changed to session

PyWorker Error Handling

.gitignore

@@ -3,3 +3,4 @@
 __pycache__
 bin/
 lib64
+.venv

README.md

@@ -1,89 +1,152 @@
-# Vast PyWorker
+# Vast PyWorker Examples
 
-Vast PyWorker is a Python web server designed to run alongside a LLM or image generation models running on vast,
+This repository contains **example PyWorkers** used by Vast.ai’s default Serverless templates (e.g., vLLM, TGI, ComfyUI, Wan, ACE). A PyWorker is a lightweight Python HTTP proxy that runs alongside your model server and:
-enabling autoscaler integration.
+
-It serves as the primary entry point for API requests, forwarding them to the model's API hosted on the
+- Exposes one or more HTTP routes (e.g., `/v1/completions`, `/generate/sync`)
-same instance. Additionally, it monitors performance metrics and estimates current workload based on factors
+- Optionally validates/transforms request payloads
-such as the number of tokens processed for LLMs or image resolution and steps for image generation models,
+- Computes per-request **workload** for autoscaling
-reporting these metrics to the autoscaler.
+- Forwards requests to the local model server
+- Optionally supports FIFO queueing when the backend cannot process concurrent requests
+- Detects readiness/failure from model logs and runs a benchmark to estimate throughput
+
+> Important: The **core PyWorker framework** (Worker, WorkerConfig, HandlerConfig, BenchmarkConfig, LogActionConfig) is provided by the **`vastai` / `vastai-sdk`** Python package (https://github.com/vast-ai/vast-sdk). This repo focuses on *worker implementations and examples*, not the framework internals.
+
+## Repository Purpose
+
+Use this repository as:
+
+- A reference for how Vast templates wire up `worker.py`
+- A starting point for implementing your own custom Serverless PyWorker
+- A collection of working examples for common model backends
+
+If you are looking for the framework code itself, refer to the Vast.ai SDK.
 
 ## Project Structure
 
-*   `lib/`: Contains the core PyWorker framework code (server logic, data types, metrics).
+Typical layout:
-*   `workers/`: Contains specific implementations (PyWorkers) for different model servers. Each subdirectory represents a worker for a particular model type.
 
-## Getting Started
+- `workers/`
+  - Example worker implementations (each worker is usually a self-contained folder)
+  - Each example typically includes:
+    - `worker.py` (the entrypoint used by Serverless)
+    - Optional sample workflows / payloads (for ComfyUI-based workers)
+    - Optional local test harness scripts
 
-1.  **Install Dependencies:**
+## How Serverless launches worker.py
-    ```bash
-    pip install -r requirements.txt
-    ```
-    You may also need `pyright` for type checking:
-    ```bash
-    sudo npm install -g pyright
-    # or use your preferred method to install pyright
-    ```
 
-2.  **Configure Environment:** Set any necessary environment variables (e.g., `MODEL_LOG` path, API keys if needed by your worker).
+On each worker instance, the template’s startup script typically:
 
-3.  **Run the Server:** Use the provided script. You'll need to specify which worker to run.
+1. Clones your repository from `PYWORKER_REPO`
-    ```bash
+2. Installs dependencies from `requirements.txt`
-    # Example for hello_world worker (assuming MODEL_LOG is set)
+3. Starts the **model server** (vLLM, TGI, ComfyUI, etc.)
-    ./start_server.sh workers.hello_world.server
+4. Runs:
-    ```
+   ```bash
-    Replace `workers.hello_world.server` with the path to the `server.py` module of the worker you want to run.
+   python worker.py
+   ```
 
-## How to Use
+Your `worker.py` builds a `WorkerConfig`, constructs a `Worker`, and starts the PyWorker HTTP server.
 
-### Using Existing Workers
+## worker.py
 
-If you are using a Vast.ai template that includes PyWorker integration (marked as autoscaler compatible), it should work out of the box. The template will typically start the appropriate PyWorker server automatically. Here's a few:
+A PyWorker is usually a single `worker.py` that uses SDK configuration objects:
 
-*   **TGI (Text Generation Inference):** [Vast.ai Template](https://cloud.vast.ai?ref_id=140778&template_id=72d8dcb41ea3a58e06c741e2c725bc00)
+```python
-*   **ComfyUI:** [Vast.ai Template](https://cloud.vast.ai?ref_id=140778&template_id=ad72c8bf7cf695c3c9ddf0eaf6da0447)
+from vastai import (
+    Worker,
+    WorkerConfig,
+    HandlerConfig,
+    BenchmarkConfig,
+    LogActionConfig,
+)
 
-Currently available workers:
+worker_config = WorkerConfig(
-*   `hello_world`: A simple example worker for a basic LLM server.
+    model_server_url="http://127.0.0.1",
-*   `comfyui`: A worker for the ComfyUI image generation backend.
+    model_server_port=18000,
-*   `tgi`: A worker for the Text Generation Inference backend.
+    model_log_file="/var/log/model/server.log",
+    handlers=[
+        HandlerConfig(
+            route="/v1/completions",
+            allow_parallel_requests=True,
+            max_queue_time=60.0,
+            workload_calculator=lambda payload: float(payload.get("max_tokens", 0)),
+            benchmark_config=BenchmarkConfig(
+                generator=lambda: {"prompt": "hello", "max_tokens": 128},
+                runs=8,
+                concurrency=10,
+            ),
+        )
+    ],
+    log_action_config=LogActionConfig(
+        on_load=["Application startup complete."],
+        on_error=["Traceback (most recent call last):", "RuntimeError:"],
+        on_info=['"message":"Download'],
+    ),
+)
 
-### Implementing a New Worker
+Worker(worker_config).run()
-
-To integrate PyWorker with a model server not already supported, you need to create a new worker implementation under the `workers/` directory. Follow these general steps:
-
-1.  **Create Worker Directory:** Add a new directory under `workers/` (e.g., `workers/my_model/`).
-2.  **Define Data Types (`data_types.py`):**
-    *   Create a class inheriting from `lib.data_types.ApiPayload`.
-    *   Implement methods like `for_test`, `generate_payload_json`, `count_workload`, and `from_json_msg` to handle request data, testing, and workload calculation specific to your model's API.
-3.  **Implement Endpoint Handlers (`server.py`):**
-    *   For each model API endpoint you want PyWorker to proxy, create a class inheriting from `lib.data_types.EndpointHandler`.
-    *   Implement methods like `endpoint`, `payload_cls`, `generate_payload_json`, `make_benchmark_payload` (for one handler), and `generate_client_response`.
-    *   Instantiate `lib.backend.Backend` with your model server details, log file path, benchmark handler, and log actions.
-    *   Define `aiohttp` routes, mapping paths to your handlers using `backend.create_handler()`.
-    *   Use `lib.server.start_server` to run the application.
-4.  **Add `__init__.py`:** Create an empty `__init__.py` file in your worker directory.
-5.  **(Optional) Add Load Testing (`test_load.py`):** Create a script using `lib.test_harness.run` to test your worker against a Vast.ai endpoint group.
-6.  **(Optional) Add Client Example (`client.py`):** Provide a script demonstrating how to call your worker's endpoints.
-
-**For a detailed walkthrough, refer to the `hello_world` example:** [workers/hello_world/README.md](workers/hello_world/README.md)
-
-
-**Type Hinting:** It is strongly recommended to use strict type hinting throughout your implementation. Use `pyright` to check for type errors.
-
-## Testing Your Worker
-
-If you implement a `test_load.py` script for your worker, you can use it to load test a Vast.ai endpoint group running your instance image.
-
-```bash
-# Example for hello_world worker
-python3 -m workers.hello_world.test_load -n 1000 -rps 0.5 -k "$API_KEY" -e "$ENDPOINT_GROUP_NAME"
 ```
 
-Replace `workers.hello_world.test_load` with the path to your worker's test script and provide your Vast.ai API Key (`-k`) and the target Endpoint Group Name (`-e`). Adjust the number of requests (`-n`) and requests per second (`-rps`) as needed.
+## Included Examples
+
+This repository contains example PyWorkers corresponding to common Vast templates, including:
+
+- **vLLM**: OpenAI-compatible completions/chat endpoints with parallel request support
+- **TGI (Text Generation Inference)**: OpenAI-compatible endpoints and log-based readiness
+- **ComfyUI (Image / JSON workflows)**: `/generate/sync` for ComfyUI workflow execution
+- **ComfyUI Wan 2.2 (T2V)**: ComfyUI workflow execution producing video outputs
+- **ComfyUI ACE Step (Text-to-Music)**: ComfyUI workflow execution producing audio outputs
+
+Exact worker paths and naming may vary by template; use the `workers/` directory as the source of truth.
+
+## Getting Started (Local)
+
+1. Install Python dependencies for the examples you plan to run:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. Start your model server locally (vLLM, TGI, ComfyUI, etc.) and ensure:
+   - You know the model server URL/port
+   - You have a log file path you can tail for readiness/error detection
+
+3. Run the worker:
+   ```bash
+   python worker.py
+   ```
+   or, if running an example from a subfolder:
+   ```bash
+   python workers/<example>/worker.py
+   ```
+
+> Note: Many examples assume they are running inside Vast templates (ports, log paths, model locations). You may need to adjust `model_server_port` and `model_log_file` for local usage.
+
+## Deploying on Vast Serverless
+
+To use a custom PyWorker with Serverless:
+
+1. Create a public Git repository containing:
+   - `worker.py`
+   - `requirements.txt`
+
+2. In your Serverless template / endpoint configuration, set:
+   - `PYWORKER_REPO` to your Git repository URL
+   - (Optional) `PYWORKER_REF` to a git ref (branch, tag, or commit)
+
+3. The template startup script will clone/install and run your `worker.py`.
+
+## Guidance for Custom Workers
+
+When implementing your own worker:
+
+- Define one `HandlerConfig` per route you want to expose.
+- Choose a workload function that correlates with compute cost:
+  - LLMs: prompt tokens + max output tokens (or `max_tokens` as a simpler proxy)
+  - Non-LLMs: constant cost per request (e.g., `100.0`) is often sufficient
+- Set `allow_parallel_requests=False` for backends that cannot handle concurrency (e.g., many ComfyUI deployments).
+- Configure exactly **one** `BenchmarkConfig` across all handlers to enable capacity estimation.
+- Use `LogActionConfig` to reliably detect “model loaded” and “fatal error” log lines.
 
 ## Community & Support
 
-Join the conversation and get help:
+- Vast.ai Discord: https://discord.gg/Pa9M29FFye
-
+- Vast.ai Subreddit: https://reddit.com/r/vastai/
-*   **Vast.ai Discord:** [https://discord.gg/Pa9M29FFye](https://discord.gg/Pa9M29FFye)
-*   **Vast.ai Subreddit:** [https://reddit.com/r/vastai/](https://reddit.com/r/vastai/)

lib/backend.py

@@ -1,398 +0,0 @@
-import os
-import json
-import time
-import base64
-import subprocess
-import dataclasses
-import logging
-from asyncio import wait, sleep, gather, Semaphore, FIRST_COMPLETED, create_task
-from typing import Tuple, Awaitable, NoReturn, List, Union, Callable, Optional
-from functools import cached_property
-from distutils.util import strtobool
-
-from anyio import open_file
-from aiohttp import web, ClientResponse, ClientSession, ClientConnectorError, ClientTimeout, TCPConnector
-
-import requests
-from Crypto.Signature import pkcs1_15
-from Crypto.Hash import SHA256
-from Crypto.PublicKey import RSA
-
-from lib.metrics import Metrics
-from lib.data_types import (
-    AuthData,
-    EndpointHandler,
-    LogAction,
-    ApiPayload_T,
-    JsonDataException,
-)
-
-MSG_HISTORY_LEN = 100
-log = logging.getLogger(__file__)
-
-# defines the minimum wait time between sending updates to autoscaler
-LOG_POLL_INTERVAL = 0.1
-BENCHMARK_INDICATOR_FILE = ".has_benchmark"
-MAX_PUBKEY_FETCH_ATTEMPTS = 3
-
-
-@dataclasses.dataclass
-class Backend:
-    """
-    This class is responsible for:
-    1. Tailing logs and updating load time metrics
-    2. Taking an EndpointHandler alongside incoming payload, preparing a json to be sent to the model, and
-    sending the request. It also updates metrics as it makes those requests.
-    3. Running a benchmark from an EndpointHandler
-    """
-
-    model_server_url: str
-    model_log_file: str
-    allow_parallel_requests: bool
-    benchmark_handler: (
-        EndpointHandler  # this endpoint handler will be used for benchmarking
-    )
-    log_actions: List[Tuple[LogAction, str]]
-    reqnum = -1
-    msg_history = []
-    sem: Semaphore = dataclasses.field(default_factory=Semaphore)
-    unsecured: bool = dataclasses.field(
-        default_factory=lambda: bool(strtobool(os.environ.get("UNSECURED", "false"))),
-    )
-
-    def __post_init__(self):
-        self.metrics = Metrics()
-        self._total_pubkey_fetch_errors = 0
-        self._pubkey = self._fetch_pubkey()
-        self.__start_healthcheck: bool = False
-
-    @property
-    def pubkey(self) -> Optional[RSA.RsaKey]:
-        if self._pubkey is None:
-            self._pubkey = self._fetch_pubkey()
-        return self._pubkey
-
-    @cached_property
-    def session(self):
-        log.debug(f"starting session with {self.model_server_url}")
-        connector = TCPConnector(
-            force_close=True, # Required for long running jobs
-            enable_cleanup_closed=True,
-        )
-        
-        timeout = ClientTimeout(total=None)
-        return ClientSession(self.model_server_url, timeout=timeout, connector=connector)
-
-    def create_handler(
-        self,
-        handler: EndpointHandler[ApiPayload_T],
-    ) -> Callable[[web.Request], Awaitable[Union[web.Response, web.StreamResponse]]]:
-        async def handler_fn(
-            request: web.Request,
-        ) -> Union[web.Response, web.StreamResponse]:
-            return await self.__handle_request(handler=handler, request=request)
-
-        return handler_fn
-
-    #######################################Private#######################################
-    def _fetch_pubkey(self):
-        command = ["curl", "-X", "GET", "https://run.vast.ai/pubkey/"]
-        result = subprocess.check_output(command, universal_newlines=True)
-        log.debug("public key:")
-        log.debug(result)
-        key = None
-        for _ in range(5):
-            try:
-                key = RSA.import_key(result)
-                break
-            except ValueError as e:
-                log.debug(f"Error downloading key: {e}")
-                time.sleep(15)
-        if key is None:
-            self._total_pubkey_fetch_errors += 1
-            if self._total_pubkey_fetch_errors >= MAX_PUBKEY_FETCH_ATTEMPTS:
-                self.backend_errored("Failed to get autoscaler pubkey")
-        return key
-
-    async def __handle_request(
-        self,
-        handler: EndpointHandler[ApiPayload_T],
-        request: web.Request,
-    ) -> Union[web.Response, web.StreamResponse]:
-        """use this function to forward requests to the model endpoint"""
-        try:
-            data = await request.json()
-            auth_data, payload = handler.get_data_from_request(data)
-        except JsonDataException as e:
-            return web.json_response(data=e.message, status=422)
-        except json.JSONDecodeError:
-            return web.json_response(dict(error="invalid JSON"), status=422)
-        workload = payload.count_workload()
-
-        async def cancel_api_call_if_disconnected() -> web.Response:
-            await request.wait_for_disconnection()
-            log.debug(f"request with reqnum: {auth_data.reqnum} was canceled")
-            self.metrics._request_canceled(workload=workload)
-            return web.Response(status=500)
-
-        async def make_request() -> Union[web.Response, web.StreamResponse]:
-            log.debug(f"got request, {auth_data.reqnum}")
-            self.metrics._request_start(workload=workload, reqnum=auth_data.reqnum)
-            if self.allow_parallel_requests is False:
-                log.debug(f"Waiting to aquire Sem for reqnum:{auth_data.reqnum}")
-                await self.sem.acquire()
-                log.debug(
-                    f"Sem acquired for reqnum:{auth_data.reqnum}, starting request..."
-                )
-            else:
-                log.debug(f"Starting request for reqnum:{auth_data.reqnum}")
-            try:
-                response = await self.__call_api(handler=handler, payload=payload)
-                status_code = response.status
-                log.debug(
-                    " ".join(
-                        [
-                            f"request with reqnum:{auth_data.reqnum}",
-                            f"returned status code: {status_code},",
-                        ]
-                    )
-                )
-                res = await handler.generate_client_response(request, response)
-                self.metrics._request_success(workload=workload)
-                return res
-            except requests.exceptions.RequestException as e:
-                log.debug(f"[backend] Request error: {e}")
-                self.metrics._request_errored(workload=workload)
-                return web.Response(status=500)
-            finally:
-                self.metrics._request_end(
-                    workload=workload,
-                    reqnum=auth_data.reqnum,
-                )
-                self.sem.release()
-
-        ###########
-
-        if self.__check_signature(auth_data) is False:
-            return web.Response(status=401)
-
-        try:
-            done, pending = await wait(
-                [
-                    create_task(make_request()),
-                    create_task(cancel_api_call_if_disconnected()),
-                ],
-                return_when=FIRST_COMPLETED,
-            )
-            [task.cancel() for task in pending]
-            return done.pop().result()
-        except Exception as e:
-            log.debug(f"Exception in main handler loop {e}")
-            return web.Response(status=500)
-
-    @cached_property  
-    def healthcheck_session(self):
-        """Dedicated session for healthchecks to avoid conflicts with API session"""
-        log.debug("creating dedicated healthcheck session")
-        connector = TCPConnector(
-            force_close=True,  # Keep this for isolation
-            enable_cleanup_closed=True,
-        )
-        timeout = ClientTimeout(total=10)  # Reasonable timeout for healthchecks
-        return ClientSession(timeout=timeout, connector=connector)
-
-    async def __healthcheck(self):
-        health_check_url = self.benchmark_handler.healthcheck_endpoint
-        if health_check_url is None:
-            log.debug("No healthcheck endpoint defined, skipping healthcheck")
-            return
-
-        while True:
-            await sleep(10)
-            if self.__start_healthcheck is False:
-                continue
-            try:
-                log.debug(f"Performing healthcheck on {health_check_url}")
-                async with self.healthcheck_session.get(health_check_url) as response:
-                    if response.status == 200:
-                        log.debug("Healthcheck successful")
-                    elif response.status == 503:
-                        log.debug(f"Healthcheck failed with status: {response.status}")
-                        self.backend_errored(
-                            f"Healthcheck failed with status: {response.status}"
-                        )
-                    else:
-                        log.debug(f"Healthcheck Endpoint not ready: {response.status}")
-            except Exception as e:
-                log.debug(f"Healthcheck failed with exception: {e}")
-                self.backend_errored(str(e))
-
-    async def _start_tracking(self) -> None:
-        await gather(
-            self.__read_logs(), self.metrics._send_metrics_loop(), self.__healthcheck()
-        )
-
-    def backend_errored(self, msg: str) -> None:
-        self.metrics._model_errored(msg)
-
-    async def __call_api(
-        self, handler: EndpointHandler[ApiPayload_T], payload: ApiPayload_T
-    ) -> ClientResponse:
-        api_payload = payload.generate_payload_json()
-        log.debug(f"posting to endpoint: '{handler.endpoint}', payload: {api_payload}")
-        return await self.session.post(url=handler.endpoint, json=api_payload)
-
-    def __check_signature(self, auth_data: AuthData) -> bool:
-        if self.unsecured is True:
-            return True
-
-        def verify_signature(message, signature):
-            if self.pubkey is None:
-                log.debug(f"No Public Key!")
-                return False
-
-            h = SHA256.new(message.encode())
-            try:
-                pkcs1_15.new(self.pubkey).verify(h, base64.b64decode(signature))
-                return True
-            except (ValueError, TypeError):
-                return False
-
-        message = {
-            key: value
-            for (key, value) in (dataclasses.asdict(auth_data).items())
-            if key != "signature"
-        }
-        if auth_data.reqnum < (self.reqnum - MSG_HISTORY_LEN):
-            log.debug(
-                f"reqnum failure, got {auth_data.reqnum}, current_reqnum: {self.reqnum}"
-            )
-            return False
-        elif message in self.msg_history:
-            log.debug(f"message: {message} already in message history")
-            return False
-        elif verify_signature(json.dumps(message, indent=4), auth_data.signature):
-            self.reqnum = max(auth_data.reqnum, self.reqnum)
-            self.msg_history.append(message)
-            self.msg_history = self.msg_history[-MSG_HISTORY_LEN:]
-            return True
-        else:
-            log.debug(
-                f"signature verification failed, sig:{auth_data.signature}, message: {message}"
-            )
-            return False
-
-    async def __read_logs(self) -> Awaitable[NoReturn]:
-
-        async def run_benchmark() -> float:
-            log.debug("starting benchmark")
-            try:
-                with open(BENCHMARK_INDICATOR_FILE, "r") as f:
-                    log.debug("already ran benchmark")
-                    # trigger model load
-                    payload = self.benchmark_handler.make_benchmark_payload()
-                    _ = await self.__call_api(
-                        handler=self.benchmark_handler, payload=payload
-                    )
-                    return float(f.readline())
-            except FileNotFoundError:
-                pass
-
-            log.debug("Initial run to trigger model loading...")
-            payload = self.benchmark_handler.make_benchmark_payload()
-            await self.__call_api(handler=self.benchmark_handler, payload=payload)
-
-            max_throughput = 0
-            sum_throughput = 0
-            concurrent_requests = 10 if self.allow_parallel_requests else 1
-
-            for run in range(1, self.benchmark_handler.benchmark_runs + 1):
-                start = time.time()
-                tasks = []
-                total_workload = 0
-
-                for _ in range(concurrent_requests):
-                    payload = self.benchmark_handler.make_benchmark_payload()
-                    total_workload += payload.count_workload()
-                    tasks.append(
-                        self.__call_api(handler=self.benchmark_handler, payload=payload)
-                    )
-
-                responses = await gather(*tasks)
-                time_elapsed = time.time() - start
-
-                throughput = total_workload / time_elapsed
-                sum_throughput += throughput
-                max_throughput = max(max_throughput, throughput)
-
-                # Log results for debugging
-                log.debug(
-                    "\n".join(
-                        [
-                            "#" * 60,
-                            f"Run: {run}, concurrent_requests: {concurrent_requests}",
-                            f"Total workload: {total_workload}, time_elapsed: {time_elapsed}s",
-                            f"Throughput: {throughput} workload/s",
-                            f"Successful responses: {len([r for r in responses if r.status == 200])}",
-                            "#" * 60,
-                        ]
-                    )
-                )
-
-            average_throughput = sum_throughput / self.benchmark_handler.benchmark_runs
-            log.debug(
-                f"benchmark result: avg {average_throughput} workload per second, max {max_throughput}"
-            )
-            with open(BENCHMARK_INDICATOR_FILE, "w") as f:
-                f.write(str(max_throughput))
-            return max_throughput
-
-        async def handle_log_line(log_line: str) -> None:
-            """
-            Implement this function to handle each log line for your model.
-            This function should mutate self.system_metrics and self.model_metrics
-            """
-            for action, msg in self.log_actions:
-                match action:
-                    case LogAction.ModelLoaded if msg in log_line:
-                        log.debug(
-                            f"Got log line indicating model is loaded: {log_line}"
-                        )
-                        # some backends need a few seconds after logging successful startup before
-                        # they can begin accepting requests
-                        await sleep(5)
-                        try:
-                            max_throughput = await run_benchmark()
-                            self.__start_healthcheck = True
-                            self.metrics._model_loaded(
-                                max_throughput=max_throughput,
-                            )
-                        except ClientConnectorError as e:
-                            log.debug(
-                                f"failed to connect to comfyui api during benchmark"
-                            )
-                            self.backend_errored(str(e))
-                    case LogAction.ModelError if msg in log_line:
-                        log.debug(f"Got log line indicating error: {log_line}")
-                        self.backend_errored(msg)
-                        break
-                    case LogAction.Info if msg in log_line:
-                        log.debug(f"Info from model logs: {log_line}")
-
-        async def tail_log():
-            log.debug(f"tailing file: {self.model_log_file}")
-            async with await open_file(self.model_log_file) as f:
-                while True:
-                    line = await f.readline()
-                    if line:
-                        await handle_log_line(line.rstrip())
-                    else:
-                        time.sleep(LOG_POLL_INTERVAL)
-
-        ###########
-
-        while True:
-            if os.path.isfile(self.model_log_file) is True:
-                return await tail_log()
-            else:
-                await sleep(1)

lib/data_types.py

@@ -1,283 +0,0 @@
-import time
-import logging
-from dataclasses import dataclass, field
-from enum import Enum
-from abc import ABC, abstractmethod
-from typing import Dict, Any, Union, Tuple, Optional, Set, TypeVar, Generic, Type
-from aiohttp import web, ClientResponse
-import inspect
-
-import psutil
-
-
-"""
-type variable representing an incoming payload to pyworker that will used to calculate load and will then
-be forwarded to the model
-"""
-
-log = logging.getLogger(__file__)
-
-
-class JsonDataException(Exception):
-    def __init__(self, json_msg: Dict[str, Any]):
-        self.message = json_msg
-
-
-ApiPayload_T = TypeVar("ApiPayload_T", bound="ApiPayload")
-
-
-@dataclass
-class ApiPayload(ABC):
-
-    @classmethod
-    @abstractmethod
-    def for_test(cls: Type[ApiPayload_T]) -> ApiPayload_T:
-        """defines how create a payload for load testing"""
-        pass
-
-    @abstractmethod
-    def generate_payload_json(self) -> Dict[str, Any]:
-        """defines how to convert an ApiPayload to JSON that will be sent to model API"""
-        pass
-
-    @abstractmethod
-    def count_workload(self) -> float:
-        """defines how to calculate workload for a payload"""
-        pass
-
-    @classmethod
-    @abstractmethod
-    def from_json_msg(
-        cls: Type[ApiPayload_T], json_msg: Dict[str, Any]
-    ) -> ApiPayload_T:
-        """
-        defines how to create an API payload from a JSON message,
-        it should throw an JsonDataException if there are issues with some fields
-        or they are missing in the format of
-        {
-            "field": "error msg"
-        }
-        """
-        pass
-
-
-@dataclass
-class AuthData:
-    """data used to authenticate requester"""
-
-    signature: str
-    cost: str
-    endpoint: str
-    reqnum: int
-    url: str
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]):
-        errors = {}
-        for param in inspect.signature(cls).parameters:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-        if errors:
-            raise JsonDataException(errors)
-        return cls(
-            **{
-                k: v
-                for k, v in json_msg.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )
-
-
-@dataclass
-class EndpointHandler(ABC, Generic[ApiPayload_T]):
-    """
-    Each model endpoint will have a handler responsible for counting workload from the incoming ApiPayload
-    and converting it to json to be forwarded to model API
-    """
-
-    benchmark_runs: int = 8
-    benchmark_words: int = 100
-
-    @property
-    @abstractmethod
-    def endpoint(self) -> str:
-        """the endpoint on the model API"""
-        pass
-
-    @property
-    @abstractmethod
-    def healthcheck_endpoint(self) -> Optional[str]:
-        """the endpoint on the model API that is used for healthchecks"""
-        pass
-
-    @classmethod
-    @abstractmethod
-    def payload_cls(cls) -> Type[ApiPayload_T]:
-        """ApiPayload class"""
-        pass
-
-    @abstractmethod
-    def make_benchmark_payload(self) -> ApiPayload_T:
-        """defines how to create an ApiPayload for benchmarking."""
-        pass
-
-    @abstractmethod
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        """
-        defines how to convert a model API response to a response to PyWorker client
-        """
-        pass
-
-    @classmethod
-    def get_data_from_request(
-        cls, req_data: Dict[str, Any]
-    ) -> Tuple[AuthData, ApiPayload_T]:
-        errors = {}
-        auth_data: Optional[AuthData] = None
-        payload: Optional[ApiPayload_T] = None
-        try:
-            if "auth_data" in req_data:
-                auth_data = AuthData.from_json_msg(req_data["auth_data"])
-            else:
-                errors["auth_data"] = "field missing"
-        except JsonDataException as e:
-            errors["auth_data"] = e.message
-        try:
-            if "payload" in req_data:
-                payload_cls = cls.payload_cls()
-                payload = payload_cls.from_json_msg(req_data["payload"])
-            else:
-                errors["payload"] = "field missing"
-        except JsonDataException as e:
-            errors["payload"] = e.message
-        if errors:
-            raise JsonDataException(errors)
-        if auth_data and payload:
-            return (auth_data, payload)
-        else:
-            raise Exception("error deserializing request data")
-
-
-@dataclass
-class SystemMetrics:
-    """General system metrics"""
-
-    model_loading_start: float
-    model_loading_time: Union[float, None]
-    last_disk_usage: float
-    additional_disk_usage: float
-    model_is_loaded: bool
-
-    @staticmethod
-    def get_disk_usage_GB():
-        return psutil.disk_usage("/").used / (2**30)  # want units of GB
-
-    @classmethod
-    def empty(cls):
-        return cls(
-            model_loading_start=time.time(),
-            model_loading_time=None,
-            last_disk_usage=SystemMetrics.get_disk_usage_GB(),
-            additional_disk_usage=0.0,
-            model_is_loaded=False,
-        )
-
-    def update_disk_usage(self):
-        disk_usage = SystemMetrics.get_disk_usage_GB()
-        self.additional_disk_usage = disk_usage - self.last_disk_usage
-        self.last_disk_usage = disk_usage
-
-    def reset(self):
-        # autoscaler excepts model_loading_time to be populated only once, when the instance has
-        # finished benchmarking and is ready to receive requests. This applies to restarted instances
-        # as well: they should send model_loading_time once when they are done loading
-        self.model_loading_time = None
-
-
-@dataclass
-class ModelMetrics:
-    """Model specific metrics"""
-
-    # these are reset after being sent to autoscaler
-    workload_served: float
-    workload_received: float
-    workload_cancelled: float
-    workload_errored: float
-    # these are not
-    workload_pending: float
-    error_msg: Optional[str]
-    max_throughput: float
-    requests_recieved: Set[int] = field(default_factory=set)
-    requests_working: Set[int] = field(default_factory=set)
-    last_update: float = field(default_factory=time.time)
-
-    @classmethod
-    def empty(cls):
-        return cls(
-            workload_pending=0.0,
-            workload_served=0.0,
-            workload_cancelled=0.0,
-            workload_errored=0.0,
-            workload_received=0.0,
-            error_msg=None,
-            max_throughput=0.0,
-        )
-
-    @property
-    def cur_perf(self) -> float:
-        return max(self.workload_served / (time.time() - self.last_update), 0.0)
-
-    @property
-    def workload_processing(self) -> float:
-        return max(self.workload_received - self.workload_cancelled, 0.0)
-
-    def set_errored(self, error_msg):
-        self.reset()
-        self.error_msg = error_msg
-
-    def reset(self):
-        self.workload_served = 0
-        self.workload_received = 0
-        self.workload_cancelled = 0
-        self.workload_errored = 0
-        self.last_update = time.time()
-
-
-@dataclass
-class AutoScalaerData:
-    """Data that is reported to autoscaler"""
-
-    id: int
-    loadtime: float
-    cur_load: float
-    error_msg: str
-    max_perf: float
-    cur_perf: float
-    cur_capacity: float
-    max_capacity: float
-    num_requests_working: int
-    num_requests_recieved: int
-    additional_disk_usage: float
-    url: str
-
-
-class LogAction(Enum):
-    """
-    These actions tell the backend what a log value means, for example:
-    actions [
-        # this marks the model server as loaded
-        (LogAction.ModelLoaded, "Starting server"),
-        # these mark the model server as errored
-        (LogAction.ModelError, "Exception loading model"),
-        (LogAction.ModelError, "Server failed to bind to port"),
-        # this tells the backend to print any logs containing the string into its own logs
-        # which are visible in the vast console instance logs
-        (LogAction.Info, "Starting model download"),
-    ]
-    """
-
-    ModelLoaded = 1
-    ModelError = 2
-    Info = 3

lib/metrics.py

@@ -1,156 +0,0 @@
-import os
-import time
-import logging
-import json
-from asyncio import sleep
-from dataclasses import dataclass, asdict, field
-from functools import cache
-
-import requests
-
-from lib.data_types import AutoScalaerData, SystemMetrics, ModelMetrics
-from typing import Awaitable, NoReturn, List
-
-METRICS_UPDATE_INTERVAL = 1
-
-log = logging.getLogger(__file__)
-
-
-@cache
-def get_url() -> str:
-    use_ssl = os.environ.get("USE_SSL", "false") == "true"
-    worker_port = os.environ[f"VAST_TCP_PORT_{os.environ['WORKER_PORT']}"]
-    public_ip = os.environ["PUBLIC_IPADDR"]
-    return f"http{'s' if use_ssl else ''}://{public_ip}:{worker_port}"
-
-
-@dataclass
-class Metrics:
-    last_metric_update: float = 0.0
-    update_pending: bool = False
-    id: int = field(default_factory=lambda: int(os.environ["CONTAINER_ID"]))
-    report_addr: List[str] = field(
-        default_factory=lambda: os.environ["REPORT_ADDR"].split(",")
-    )
-    url: str = field(default_factory=get_url)
-    system_metrics: SystemMetrics = field(default_factory=SystemMetrics.empty)
-    model_metrics: ModelMetrics = field(default_factory=ModelMetrics.empty)
-
-    def _request_start(self, workload: float, reqnum: int) -> None:
-        """
-        this function is called prior to forwarding a request to a model API.
-        """
-        log.debug("request start")
-        self.model_metrics.workload_pending += workload
-        self.model_metrics.workload_received += workload
-        self.model_metrics.requests_recieved.add(reqnum)
-        self.model_metrics.requests_working.add(reqnum)
-        self.update_pending = True
-
-    def _request_end(self, workload: float, reqnum: int) -> None:
-        """
-        this function is called after handling of a request ends, regardless of the outcome
-        """
-        self.model_metrics.workload_pending -= workload
-        self.model_metrics.requests_working.discard(reqnum)
-
-    def _request_success(self, workload: float) -> None:
-        """
-        this function is called after a response from model API is received and forwarded.
-        """
-        self.model_metrics.workload_served += workload
-        self.update_pending = True
-
-    def _request_errored(self, workload: float) -> None:
-        """
-        this function is called if model API returns an error
-        """
-        self.model_metrics.workload_errored += workload
-
-    def _request_canceled(self, workload: float) -> None:
-        """
-        this function is called if client drops connection before model API has responded
-        """
-        self.model_metrics.workload_cancelled += workload
-
-    async def _send_metrics_loop(self) -> Awaitable[NoReturn]:
-        while True:
-            await sleep(METRICS_UPDATE_INTERVAL)
-            elapsed = time.time() - self.last_metric_update
-            if self.system_metrics.model_is_loaded is False and elapsed >= 10:
-                log.debug(f"sending loading model metrics after {int(elapsed)}s wait")
-                self.__send_metrics_and_reset()
-            elif self.update_pending or elapsed > 10:
-                log.debug(f"sending loaded model metrics after {int(elapsed)}s wait")
-                self.__send_metrics_and_reset()
-
-    def _model_loaded(self, max_throughput: float) -> None:
-        self.system_metrics.model_loading_time = (
-            time.time() - self.system_metrics.model_loading_start
-        )
-        self.system_metrics.model_is_loaded = True
-        self.model_metrics.max_throughput = max_throughput
-
-    def _model_errored(self, error_msg: str) -> None:
-        self.model_metrics.set_errored(error_msg)
-        self.system_metrics.model_is_loaded = True
-
-    #######################################Private#######################################
-
-    def __send_metrics_and_reset(self):
-
-        def compute_autoscaler_data() -> AutoScalaerData:
-            return AutoScalaerData(
-                id=self.id,
-                loadtime=(self.system_metrics.model_loading_time or 0.0),
-                cur_load=(self.model_metrics.workload_processing),
-                max_perf=self.model_metrics.max_throughput,
-                cur_perf=self.model_metrics.cur_perf,
-                error_msg=self.model_metrics.error_msg or "",
-                num_requests_working=len(self.model_metrics.requests_working),
-                num_requests_recieved=len(self.model_metrics.requests_recieved),
-                additional_disk_usage=self.system_metrics.additional_disk_usage,
-                cur_capacity=0,
-                max_capacity=0,
-                url=self.url,
-            )
-
-        def send_data(report_addr: str) -> bool:
-            data = compute_autoscaler_data()
-            full_path = report_addr.rstrip("/") + "/worker_status/"
-            log.debug(
-                "\n".join(
-                    [
-                        "#" * 60,
-                        f"sending data to autoscaler",
-                        f"{json.dumps((asdict(data)), indent=2)}",
-                        "#" * 60,
-                    ]
-                )
-            )
-            for attempt in range(1, 4):
-                try:
-                    res = requests.post(full_path, json=asdict(data), timeout=1)
-                    res.raise_for_status()
-                    return True
-                except requests.Timeout:
-                    log.debug(f"autoscaler status update timed out")
-                except Exception as e:
-                    log.debug(f"autoscaler status update failed with error: {e}")
-                time.sleep(2)
-                log.debug(f"retrying autoscaler status update, attempt: {attempt}")
-            log.debug(f"failed to send update through {report_addr}")
-            return False
-
-        ###########
-
-        self.system_metrics.update_disk_usage()
-
-        for report_addr in self.report_addr:
-            success = send_data(report_addr)
-            if success is True:
-                break
-        self.update_pending = False
-        self.model_metrics.reset()
-        self.system_metrics.reset()
-        self.last_metric_update = time.time()

lib/server.py

@@ -1,40 +0,0 @@
-import os
-import logging
-from typing import List
-import ssl
-from asyncio import run, gather
-
-
-from lib.backend import Backend
-from aiohttp import web
-
-log = logging.getLogger(__file__)
-
-
-def start_server(backend: Backend, routes: List[web.RouteDef], **kwargs):
-    log.debug("getting certificate...")
-    use_ssl = os.environ.get("USE_SSL", "false") == "true"
-    if use_ssl is True:
-        ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
-        ssl_context.load_cert_chain(
-            certfile="/etc/instance.crt",
-            keyfile="/etc/instance.key",
-        )
-    else:
-        ssl_context = None
-
-    async def main():
-        log.debug("starting server...")
-        app = web.Application()
-        app.add_routes(routes)
-        runner = web.AppRunner(app)
-        await runner.setup()
-        site = web.TCPSite(
-            runner,
-            ssl_context=ssl_context,
-            port=int(os.environ["WORKER_PORT"]),
-            **kwargs
-        )
-        await gather(site.start(), backend._start_tracking())
-
-    run(main())

lib/test_utils.py

@@ -1,310 +0,0 @@
-import logging
-import os
-import time
-import argparse
-from typing import Callable, List, Dict, Tuple, Dict, Any, Type
-from time import sleep
-import threading
-from enum import Enum
-from collections import Counter
-from dataclasses import dataclass, field, asdict
-from urllib.parse import urljoin
-from utils.endpoint_util import Endpoint
-from utils.ssl import get_cert_file_path
-import requests
-
-from lib.data_types import AuthData, ApiPayload
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-
-class ClientStatus(Enum):
-    FetchEndpoint = 1
-    Generating = 2
-    Done = 3
-    Error = 4
-
-
-total_success = 0
-last_res = []
-stop_event = threading.Event()
-
-start_time = time.time()
-test_args = argparse.ArgumentParser(description="Test inference endpoint")
-test_args.add_argument(
-    "-k", dest="api_key", type=str, required=True, help="Your vast account API key"
-)
-test_args.add_argument(
-    "-e",
-    dest="endpoint_group_name",
-    type=str,
-    required=True,
-    help="Endpoint group name",
-)
-test_args.add_argument(
-    "-l",
-    dest="server_url",
-    action="store_const",
-    const="http://localhost:8081",
-    default="https://run.vast.ai",
-    help="Call local autoscaler instead of prod, for dev use only",
-)
-test_args.add_argument(
-    "-i",
-    dest="instance",
-    type=str,
-    default="prod",
-    help="Autoscaler shard to run the command against, default: prod",
-)
-
-GetPayloadAndWorkload = Callable[[], Tuple[Dict[str, Any], float]]
-
-
-def print_truncate_res(res: str):
-    if len(res) > 150:
-        print(f"{res[:50]}....{res[-100:]}")
-    else:
-        print(res)
-
-
-@dataclass
-class ClientState:
-    endpoint_group_name: str
-    api_key: str
-    server_url: str
-    worker_endpoint: str
-    instance: str
-    payload: ApiPayload
-    url: str = ""
-    status: ClientStatus = ClientStatus.FetchEndpoint
-    as_error: List[str] = field(default_factory=list)
-    infer_error: List[str] = field(default_factory=list)
-    conn_errors: Counter = field(default_factory=Counter)
-
-    def make_call(self):
-        self.status = ClientStatus.FetchEndpoint
-        if not self.api_key:
-            self.as_error.append(
-                f"Endpoint {self.endpoint_group_name} not found for API key",
-            )
-            self.status = ClientStatus.Error
-            return
-        route_payload = {
-            "endpoint": self.endpoint_group_name,
-            "api_key": self.api_key,
-            "cost": self.payload.count_workload(),
-        }
-        headers = {"Authorization": f"Bearer {self.api_key}"}
-        response = requests.post(
-            urljoin(self.server_url, "/route/"),
-            json=route_payload,
-            headers=headers,
-            timeout=4,
-        )
-        if response.status_code != 200:
-            self.as_error.append(
-                f"code: {response.status_code}, body: {response.text}",
-            )
-            self.status = ClientStatus.Error
-            return
-        message = response.json()
-        worker_address = message["url"]
-        req_data = dict(
-            payload=asdict(self.payload),
-            auth_data=asdict(AuthData.from_json_msg(message)),
-        )
-        self.url = worker_address
-        url = urljoin(worker_address, self.worker_endpoint)
-        self.status = ClientStatus.Generating
-
-        response = requests.post(
-            url,
-            json=req_data,
-            verify=get_cert_file_path(),
-        )
-        if response.status_code != 200:
-            self.infer_error.append(
-                f"code: {response.status_code}, body: {response.text}, url: {url}",
-            )
-            self.status = ClientStatus.Error
-            return
-        res = str(response.json())
-        global total_success
-        global last_res
-        total_success += 1
-        last_res.append(res)
-        self.status = ClientStatus.Done
-
-    def simulate_user(self) -> None:
-        try:
-            self.make_call()
-        except Exception as e:
-            print(e)
-            self.status = ClientStatus.Error
-            _ = e
-            self.conn_errors[self.url] += 1
-
-
-def print_state(clients: List[ClientState], num_clients: int) -> None:
-    print("starting up...")
-    sleep(2)
-    center_size = 14
-    global start_time
-    while len(clients) < num_clients or (
-        any(
-            map(
-                lambda client: client.status
-                in [ClientStatus.FetchEndpoint, ClientStatus.Generating],
-                clients,
-            )
-        )
-    ):
-        sleep(0.5)
-        os.system("clear")
-        print(
-            " | ".join(
-                [member.name.center(center_size) for member in ClientStatus]
-                + [
-                    item.center(center_size)
-                    for item in [
-                        "urls",
-                        "as_error",
-                        "infer_error",
-                        "conn_error",
-                        "total_success",
-                    ]
-                ]
-            )
-        )
-        unique_urls = len(set([c.url for c in clients if c.url != ""]))
-        as_errors = sum(
-            map(
-                lambda client: len(client.as_error),
-                [client for client in clients],
-            )
-        )
-        infer_errors = sum(
-            map(
-                lambda client: len(client.infer_error),
-                [client for client in clients],
-            )
-        )
-        conn_errors = sum([client.conn_errors for client in clients], start=Counter())
-        conn_errors_str = ",".join(map(str, conn_errors.values())) or "0"
-        elapsed = time.time() - start_time
-        print(
-            " | ".join(
-                map(
-                    lambda item: str(item).center(center_size),
-                    [
-                        len(list(filter(lambda x: x.status == member, clients)))
-                        for member in ClientStatus
-                    ]
-                    + [
-                        unique_urls,
-                        as_errors,
-                        infer_errors,
-                        conn_errors_str,
-                        f"{total_success}({((total_success/elapsed) * 60):.2f}/minute)",
-                    ],
-                )
-            )
-        )
-        if conn_errors:
-            print("conn_errors:")
-            for url, count in conn_errors.items():
-                print(url.ljust(28), ": ", str(count))
-        elapsed = time.time() - start_time
-        print(f"\n elapsed: {int(elapsed // 60)}:{int(elapsed % 60)}")
-        if last_res:
-            for i, res in enumerate(last_res[-10:]):
-                print_truncate_res(f"res #{1+i+max(len(last_res )-10,0)}: {res}")
-        if stop_event.is_set():
-            print("\n### waiting for existing connections to close ###")
-
-
-def run_test(
-    num_requests: int,
-    requests_per_second: int,
-    endpoint_group_name: str,
-    api_key: str,
-    server_url: str,
-    worker_endpoint: str,
-    payload_cls: Type[ApiPayload],
-    instance: str,
-):
-    threads = []
-
-    clients = []
-    print_thread = threading.Thread(target=print_state, args=(clients, num_requests))
-    print_thread.daemon = True  # makes threads get killed on program exit
-    print_thread.start()
-    endpoint_api_key = Endpoint.get_endpoint_api_key(
-        endpoint_name=endpoint_group_name, account_api_key=api_key, instance=instance
-    )
-    if not endpoint_api_key:
-        log.debug(f"Endpoint {endpoint_group_name} not found for API key")
-        return
-    try:
-        for _ in range(num_requests):
-            client = ClientState(
-                endpoint_group_name=endpoint_group_name,
-                api_key=endpoint_api_key,
-                server_url=server_url,
-                worker_endpoint=worker_endpoint,
-                payload=payload_cls.for_test(),
-                instance=instance,
-            )
-            clients.append(client)
-            thread = threading.Thread(target=client.simulate_user, args=())
-            threads.append(thread)
-            thread.start()
-            sleep(1 / requests_per_second)
-        for thread in threads:
-            thread.join()
-        print("done spawning workers")
-    except KeyboardInterrupt:
-        stop_event.set()
-
-
-def test_load_cmd(
-    payload_cls: Type[ApiPayload], endpoint: str, arg_parser: argparse.ArgumentParser
-):
-    arg_parser.add_argument(
-        "-n",
-        dest="num_requests",
-        type=int,
-        required=True,
-        help="total number of requests",
-    )
-    arg_parser.add_argument(
-        "-rps",
-        dest="requests_per_second",
-        type=float,
-        required=True,
-        help="requests per second",
-    )
-    args = arg_parser.parse_args()
-    if hasattr(args, "comfy_model"):
-        os.environ["COMFY_MODEL"] = args.comfy_model
-    server_url = dict(
-        prod="https://run.vast.ai",
-        alpha="https://run-alpha.vast.ai",
-        candidate="https://run-candidate.vast.ai",
-        local="http://localhost:8080",
-    )[args.instance]
-    run_test(
-        num_requests=args.num_requests,
-        requests_per_second=args.requests_per_second,
-        api_key=args.api_key,
-        server_url=server_url,
-        endpoint_group_name=args.endpoint_group_name,
-        worker_endpoint=endpoint,
-        payload_cls=payload_cls,
-        instance=args.instance,
-    )

lib/wheres-my-pyworker.txt

@@ -0,0 +1,22 @@
+# Where did the PyWorker code go?
+We have moved the PyWorker source code into the `vastai-sdk` Python SDK.
+You can install it with
+```
+pip install vastai-sdk
+```
+
+All of the source code can be found here:
+https://github.com/vast-ai/vast-sdk
+
+And can be imported from vastai.serverless.server.lib
+
+Serverless instances automatically run the start_server.sh script, which installs the vastai-sdk.
+This is how the PyWorker source code makes it onto your serverless instances.
+You provide a worker.py file in your PYWORKER_REPO, and the start_server.sh will 
+create and run a PyWorker according to your configuration defined in the file.
+
+While you can still create and run PyWorkers for serverless using your old PyWorker code,
+we **strongly** encourage you to use the new worker.py configuration method, since
+we can guarantee backwards compatibility for all your worker definitions. No more forking pyworker :)
+
+If you encounter and issues with using PyWorker, please create a GitHub issue and we will be happy to assist.

requirements.txt

@@ -8,3 +8,4 @@ Requests~=2.32
 transformers~=4.52
 utils==1.0.*
 hf_transfer>=0.1.9
+git+https://github.com/vast-ai/vast-sdk.git@session

start_server.sh

@@ -9,24 +9,46 @@ ENV_PATH="$WORKSPACE_DIR/worker-env"
 DEBUG_LOG="$WORKSPACE_DIR/debug.log"
 PYWORKER_LOG="$WORKSPACE_DIR/pyworker.log"
 
-REPORT_ADDR="${REPORT_ADDR:-https://cloud.vast.ai/api/v0,https://run.vast.ai}"
+REPORT_ADDR="${REPORT_ADDR:-https://run.vast.ai}"
 USE_SSL="${USE_SSL:-true}"
 WORKER_PORT="${WORKER_PORT:-3000}"
 mkdir -p "$WORKSPACE_DIR"
 cd "$WORKSPACE_DIR"
 
-# make all output go to $DEBUG_LOG and stdout without having to add `... | tee -a $DEBUG_LOG` to every command
 exec &> >(tee -a "$DEBUG_LOG")
 
 function echo_var(){
     echo "$1: ${!1}"
 }
 
-[ -z "$BACKEND" ] && echo "BACKEND must be set!" && exit 1
+function report_error_and_exit(){
-[ -z "$MODEL_LOG" ] && echo "MODEL_LOG must be set!" && exit 1
+    local error_msg="$1"
-[ -z "$HF_TOKEN" ] && echo "HF_TOKEN must be set!" && exit 1
+    echo "ERROR: $error_msg"
-[ "$BACKEND" = "comfyui" ] && [ -z "$COMFY_MODEL" ] && echo "For comfyui backends, COMFY_MODEL must be set!" && exit 1
 
+    MTOKEN="${MASTER_TOKEN:-}"
+    VERSION="${PYWORKER_VERSION:-0}"
+
+    IFS=',' read -r -a REPORT_ADDRS <<< "${REPORT_ADDR}"
+    for addr in "${REPORT_ADDRS[@]}"; do
+        curl -sS -X POST -H 'Content-Type: application/json' \
+            -d "$(cat <<JSON
+{
+  "id": ${CONTAINER_ID:-0},
+  "mtoken": "${MTOKEN}",
+  "version": "${VERSION}",
+  "error_msg": "${error_msg}",
+  "url": "${URL:-}"
+}
+JSON
+)" "${addr%/}/worker_status/" || true
+    done
+
+    exit 1
+}
+
+[ -n "$BACKEND" ] && [ -z "$HF_TOKEN" ] && report_error_and_exit "HF_TOKEN must be set when BACKEND is set!"
+[ -z "$CONTAINER_ID" ] && report_error_and_exit "CONTAINER_ID must be set!"
+[ "$BACKEND" = "comfyui" ] && [ -z "$COMFY_MODEL" ] && report_error_and_exit "For comfyui backends, COMFY_MODEL must be set!"
 
 echo "start_server.sh"
 date
@@ -41,47 +63,85 @@ echo_var DEBUG_LOG
 echo_var PYWORKER_LOG
 echo_var MODEL_LOG
 
+if [ -e "$MODEL_LOG" ]; then
+    echo "Rotating model log at $MODEL_LOG to $MODEL_LOG.old"
+    if ! cat "$MODEL_LOG" >> "$MODEL_LOG.old"; then
+        report_error_and_exit "Failed to rotate model log"
+    fi
+    if ! : > "$MODEL_LOG"; then
+        report_error_and_exit "Failed to truncate model log"
+    fi
+fi
+
 # Populate /etc/environment with quoted values
 if ! grep -q "VAST" /etc/environment; then
-    env -0 | grep -zEv "^(HOME=|SHLVL=)|CONDA" | while IFS= read -r -d '' line; do
+    if ! env -0 | grep -zEv "^(HOME=|SHLVL=)|CONDA" | while IFS= read -r -d '' line; do
             name=${line%%=*}
             value=${line#*=}
             printf '%s="%s"\n' "$name" "$value"
-        done > /etc/environment
+        done > /etc/environment; then
+        echo "WARNING: Failed to populate /etc/environment, continuing anyway"
+    fi
 fi
 
 if [ ! -d "$ENV_PATH" ]
 then
     echo "setting up venv"
     if ! which uv; then
-        curl -LsSf https://astral.sh/uv/install.sh | sh
+        if ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
-        source ~/.local/bin/env
+            report_error_and_exit "Failed to install uv package manager"
+        fi
+        if [[ -f ~/.local/bin/env ]]; then
+            if ! source ~/.local/bin/env; then
+                report_error_and_exit "Failed to source uv environment"
+            fi
+        else
+            echo "WARNING: ~/.local/bin/env not found after uv installation"
+        fi
     fi
 
-    # Fork testing
+    if [[ ! -d $SERVER_DIR ]]; then
-    [[ ! -d $SERVER_DIR ]] && git clone "${PYWORKER_REPO:-https://github.com/vast-ai/pyworker}" "$SERVER_DIR"
+        if ! git clone "${PYWORKER_REPO:-https://github.com/vast-ai/pyworker}" "$SERVER_DIR"; then
+            report_error_and_exit "Failed to clone pyworker repository"
+        fi
+    fi
     if [[ -n ${PYWORKER_REF:-} ]]; then
-        (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF")
+        if ! (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF"); then
+            report_error_and_exit "Failed to checkout pyworker reference: $PYWORKER_REF"
+        fi
     fi
 
-    uv venv --python-preference only-managed "$ENV_PATH" -p 3.10
+    if ! uv venv --python-preference only-managed "$ENV_PATH" -p 3.10; then
-    source "$ENV_PATH/bin/activate"
+        report_error_and_exit "Failed to create virtual environment"
+    fi
 
-    uv pip install -r "${SERVER_DIR}/requirements.txt"
+    if ! source "$ENV_PATH/bin/activate"; then
+        report_error_and_exit "Failed to activate virtual environment"
+    fi
 
-    touch ~/.no_auto_tmux
+    if ! uv pip install -r "${SERVER_DIR}/requirements.txt"; then
+        report_error_and_exit "Failed to install Python requirements"
+    fi
+
+    if ! touch ~/.no_auto_tmux; then
+        report_error_and_exit "Failed to create ~/.no_auto_tmux"
+    fi
 else
-    [[ -f ~/.local/bin/env ]] && source ~/.local/bin/env
+    if [[ -f ~/.local/bin/env ]]; then
-    source "$WORKSPACE_DIR/worker-env/bin/activate"
+        if ! source ~/.local/bin/env; then
+            report_error_and_exit "Failed to source uv environment"
+        fi
+    fi
+    if ! source "$WORKSPACE_DIR/worker-env/bin/activate"; then
+        report_error_and_exit "Failed to activate existing virtual environment"
+    fi
     echo "environment activated"
     echo "venv: $VIRTUAL_ENV"
 fi
 
-[ ! -d "$SERVER_DIR/workers/$BACKEND" ] && echo "$BACKEND not supported!" && exit 1
-
 if [ "$USE_SSL" = true ]; then
 
-    cat << EOF > /etc/openssl-san.cnf
+    if ! cat << EOF > /etc/openssl-san.cnf
     [req]
     default_bits       = 2048
     distinguished_name = req_distinguished_name
@@ -101,32 +161,64 @@ if [ "$USE_SSL" = true ]; then
     [alt_names]
     IP.1   = 0.0.0.0
 EOF
+    then
+        report_error_and_exit "Failed to write OpenSSL config"
+    fi
 
-    openssl req -newkey rsa:2048 -subj "/C=US/ST=CA/CN=pyworker.vast.ai/" \
+    if ! openssl req -newkey rsa:2048 -subj "/C=US/ST=CA/CN=pyworker.vast.ai/" \
         -nodes \
         -sha256 \
         -keyout /etc/instance.key \
         -out /etc/instance.csr \
-        -config /etc/openssl-san.cnf
+        -config /etc/openssl-san.cnf; then
+        report_error_and_exit "Failed to generate SSL certificate request"
+    fi
 
-    curl --header 'Content-Type: application/octet-stream' \
+    if ! curl --header 'Content-Type: application/octet-stream' \
-        --data-binary @//etc/instance.csr \
+        --data-binary @/etc/instance.csr \
         -X \
-        POST "https://console.vast.ai/api/v0/sign_cert/?instance_id=$CONTAINER_ID" > /etc/instance.crt;
+        POST "https://console.vast.ai/api/v0/sign_cert/?instance_id=$CONTAINER_ID" > /etc/instance.crt; then
+        report_error_and_exit "Failed to sign SSL certificate"
+    fi
 fi
 
-
-
-
 export REPORT_ADDR WORKER_PORT USE_SSL UNSECURED
 
-cd "$SERVER_DIR"
+if ! cd "$SERVER_DIR"; then
+    report_error_and_exit "Failed to cd into SERVER_DIR: $SERVER_DIR"
+fi
 
 echo "launching PyWorker server"
 
-# if instance is rebooted, we want to clear out the log file so pyworker doesn't read lines
+set +e
-# from the run prior to reboot. past logs are saved in $MODEL_LOG.old for debugging only
+
-[ -e "$MODEL_LOG" ] && cat "$MODEL_LOG" >> "$MODEL_LOG.old" && : > "$MODEL_LOG"
+PY_STATUS=1
+
+if [ -f "$SERVER_DIR/worker.py" ]; then
+    echo "trying worker.py"
+    python3 -m "worker" |& tee -a "$PYWORKER_LOG"
+    PY_STATUS=${PIPESTATUS[0]}
+fi
+
+if [ "${PY_STATUS}" -ne 0 ] && [ -f "$SERVER_DIR/workers/$BACKEND/worker.py" ]; then
+    echo "trying workers.${BACKEND}.worker"
+    python3 -m "workers.${BACKEND}.worker" |& tee -a "$PYWORKER_LOG"
+    PY_STATUS=${PIPESTATUS[0]}
+fi
+
+if [ "${PY_STATUS}" -ne 0 ] && [ -f "$SERVER_DIR/workers/$BACKEND/server.py" ]; then
+    echo "trying workers.${BACKEND}.server"
+    python3 -m "workers.${BACKEND}.server" |& tee -a "$PYWORKER_LOG"
+    PY_STATUS=${PIPESTATUS[0]}
+fi
+
+set -e
+
+if [ "${PY_STATUS}" -ne 0 ]; then
+    if [ ! -f "$SERVER_DIR/worker.py" ] && [ ! -f "$SERVER_DIR/workers/$BACKEND/worker.py" ] && [ ! -f "$SERVER_DIR/workers/$BACKEND/server.py" ]; then
+        report_error_and_exit "Failed to find PyWorker"
+    fi
+    report_error_and_exit "PyWorker exited with status ${PY_STATUS}"
+fi
 
-(python3 -m "workers.$BACKEND.server" |& tee -a "$PYWORKER_LOG") &
 echo "launching PyWorker server done"

utils/endpoint_util.py

@@ -1,98 +0,0 @@
-import logging
-from typing import Any, Dict, Optional
-
-import requests
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-
-class Endpoint:
-    """
-    Utility class for handling endpoint operations.
-    """
-
-    @staticmethod
-    def get_autoscaler_server_url(instance: str) -> str:
-        endpoints = {
-            "alpha": "run-alpha",
-            "candidate": "run-candidate",
-            "prod": "run",
-        }
-        return f"https://{endpoints[instance]}.vast.ai/"
-
-    @staticmethod
-    def get_server_url(instance: str) -> str:
-        endpoints = {
-            "alpha": "alpha",
-            "candidate": "candidate",
-            "prod": "console",
-        }
-        return f"https://{endpoints[instance]}.vast.ai/api/v0/endptjobs/"
-
-    @staticmethod
-    def get_endpoint_api_key(
-        endpoint_name: str, account_api_key: str, instance: str
-    ) -> Optional[str]:
-        """
-        Fetch endpoint API key from VastAI console following the healthcheck pattern.
-
-        Args:
-            endpoint_name: Name of the endpoint
-            account_api_key: Account API key for authentication
-
-        Returns:
-            Endpoint API key if successful, None otherwise
-        """
-        headers = {"Authorization": f"Bearer {account_api_key}"}
-
-        try:
-            log.debug(f"Fetching endpoint API key for endpoint: {endpoint_name}")
-            response = requests.get(
-                f"{Endpoint.get_server_url(instance)}?autoscaler_instance={instance}",
-                headers=headers,
-            )
-
-            if response.status_code != 200:
-                error_msg = f"Failed to fetch endpoint API key: {response.status_code} - {response.text}"
-                log.debug(error_msg)
-                return None
-
-            try:
-                data = response.json()
-            except requests.exceptions.JSONDecodeError as e:
-                log.debug(f"Failed to parse JSON response: {e}")
-                return None
-
-            result = data.get("results", [])
-
-            endpoint: Optional[Dict[str, Any]] = next(
-                (item for item in result if item["endpoint_name"] == endpoint_name),
-                None,
-            )
-            if not endpoint:
-                error_msg = f"Endpoint '{endpoint_name}' not found."
-                log.debug(error_msg)
-                return None
-
-            endpoint_api_key = endpoint.get("api_key")
-            if not endpoint_api_key:
-                error_msg = f"API key for endpoint '{endpoint_name}' not found."
-                log.debug(error_msg)
-                return None
-
-            log.debug(f"Successfully retrieved API key for endpoint: {endpoint_name}")
-            return endpoint_api_key
-
-        except requests.exceptions.RequestException as e:
-            error_msg = f"Request error while fetching endpoint API key: {e}"
-            log.debug(error_msg)
-            return None
-        except Exception as e:
-            error_msg = f"Unexpected error while fetching endpoint API key: {e}"
-            log.debug(error_msg)
-            return None

utils/ssl.py

@@ -1,15 +0,0 @@
-import tempfile
-from functools import cache
-
-import requests
-
-
-@cache
-def get_cert_file_path():
-    cert_url = "https://console.vast.ai/static/jvastai_root.cer"
-    response = requests.get(cert_url)
-    response.raise_for_status()
-    # Use a temporary file that is not deleted on close
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".cer", mode="wb") as f:
-        f.write(response.content)
-        return f.name

workers/ace/README.md

@@ -0,0 +1,168 @@
+# ComfyUI ACE Step PyWorker
+
+This is the PyWorker implementation for running **ACE Step v1 3.5B** text-to-music workflows in ComfyUI. It provides a unified interface for executing complete ComfyUI audio-generation workflows through a proxy-based architecture and returning generated audio assets.
+
+Each request has a static cost of `100`. ComfyUI does not support concurrent workloads, and there is no provision to run multiple ComfyUI instances per worker node.
+
+## Requirements
+
+This worker requires the following components:
+
+- ComfyUI (https://github.com/comfyanonymous/ComfyUI)
+- ComfyUI API Wrapper (https://github.com/ai-dock/comfyui-api-wrapper)
+- ACE Step v1 3.5B model and required custom nodes
+
+A Docker image is provided with the ACE Step model pre-installed, but any image may be used if the above requirements are met.
+
+## Endpoint
+
+The worker exposes a single synchronous endpoint:
+
+- `/generate/sync`: Processes a complete ComfyUI workflow JSON and generates audio output
+
+## Request Format
+
+The ACE Step worker **only supports custom workflow mode**. Modifier-based workflows are not supported.
+
+```json
+{
+  "input": {
+    "request_id": "uuid-string",
+    "workflow_json": {
+      // Complete ComfyUI ACE Step workflow JSON
+    },
+    "s3": { },
+    "webhook": { }
+  }
+}
+```
+
+## Request Fields
+
+### Required Fields
+
+- `input`: Container for all request parameters
+- `input.workflow_json`: Complete ComfyUI workflow graph for ACE Step audio generation
+
+### Optional Fields
+
+- `input.request_id`: Client-defined request identifier
+- `input.s3`: S3-compatible storage configuration
+- `input.webhook`: Webhook configuration for completion notifications
+
+The special string `"__RANDOM_INT__"` may be used in the workflow JSON and will be replaced with a random integer before submission to ComfyUI.
+
+## S3 Configuration
+
+Generated audio assets can be automatically uploaded to S3-compatible storage. Configuration can be supplied per request or via environment variables. Request-level values take precedence.
+
+### Via Request JSON
+
+```json
+"s3": {
+  "access_key_id": "your-s3-access-key",
+  "secret_access_key": "your-s3-secret-access-key",
+  "endpoint_url": "https://s3.amazonaws.com",
+  "bucket_name": "your-bucket",
+  "region": "us-east-1"
+}
+```
+
+### Via Environment Variables
+
+```bash
+S3_ACCESS_KEY_ID=your-key
+S3_SECRET_ACCESS_KEY=your-secret
+S3_BUCKET_NAME=your-bucket
+S3_ENDPOINT_URL=https://s3.amazonaws.com
+S3_REGION=us-east-1
+```
+
+## Webhook Configuration
+
+Webhooks are triggered on request completion or failure.
+
+### Via Request JSON
+
+```json
+"webhook": {
+  "url": "https://your-webhook-url",
+  "extra_params": {
+    "custom_field": "value"
+  }
+}
+```
+
+### Via Environment Variables
+
+```bash
+WEBHOOK_URL=https://your-webhook-url
+WEBHOOK_TIMEOUT=30
+```
+
+## Example Request
+
+### ACE Step Text-to-Music Workflow
+
+```json
+{
+  "input": {
+    "workflow_json": {
+      "14": {
+        "inputs": {
+          "tags": "funk, pop, upbeat, 105 BPM",
+          "lyrics": "Turn it up and let it flow",
+          "lyrics_strength": 0.99,
+          "clip": ["40", 1]
+        },
+        "class_type": "TextEncodeAceStepAudio"
+      },
+      "17": {
+        "inputs": {
+          "seconds": 180,
+          "batch_size": 1
+        },
+        "class_type": "EmptyAceStepLatentAudio"
+      },
+      "40": {
+        "inputs": {
+          "ckpt_name": "ace_step_v1_3.5b.safetensors"
+        },
+        "class_type": "CheckpointLoaderSimple"
+      }
+    }
+  }
+}
+```
+
+## Response Format
+
+A successful response includes execution metadata, ComfyUI output details, and generated audio assets.
+
+### Response Fields
+
+- `id`: Unique request identifier
+- `status`: `completed`, `failed`, `processing`, `generating`, or `queued`
+- `message`: Human-readable status message
+- `comfyui_response`: Raw response from ComfyUI, including execution status and progress
+- `output`: Array of generated outputs
+- `timings`: Timing information for the request
+
+### Output Object
+
+Each entry in `output` includes:
+
+- `filename`: Generated file name (e.g., `.mp3`)
+- `local_path`: File path on the worker
+- `url`: Pre-signed download URL (if S3 is configured)
+- `type`: Output type (`output`)
+- `subfolder`: Output directory (e.g., `audio`)
+- `node_id`: ComfyUI node that produced the output
+- `output_type`: Output category (e.g., `audio`)
+
+## Notes and Limitations
+
+- Only full ComfyUI workflow JSONs are supported
+- Concurrent requests are not supported per worker
+- ACE Step model must be installed before processing requests
+- Audio generation duration and runtime depend on workflow configuration

workers/ace/client.py

@@ -0,0 +1,149 @@
+from vastai import Serverless
+import asyncio
+
+
+async def main():
+    async with Serverless() as client:
+        endpoint = await client.get_endpoint(name="my-ace-endpoint")
+
+        # ComfyUI API compatible json workflow for ACE Step
+        workflow = {
+          "14": {
+            "inputs": {
+              "tags": "funk, pop, soul, rock, melodic, guitar, drums, bass, keyboard, percussion, 105 BPM, energetic, upbeat, groovy, vibrant, dynamic",
+              "lyrics": "[verse]\nNeon lights they flicker bright\nCity hums in dead of night\nRhythms pulse through concrete veins\nLost in echoes of refrains\n\n[verse]\nBassline groovin in my chest\nHeartbeats match the citys zest\nElectric whispers fill the air\nSynthesized dreams everywhere\n\n[chorus]\nTurn it up and let it flow\nFeel the fire let it grow\nIn this rhythm we belong\nHear the night sing out our song",
+              "lyrics_strength": 0.99,
+              "clip": ["40", 1]
+            },
+            "class_type": "TextEncodeAceStepAudio",
+            "_meta": {
+              "title": "TextEncodeAceStepAudio"
+            }
+          },
+          "17": {
+            "inputs": {
+              "seconds": 180,
+              "batch_size": 1
+            },
+            "class_type": "EmptyAceStepLatentAudio",
+            "_meta": {
+              "title": "EmptyAceStepLatentAudio"
+            }
+          },
+          "18": {
+            "inputs": {
+              "samples": ["52", 0],
+              "vae": ["40", 2]
+            },
+            "class_type": "VAEDecodeAudio",
+            "_meta": {
+              "title": "VAE Decode Audio"
+            }
+          },
+          "40": {
+            "inputs": {
+              "ckpt_name": "ace_step_v1_3.5b.safetensors"
+            },
+            "class_type": "CheckpointLoaderSimple",
+            "_meta": {
+              "title": "Load Checkpoint"
+            }
+          },
+          "44": {
+            "inputs": {
+              "conditioning": ["14", 0]
+            },
+            "class_type": "ConditioningZeroOut",
+            "_meta": {
+              "title": "ConditioningZeroOut"
+            }
+          },
+          "49": {
+            "inputs": {
+              "model": ["51", 0],
+              "operation": ["50", 0]
+            },
+            "class_type": "LatentApplyOperationCFG",
+            "_meta": {
+              "title": "LatentApplyOperationCFG"
+            }
+          },
+          "50": {
+            "inputs": {
+              "multiplier": 1.15
+            },
+            "class_type": "LatentOperationTonemapReinhard",
+            "_meta": {
+              "title": "LatentOperationTonemapReinhard"
+            }
+          },
+          "51": {
+            "inputs": {
+              "shift": 6,
+              "model": ["40", 0]
+            },
+            "class_type": "ModelSamplingSD3",
+            "_meta": {
+              "title": "ModelSamplingSD3"
+            }
+          },
+          "52": {
+            "inputs": {
+              "seed": "__RANDOM_INT__",
+              "steps": 65,
+              "cfg": 4,
+              "sampler_name": "er_sde",
+              "scheduler": "linear_quadratic",
+              "denoise": 1,
+              "model": ["49", 0],
+              "positive": ["14", 0],
+              "negative": ["44", 0],
+              "latent_image": ["17", 0]
+            },
+            "class_type": "KSampler",
+            "_meta": {
+              "title": "KSampler"
+            }
+          },
+          "59": {
+            "inputs": {
+              "filename_prefix": "audio/ComfyUI",
+              "quality": "V0",
+              "audioUI": "",
+              "audio": ["18", 0]
+            },
+            "class_type": "SaveAudioMP3",
+            "_meta": {
+              "title": "Save Audio (MP3)"
+            }
+          }
+        }
+
+        payload = {
+          "input": {
+            "request_id": "",
+            "workflow_json": workflow,
+            "s3": {
+              "access_key_id": "",
+              "secret_access_key": "",
+              "endpoint_url": "",
+              "bucket_name": "",
+              "region": ""
+            },
+            "webhook": {
+              "url": "",
+              "extra_params": {
+                "user_id": "12345",
+                "project_id": "abc-def"
+              }
+            }
+          }
+        }
+
+        response = await endpoint.request("/generate/sync", payload)
+
+        # Response contains status, output, and any errors
+        print(response["response"])
+
+if __name__ == "__main__":
+    asyncio.run(main())

workers/ace/worker.py

@@ -0,0 +1,184 @@
+import random
+import sys
+
+from vastai import Worker, WorkerConfig, HandlerConfig, LogActionConfig, BenchmarkConfig
+
+# ComyUI model configuration
+MODEL_SERVER_URL           = 'http://127.0.0.1'
+MODEL_SERVER_PORT          = 18288
+MODEL_LOG_FILE             = '/var/log/portal/comfyui.log'
+MODEL_HEALTHCHECK_ENDPOINT = "/health"
+
+# ComyUI-specific log messages
+MODEL_LOAD_LOG_MSG = [
+    "To see the GUI go to: "
+]
+
+MODEL_ERROR_LOG_MSGS = [
+    "MetadataIncompleteBuffer",
+    "Value not in list: ",
+    "[ERROR] Provisioning Script failed"
+]
+
+MODEL_INFO_LOG_MSGS = [
+    '"message":"Downloading'
+]
+
+benchmark_lyrics = [
+    "[verse]\nGuardian cloaked in twilight hue\nShadows melt where he breaks through\nEchoes swirl in mystic flight\nHooded hero owns the night\n\n[verse]\nThrough the chaos shapes arise\nFeral whispers, glowing eyes\nOrcs and creatures side by side\nMarch within the inky tide\n\n[chorus]\nRise above the fear and gloom\nLet your courage fully bloom\nIn the darkness stand your ground\nHear the night proclaim your sound",
+    "[verse]\nMorning sun on fields of gold\nGentle stories unfold\nEvery breeze a quiet song\nWhere the peaceful hearts belong\n\n[verse]\nLanterns glow at stable doors\nRustling leaves on orchard floors\nSimple joys in every hand\nLife grows soft in fertile land\n\n[chorus]\nLet the day drift slow and free\nRoot your soul where you can be\nIn this haven warm and bright\nFeel the earth breathe pure delight",
+    "[verse]\nLittle feet on dusty ground\nChasing dreams without a sound\nSoccer ball in morning light\nHopes take wing in youthful flight\n\n[verse]\nChrome reflections paint the day\nSwagger in the steps that play\nCopper tones in shining air\nChildhood gleaming everywhere\n\n[chorus]\nKick the world with boundless cheer\nHold the magic close and near\nIn each moment bold and true\nLet the sky belong to you",
+    "[verse]\nSunset bleeds across the street\nGilded calm in summer heat\nLow-rise towers rimmed with fire\nDreams ignite as lights climb higher\n\n[verse]\nFootsteps scatter through the haze\nFutures shimmer in the blaze\nEvery window tells a tale\nFloating through a tangerine veil\n\n[chorus]\nLet the neon softly glow\nLet your restless heartbeat slow\nIn this city forged in light\nCarry hope into the night",
+    "[verse]\nOcean breathes in rolling arcs\nSprays of diamond, glowing sparks\nWaves unfold a perfect line\nNature’s rhythm feels divine\n\n[verse]\nSun above in golden sweep\nPaints the rise of every deep\nShimmer drifting through the blue\nWorld reborn in every view\n\n[chorus]\nLet the tide pull you along\nHear the water’s ancient song\nIn the cresting waves you’ll find\nQuiet peace for heart and mind",
+    "[verse]\nGlass aglow with swirling light\nFruits and mints in colors bright\nIcy whispers clink and chime\nFlowing forms suspend in time\n\n[verse]\nCreamy spirals drift within\nGentle currents slowly spin\nWarm reflections lingering sweet\nMixing flavors at your feet\n\n[chorus]\nSip the glow and let it rise\nTaste the sunset in disguise\nIn this moment clear and true\nLet the warmth flow into you",
+    "[verse]\nEngines rumble down the lane\nCopper clouds of steam and rain\nOilpunk dreams in metal shine\nRider drifting down the line\n\n[verse]\nLeather jacket, steady glare\nStories sparking in the air\nMagazine lights frame his face\nKing of roads in timeless grace\n\n[chorus]\nThrottle up beyond the bend\nFeel the force of steel ascend\nRide the night and hold on tight\nClaim the world in streaks of light",
+    "[verse]\nCut-out shapes in swirling play\nTextures dance in bold array\nCats in denim, grinning wide\nStrut across the patterned tide\n\n[verse]\nPosters hum with neon glow\nSurreal scenes begin to grow\nColors crisp as folded art\nPatchwork beating like a heart\n\n[chorus]\nLet the collage come alive\nWatch the vibrant pieces thrive\nIn this joyful, crafted space\nEvery shape finds its own place",
+    "[verse]\nTiny world in crystal glass\nAncient tales behind the mass\nVillage lights in winter gleam\nFrozen in a mystic dream\n\n[verse]\nLantern beams in swirling air\nSoft enchantment everywhere\nShadows drift with gentle grace\nMagic sealed within the space\n\n[chorus]\nHold the sphere and you will see\nEchoes of a memory\nIn the glow of fragile light\nLives a realm of pure delight",
+    "[verse]\nArmor hums with power bright\nChopping sparks in jungle night\nMecha spirits shift and scream\nThrough the ferns like shattered beams\n\n[verse]\nAxes blaze in glowing arcs\nLighting up the shadowed marks\nNature roars in trembling air\nClash of steel and cosmic flare\n\n[chorus]\nRaise the fire, strike the ground\nLet your legend shake the sound\nIn the wild where echoes roam\nForge the fight and carve your home",
+    "[verse]\nCrowds ignite in vibrant flare\nBeats explode through smoky air\nDJ robes replaced with flame\nPope on decks in holy frame\n\n[verse]\nLeather gleams in blinding light\nTurntables spin with sacred might\nChoirs echo in the bass\nHeaven pulses through the place\n\n[chorus]\nLift the roof and shake the floor\nSacred rhythm evermore\nLet the music take control\nFeel the blessing in your soul",
+]
+
+benchmark_dataset = [
+    {
+        "input": {
+            "request_id": "",
+            "workflow_json": {
+                "14": {
+                "inputs": {
+                    "tags": "funk, pop, soul, rock, melodic, guitar, drums, bass, keyboard, percussion, 105 BPM, energetic, upbeat, groovy, vibrant, dynamic",
+                    "lyrics": lyrics,
+                    "lyrics_strength": 0.99,
+                    "clip": ["40", 1]
+                },
+                "class_type": "TextEncodeAceStepAudio",
+                "_meta": {
+                    "title": "TextEncodeAceStepAudio"
+                }
+                },
+                "17": {
+                "inputs": {
+                    "seconds": 180,
+                    "batch_size": 1
+                },
+                "class_type": "EmptyAceStepLatentAudio",
+                "_meta": {
+                    "title": "EmptyAceStepLatentAudio"
+                }
+                },
+                "18": {
+                "inputs": {
+                    "samples": ["52", 0],
+                    "vae": ["40", 2]
+                },
+                "class_type": "VAEDecodeAudio",
+                "_meta": {
+                    "title": "VAE Decode Audio"
+                }
+                },
+                "40": {
+                "inputs": {
+                    "ckpt_name": "ace_step_v1_3.5b.safetensors"
+                },
+                "class_type": "CheckpointLoaderSimple",
+                "_meta": {
+                    "title": "Load Checkpoint"
+                }
+                },
+                "44": {
+                "inputs": {
+                    "conditioning": ["14", 0]
+                },
+                "class_type": "ConditioningZeroOut",
+                "_meta": {
+                    "title": "ConditioningZeroOut"
+                }
+                },
+                "49": {
+                "inputs": {
+                    "model": ["51", 0],
+                    "operation": ["50", 0]
+                },
+                "class_type": "LatentApplyOperationCFG",
+                "_meta": {
+                    "title": "LatentApplyOperationCFG"
+                }
+                },
+                "50": {
+                "inputs": {
+                    "multiplier": 1.15
+                },
+                "class_type": "LatentOperationTonemapReinhard",
+                "_meta": {
+                    "title": "LatentOperationTonemapReinhard"
+                }
+                },
+                "51": {
+                "inputs": {
+                    "shift": 6,
+                    "model": ["40", 0]
+                },
+                "class_type": "ModelSamplingSD3",
+                "_meta": {
+                    "title": "ModelSamplingSD3"
+                }
+                },
+                "52": {
+                "inputs": {
+                    "seed": "__RANDOM_INT__",
+                    "steps": 65,
+                    "cfg": 4,
+                    "sampler_name": "er_sde",
+                    "scheduler": "linear_quadratic",
+                    "denoise": 1,
+                    "model": ["49", 0],
+                    "positive": ["14", 0],
+                    "negative": ["44", 0],
+                    "latent_image": ["17", 0]
+                },
+                "class_type": "KSampler",
+                "_meta": {
+                    "title": "KSampler"
+                }
+                },
+                "59": {
+                "inputs": {
+                    "filename_prefix": "audio/ComfyUI",
+                    "quality": "V0",
+                    "audioUI": "",
+                    "audio": ["18", 0]
+                },
+                "class_type": "SaveAudioMP3",
+                "_meta": {
+                    "title": "Save Audio (MP3)"
+                }
+                }
+            }
+        }
+    } for lyrics in benchmark_lyrics
+]
+
+worker_config = WorkerConfig(
+    model_server_url=MODEL_SERVER_URL,
+    model_server_port=MODEL_SERVER_PORT,
+    model_log_file=MODEL_LOG_FILE,
+    model_healthcheck_url=MODEL_HEALTHCHECK_ENDPOINT,
+    handlers=[
+        HandlerConfig(
+            route="/generate/sync",
+            allow_parallel_requests=False,
+            max_queue_time=10.0,
+            benchmark_config=BenchmarkConfig(
+                dataset=benchmark_dataset,
+                runs=1
+            ),
+            workload_calculator= lambda _ : 1000.0
+        )
+    ],
+    log_action_config=LogActionConfig(
+        on_load=MODEL_LOAD_LOG_MSG,
+        on_error=MODEL_ERROR_LOG_MSGS,
+        on_info=MODEL_INFO_LOG_MSGS
+    )
+)
+
+Worker(worker_config).run()

workers/comfyui-json/README.md

@@ -2,7 +2,7 @@
 
 This is the base PyWorker for ComfyUI. It provides a unified interface for running any ComfyUI workflow through a proxy-based architecture.
 
-The cost for each request has a static value of `1`.  ComfyUI does not handle concurrent workloads and there is no current provision to load multiple instances of ComfyUI per worker node.
+The cost for each request has a static value of `100`.  ComfyUI does not handle concurrent workloads and there is no current provision to load multiple instances of ComfyUI per worker node.
 
 ## Requirements
 
@@ -10,55 +10,6 @@ This worker requires both [ComfyUI](https://github.com/comfyanonymous/ComfyUI) a
 
 A docker image is provided but you may use any if the above requirements are met.
 
-## Benchmarking
-
-### Custom Benchmark Workflows
-
-You can provide a custom ComfyUI workflow for benchmarking by creating `workers/comfyui-json/misc/benchmark.json`. This allows you to test performance using your preferred models and workflow complexity.
-
-**Ways to provide the benchmark file:**
-- Fork this repository and add your `benchmark.json` file
-- Write the file during worker provisioning (onstart script or setup phase)
-
-An example file is provided in the repository. To ensure varied generations, use the placeholder `__RANDOM_INT__` in place of static seed values - it will be replaced with a random integer for each benchmark run.
-
-### Default Benchmark (Fallback)
-
-If `benchmark.json` is not available, a simple image generation benchmark runs when each worker initializes. This validates GPU performance and helps identify underperforming machines.
-
-The default benchmark uses Stable Diffusion v1.5 with ComfyUI's standard text-to-image workflow. Configure it using these environment variables:
-
-| Environment Variable | Default Value | Description |
-| -------------------- | ------------- | ----------- |
-| BENCHMARK_TEST_WIDTH | 512 | Image width (pixels) |
-| BENCHMARK_TEST_HEIGHT | 512 | Image height (pixels) |
-| BENCHMARK_TEST_STEPS | 20 | Number of denoising steps |
-
-Each benchmark run uses a random prompt from `misc/test_prompts.txt` and a random seed to ensure consistent GPU load patterns.
-
-#### Calibrating Fallback Benchmark Duration
-
-To screen for underperforming hardware, set `BENCHMARK_TEST_STEPS` to match your expected production workflow duration. This allows you to identify machines that won't meet performance requirements.
-
-**Example:** If your typical workflow should complete in 90 seconds on acceptable hardware:
-
-```bash
-# 1. Measure it/sec on your reference machine
-# RTX 4090 typically achieves ~43 it/sec with SD1.5
-
-# 2. Calculate required steps
-# 90 seconds × 43 it/sec = 3870 steps
-
-# 3. Configure benchmark
-export BENCHMARK_TEST_STEPS=3870
-
-# 4. Machines completing significantly slower than 90s indicate hardware issues
-```
-
-**Performance expectations:**
-- Benchmark duration should remain consistent across identical GPU models
-- Significant variation (>20%) may indicate thermal, power, or configuration issues
-
 ## Endpoint
 
 The worker provides a single endpoint:
@@ -215,7 +166,7 @@ WEBHOOK_TIMEOUT=30                   # Webhook timeout in seconds
 
 ## Client Libraries
 
-See the test client examples for implementation details on how to integrate with the ComfyUI worker.
+See the client example for implementation details on how to integrate with the ComfyUI worker.
 
 ---
 

workers/comfyui-json/client.py

@@ -1,156 +1,34 @@
-import logging
 import uuid
 import random
-from urllib.parse import urljoin
+import asyncio
-import json
+import random
 
-import requests
+from vastai import Serverless
 
-from lib.test_utils import print_truncate_res
+async def main():
-from utils.endpoint_util import Endpoint
+    async with Serverless() as client:
-from utils.ssl import get_cert_file_path
+        endpoint = await client.get_endpoint(name="my-comfy-endpoint") # Change this to your endpoint name
-from .data_types import count_workload
 
-logging.basicConfig(
+        payload = {
-    level=logging.DEBUG,
+            "input": {
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
+                "request_id": str(uuid.uuid4()),
-    datefmt="%Y-%m-%d %H:%M:%S",
+                "modifier": "Text2Image",
-)
+                "modifications": {
-log = logging.getLogger(__file__)
+                    "prompt": "a beautiful landscape with mountains and lakes",
-
+                    "width": 1024,
-
+                    "height": 1024,
-def call_text2image_workflow(
+                    "steps": 20,
-    endpoint_group_name: str, api_key: str, server_url: str
+                    "seed": random.randint(0, 2**32 - 1)
-) -> None:
+                },
-    """Simple Text2Image using the new modifier-based approach"""
+                "workflow_json": {}  # Empty since using modifier approach
-    
+            }
-    def make_request(url: str, payload: dict, timeout: int = None, verify=True, context: str = "request"):
-        """Helper function for making requests with consistent error handling"""
-        try:
-            response = requests.post(
-                url,
-                json=payload,
-                timeout=timeout,
-                verify=verify
-            )
-            
-            response.raise_for_status()
-            return response.json()
-            
-        except requests.exceptions.HTTPError as http_err:
-            log.error(f"HTTP error occurred during {context}: {http_err}")
-            log.error(f"Status Code: {response.status_code}")
-            log.error("Response content:", response.text)
-            return None
-        except requests.exceptions.Timeout:
-            log.error(f"Timeout occurred during {context}: {url}")
-            return None
-        except requests.exceptions.ConnectionError:
-            log.error(f"Connection error occurred during {context}: {url}")
-            return None
-        except json.JSONDecodeError as json_err:
-            log.error(f"Failed to decode JSON response during {context}: {json_err}")
-            if 'response' in locals():
-                print("Response content:", response.text)
-            return None
-        except Exception as err:
-            log.error(f"An unexpected error occurred during {context}: {err}")
-            if 'response' in locals():
-                log.error("Response content (if available):", response.text)
-            return None
-    
-    WORKER_ENDPOINT = "/generate/sync"
-
-    # This worker has concurrency = 1.  All workloads have cost value 1.0
-    COST = count_workload()
-    
-    # Route to get worker URL
-    route_payload = {
-        "endpoint": endpoint_group_name,
-        "api_key": api_key,
-        "cost": COST,
-    }
-    
-    # First request - get routing information
-    route_response = make_request(
-        url=urljoin(server_url, "/route/"),
-        payload=route_payload,
-        timeout=4,
-        context="route request"
-    )
-    
-    if route_response is None:
-        return None
-    
-    if "url" not in route_response or not route_response["url"]:
-        log.error("Error: No worker in 'Ready' state. Please wait while the serverless engine removes errored workers or finishes loading new workers.")
-        return None
-    
-    if "status" in route_response:
-        print(f"Autoscaler status: {route_response['status']}")
-        return None
-    
-    # Extract data from route response
-    url = route_response["url"]
-    auth_data = dict(
-        signature=route_response["signature"],
-        cost=route_response["cost"],
-        endpoint=route_response["endpoint"],
-        reqnum=route_response["reqnum"],
-        url=route_response["url"],
-        request_idx=route_response["request_idx"],
-    )
-    
-    # Build the payload for the worker request
-    worker_payload = {
-        "input": {
-            "request_id": str(uuid.uuid4()),
-            "modifier": "Text2Image",
-            "modifications": {
-                "prompt": "a beautiful landscape with mountains and lakes",
-                "width": 1024,
-                "height": 1024,
-                "steps": 20,
-                "seed": random.randint(0, 2**32 - 1)
-            },
-            "workflow_json": {}  # Empty since using modifier approach
         }
-    }
         
-    req_data = dict(payload=worker_payload, auth_data=auth_data)
+        response = await endpoint.request("/generate/sync", payload, cost=100)
-    worker_url = urljoin(url, WORKER_ENDPOINT)
-    print(f"url: {worker_url}")
-    
-    # Second request - call the worker endpoint
-    worker_response = make_request(
-        url=worker_url,
-        payload=req_data,
-        verify=get_cert_file_path(),
-        context="worker request"
-    )
-    
-    return worker_response
 
+        # Get the file from the path on the local machine using SCP or SFTP
+        # or configure S3 to upload to cloud storage.
+        print(response["response"]["output"][0]["local_path"])
 
 if __name__ == "__main__":
-    from lib.test_utils import test_args
+    asyncio.run(main())
-
-    args = test_args.parse_args()
-    endpoint_api_key = Endpoint.get_endpoint_api_key(
-        endpoint_name=args.endpoint_group_name,
-        account_api_key=args.api_key,
-        instance=args.instance,
-    )
-
-    if endpoint_api_key:
-        result = call_text2image_workflow(
-            api_key=endpoint_api_key,
-            endpoint_group_name=args.endpoint_group_name,
-            server_url=args.server_url,
-        )
-        if result is None:
-            log.error("Text2Image workflow failed")
-        else:   
-            print(result)
-    else:
-        log.error(f"Failed to get API key for endpoint {args.endpoint_group_name}")

workers/comfyui-json/data_types.py

@@ -1,84 +0,0 @@
-import os
-import sys
-import random
-import dataclasses
-from typing import Dict, Any
-from functools import cache
-from math import ceil
-from pathlib import Path
-import json
-import logging
-
-from lib.data_types import ApiPayload, JsonDataException
-
-log = logging.getLogger(__file__)
-
-def count_workload() -> float:
-    # Always 100.0 where there is a single instance of ComfyUI handling requests
-    # Results will indicate % or a job completed per second.  Avoids sub 0.1 sec performance indication
-    return 100.0
-
-@dataclasses.dataclass
-class ComfyWorkflowData(ApiPayload):
-    input: dict
-
-    @classmethod
-    def for_test(cls):
-        """
-        If the user has provided a benchmark workflow we can use it here to properly gauge performance.
-        Otherwise, use the variables available to simulate workflows of the required running time
-        Example: SD1.5, simple image gen 10000 steps, 512px x 512px will run for approximately 9 minutes @ ~18 it/s (RTX 4090)
-        """
-        # Try to load benchmark.json
-        benchmark_file = Path("workers/comfyui-json/misc/benchmark.json")
-        
-        if benchmark_file.exists():
-            try:
-                with open(benchmark_file, "r") as f:
-                    benchmark_workflow = json.load(f)
-                return cls(
-                    input={
-                        "request_id": f"test-{random.randint(1000, 99999)}",
-                        "workflow_json": benchmark_workflow
-                    }
-                )
-            except (json.JSONDecodeError, IOError):
-                # JSON is malformed or file can't be read, fall through to default
-                log.error(f"Failed to benchmark using {benchmark_file}")
-        
-        # Fallback: read prompts and construct payload
-        log.info("Using fallback method for benchmarking")
-        with open("workers/comfyui-json/misc/test_prompts.txt", "r") as f:
-            test_prompts = f.readlines()
-        
-        test_prompt = random.choice(test_prompts).rstrip()
-        return cls(
-            input={
-                "request_id": f"test-{random.randint(1000, 99999)}",
-                "modifier": "Text2Image",
-                "modifications": {
-                    "prompt": test_prompt,
-                    "width": os.getenv('BENCHMARK_TEST_WIDTH', 512),
-                    "height": os.getenv('BENCHMARK_TEST_HEIGHT', 512),
-                    "steps": os.getenv('BENCHMARK_TEST_STEPS', 20),
-                    "seed": random.randint(0, sys.maxsize),
-                }
-            }
-        )
-
-    def generate_payload_json(self) -> Dict[str, Any]:
-        # input is already a dict, just return it wrapped in the expected structure
-        return {"input": self.input}
-
-    def count_workload(self) -> float:
-        return count_workload()
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "ComfyWorkflowData":
-        # Extract required fields
-        if "input" not in json_msg:
-            raise JsonDataException({"input": "missing parameter"})
-        
-        return cls(
-            input=json_msg["input"]
-        )

workers/comfyui-json/misc/benchmark.json.example

@@ -1,107 +0,0 @@
-{
-    "3": {
-        "inputs": {
-            "seed": "__RANDOM_INT__",
-            "steps": 20,
-            "cfg": 8,
-            "sampler_name": "euler",
-            "scheduler": "normal",
-            "denoise": 1,
-            "model": [
-            "4",
-            0
-            ],
-            "positive": [
-            "6",
-            0
-            ],
-            "negative": [
-            "7",
-            0
-            ],
-            "latent_image": [
-            "5",
-            0
-            ]
-        },
-        "class_type": "KSampler",
-        "_meta": {
-            "title": "KSampler"
-        }
-    },
-    "4": {
-        "inputs": {
-            "ckpt_name": "v1-5-pruned-emaonly-fp16.safetensors"
-        },
-        "class_type": "CheckpointLoaderSimple",
-        "_meta": {
-            "title": "Load Checkpoint"
-        }
-    },
-    "5": {
-        "inputs": {
-            "width": 512,
-            "height": 512,
-            "batch_size": 1
-        },
-        "class_type": "EmptyLatentImage",
-        "_meta": {
-            "title": "Empty Latent Image"
-        }
-    },
-    "6": {
-        "inputs": {
-            "text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
-            "clip": [
-            "4",
-            1
-            ]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-            "title": "CLIP Text Encode (Prompt)"
-        }
-    },
-    "7": {
-        "inputs": {
-            "text": "text, watermark",
-            "clip": [
-            "4",
-            1
-            ]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-            "title": "CLIP Text Encode (Prompt)"
-        }
-    },
-    "8": {
-        "inputs": {
-            "samples": [
-            "3",
-            0
-            ],
-            "vae": [
-            "4",
-            2
-            ]
-        },
-        "class_type": "VAEDecode",
-        "_meta": {
-            "title": "VAE Decode"
-        }
-    },
-    "9": {
-        "inputs": {
-            "filename_prefix": "ComfyUI",
-            "images": [
-            "8",
-            0
-            ]
-        },
-        "class_type": "SaveImage",
-        "_meta": {
-            "title": "Save Image"
-        }
-    }
-}

workers/comfyui-json/misc/note.txt

@@ -0,0 +1 @@
+# This folder is required for the provisioning scripts of ace and wan to complete.

workers/comfyui-json/misc/test_prompts.txt

@@ -1,34 +0,0 @@
-cartoon character of a person with a hoodie , in style of cytus and deemo, ork, gold chains, realistic anime cat, dripping black goo, lineage revolution style, thug life, cute anthropomorphic bunny, balrog, arknights, aliased, very buff, black and red and yellow paint, painting illustration collage style, character composition in vector with white background
-stardew valley, fine details
-2D Vector Illustration of a child with soccer ball Art for Sublimation, Design Art, Chrome Art, Painting and Stunning Artwork, Highly Detailed Digital Painting, Airbrush Art, Highly Detailed Digital Artwork, Dramatic Artwork, stained antique yellow copper paint, digital airbrush art, detailed by Mark Brooks, Chicano airbrush art, Swagger! snake Culture
-realistic futuristic city-downtown with short buildings, sunset
-seascape by Ray Collins and artgerm, front view of a perfect wave, sunny background, ultra detailed water
-inspired by realflow-cinema4d editor features, create image of a transparent luxury cup with ice fruits and mint, connected with white, yellow and pink cream, Slow - High Speed MO Photography, YouTube Video Screenshot, Abstract Clay, Transparent Cup , molecular gastronomy, wheel, 3D fluid,Simulation rendering, still video, 4k polymer clay futras photography, very surreal, Houdini Fluid Simulation, hyperrealistic CGI and FLUIDS & MULTIPHYSICS SIMULATION effect, with Somali Stain Lurex, Metallic Jacquard, Gold Thread, Mulberry Silk, Toub Saree, Warm background, a fantastic image worthy of an award.
-biker with backpack on his back riding a motorcycle, Style by Ade Santora, Oilpunk, Cover photo, craig mullins style, on the cover of a magazine, Outdoor Magazine, inspired by Alex Petruk APe, image of a male biker, Cover of an award-winning magazine, the man has a backpack, photo for magazine, with a backpack, magazine cover
-generate a collage-style illustration inspired by the Procreate raster graphic editor, photographic illustration with the theme, 2D vector, art for textile sublimation, containing surrealistic cartoon cat wearing a baseball cap and jeans standing in front of a poster, inspired by Sadao Watanabe, Doraemon, Japanese cartoon style, Eichiro Oda, Iconic high detail character, Director: Nakahara Nantenbō, Kastuhiro Otomo, image detailed, by Miyamoto, Hidetaka Miyazaki, Katsuhiro illustration, 8k, masterpiece, Minimize noise and grain in photo quality without lose quality and increase brightness and lighting,Symmetry and Alignment, Avoid asymmetrical shapes and out-of-focus points. Focus and Sharpness: Make sure the image is focused and sharp and encourages the viewer to see it as a work of art printed on fabric.
-fantasy medieval village world inside a glass sphere , high detail, fantasy, realistic, light effect, hyper detail, volumetric lighting, cinematic, macro, depth of field, blur, red light and clouds from the back, highly detailed epic cinematic concept art cg render made in maya, blender and photoshop, octane render, excellent composition, dynamic dramatic cinematic lighting, aesthetic, very inspirational, world inside a glass sphere by james gurney by artgerm with james jean, joe fenton and tristan eaton by ross tran, fine details
-Iron Man, (Arnold Tsang, Toru Nakayama), Masterpiece, Studio Quality, 6k , toa, toaair, 1boy, glowing, axe, mecha, science_fiction, solo, weapon, jungle , green_background, nature, outdoors, solo, tree, weapon, mask, dynamic lighting, detailed shading, digital texture painting
-(Pope Francis) wearing leather jacket is a DJ in a nightclub, mixing live on stage, giant mixing table, a masterpiece
-Pope Francis wearing biker (leather jacket), a masterpiece
-Luke Skywalker ordering a burger and fries from the Death Star canteen.
-I want to generate a group avatar for a Feishu group chat. The role of this group is daily software technical communication. Now the subject technology stacks that members of this group discuss daily include: algorithms, data structures, optimization, functional programming, and the programming languages often discussed are: TypeScript, Java, python, etc. I hope this avatar has a simple aesthetic, this avatar is a single person avatar
-portrait Anime black girl cute-fine-face, pretty face, realistic shaded Perfect face, fine details. Anime. realistic shaded lighting by Ilya Kuvshinov Giuseppe Dangelico Pino and Michael Garmash and Rob Rey, IAMAG premiere, WLOP matte print, cute freckles, masterpiece
-young Disney socialite wearing a beige miniskirt, dark brown turtleneck sweater, small neckless, cute-fine-face, anime. illustration, realistic shaded perfect face, brown hair, grey eyes, fine details, realistic shaded lighting by ilya kuvshinov giuseppe dangelico pino and michael garmash and rob rey, iamag premiere, wlop matte print, a masterpiece
-Cute small cat sitting in a movie theater eating chicken wiggs watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
-Cute small dog sitting in a movie theater eating popcorn watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
-fox bracelet made of buckskin with fox features, rich details, fine carvings, studio lighting
-crane buckskin bracelet with crane features, rich details, fine carvings, studio lighting
-london luxurious interior living-room, light walls
-Parisian luxurious interior penthouse bedroom, dark walls, wooden panels
-cute girl, crop-top, blond hair, black glasses, stretching, with background by greg rutkowski makoto shinkai kyoto animation key art feminine mid shot
-houses in front, houses background, straight houses, digital art, smooth, sharp focus, gravity falls style, doraemon style, shinchan style, anime style
-Simplified technical drawing, Leonardo da Vinci, Mechanical Dinosaur Skeleton, Minimalistic annotations, Hand-drawn illustrations, Basic design and engineering, Wonder and curiosity
-High quality 8K painting impressionist style of a Japanese modern city street with a girl on the foreground wearing a traditional wedding dress with a fox mask, staring at the sky, daylight
-a landscape from the Moon with the Earth setting on the horizon, realistic, detailed
-Isometric Atlantis city,great architecture with columns, great details, ornaments,seaweed, blue ambiance, 3D cartoon style, soft light, 45° view
-A hyper realistic avatar of a guy riding on a black honda cbr 650r in leather suit,high detail, high quality,8K,photo realism
-the street of amedieval fantasy town, at dawn, dark, highly detailed
-overwhelmingly beautiful eagle framed with vector flowers, long shiny wavy flowing hair, polished, ultra detailed vector floral illustration mixed with hyper realism, muted pastel colors, vector floral details in background, muted colors, hyper detailed ultra intricate overwhelming realism in detailed complex scene with magical fantasy atmosphere, no signature, no watermark
-a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece | hyperrealism| highly detailed| insanely detailed| intricate| cinematic lighting| depth of field
-electronik robot and ofice ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
-exquisitely intricately detailed illustration, of a small world with a lake and a rainbow, inside a closed glass jar.

workers/comfyui-json/server.py

@@ -1,117 +0,0 @@
-import os
-import logging
-import dataclasses
-import base64
-from typing import Optional, Union, Type
-
-from aiohttp import web, ClientResponse
-
-from lib.backend import Backend, LogAction
-from lib.data_types import EndpointHandler
-from lib.server import start_server
-from .data_types import ComfyWorkflowData
-
-
-MODEL_SERVER_URL = os.getenv("MODEL_SERVER_URL", "http://127.0.0.1:18288")
-
-# This is the last log line that gets emitted once comfyui+extensions have been fully loaded
-MODEL_SERVER_START_LOG_MSG = "To see the GUI go to: "
-MODEL_SERVER_ERROR_LOG_MSGS = [
-    "MetadataIncompleteBuffer",  # This error is emitted when the downloaded model is corrupted
-    "Value not in list: ",  # This error is emitted when the model file is not there at all
-    "[ERROR] Provisioning Script failed", # Error inserted by provisioning script if models/nodes fail to download
-]
-
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-
-async def generate_client_response(
-        client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        # Check if the response is actually streaming based on response headers/content-type
-        is_streaming_response = (
-            model_response.content_type == "text/event-stream"
-            or model_response.content_type == "application/x-ndjson"
-            or model_response.headers.get("Transfer-Encoding") == "chunked"
-            or "stream" in model_response.content_type.lower()
-        )
-
-        if is_streaming_response:
-            log.debug("Detected streaming response...")
-            res = web.StreamResponse()
-            res.content_type = model_response.content_type
-            await res.prepare(client_request)
-            async for chunk in model_response.content:
-                await res.write(chunk)
-            await res.write_eof()
-            log.debug("Done streaming response")
-            return res
-        else:
-            log.debug("Detected non-streaming response...")
-            content = await model_response.read()
-            return web.Response(
-                body=content,
-                status=model_response.status,
-                content_type=model_response.content_type
-            )
-            
-
-@dataclasses.dataclass
-class ComfyWorkflowHandler(EndpointHandler[ComfyWorkflowData]):
-
-    @property
-    def endpoint(self) -> str:
-        return "/generate/sync"
-
-    @property
-    def healthcheck_endpoint(self) -> Optional[str]:
-        return f"{MODEL_SERVER_URL}/health"
-
-    @classmethod
-    def payload_cls(cls) -> Type[ComfyWorkflowData]:
-        return ComfyWorkflowData
-
-    def make_benchmark_payload(self) -> ComfyWorkflowData:
-        return ComfyWorkflowData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        return await generate_client_response(client_request, model_response)
-
-
-backend = Backend(
-    model_server_url=MODEL_SERVER_URL,
-    model_log_file=os.environ["MODEL_LOG"],
-    allow_parallel_requests=False,
-    benchmark_handler=ComfyWorkflowHandler(
-        benchmark_runs=3, benchmark_words=100
-    ),
-    log_actions=[
-        (LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
-        (LogAction.Info, "Downloading:"),
-        *[
-            (LogAction.ModelError, error_msg)
-            for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
-        ],
-    ],
-)
-
-
-async def handle_ping(_):
-    return web.Response(body="pong")
-
-
-routes = [
-    web.post("/generate/sync", backend.create_handler(ComfyWorkflowHandler())),
-    web.get("/ping", handle_ping),
-]
-
-if __name__ == "__main__":
-    start_server(backend, routes)

workers/comfyui-json/test_load.py

@@ -1,8 +0,0 @@
-from lib.test_utils import test_load_cmd, test_args
-from .data_types import ComfyWorkflowData
-
-WORKER_ENDPOINT = "/generate/sync"
-
-
-if __name__ == "__main__":
-    test_load_cmd(ComfyWorkflowData, WORKER_ENDPOINT, arg_parser=test_args)

workers/comfyui-json/worker.py

@@ -0,0 +1,81 @@
+import random
+import sys
+
+from vastai import Worker, WorkerConfig, HandlerConfig, LogActionConfig, BenchmarkConfig
+
+# ComyUI model configuration
+MODEL_SERVER_URL           = 'http://127.0.0.1'
+MODEL_SERVER_PORT          = 18288
+MODEL_LOG_FILE             = '/var/log/portal/comfyui.log'
+MODEL_HEALTHCHECK_ENDPOINT = "/health"
+
+# ComyUI-specific log messages
+MODEL_LOAD_LOG_MSG = [
+    "To see the GUI go to: "
+]
+
+MODEL_ERROR_LOG_MSGS = [
+    "MetadataIncompleteBuffer",
+    "Value not in list: ",
+    "[ERROR] Provisioning Script failed"
+]
+
+MODEL_INFO_LOG_MSGS = [
+    '"message":"Downloading'
+]
+
+benchmark_prompts = [
+    "Cartoon hoodie hero; orc, anime cat, bunny; black goo; buff; vector on white.",
+    "Cozy farming-game scene with fine details.",
+    "2D vector child with soccer ball; airbrush chrome; swagger; antique copper.",
+    "Realistic futuristic downtown of low buildings at sunset.",
+    "Perfect wave front view; sunny seascape; ultra-detailed water; artful feel.",
+    "Clear cup with ice, fruit, mint; creamy swirls; fluid-sim CGI; warm glow.",
+    "Male biker with backpack on motorcycle; oilpunk; award-worthy magazine cover.",
+    "Collage for textile; surreal cartoon cat in cap/jeans before poster; crisp.",
+    "Medieval village inside glass sphere; volumetric light; macro focus.",
+    "Iron Man with glowing axe; mecha sci-fi; jungle scene; dynamic light.",
+    "Pope Francis DJ in leather jacket, mixing on giant console; dramatic.",
+]
+
+
+
+benchmark_dataset = [
+    {
+        "input": {
+            "request_id": f"test-{random.randint(1000, 99999)}",
+            "modifier": "Text2Image",
+            "modifications": {
+                "prompt": prompt,
+                "width": 512,
+                "height": 512,
+                "steps": 20,
+                "seed": random.randint(0, sys.maxsize)
+            }
+        }
+    } for prompt in benchmark_prompts
+]
+
+worker_config = WorkerConfig(
+    model_server_url=MODEL_SERVER_URL,
+    model_server_port=MODEL_SERVER_PORT,
+    model_log_file=MODEL_LOG_FILE,
+    model_healthcheck_url=MODEL_HEALTHCHECK_ENDPOINT,
+    handlers=[
+        HandlerConfig(
+            route="/generate/sync",
+            allow_parallel_requests=False,
+            max_queue_time=10.0,
+            benchmark_config=BenchmarkConfig(
+                dataset=benchmark_dataset,
+            )
+        )
+    ],
+    log_action_config=LogActionConfig(
+        on_load=MODEL_LOAD_LOG_MSG,
+        on_error=MODEL_ERROR_LOG_MSGS,
+        on_info=MODEL_INFO_LOG_MSGS
+    )
+)
+
+Worker(worker_config).run()

workers/comfyui/README.md

@@ -1,92 +0,0 @@
-This is the base PyWorker for comfyui. It can be used to create PyWorker that use various models and
-workflows. It provides two endpoints:
-
-1. `/prompt`: Uses the default comfy workflow defined under `misc/default_workflows`
-2. `/custom_workflow`: Allows the client to send their own comfy workflow with each API request.
-
-To use the comfyui PyWorker, `$COMFY_MODEL` env variable must be set in the template. Current options are
-`sd3` and `flux`. Each have example clients.
-
-To add new models, a JSON with name `$COMFY_MODEL.json` must be created under `misc/default_workflows`
-
-NOTE: default workflows follow this format:
-
-```json
-{
-  "input": {
-    "handler": "RawWorkflow",
-    "aws_access_key_id": "your-s3-access-key",
-    "aws_secret_access_key": "your-s3-secret-access-key",
-    "aws_endpoint_url": "https://my-endpoint.backblaze.com",
-    "aws_bucket_name": "your-bucket",
-    "webhook_url": "your-webhook-url",
-    "webhook_extra_params": {},
-    "workflow_json": {}
-  }
-}
-```
-
-You can ignore all of these fields except for `workflow_json`.
-
-Fields written as "{{FOO}}" will be replaced using data from a user request. For example, SD3's workflow has the
-following nodes:
-
-```json
-      "5": {
-        "inputs": {
-          "width": "{{WIDTH}}",
-          "height": "{{HEIGHT}}",
-          "batch_size": 1
-        },
-
-      "6": {
-        "inputs": {
-          "text": "{{PROMPT}}",
-          "clip": ["11", 0]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-          "title": "CLIP Text Encode (Prompt)"
-        }
-      },
-      ...
-      "17": {
-        "inputs": {
-          "scheduler": "simple",
-          "steps": "{{STEPS}}",
-          "denoise": 1,
-          "model": ["12", 0]
-        },
-        "class_type": "BasicScheduler",
-        "_meta": {
-          "title": "BasicScheduler"
-        }
-      },
-      ...
-      "25": {
-        "inputs": {
-          "noise_seed": "{{SEED}}"
-        },
-        "class_type": "RandomNoise",
-        "_meta": {
-          "title": "RandomNoise"
-        }
-      }
-
-```
-
-Incoming requests have the following JSON format:
-
-```json
-{
-    prompt: str
-    width: int
-    height: int
-    steps: int
-    seed: int
-}
-```
-
-Each value in those fields with replace the placeholder of the same name in the default workflow.
-
-See Vast's serverless documentation for more details on how to use comfyui with autoscaler

workers/comfyui/client.py

@@ -1,177 +0,0 @@
-import logging
-from urllib.parse import urljoin
-
-import requests
-
-from lib.test_utils import print_truncate_res
-from utils.endpoint_util import Endpoint
-from utils.ssl import get_cert_file_path
-
-"""
-NOTE: this client example uses a custom comfy workflow compatible with SD3 only
-"""
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-
-def call_default_workflow(
-    endpoint_group_name: str, api_key: str, server_url: str
-) -> None:
-    WORKER_ENDPOINT = "/prompt"
-    COST = 100
-    route_payload = {
-        "endpoint": endpoint_group_name,
-        "api_key": api_key,
-        "cost": COST,
-    }
-    response = requests.post(
-        urljoin(server_url, "/route/"),
-        json=route_payload,
-        timeout=4,
-    )
-    response.raise_for_status()
-    message = response.json()
-    url = message["url"]
-    auth_data = dict(
-        signature=message["signature"],
-        cost=message["cost"],
-        endpoint=message["endpoint"],
-        reqnum=message["reqnum"],
-        url=message["url"],
-    )
-    payload = dict(
-        prompt="a fat fluffy cat", width=1024, height=1024, steps=20, seed=123456789
-    )
-    req_data = dict(payload=payload, auth_data=auth_data)
-    url = urljoin(url, WORKER_ENDPOINT)
-    print(f"url: {url}")
-    response = requests.post(
-        url,
-        json=req_data,
-        verify=get_cert_file_path(),
-    )
-    response.raise_for_status()
-    print_truncate_res(str(response.json()))
-
-
-def call_custom_workflow_for_sd3(
-    endpoint_group_name: str, api_key: str, server_url: str
-) -> None:
-    WORKER_ENDPOINT = "/custom-workflow"
-    COST = 100
-    route_payload = {
-        "endpoint": endpoint_group_name,
-        "api_key": api_key,
-        "cost": COST,
-    }
-    response = requests.post(
-        urljoin(server_url, "/route/"),
-        json=route_payload,
-        timeout=4,
-    )
-    response.raise_for_status()
-    message = response.json()
-    url = message["url"]
-    auth_data = dict(
-        signature=message["signature"],
-        cost=message["cost"],
-        endpoint=message["endpoint"],
-        reqnum=message["reqnum"],
-        url=message["url"],
-        request_idx=message["request_idx"],
-    )
-    workflow = {
-        "3": {
-            "inputs": {
-                "seed": 156680208700286,
-                "steps": 20,
-                "cfg": 8,
-                "sampler_name": "euler",
-                "scheduler": "normal",
-                "denoise": 1,
-                "model": ["4", 0],
-                "positive": ["6", 0],
-                "negative": ["7", 0],
-                "latent_image": ["5", 0],
-            },
-            "class_type": "KSampler",
-        },
-        "4": {
-            "inputs": {"ckpt_name": "sd3_medium_incl_clips_t5xxlfp16.safetensors"},
-            "class_type": "CheckpointLoaderSimple",
-        },
-        "5": {
-            "inputs": {"width": 512, "height": 512, "batch_size": 1},
-            "class_type": "EmptyLatentImage",
-        },
-        "6": {
-            "inputs": {
-                "text": "beautiful scenery nature glass bottle landscape, purple galaxy bottle",
-                "clip": ["4", 1],
-            },
-            "class_type": "CLIPTextEncode",
-        },
-        "7": {
-            "inputs": {"text": "text, watermark", "clip": ["4", 1]},
-            "class_type": "CLIPTextEncode",
-        },
-        "8": {
-            "inputs": {"samples": ["3", 0], "vae": ["4", 2]},
-            "class_type": "VAEDecode",
-        },
-        "9": {
-            "inputs": {"filename_prefix": "ComfyUI", "images": ["8", 0]},
-            "class_type": "SaveImage",
-        },
-    }
-    # these values should match the values in the custom workflow above,
-    # they are used to calculate workload
-    custom_fields = dict(
-        steps=20,
-        width=512,
-        height=512,
-    )
-    req_data = dict(
-        payload=dict(custom_fields=custom_fields, workflow=workflow),
-        auth_data=auth_data,
-    )
-    url = urljoin(url, WORKER_ENDPOINT)
-    print(f"url: {url}")
-    response = requests.post(
-        url,
-        json=req_data,
-        verify=get_cert_file_path(),
-    )
-    response.raise_for_status()
-    print_truncate_res(str(response.json()))
-
-
-if __name__ == "__main__":
-    from lib.test_utils import test_args
-
-    args = test_args.parse_args()
-    endpoint_api_key = Endpoint.get_endpoint_api_key(
-        endpoint_name=args.endpoint_group_name,
-        account_api_key=args.api_key,
-        instance=args.instance,
-    )
-    if endpoint_api_key:
-        try:
-            call_default_workflow(
-                api_key=endpoint_api_key,
-                endpoint_group_name=args.endpoint_group_name,
-                server_url=args.server_url,
-            )
-            call_custom_workflow_for_sd3(
-                api_key=endpoint_api_key,
-                endpoint_group_name=args.endpoint_group_name,
-                server_url=args.server_url,
-            )
-        except Exception as e:
-            log.error(f"Error during API call: {e}")
-    else:
-        log.error(f"Failed to get API key for endpoint {args.endpoint_group_name} ")

workers/comfyui/data_types.py

@@ -1,205 +0,0 @@
-import sys
-import os
-import json
-import random
-import dataclasses
-import inspect
-from typing import Dict, Any
-from functools import cache
-from math import ceil
-from enum import Enum
-
-from lib.data_types import ApiPayload, JsonDataException
-
-
-with open("workers/comfyui/misc/test_prompts.txt", "r") as f:
-    test_prompts = f.readlines()
-
-
-class Model(Enum):
-    Flux = "flux"
-    Sd3 = "sd3"
-
-    def get_request_time(self) -> int:
-        match self:
-            case Model.Flux:
-                return 23
-            case Model.Sd3:
-                return 6
-
-
-@cache
-def get_model() -> Model:
-    match os.environ.get("COMFY_MODEL"):
-        case "flux":
-            return Model.Flux
-        case "sd3":
-            return Model.Sd3
-        case None:
-            raise Exception(
-                "For comfyui pyworker, $COMFY_MODEL must be set in the vast template"
-            )
-        case model:
-            raise Exception(f"Unsupported comfyui model: {model}")
-
-
-@cache
-def get_request_template() -> str:
-    with open(f"workers/comfyui/misc/default_workflows/{get_model().value}.json") as f:
-        return f.read()
-
-
-def count_workload(width: int, height: int, steps: int) -> float:
-    """
-    we want to normalize the workload is a number such that cur_perf(tokens/second) for 1024x1024 image with
-    28 steps is 200 tokens on a 4090.
-
-    in order get that we calculate the
-
-    A = ( absolute workload based on given data )
-    B = ( absolute workload for a 1024x1024 image with 28 steps )
-
-    and adjust the workload to 200 tokens by A/B.
-
-    we then adjust for difference between Flux and SD3 by multiplying this value by expected request time for a
-    standard image(23s for Flux, 6s for SD3).
-    On a 4090, this would give us a workload that would give a cur_perf(workload / request_time) of around 200
-    """
-
-    def _calculate_absolute_tokens(width_: int, height_: int, steps_: int) -> float:
-        """
-        This is based on how openai counts image generation tokens, see: https://openai.com/api/pricing/
-
-        we count how many 512x512 grids are needed to cover the image.
-        each tile is then counted as 175 tokens.
-        each image generation also has constant of 85 base tokens.
-
-        we then adjust the count based on the number of steps. The baseline number of steps is assumed to be 28.
-        Some testing with flux gave me this data:
-
-        steps(X)  | request time(Y)
-        __________|_________________
-        07(0.25x) | 11s (0.47x)
-        14(0.50x) | 15s (0.65x)
-        21(0.75x) | 20s (0.86x)
-        28(1.00x) | 23s (1.00x)
-        35(1.25x) | 28s (1.21x)
-        42(1.50x) | 32s (1.39x)
-        49(1.75x) | 37s (1.60x)
-
-        this gives a linear regression of Y = 0.61*X + 6.57
-
-        we can use this as an adjustment_factor for token count
-
-        adjustment_factor = (0.61 * steps + 6.57)
-        """
-
-        width_grids = ceil(width_ / 512)
-        height_grids = ceil(height_ / 512)
-        tokens = 85 + width_grids * height_grids * 175
-        adjustment_factor = 0.61 * steps_ + 6.57
-        return tokens * adjustment_factor
-
-    REQUEST_TIME_FOR_STANDARD_IMAGE = get_model().get_request_time()
-
-    absolute_tokens = _calculate_absolute_tokens(
-        width_=width, height_=height, steps_=steps
-    )
-    absolute_tokens_standard_image = _calculate_absolute_tokens(
-        width_=1024, height_=1024, steps_=28
-    )
-    return REQUEST_TIME_FOR_STANDARD_IMAGE * (
-        (absolute_tokens / absolute_tokens_standard_image) * 200
-    )
-
-
-@dataclasses.dataclass
-class DefaultComfyWorkflowData(ApiPayload):
-    prompt: str
-    width: int
-    height: int
-    steps: int
-    seed: int
-
-    @classmethod
-    def for_test(cls):
-
-        test_prompt = random.choice(test_prompts).rstrip()
-        return cls(
-            prompt=test_prompt,
-            width=1024,
-            height=1024,
-            steps=28,
-            seed=random.randint(0, sys.maxsize),
-        )
-
-    def generate_payload_json(
-        self,
-    ) -> Dict[str, Any]:
-        return json.loads(
-            get_request_template()
-            .replace("{{PROMPT}}", self.prompt)
-            # these values should be of int type. Since "{{VAR}}" is wrapped with " in the template
-            # to make the JSON valid, we must replace the double quotes. i.e. "{{WIDTH}}" -> 1024 and not "1024"
-            .replace('"{{WIDTH}}"', str(self.width))
-            .replace('"{{HEIGHT}}"', str(self.height))
-            .replace('"{{STEPS}}"', str(self.steps))
-            .replace('"{{SEED}}"', str(self.seed))
-        )
-
-    def count_workload(self) -> float:
-        return count_workload(width=self.width, height=self.height, steps=self.steps)
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "DefaultComfyWorkflowData":
-        errors = {}
-        for param in inspect.signature(cls).parameters:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-        if errors:
-            raise JsonDataException(errors)
-        return cls(
-            **{
-                k: v
-                for k, v in json_msg.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )
-
-
-@dataclasses.dataclass
-class CustomComfyWorkflowData(ApiPayload):
-    custom_fields: Dict[str, int]
-    workflow: Dict[str, Any]
-
-    @classmethod
-    def for_test(cls):
-        raise NotImplementedError("Custom comfy workflow is not used for testing")
-
-    def count_workload(self) -> float:
-        return count_workload(
-            width=int(self.custom_fields.get("width", 1024)),
-            height=int(self.custom_fields.get("height", 1024)),
-            steps=int(self.custom_fields.get("steps", 28)),
-        )
-
-    def generate_payload_json(self) -> Dict[str, Any]:
-        template_json = json.loads(get_request_template())
-        template_json["input"]["workflow_json"] = self.workflow
-        return template_json
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "CustomComfyWorkflowData":
-        errors = {}
-        for param in inspect.signature(cls).parameters:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-        if errors:
-            raise JsonDataException(errors)
-        return cls(
-            **{
-                k: v
-                for k, v in json_msg.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )

workers/comfyui/misc/default_workflows/flux.json

@@ -1,137 +0,0 @@
-{
-  "input": {
-    "handler": "RawWorkflow",
-    "aws_access_key_id": "your-s3-access-key",
-    "aws_secret_access_key": "your-s3-secret-access-key",
-    "aws_endpoint_url": "https://my-endpoint.backblaze.com",
-    "aws_bucket_name": "your-bucket",
-    "webhook_url": "your-webhook-url",
-    "webhook_extra_params": {},
-    "workflow_json": {
-      "5": {
-        "inputs": {
-          "width": "{{WIDTH}}",
-          "height": "{{HEIGHT}}",
-          "batch_size": 1
-        },
-        "class_type": "EmptyLatentImage",
-        "_meta": {
-          "title": "Empty Latent Image"
-        }
-      },
-      "6": {
-        "inputs": {
-          "text": "{{PROMPT}}",
-          "clip": ["11", 0]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-          "title": "CLIP Text Encode (Prompt)"
-        }
-      },
-      "8": {
-        "inputs": {
-          "samples": ["13", 0],
-          "vae": ["10", 0]
-        },
-        "class_type": "VAEDecode",
-        "_meta": {
-          "title": "VAE Decode"
-        }
-      },
-      "9": {
-        "inputs": {
-          "filename_prefix": "ComfyUI",
-          "images": ["8", 0]
-        },
-        "class_type": "SaveImage",
-        "_meta": {
-          "title": "Save Image"
-        }
-      },
-      "10": {
-        "inputs": {
-          "vae_name": "ae.safetensors"
-        },
-        "class_type": "VAELoader",
-        "_meta": {
-          "title": "Load VAE"
-        }
-      },
-      "11": {
-        "inputs": {
-          "clip_name1": "t5xxl_fp16.safetensors",
-          "clip_name2": "clip_l.safetensors",
-          "type": "flux"
-        },
-        "class_type": "DualCLIPLoader",
-        "_meta": {
-          "title": "DualCLIPLoader"
-        }
-      },
-      "12": {
-        "inputs": {
-          "unet_name": "flux1-dev.safetensors",
-          "weight_dtype": "default"
-        },
-        "class_type": "UNETLoader",
-        "_meta": {
-          "title": "Load Diffusion Model"
-        }
-      },
-      "13": {
-        "inputs": {
-          "noise": ["25", 0],
-          "guider": ["22", 0],
-          "sampler": ["16", 0],
-          "sigmas": ["17", 0],
-          "latent_image": ["5", 0]
-        },
-        "class_type": "SamplerCustomAdvanced",
-        "_meta": {
-          "title": "SamplerCustomAdvanced"
-        }
-      },
-      "16": {
-        "inputs": {
-          "sampler_name": "euler"
-        },
-        "class_type": "KSamplerSelect",
-        "_meta": {
-          "title": "KSamplerSelect"
-        }
-      },
-      "17": {
-        "inputs": {
-          "scheduler": "simple",
-          "steps": "{{STEPS}}",
-          "denoise": 1,
-          "model": ["12", 0]
-        },
-        "class_type": "BasicScheduler",
-        "_meta": {
-          "title": "BasicScheduler"
-        }
-      },
-      "22": {
-        "inputs": {
-          "model": ["12", 0],
-          "conditioning": ["6", 0]
-        },
-        "class_type": "BasicGuider",
-        "_meta": {
-          "title": "BasicGuider"
-        }
-      },
-      "25": {
-        "inputs": {
-          "noise_seed": "{{SEED}}"
-        },
-        "class_type": "RandomNoise",
-        "_meta": {
-          "title": "RandomNoise"
-        }
-      }
-    }
-  }
-}

workers/comfyui/misc/default_workflows/sd3.json

@@ -1,142 +0,0 @@
-{
-  "input": {
-    "handler": "RawWorkflow",
-    "aws_access_key_id": "your-s3-access-key",
-    "aws_secret_access_key": "your-s3-secret-access-key",
-    "aws_endpoint_url": "https://my-endpoint.backblaze.com",
-    "aws_bucket_name": "your-bucket",
-    "webhook_url": "your-webhook-url",
-    "webhook_extra_params": {},
-    "workflow_json": {
-      "6": {
-        "inputs": {
-          "text": "{{PROMPT}}",
-          "clip": ["252", 1]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-          "title": "CLIP Text Encode (Prompt)"
-        }
-      },
-      "13": {
-        "inputs": {
-          "shift": 3,
-          "model": ["252", 0]
-        },
-        "class_type": "ModelSamplingSD3",
-        "_meta": {
-          "title": "ModelSamplingSD3"
-        }
-      },
-      "67": {
-        "inputs": {
-          "conditioning": ["71", 0]
-        },
-        "class_type": "ConditioningZeroOut",
-        "_meta": {
-          "title": "ConditioningZeroOut"
-        }
-      },
-      "68": {
-        "inputs": {
-          "start": 0.1,
-          "end": 1,
-          "conditioning": ["67", 0]
-        },
-        "class_type": "ConditioningSetTimestepRange",
-        "_meta": {
-          "title": "ConditioningSetTimestepRange"
-        }
-      },
-      "69": {
-        "inputs": {
-          "conditioning_1": ["68", 0],
-          "conditioning_2": ["70", 0]
-        },
-        "class_type": "ConditioningCombine",
-        "_meta": {
-          "title": "Conditioning (Combine)"
-        }
-      },
-      "70": {
-        "inputs": {
-          "start": 0,
-          "end": 0.1,
-          "conditioning": ["71", 0]
-        },
-        "class_type": "ConditioningSetTimestepRange",
-        "_meta": {
-          "title": "ConditioningSetTimestepRange"
-        }
-      },
-      "71": {
-        "inputs": {
-          "text": "bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
-          "clip": ["252", 1]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-          "title": "CLIP Text Encode (Negative Prompt)"
-        }
-      },
-      "135": {
-        "inputs": {
-          "width": "{{WIDTH}}",
-          "height": "{{HEIGHT}}",
-          "batch_size": 1
-        },
-        "class_type": "EmptySD3LatentImage",
-        "_meta": {
-          "title": "EmptySD3LatentImage"
-        }
-      },
-      "231": {
-        "inputs": {
-          "samples": ["271", 0],
-          "vae": ["252", 2]
-        },
-        "class_type": "VAEDecode",
-        "_meta": {
-          "title": "VAE Decode"
-        }
-      },
-      "233": {
-        "inputs": {
-          "filename_prefix": "ComfyUI",
-          "images": ["231", 0]
-        },
-        "class_type": "SaveImage",
-        "_meta": {
-          "title": "Save Image"
-        }
-      },
-      "252": {
-        "inputs": {
-          "ckpt_name": "sd3_medium_incl_clips_t5xxlfp16.safetensors"
-        },
-        "class_type": "CheckpointLoaderSimple",
-        "_meta": {
-          "title": "Load Checkpoint"
-        }
-      },
-      "271": {
-        "inputs": {
-          "seed": "{{SEED}}",
-          "steps": "{{STEPS}}",
-          "cfg": 4.5,
-          "sampler_name": "dpmpp_2m",
-          "scheduler": "sgm_uniform",
-          "denoise": 1,
-          "model": ["13", 0],
-          "positive": ["6", 0],
-          "negative": ["69", 0],
-          "latent_image": ["135", 0]
-        },
-        "class_type": "KSampler",
-        "_meta": {
-          "title": "KSampler"
-        }
-      }
-    }
-  }
-}

workers/comfyui/misc/test_prompts.txt

@@ -1,34 +0,0 @@
-cartoon character of a person with a hoodie , in style of cytus and deemo, ork, gold chains, realistic anime cat, dripping black goo, lineage revolution style, thug life, cute anthropomorphic bunny, balrog, arknights, aliased, very buff, black and red and yellow paint, painting illustration collage style, character composition in vector with white background
-stardew valley, fine details
-2D Vector Illustration of a child with soccer ball Art for Sublimation, Design Art, Chrome Art, Painting and Stunning Artwork, Highly Detailed Digital Painting, Airbrush Art, Highly Detailed Digital Artwork, Dramatic Artwork, stained antique yellow copper paint, digital airbrush art, detailed by Mark Brooks, Chicano airbrush art, Swagger! snake Culture
-realistic futuristic city-downtown with short buildings, sunset
-seascape by Ray Collins and artgerm, front view of a perfect wave, sunny background, ultra detailed water
-inspired by realflow-cinema4d editor features, create image of a transparent luxury cup with ice fruits and mint, connected with white, yellow and pink cream, Slow - High Speed MO Photography, YouTube Video Screenshot, Abstract Clay, Transparent Cup , molecular gastronomy, wheel, 3D fluid,Simulation rendering, still video, 4k polymer clay futras photography, very surreal, Houdini Fluid Simulation, hyperrealistic CGI and FLUIDS & MULTIPHYSICS SIMULATION effect, with Somali Stain Lurex, Metallic Jacquard, Gold Thread, Mulberry Silk, Toub Saree, Warm background, a fantastic image worthy of an award.
-biker with backpack on his back riding a motorcycle, Style by Ade Santora, Oilpunk, Cover photo, craig mullins style, on the cover of a magazine, Outdoor Magazine, inspired by Alex Petruk APe, image of a male biker, Cover of an award-winning magazine, the man has a backpack, photo for magazine, with a backpack, magazine cover
-generate a collage-style illustration inspired by the Procreate raster graphic editor, photographic illustration with the theme, 2D vector, art for textile sublimation, containing surrealistic cartoon cat wearing a baseball cap and jeans standing in front of a poster, inspired by Sadao Watanabe, Doraemon, Japanese cartoon style, Eichiro Oda, Iconic high detail character, Director: Nakahara Nantenbō, Kastuhiro Otomo, image detailed, by Miyamoto, Hidetaka Miyazaki, Katsuhiro illustration, 8k, masterpiece, Minimize noise and grain in photo quality without lose quality and increase brightness and lighting,Symmetry and Alignment, Avoid asymmetrical shapes and out-of-focus points. Focus and Sharpness: Make sure the image is focused and sharp and encourages the viewer to see it as a work of art printed on fabric.
-fantasy medieval village world inside a glass sphere , high detail, fantasy, realistic, light effect, hyper detail, volumetric lighting, cinematic, macro, depth of field, blur, red light and clouds from the back, highly detailed epic cinematic concept art cg render made in maya, blender and photoshop, octane render, excellent composition, dynamic dramatic cinematic lighting, aesthetic, very inspirational, world inside a glass sphere by james gurney by artgerm with james jean, joe fenton and tristan eaton by ross tran, fine details
-Iron Man, (Arnold Tsang, Toru Nakayama), Masterpiece, Studio Quality, 6k , toa, toaair, 1boy, glowing, axe, mecha, science_fiction, solo, weapon, jungle , green_background, nature, outdoors, solo, tree, weapon, mask, dynamic lighting, detailed shading, digital texture painting
-(Pope Francis) wearing leather jacket is a DJ in a nightclub, mixing live on stage, giant mixing table, a masterpiece
-Pope Francis wearing biker (leather jacket), a masterpiece
-Luke Skywalker ordering a burger and fries from the Death Star canteen.
-I want to generate a group avatar for a Feishu group chat. The role of this group is daily software technical communication. Now the subject technology stacks that members of this group discuss daily include: algorithms, data structures, optimization, functional programming, and the programming languages often discussed are: TypeScript, Java, python, etc. I hope this avatar has a simple aesthetic, this avatar is a single person avatar
-portrait Anime black girl cute-fine-face, pretty face, realistic shaded Perfect face, fine details. Anime. realistic shaded lighting by Ilya Kuvshinov Giuseppe Dangelico Pino and Michael Garmash and Rob Rey, IAMAG premiere, WLOP matte print, cute freckles, masterpiece
-young Disney socialite wearing a beige miniskirt, dark brown turtleneck sweater, small neckless, cute-fine-face, anime. illustration, realistic shaded perfect face, brown hair, grey eyes, fine details, realistic shaded lighting by ilya kuvshinov giuseppe dangelico pino and michael garmash and rob rey, iamag premiere, wlop matte print, a masterpiece
-Cute small cat sitting in a movie theater eating chicken wiggs watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
-Cute small dog sitting in a movie theater eating popcorn watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
-fox bracelet made of buckskin with fox features, rich details, fine carvings, studio lighting
-crane buckskin bracelet with crane features, rich details, fine carvings, studio lighting
-london luxurious interior living-room, light walls
-Parisian luxurious interior penthouse bedroom, dark walls, wooden panels
-cute girl, crop-top, blond hair, black glasses, stretching, with background by greg rutkowski makoto shinkai kyoto animation key art feminine mid shot
-houses in front, houses background, straight houses, digital art, smooth, sharp focus, gravity falls style, doraemon style, shinchan style, anime style
-Simplified technical drawing, Leonardo da Vinci, Mechanical Dinosaur Skeleton, Minimalistic annotations, Hand-drawn illustrations, Basic design and engineering, Wonder and curiosity
-High quality 8K painting impressionist style of a Japanese modern city street with a girl on the foreground wearing a traditional wedding dress with a fox mask, staring at the sky, daylight
-a landscape from the Moon with the Earth setting on the horizon, realistic, detailed
-Isometric Atlantis city,great architecture with columns, great details, ornaments,seaweed, blue ambiance, 3D cartoon style, soft light, 45° view
-A hyper realistic avatar of a guy riding on a black honda cbr 650r in leather suit,high detail, high quality,8K,photo realism
-the street of amedieval fantasy town, at dawn, dark, highly detailed
-overwhelmingly beautiful eagle framed with vector flowers, long shiny wavy flowing hair, polished, ultra detailed vector floral illustration mixed with hyper realism, muted pastel colors, vector floral details in background, muted colors, hyper detailed ultra intricate overwhelming realism in detailed complex scene with magical fantasy atmosphere, no signature, no watermark
-a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece | hyperrealism| highly detailed| insanely detailed| intricate| cinematic lighting| depth of field
-electronik robot and ofice ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
-exquisitely intricately detailed illustration, of a small world with a lake and a rainbow, inside a closed glass jar.

workers/comfyui/server.py

@@ -1,143 +0,0 @@
-import os
-import logging
-import dataclasses
-import base64
-from typing import Optional, Union, Type
-
-from aiohttp import web, ClientResponse
-from anyio import open_file
-
-from lib.backend import Backend, LogAction
-from lib.data_types import EndpointHandler
-from lib.server import start_server
-from .data_types import DefaultComfyWorkflowData, CustomComfyWorkflowData
-
-
-MODEL_SERVER_URL = "http://127.0.0.1:18288" # API Wrapper Service
-
-# This is the last log line that gets emitted once comfyui+extensions have been fully loaded
-MODEL_SERVER_START_LOG_MSG = "To see the GUI go to: http://127.0.0.1:18188"
-MODEL_SERVER_ERROR_LOG_MSGS = [
-    "MetadataIncompleteBuffer",  # This error is emitted when the downloaded model is corrupted
-    "Value not in list: unet_name",  # This error is emitted when the model file is not there at all
-]
-
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-
-async def generate_client_response(
-    request: web.Request, response: ClientResponse
-) -> Union[web.Response, web.StreamResponse]:
-    _ = request
-    match response.status:
-        case 200:
-            log.debug("SUCCESS")
-            res = await response.json()
-            if "output" not in res:
-                return web.json_response(
-                    data=dict(error="there was an error in the workflow"),
-                    status=422,
-                )
-            image_paths = [path["local_path"] for path in res["output"]["images"]]
-            if not image_paths:
-                return web.json_response(
-                    data=dict(error="workflow did not produce any images"),
-                    status=422,
-                )
-            images = []
-            for image_path in image_paths:
-                async with await open_file(image_path, mode="rb") as f:
-                    contents = await f.read()
-                    images.append(
-                        f"data:image/png;base64,{base64.b64encode(contents).decode('utf-8')}"
-                    )
-            return web.json_response(data=dict(images=images))
-        case code:
-            log.debug("SENDING RESPONSE: ERROR: unknown code")
-            return web.Response(status=code)
-
-
-@dataclasses.dataclass
-class DefaultComfyWorkflowHandler(EndpointHandler[DefaultComfyWorkflowData]):
-
-    @property
-    def endpoint(self) -> str:
-        return "/runsync"
-
-    @property
-    def healthcheck_endpoint(self) -> Optional[str]:
-        return None
-
-    @classmethod
-    def payload_cls(cls) -> Type[DefaultComfyWorkflowData]:
-        return DefaultComfyWorkflowData
-
-    def make_benchmark_payload(self) -> DefaultComfyWorkflowData:
-        return DefaultComfyWorkflowData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        return await generate_client_response(client_request, model_response)
-
-
-@dataclasses.dataclass
-class CustomComfyWorkflowHandler(EndpointHandler[CustomComfyWorkflowData]):
-
-    @property
-    def endpoint(self) -> str:
-        return "/runsync"
-
-    @property
-    def healthcheck_endpoint(self) -> Optional[str]:
-        return None
-
-    @classmethod
-    def payload_cls(cls) -> Type[CustomComfyWorkflowData]:
-        return CustomComfyWorkflowData
-
-    def make_benchmark_payload(self) -> CustomComfyWorkflowData:
-        return CustomComfyWorkflowData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        return await generate_client_response(client_request, model_response)
-
-
-backend = Backend(
-    model_server_url=MODEL_SERVER_URL,
-    model_log_file=os.environ["MODEL_LOG"],
-    allow_parallel_requests=False,
-    benchmark_handler=DefaultComfyWorkflowHandler(
-        benchmark_runs=3, benchmark_words=100
-    ),
-    log_actions=[
-        (LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
-        (LogAction.Info, "Downloading:"),
-        *[
-            (LogAction.ModelError, error_msg)
-            for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
-        ],
-    ],
-)
-
-
-async def handle_ping(_):
-    return web.Response(body="pong")
-
-
-routes = [
-    web.post("/prompt", backend.create_handler(DefaultComfyWorkflowHandler())),
-    web.post("/custom-workflow", backend.create_handler(CustomComfyWorkflowHandler())),
-    web.get("/ping", handle_ping),
-]
-
-if __name__ == "__main__":
-    start_server(backend, routes)

workers/comfyui/test_load.py

@@ -1,15 +0,0 @@
-from lib.test_utils import test_load_cmd, test_args
-from .data_types import DefaultComfyWorkflowData, Model
-
-WORKER_ENDPOINT = "/prompt"
-
-
-if __name__ == "__main__":
-    test_args.add_argument(
-        "-m",
-        dest="comfy_model",
-        choices=list(map(lambda x: x.value, Model)),
-        required=True,
-        help="Image generation model name",
-    )
-    test_load_cmd(DefaultComfyWorkflowData, WORKER_ENDPOINT, arg_parser=test_args)

workers/hello_world/README.md

@@ -1,321 +0,0 @@
-# Vast PyWorker
-
-## Hello_world example
-
-There is a hello_world PyWorker implementation under `workers/hello_world`. This PyWorker is
-created for an LLM model server that runs on port 5001 has two API endpoints:
-
-1. `/generate`: generates an full response to the prompt and sends a JSON response
-2. `/generate_stream`: streams a response one token at a time
-
-Both of these endpoints take the same API JSON payload:
-
-```
-{
-    "prompt": String,
-    "max_response_tokens": Number | null
-}
-```
-
-We want the PyWorker to also expose two endpoints that correspond to the above endpoints.
-
-### Structure
-
-All PyWorkers have four files:
-
-```
-.
-└── workers
-    └── hello_world
-        ├── __init__.py
-        ├── data_types.py # contains data types representing model API endpoints
-        ├── server.py # contains endpoint handlers
-        └── test_load.py # script for load testing
-
-```
-
-All of the classes follow strict type hinting. It is recommended that you type hint all of your function.
-This will allow your IDE or VSCode with `pyright` plugin to find any type errors in your implementation.
-You can also install `pyright` with `sudo npm install -g pyright` and run `pyright` in the root of the project to find
-any type errors.
-
-### data_types.py: Contains data types representing model API endpoints
-
-This file defines the structure of the data your model server expects (its API contract) and, critically, how PyWorker *interprets* that data for autoscaling purposes. You define Python data classes that mirror the JSON payloads your model's API uses.
-
-These classes **must** inherit from `lib.data_types.ApiPayload`. This inheritance is not just for structure; it's how PyWorker knows how to:
-
-*   **Parse Incoming Requests:** Convert JSON from clients into usable Python objects.
-*   **Calculate Workload:** Determine the computational cost of a request.
-*   **Generate Test Data:** Create realistic inputs for benchmarking.
-*   **Format Requests for the Model Server:** Prepare data for the underlying model.
-
-
-```python
-import dataclasses
-import random
-from typing import Dict, Any
-
-from transformers import OpenAIGPTTokenizer # used to count tokens in a prompt
-import nltk # used to download a list of all words to generate a random prompt and benchmark the LLM model
-
-from lib.data_types import ApiPayload
-
-nltk.download("words")
-WORD_LIST = nltk.corpus.words.words()
-
-# you can use any tokenizer that fits your LLM. `openai-gpt` is free to use and is a good fit for most LLMs
-tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
-
-@dataclasses.dataclass
-class InputData(ApiPayload):
-    prompt: str
-    max_response_tokens: int
-
-    @classmethod
-    def for_test(cls) -> "ApiPayload":
-        """defines how create a payload for load testing"""
-        prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
-        return cls(prompt=prompt, max_response_tokens=300)
-
-    def generate_payload_json(self) -> Dict[str, Any]:
-        """defines how to convert an ApiPayload to JSON that will be sent to model API"""
-        return dataclasses.asdict(self)
-
-    def count_workload(self) -> float:
-        """defines how to calculate workload for a payload"""
-        return len(tokenizer.tokenize(self.prompt))
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputData":
-        """
-        defines how to transform JSON data to AuthData and payload type,
-        in this case `InputData` defined above represents the data sent to the model API.
-        AuthData is data generated by autoscaler in order to authenticate payloads.
-        In this case, the transformation is simple and 1:1. That is not always the case. See comfyui's PyWorker
-        for more complicated examples
-        """
-        errors = {}
-        for param in inspect.signature(cls).parameters:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-        if errors:
-            raise JsonDataException(errors)
-        return cls(
-            **{
-                k: v
-                for k, v in json_msg.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )
-
-```
-
-### server.py: Creating Your Model's API Endpoints
-
-This section guides you through creating the core of your custom model API: the `EndpointHandler`.  Think of `EndpointHandler` as the bridge between incoming requests from users and your underlying model.  It's the key to making your model accessible and scalable.
-
-**Why use an `EndpointHandler`?**
-
-*   **Organized Request Handling:** It provides a structured way to handle different types of requests (like generating text, generating images, or performing other model-specific tasks).
-*   **Scalability:** By separating request handling from the model itself, you can easily scale your API to handle many concurrent users.
-*   **Flexibility:** You can customize how requests are processed, validated, and transformed before being sent to your model.
-*   **Standard Interface:** It provides a consistent interface for interacting with your model, regardless of the underlying implementation.
-
-For every model API endpoint you want to expose (e.g., `/generate`, `/generate_stream`), you'll implement an `EndpointHandler`. This class is responsible for:
-The `EndpointHandler` achieves this through several key methods:
-
-*   **Receiving and validating incoming requests (`get_data_from_request`):** This method ensures the request contains the necessary data (authentication and payload) and is in the correct format. It's the entry point for all requests.
-*   **Defining the endpoint (`endpoint`):**  This method specifies the URL endpoint on the model API server where requests will be sent (e.g., `/generate`).
-*   **Specifying the payload type (`payload_cls`):** This method indicates the specific `ApiPayload` class used for this endpoint, defining the structure of the request data.
-*   **Creating benchmark payloads (`make_benchmark_payload`):**  This method creates payloads specifically for benchmarking the model's performance.
-*   **Handling the model's response (`generate_client_response`):** This method takes the response from the model API server and transforms it into the format expected by the client making the request to your PyWorker.  This allows you to customize the output as needed.
-
-The `EndpointHandler` class has several abstract functions that you *must* implement to define the behavior of your specific endpoints.  Here, we'll implement two common endpoints: `/generate` (for synchronous requests) and `/generate_stream` (for streaming responses):
-
-```python
-
-"""
-AuthData is a dataclass that represents Authentication data sent from Autoscaler to client requesting a route.
-When a user requests a route from autoscaler, see Vast's Serverless documentation for how routing and AuthData
-work.
-When a user receives a route for this PyWorker, they'll call PyWorkers API with the following JSON:
-{
-    auth_data: AuthData,
-    payload : InputData # defined above
-}
-"""
-from aiohttp import web
-
-from lib.data_types import EndpointHandler, JsonDataException
-from lib.server import start_server
-from .data_types import InputData
-
-# This class is the implementer for the '/generate' endpoint of model API
-@dataclasses.dataclass
-class GenerateHandler(EndpointHandler[InputData]):
-
-    @property
-    def endpoint(self) -> str:
-        # the API endpoint
-        return "/generate"
-
-    @classmethod
-    def payload_cls(cls) -> Type[InputData]:
-        """this function should just return ApiPayload subclass used by this handler"""
-        return InputData
-
-    def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
-        """
-        defines how to convert `InputData` defined above, to
-        JSON data to be sent to the model API. This function too is a simple dataclass -> JSON, but
-        can be more complicated, See comfyui for an example
-        """
-        return dataclasses.asdict(payload)
-
-    def make_benchmark_payload(self) -> InputData:
-        """
-        defines how to generate an InputData for benchmarking. This needs to be defined in only
-        one EndpointHandler, the one passed to the backend as the benchmark handler. Here we use the .for_test()
-        method on InputData. However, in some cases you might need to fine tune your InputData used for
-        benchmarking to closely resemble the average request users call the endpoint with in order to get best
-        autoscaling performance
-        """
-        return InputData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        """
-        defines how to convert a model API response to a response to PyWorker client
-        """
-        _ = client_request
-        match model_response.status:
-            case 200:
-                log.debug("SUCCESS")
-                data = await model_response.json()
-                return web.json_response(data=data)
-            case code:
-                log.debug("SENDING RESPONSE: ERROR: unknown code")
-                return web.Response(status=code)
-
-
-```
-
-We also handle `GenerateStreamHandler` for streaming responses. It is identical to `GenerateHandler`, except for
-the endpoint name and how we create a web response, as it is a streaming response:
-
-```python
-class GenerateStreamHandler(EndpointHandler[InputData]):
-    @property
-    def endpoint(self) -> str:
-        return "/generate_stream"
-
-    @classmethod
-    def payload_cls(cls) -> Type[InputData]:
-        return InputData
-
-    def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
-        return dataclasses.asdict(payload)
-
-    def make_benchmark_payload(self) -> InputData:
-        return InputData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        match model_response.status:
-            case 200:
-                log.debug("Streaming response...")
-                res = web.StreamResponse()
-                res.content_type = "text/event-stream"
-                await res.prepare(client_request)
-                async for chunk in model_response.content:
-                    await res.write(chunk)
-                await res.write_eof()
-                log.debug("Done streaming response")
-                return res
-            case code:
-                log.debug("SENDING RESPONSE: ERROR: unknown code")
-                return web.Response(status=code)
-
-
-```
-
-You can now instantiate a Backend and use it to handle requests.
-
-```python
-from lib.backend import Backend, LogAction
-
-# the url and port of model API
-MODEL_SERVER_URL = "http://0.0.0.0:5001"
-
-
-# This is the log line that is emitted once the server has started
-MODEL_SERVER_START_LOG_MSG = "server has started"
-MODEL_SERVER_ERROR_LOG_MSGS = [
-    "Exception: corrupted model file"  # message in the logs indicating the unrecoverable error
-]
-
-backend = Backend(
-    model_server_url=MODEL_SERVER_URL,
-    # location of model log file
-    model_log_file=os.environ["MODEL_LOG"],
-    # for some model backends that can only handle one request at a time, be sure to set this to False to
-    # let PyWorker handling queueing requests.
-    allow_parallel_requests=True,
-    # give the backend an EndpointHandler instance that is used for benchmarking
-    # number of benchmark run and number of words for a random benchmark run are given
-    benchmark_handler=GenerateHandler(benchmark_runs=3, benchmark_words=256),
-    # defines how to handle specific log messages. See docstring of LogAction for details
-    log_actions=[
-        (LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
-        (LogAction.Info, '"message":"Download'),
-        *[
-            (LogAction.ModelError, error_msg)
-            for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
-        ],
-    ],
-)
-
-# this is a simple ping handler for PyWorker
-async def handle_ping(_: web.Request):
-    return web.Response(body="pong")
-
-# this is a handler for forwarding a health check to model API
-async def handle_healthcheck(_: web.Request):
-    healthcheck_res = await backend.session.get("/healthcheck")
-    return web.Response(body=healthcheck_res.content, status=healthcheck_res.status)
-
-routes = [
-    web.post("/generate", backend.create_handler(GenerateHandler())),
-    web.post("/generate_stream", backend.create_handler(GenerateStreamHandler())),
-    web.get("/ping", handle_ping),
-    web.get("/healthcheck", handle_healthcheck),
-]
-
-if __name__ == "__main__":
-    # start server, called from start_server.sh
-    start_server(backend, routes)
-```
-
-### test_load.py
-
-Here you can create a script that allows you test an endpoint group running instances with this PyWorker
-
-```python
-from lib.test_harness import run
-from .data_types import InputData
-
-WORKER_ENDPOINT = "/generate"
-
-if __name__ == "__main__":
-    run(InputData.for_test(), WORKER_ENDPOINT)
-```
-
-You can then run the following command from the root of this repo to load test endpoint group:
-
-```sh
-# sends 1000 requests at the rate of 0.5 requests per second
-python3 workers.hello_world.test_load -n 1000 -rps 0.5 -k "$API_KEY" -e "$ENDPOINT_GROUP_NAME"
-```

workers/hello_world/data_types.py

@@ -1,48 +0,0 @@
-import dataclasses
-import random
-import inspect
-from typing import Dict, Any
-
-from transformers import OpenAIGPTTokenizer
-import nltk
-
-from lib.data_types import ApiPayload, JsonDataException
-
-nltk.download("words")
-WORD_LIST = nltk.corpus.words.words()
-
-# used to count to count tokens and workload for LLM
-tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
-
-
-@dataclasses.dataclass
-class InputData(ApiPayload):
-    prompt: str
-    max_response_tokens: int
-
-    @classmethod
-    def for_test(cls) -> "InputData":
-        prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
-        return cls(prompt=prompt, max_response_tokens=300)
-
-    def generate_payload_json(self) -> Dict[str, Any]:
-        return dataclasses.asdict(self)
-
-    def count_workload(self) -> int:
-        return len(tokenizer.tokenize(self.prompt))
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputData":
-        errors = {}
-        for param in inspect.signature(cls).parameters:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-        if errors:
-            raise JsonDataException(errors)
-        return cls(
-            **{
-                k: v
-                for k, v in json_msg.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )

workers/hello_world/server.py

@@ -1,175 +0,0 @@
-"""
-PyWorker works as a man-in-the-middle between the client and model API. It's function is:
-1. receive request from client, update metrics such as workload of a request, number of pending requests, etc.
-2a. transform the data and forward the transformed data to model API
-2b. send updated metrics to autoscaler
-3. transform response from model API(if needed) and forward the response to client
-
-PyWorker forward requests to many model API endpoint. each endpoint must have an EndpointHandler. You can also
-write function to just forward requests that don't generate anything with the model to model API without an
-EndpointHandler. This is useful for endpoints such as healthchecks. See below for example
-"""
-
-import os
-import logging
-import dataclasses
-from typing import Dict, Any, Optional, Union, Type
-
-from aiohttp import web, ClientResponse
-
-from lib.backend import Backend, LogAction
-from lib.data_types import EndpointHandler
-from lib.server import start_server
-from .data_types import InputData
-
-# the url and port of model API
-MODEL_SERVER_URL = "http://0.0.0.0:5001"
-
-
-# This is the log line that is emitted once the server has started
-MODEL_SERVER_START_LOG_MSG = "infer server has started"
-MODEL_SERVER_ERROR_LOG_MSGS = [
-    "Exception: corrupted model file"  # message in the logs indicating the unrecoverable error
-]
-
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-
-# This class is the implementer for the '/generate' endpoint of model API
-@dataclasses.dataclass
-class GenerateHandler(EndpointHandler[InputData]):
-
-    @property
-    def endpoint(self) -> str:
-        # the API endpoint
-        return "/generate"
-
-    @property
-    def healthcheck_endpoint(self) -> Optional[str]:
-        return None
-
-    @classmethod
-    def payload_cls(cls) -> Type[InputData]:
-        return InputData
-
-    def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
-        """
-        defines how to convert `InputData` defined above, to
-        json data to be sent to the model API
-        """
-        return dataclasses.asdict(payload)
-
-    def make_benchmark_payload(self) -> InputData:
-        """
-        defines how to generate an InputData for benchmarking. This needs to be defined in only
-        one EndpointHandler, the one passed to the backend as the benchmark handler
-        """
-        return InputData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        """
-        defines how to convert a model API response to a response to PyWorker client
-        """
-        _ = client_request
-        match model_response.status:
-            case 200:
-                log.debug("SUCCESS")
-                data = await model_response.json()
-                return web.json_response(data=data)
-            case code:
-                log.debug("SENDING RESPONSE: ERROR: unknown code")
-                return web.Response(status=code)
-
-
-# This is the same as GenerateHandler, except that it calls a streaming endpoint of the model API and streams the
-# response, which itself is streaming, back to the client.
-# it is nearly identical to handler as above, but it calls a different model API endpoint and it streams the
-# streaming response from model API to client
-class GenerateStreamHandler(EndpointHandler[InputData]):
-    @property
-    def endpoint(self) -> str:
-        return "/generate_stream"
-
-    @property
-    def healthcheck_endpoint(self) -> Optional[str]:
-        return None
-
-    @classmethod
-    def payload_cls(cls) -> Type[InputData]:
-        return InputData
-
-    def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
-        return dataclasses.asdict(payload)
-
-    def make_benchmark_payload(self) -> InputData:
-        return InputData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        match model_response.status:
-            case 200:
-                log.debug("Streaming response...")
-                res = web.StreamResponse()
-                res.content_type = "text/event-stream"
-                await res.prepare(client_request)
-                async for chunk in model_response.content:
-                    await res.write(chunk)
-                await res.write_eof()
-                log.debug("Done streaming response")
-                return res
-            case code:
-                log.debug("SENDING RESPONSE: ERROR: unknown code")
-                return web.Response(status=code)
-
-
-# This is the backend instance of pyworker. Only one must be made which uses EndpointHandlers to process
-# incoming requests
-backend = Backend(
-    model_server_url=MODEL_SERVER_URL,
-    model_log_file=os.environ["MODEL_LOG"],
-    allow_parallel_requests=True,
-    # give the backend a handler instance that is used for benchmarking
-    # number of benchmark run and number of words for a random benchmark run are given
-    benchmark_handler=GenerateHandler(benchmark_runs=3, benchmark_words=256),
-    # defines how to handle specific log messages. See docstring of LogAction for details
-    log_actions=[
-        (LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
-        (LogAction.Info, '"message":"Download'),
-        *[
-            (LogAction.ModelError, error_msg)
-            for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
-        ],
-    ],
-)
-
-
-# this is a simple ping handler for pyworker
-async def handle_ping(_: web.Request):
-    return web.Response(body="pong")
-
-
-# this is a handler for forwarding a health check to modelAPI
-async def handle_healthcheck(_: web.Request):
-    healthcheck_res = await backend.session.get("/healthcheck")
-    return web.Response(body=healthcheck_res.content, status=healthcheck_res.status)
-
-
-routes = [
-    web.post("/generate", backend.create_handler(GenerateHandler())),
-    web.post("/generate_stream", backend.create_handler(GenerateStreamHandler())),
-    web.get("/ping", handle_ping),
-    web.get("/healthcheck", handle_healthcheck),
-]
-
-if __name__ == "__main__":
-    # start the PyWorker server
-    start_server(backend, routes)

workers/hello_world/test_load.py

@@ -1,7 +0,0 @@
-from lib.test_utils import test_load_cmd, test_args
-from .data_types import InputData
-
-WORKER_ENDPOINT = "/generate"
-
-if __name__ == "__main__":
-    test_load_cmd(InputData, WORKER_ENDPOINT, arg_parser=test_args)

workers/openai/README.templates.md

@@ -1,77 +0,0 @@
-# <INFERENCE_SERVER> + <MODEL_NAME> (serverless)
-
-Run <INFERENCE_SERVER> with our serverless autoscaling infrastructure.
-
-See the [serverless documentation](https://docs.vast.ai/serverless) and the [Getting Started](https://docs.vast.ai/serverless/getting-started) guide for in-depth details about how to use these templates.
-
-## Configuration
-
-Two environment variables are provided to help you configure the <INFERENCE_SERVER> server:
-
-| Variable | Default Value | Used For |
-| --- | --- | --- |
-| `MODEL_NAME` | `<MODEL_NAME>` | The model to load.  Also accepts [hf.co/repo/model](#) links |
-| `<ARGS_VAR>` | `<ARGS_VAL>` | Arguments to pass to the `<ARGS_RECEIVER>` command |
-
-This template has been configured to work with <MIN_VRAM> VRAM. Setting alternative models and server arguments will change the VRAM requirements. Check model cards and <INFERENCE_SERVER_DOCS> for guidance.
-
-## Usage
-
-We have provided a demonstration client to help you implement this template into your own infrastructure
-
-### Client Setup
-
-Clone the PyWorker repository to your local machine and install the necessary requirements for running the test client.
-
-```bash
-git clone https://github.com/vast-ai/pyworker
-cd pyworker
-pip install uv
-uv venv -p 3.12
-source .venv/bin/activate
-uv pip install -r requirements.txt
-```
-
-### Completions
-
-Call to `/v1/completions` with json response
-
-```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --completion --model <MODEL_NAME>
-```
-
-### Chat Completion (json)
-
-Call to `/v1/chat/completions` with json response
-
-```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --chat --model <MODEL_NAME>
-```
-
-### Chat Completion (streaming)
-
-Call to `/v1/chat/completions` with streaming response
-
-```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --chat-stream --model <MODEL_NAME>
-```
-
-### Tool Use (json)
-
-Call to `/v1/chat/completions` with tool and json response.
-
-This test defines a simple tool which will list the contents of the local pyworker directory.  The output is then analysed by the model.
-
-```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --tools --model <MODEL_NAME>
-```
-
-### Interactive Chat (streaming)
-
-Interactive session with calls to `/v1/chat/completions`.
-
-Type `clear` to clear the chat history or `quit` to exit.
-
-```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --interactive --model <MODEL_NAME>
-```

workers/openai/client.py

@@ -1,14 +1,15 @@
 import logging
-import sys
 import json
+import os
+import sys
 import subprocess
-from urllib.parse import urljoin
+import argparse
-from typing import Dict, Any, Optional, Iterator, Union, List
+from typing import Any, Dict, List, Optional
-import requests
-from utils.endpoint_util import Endpoint
-from utils.ssl import get_cert_file_path
-from .data_types.client import CompletionConfig, ChatCompletionConfig
 
+from vastai import Serverless
+import asyncio
+
+# ---------------------- Logging ----------------------
 logging.basicConfig(
     level=logging.DEBUG,
     format="%(asctime)s[%(levelname)-5s] %(message)s",
@@ -16,135 +17,20 @@ logging.basicConfig(
 )
 log = logging.getLogger(__file__)
 
+# ---------------------- Prompts ----------------------
 COMPLETIONS_PROMPT = "the capital of USA is"
 CHAT_PROMPT = "Think step by step: Tell me about the Python programming language."
-TOOLS_PROMPT = "Can you list the files in the current working directory and tell me what you see? What do you think this directory might be for?"
+TOOLS_PROMPT = (
-
+    "Can you list the files in the current working directory and tell me what you see? "
-
+    "What do you think this directory might be for?"
-class APIClient:
+)
-    """Lightweight client focused solely on API communication"""
-
-    # Remove the generic WORKER_ENDPOINT since we're now going direct
-    DEFAULT_COST = 100
-    DEFAULT_TIMEOUT = 4
-
-    def __init__(
-        self,
-        endpoint_group_name: str,
-        api_key: str,
-        server_url: str,
-        endpoint_api_key: str,
-    ):
-        self.endpoint_group_name = endpoint_group_name
-        self.api_key = api_key
-        self.server_url = server_url
-        self.endpoint_api_key = endpoint_api_key
-
-    def _get_worker_url(self, cost: int = DEFAULT_COST) -> Dict[str, Any]:
-        """Get worker URL and auth data from routing service"""
-        if not self.endpoint_api_key:
-            raise ValueError("No valid endpoint API key available")
-
-        route_payload = {
-            "endpoint": self.endpoint_group_name,
-            "api_key": self.endpoint_api_key,
-            "cost": cost,
-        }
-
-        response = requests.post(
-            urljoin(self.server_url, "/route/"),
-            json=route_payload,
-            timeout=self.DEFAULT_TIMEOUT,
-        )
-        response.raise_for_status()
-        return response.json()
-
-    def _create_auth_data(self, message: Dict[str, Any]) -> Dict[str, Any]:
-        """Create auth data from routing response"""
-        return {
-            "signature": message["signature"],
-            "cost": message["cost"],
-            "endpoint": message["endpoint"],
-            "reqnum": message["reqnum"],
-            "url": message["url"],
-        }
-
-    def _make_request(
-        self,
-        payload: Dict[str, Any],
-        endpoint: str,
-        method: str = "POST",
-        stream: bool = False,
-    ) -> Union[Dict[str, Any], Iterator[str]]:
-        """Make request directly to the specific worker endpoint"""
-        # Get worker URL and auth data
-        cost = payload.get("max_tokens", self.DEFAULT_COST)
-        message = self._get_worker_url(cost=cost)
-        worker_url = message["url"]
-        auth_data = self._create_auth_data(message)
-
-        req_data = {"payload": {"input": payload}, "auth_data": auth_data}
-
-        url = urljoin(worker_url, endpoint)
-        log.debug(f"Making direct request to: {url}")
-        log.debug(f"Payload: {req_data}")
-
-        # Make the request using the specified method
-        if method.upper() == "POST":
-            response = requests.post(
-                url, json=req_data, stream=stream, verify=get_cert_file_path()
-            )
-        elif method.upper() == "GET":
-            response = requests.get(
-                url, params=req_data, stream=stream, verify=get_cert_file_path()
-            )
-        else:
-            raise ValueError(f"Unsupported HTTP method: {method}")
-
-        response.raise_for_status()
-
-        if stream:
-            return self._handle_streaming_response(response)
-        else:
-            return response.json()
-
-    def _handle_streaming_response(self, response: requests.Response) -> Iterator[str]:
-        """Handle streaming response and yield tokens"""
-        try:
-            for line in response.iter_lines(decode_unicode=True):
-                if line:
-                    if line.startswith("data: "):
-                        data_str = line[6:]
-                        if data_str.strip() == "[DONE]":
-                            break
-                        try:
-                            data = json.loads(data_str)
-                            yield data  # Yield the full chunk
-                        except json.JSONDecodeError:
-                            continue
-        except Exception as e:
-            log.error(f"Error handling streaming response: {e}")
-            raise
-
-    def call_completions(
-        self, config: CompletionConfig
-    ) -> Union[Dict[str, Any], Iterator[str]]:
-        payload = config.to_dict()
-
-        return self._make_request(
-            payload=payload, endpoint="/v1/completions", stream=config.stream
-        )
-
-    def call_chat_completions(
-        self, config: ChatCompletionConfig
-    ) -> Union[Dict[str, Any], Iterator[str]]:
-        payload = config.to_dict()
-
-        return self._make_request(
-            payload=payload, endpoint="/v1/chat/completions", stream=config.stream
-        )
 
+ENDPOINT_NAME = "my-vllm-endpoint"       # change this to your vLLM endpoint name
+DEFAULT_MODEL = "Qwen/Qwen3-8B"          # must support tool calling
+MAX_TOKENS = 1024
+DEFAULT_TEMPERATURE = 0.7
 
+# ---------------------- Tooling ----------------------
 class ToolManager:
     """Handles tool definitions and execution"""
 
@@ -164,7 +50,7 @@ class ToolManager:
 
     @staticmethod
     def get_ls_tool_definition() -> List[Dict[str, Any]]:
-        """Get the ls tool definition"""
+        """OpenAI-compatible tool schema"""
         return [
             {
                 "type": "function",
@@ -178,98 +64,209 @@ class ToolManager:
 
     def execute_tool_call(self, tool_call: Dict[str, Any]) -> str:
         """Execute a tool call and return the result"""
-        function_name = tool_call["function"]["name"]
+        function_name = (tool_call.get("function") or {}).get("name")
-
         if function_name == "list_files":
             return self.list_files()
-        else:
+        raise ValueError(f"Unknown tool function: {function_name}")
-            raise ValueError(f"Unknown tool function: {function_name}")
 
 
+# ----- Helpers to handle streamed tool_calls assembly -----
+def _merge_tool_call_delta(state: Dict[int, Dict[str, Any]], tc_delta: Dict[str, Any]) -> None:
+    """
+    OpenAI-style streaming sends partial tool_calls with an index and partial fields.
+    We merge into a per-index state dict until the assistant message finishes.
+    """
+    idx = tc_delta.get("index")
+    if idx is None:
+        return
+
+    entry = state.setdefault(idx, {"id": None, "function": {"name": None, "arguments": ""}, "type": "function"})
+
+    if tc_delta.get("id"):
+        entry["id"] = tc_delta["id"]
+
+    fn_delta = tc_delta.get("function") or {}
+    if "name" in fn_delta and fn_delta["name"]:
+        entry["function"]["name"] = fn_delta["name"]
+    if "arguments" in fn_delta and fn_delta["arguments"]:
+        entry["function"]["arguments"] += fn_delta["arguments"]
+
+
+def _tool_state_to_message_tool_calls(state: Dict[int, Dict[str, Any]]) -> List[Dict[str, Any]]:
+    return [state[i] for i in sorted(state.keys())]
+
+
+# ---- OpenAI-compatible calls (non-streaming) ----
+async def call_completions(client: Serverless, *, model: str, prompt: str, **kwargs) -> Dict[str, Any]:
+
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+    }
+    log.debug("POST /v1/completions %s", json.dumps(payload)[:500])
+    resp = await endpoint.request("/v1/completions", payload, cost=payload["max_tokens"])
+    return resp["response"]
+
+async def call_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], **kwargs) -> Dict[str, Any]:
+
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+
+    payload = {
+        "model": model,
+        "messages": messages,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+        **({"tools": kwargs["tools"]} if "tools" in kwargs else {}),
+        **({"tool_choice": kwargs["tool_choice"]} if "tool_choice" in kwargs else {}),
+    }
+    log.debug("POST /v1/chat/completions %s", json.dumps(payload)[:500])
+    resp = await endpoint.request("/v1/chat/completions", payload, cost=payload["max_tokens"])
+    return resp["response"]
+
+# ---- Streaming variants ----
+async def stream_completions(client: Serverless, *, model: str, prompt: str, **kwargs):
+
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+        "stream": True,
+        **({"stop": kwargs["stop"]} if "stop" in kwargs else {}),
+    }
+    log.debug("STREAM /v1/completions %s", json.dumps(payload)[:500])
+    resp = await endpoint.request("/v1/completions", payload, cost=payload["max_tokens"], stream=True)
+    return resp["response"]  # async generator
+
+async def stream_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], **kwargs):
+
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+
+    payload = {
+        "model": model,
+        "messages": messages,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+        "stream": True,
+        **({"tools": kwargs["tools"]} if "tools" in kwargs else {}),
+        **({"tool_choice": kwargs["tool_choice"]} if "tool_choice" in kwargs else {}),
+    }
+    log.debug("STREAM /v1/chat/completions %s", json.dumps(payload)[:500])
+    resp = await endpoint.request("/v1/chat/completions", payload, cost=payload["max_tokens"], stream=True)
+    return resp["response"]  # async generator
+
+
+# ---------------------- Demo Runner ----------------------
 class APIDemo:
     """Demo and testing functionality for the API client"""
 
-    def __init__(
+    def __init__(self, client: Serverless, model: str, tool_manager: Optional[ToolManager] = None):
-        self, client: APIClient, model: str, tool_manager: Optional[ToolManager] = None
-    ):
         self.client = client
         self.model = model
         self.tool_manager = tool_manager or ToolManager()
 
-    def handle_streaming_response(
+    # ----- Streaming handler -----
-        self, response_stream, show_reasoning: bool = True
+    async def handle_streaming_response(self, stream, show_reasoning: bool = True) -> str:
-    ) -> str:
-        """
-        Handle streaming chat response and display all output.
-        """
-
         full_response = ""
         reasoning_content = ""
-        reasoning_started = False
+        printed_reasoning = False
-        content_started = False
+        printed_answer = False
 
-        for chunk in response_stream:
+        async for chunk in stream:
-            # Normalize the chunk
+            choice = (chunk.get("choices") or [{}])[0]
-            if isinstance(chunk, str):
+            delta = choice.get("delta", {})
-                chunk = chunk.strip()
-                if chunk.startswith("data: "):
-                    chunk = chunk[6:].strip()
-                if chunk in ["[DONE]", ""]:
-                    continue
-                try:
-                    parsed_chunk = json.loads(chunk)
-                except json.JSONDecodeError:
-                    continue
-            elif isinstance(chunk, dict):
-                parsed_chunk = chunk
-            else:
-                continue
 
-            # Parse delta from the chunk
+            # reasoning tokens
-            choices = parsed_chunk.get("choices", [])
+            rc = delta.get("reasoning_content")
-            if not choices:
+            if rc and show_reasoning:
-                continue
+                if not printed_reasoning:
-
-            delta = choices[0].get("delta", {})
-            reasoning_token = delta.get("reasoning_content", "")
-            content_token = delta.get("content", "")
-
-            # Print reasoning token if applicable
-            if show_reasoning and reasoning_token:
-                if not reasoning_started:
                     print("\n🧠 Reasoning: ", end="", flush=True)
-                    reasoning_started = True
+                    printed_reasoning = True
-                print(f"\033[90m{reasoning_token}\033[0m", end="", flush=True)
+                print(rc, end="", flush=True)
-                reasoning_content += reasoning_token
+                reasoning_content += rc
 
-            # Print content token
+            # content tokens
-            if content_token:
+            content_part = delta.get("content")
-                if not content_started:
+            if content_part:
-                    if show_reasoning and reasoning_started:
+                if not printed_answer:
-                        print(f"\n💬 Response: ", end="", flush=True)
+                    if show_reasoning and printed_reasoning:
+                        print("\n💬 Response: ", end="", flush=True)
                     else:
                         print("Assistant: ", end="", flush=True)
-                    content_started = True
+                    printed_answer = True
-                print(content_token, end="", flush=True)
+                print(content_part, end="", flush=True)
-                full_response += content_token
+                full_response += content_part
-
-        print()  # Ensure newline after response
 
+        print()  # newline
         if show_reasoning:
-            if reasoning_started or content_started:
+            if printed_reasoning or printed_answer:
                 print("\nStreaming completed.")
-            if reasoning_started:
+            if printed_reasoning:
                 print(f"Reasoning tokens: {len(reasoning_content.split())}")
-            if content_started:
+            if printed_answer:
                 print(f"Response tokens: {len(full_response.split())}")
 
         return full_response
     
-    def test_tool_support(self) -> bool:
+    async def demo_completions(self) -> None:
-        """Test if the endpoint supports function calling"""
+        print("=" * 60)
-        log.debug("Testing endpoint tool calling support...")
+        print("COMPLETIONS DEMO")
+        print("=" * 60)
 
-        # Try a simple request with minimal tools to test support
+        response = await call_completions(
+            client=self.client,
+            model=self.model,
+            prompt=COMPLETIONS_PROMPT,
+            max_tokens=MAX_TOKENS,
+            temperature=DEFAULT_TEMPERATURE,
+        )
+        print("\nResponse:")
+        print(json.dumps(response, indent=2))
+
+    async def demo_chat(self, use_streaming: bool = True) -> None:
+        print("=" * 60)
+        print(f"CHAT COMPLETIONS DEMO {'(STREAMING)' if use_streaming else '(NON-STREAMING)'}")
+        print("=" * 60)
+
+        messages = [{"role": "user", "content": CHAT_PROMPT}]
+
+        if use_streaming:
+            stream = await stream_chat_completions(
+                client=self.client,
+                model=self.model, 
+                messages=messages,
+                max_tokens=MAX_TOKENS,
+                temperature=DEFAULT_TEMPERATURE
+            )
+            try:
+                await self.handle_streaming_response(stream, show_reasoning=True)
+            except Exception as e:
+                log.error("\nError during streaming: %s", e, exc_info=True)
+        else:
+            response = await call_chat_completions(
+                client=self.client,
+                model=self.model, 
+                messages=messages,
+                max_tokens=MAX_TOKENS,
+                temperature=DEFAULT_TEMPERATURE
+            )
+            choice = (response.get("choices") or [{}])[0]
+            message = choice.get("message", {})
+            content = message.get("content", "")
+            reasoning = message.get("reasoning_content", "") or message.get("reasoning", "")
+            if reasoning:
+                print(f"\n🧠 Reasoning: \033[90m{reasoning}\033[0m")
+            print(f"\n💬 Assistant: {content}")
+            print(f"\nFull Response:\n{json.dumps(response, indent=2)}")
+
+    async def test_tool_support(self) -> bool:
+        """Probe that tool schema is accepted (no actual call)"""
         messages = [{"role": "user", "content": "Hello"}]
         minimal_tool = [
             {
@@ -277,170 +274,147 @@ class APIDemo:
                 "function": {"name": "test_function", "description": "Test function"},
             }
         ]
-
-        config = ChatCompletionConfig(
-            model=self.model,
-            messages=messages,
-            max_tokens=10,
-            tools=minimal_tool,
-            tool_choice="none",  # Don't actually call the tool
-        )
-
         try:
-            response = self.client.call_chat_completions(config)
+            _ = await call_chat_completions(
+                client=self.client,
+                model=self.model,
+                messages=messages,
+                tools=minimal_tool,
+                tool_choice="none",
+                max_tokens=10
+            )
             return True
         except Exception as e:
-            log.error(f"Error: Endpoint does not support tool calling: {e}")
+            log.error("Endpoint does not support tool calling: %s", e)
             return False
 
-    def demo_completions(self) -> None:
+    async def demo_ls_tool(self) -> None:
-        """Demo: test basic completions endpoint"""
+        """Ask to list files using function calling, then provide final analysis"""
-        print("=" * 60)
-        print("COMPLETIONS DEMO")
-        print("=" * 60)
-
-        config = CompletionConfig(
-            model=self.model, prompt=COMPLETIONS_PROMPT, stream=False
-        )
-
-        log.info(
-            f"Testing completions with model '{self.model}' and prompt: '{config.prompt}'"
-        )
-        response = self.client.call_completions(config)
-
-        if isinstance(response, dict):
-            print("\nResponse:")
-            print(json.dumps(response, indent=2))
-        else:
-            log.error("Unexpected response format")
-
-    def demo_chat(self, use_streaming: bool = True) -> None:
-        """
-        Demo: test chat completions endpoint with optional streaming
-        """
-        print("=" * 60)
-        print(
-            f"CHAT COMPLETIONS DEMO {'(STREAMING)' if use_streaming else '(NON-STREAMING)'}"
-        )
-        print("=" * 60)
-
-        config = ChatCompletionConfig(
-            model=self.model,
-            messages=[{"role": "user", "content": CHAT_PROMPT}],
-            stream=use_streaming,
-        )
-
-        log.info(f"Testing chat completions with model '{self.model}'...")
-        response = self.client.call_chat_completions(config)
-
-        if use_streaming:
-            try:
-                self.handle_streaming_response(response, show_reasoning=True)
-            except Exception as e:
-                log.error(f"\nError during streaming: {e}")
-                import traceback
-
-                traceback.print_exc()
-                return
-
-        else:
-            if isinstance(response, dict):
-                choice = response.get("choices", [{}])[0]
-                message = choice.get("message", {})
-                content = message.get("content", "")
-                reasoning = message.get("reasoning_content", "") or message.get(
-                    "reasoning", ""
-                )
-
-                if reasoning:
-                    print(f"\n🧠 Reasoning: \033[90m{reasoning}\033[0m")
-
-                print(f"\n💬 Assistant: {content}")
-                print(f"\nFull Response:")
-                print(json.dumps(response, indent=2))
-            else:
-                log.error("Unexpected response format")
-
-    def demo_ls_tool(self) -> None:
-        """Demo: ask LLM to list files in the current directory and describe what it sees"""
         print("=" * 60)
         print("TOOL USE DEMO: List Directory Contents")
         print("=" * 60)
 
-        # Test if tools are supported first
+        if not await self.test_tool_support():
-        if not self.test_tool_support():
             return
 
-        # Request with tool available
+        messages: List[Dict[str, Any]] = [{"role": "user", "content": TOOLS_PROMPT}]
-        messages = [{"role": "user", "content": TOOLS_PROMPT}]
 
-        config = ChatCompletionConfig(
+        # First pass: let the model decide tools, stream tool_calls and partial content
+        stream = await stream_chat_completions(
+            client=self.client,
             model=self.model,
             messages=messages,
             tools=self.tool_manager.get_ls_tool_definition(),
             tool_choice="auto",
+            max_tokens=MAX_TOKENS,
+            temperature=DEFAULT_TEMPERATURE,
         )
 
-        log.info(f"Making initial request with tool using model '{self.model}'...")
+        assistant_content_buf: List[str] = []
-        response = self.client.call_chat_completions(config)
+        tool_calls_state: Dict[int, Dict[str, Any]] = {}
+        printed_reasoning = False
+        printed_answer = False
 
-        if not isinstance(response, dict):
+        async for chunk in stream:
-            raise ValueError("Expected dict response for tool use")
+            choice = (chunk.get("choices") or [{}])[0]
+            delta = choice.get("delta", {})
 
-        choice = response.get("choices", [{}])[0]
+            rc = delta.get("reasoning_content")
-        message = choice.get("message", {})
+            if rc:
+                if not printed_reasoning:
+                    printed_reasoning = True
+                    print("🧠 Reasoning: ", end="", flush=True)
+                print(rc, end="", flush=True)
 
-        print(f"Assistant response: {message.get('content', 'No content')}")
+            content_part = delta.get("content")
+            if content_part:
+                assistant_content_buf.append(content_part)
+                if not printed_answer:
+                    printed_answer = True
+                    print("\n💬 Response: ", end="", flush=True)
+                print(content_part, end="", flush=True)
 
-        # Check for tool calls
+            if "tool_calls" in delta and delta["tool_calls"]:
-        tool_calls = message.get("tool_calls")
+                for tc_delta in delta["tool_calls"]:
-        if not tool_calls:
+                    _merge_tool_call_delta(tool_calls_state, tc_delta)
-            raise ValueError(
-                "No tool calls made - model may not support function calling"
-            )
 
-        print(f"Tool calls detected: {len(tool_calls)}")
+        # If no tool calls, we’re done.
+        if not tool_calls_state:
+            print("\n(No tool calls were made.)")
+            return
 
-        # Execute the tool call
+        # Build assistant message with tool_calls
-        for tool_call in tool_calls:
+        assistant_message = {
-            function_name = tool_call["function"]["name"]
+            "role": "assistant",
-            print(f"Executing tool: {function_name}")
+            "content": "".join(assistant_content_buf) if assistant_content_buf else None,
+            "tool_calls": _tool_state_to_message_tool_calls(tool_calls_state),
+        }
+        messages.append(assistant_message)
 
-            tool_result = self.tool_manager.execute_tool_call(tool_call)
+        # Execute tools and feed results back
-            print(f"Tool result:\n{tool_result}")
+        for tc in assistant_message["tool_calls"]:
+            tool_name = (tc.get("function") or {}).get("name")
+            call_id = tc.get("id")
+            raw_args = (tc.get("function") or {}).get("arguments") or "{}"
 
-            # Add tool result and continue conversation
+            try:
-            messages.append(message)  # Add assistant's message with tool call
+                args = json.loads(raw_args) if raw_args.strip() else {}
-            messages.append(
+            except Exception as e:
-                {
+                tool_result = json.dumps({"error": f"Argument parse failed: {str(e)}", "raw_arguments": raw_args})
-                    "role": "tool",
+                messages.append({"role": "tool", "tool_call_id": call_id, "content": tool_result})
-                    "tool_call_id": tool_call["id"],
+                continue
-                    "content": tool_result,
-                }
-            )
 
-            # Get final response
+            try:
-            final_config = ChatCompletionConfig(
+                if tool_name == "list_files":
-                model=self.model,
+                    tool_result = self.tool_manager.list_files()
-                messages=messages,
+                else:
-                tools=self.tool_manager.get_ls_tool_definition(),
+                    tool_result = json.dumps({"error": f"Unknown tool '{tool_name}'"})
-            )
+            except Exception as e:
+                tool_result = json.dumps({"error": f"Tool '{tool_name}' failed: {str(e)}"})
 
-            print("Getting final response...")
+            print("\n[Tool executed]", tool_name)
-            final_response = self.client.call_chat_completions(final_config)
+            print(tool_result[:500] + ("..." if len(tool_result) > 500 else ""))
+            messages.append({"role": "tool", "tool_call_id": call_id, "content": tool_result})
 
-            if isinstance(final_response, dict):
+        # Second pass: get final streamed answer after tool results
-                final_choice = final_response.get("choices", [{}])[0]
+        stream2 = await stream_chat_completions(
-                final_message = final_choice.get("message", {})
+            client=self.client,
-                final_content = final_message.get("content", "")
+            model=self.model,
+            messages=messages,
+            max_tokens=MAX_TOKENS,
+            temperature=DEFAULT_TEMPERATURE,
+        )
 
-                print("\n" + "=" * 60)
+        final_buf = []
-                print("FINAL LLM ANALYSIS:")
+        printed_reasoning2 = False
-                print("=" * 60)
+        printed_answer2 = False
-                print(final_content)
-                print("=" * 60)
 
-    def interactive_chat(self) -> None:
+        async for chunk in stream2:
+            choice = (chunk.get("choices") or [{}])[0]
+            delta = choice.get("delta", {})
+
+            rc2 = delta.get("reasoning_content")
+            if rc2:
+                if not printed_reasoning2:
+                    printed_reasoning2 = True
+                    print("\n🧠 Reasoning (post-tools): ", end="", flush=True)
+                print(rc2, end="", flush=True)
+
+            c2 = delta.get("content")
+            if c2:
+                final_buf.append(c2)
+                if not printed_answer2:
+                    printed_answer2 = True
+                    print("\n💬 Response (final): ", end="", flush=True)
+                print(c2, end="", flush=True)
+
+        print("\n" + "=" * 60)
+        print("FINAL LLM ANALYSIS:")
+        print("=" * 60)
+        print("".join(final_buf))
+        print("=" * 60)
+
+    async def interactive_chat(self) -> None:
         """Interactive chat session with streaming"""
         print("=" * 60)
         print("INTERACTIVE STREAMING CHAT")
@@ -449,7 +423,7 @@ class APIDemo:
         print("Type 'quit' to exit, 'clear' to clear history")
         print()
 
-        messages = []
+        messages: List[Dict[str, Any]] = []
 
         while True:
             try:
@@ -467,16 +441,15 @@ class APIDemo:
 
                 messages.append({"role": "user", "content": user_input})
 
-                config = ChatCompletionConfig(
-                    model=self.model, messages=messages, stream=True, temperature=0.7
-                )
-
                 print("Assistant: ", end="", flush=True)
-
+                stream = await stream_chat_completions(
-                response = self.client.call_chat_completions(config)
+                    client=self.client,
-                assistant_content = self.handle_streaming_response(
+                    model=self.model, 
-                    response, show_reasoning=True
+                    messages=messages, 
+                    max_tokens=MAX_TOKENS, 
+                    temperature=0.7
                 )
+                assistant_content = await self.handle_streaming_response(stream, show_reasoning=True)
 
                 # Add assistant response to conversation history
                 messages.append({"role": "assistant", "content": assistant_content})
@@ -485,115 +458,64 @@ class APIDemo:
                 print("\n👋 Chat interrupted. Goodbye!")
                 break
             except Exception as e:
-                log.error(f"\nError: {e}")
+                log.error("\nError: %s", e)
                 continue
 
 
-def main():
+# ---------------------- CLI ----------------------
-    """Main function with CLI switches for different tests"""
+def build_arg_parser() -> argparse.ArgumentParser:
-    from lib.test_utils import test_args
+    p = argparse.ArgumentParser(description="Vast vLLM Demo (Serverless SDK)")
+    p.add_argument("--model", required=True, help="Model to use for requests (required)")
+    p.add_argument("--endpoint", default="my-vllm-endpoint", help="Vast endpoint name (default: my-vllm-endpoint)")
 
-    # Add mandatory model argument
+    modes = p.add_mutually_exclusive_group(required=False)
-    test_args.add_argument(
+    modes.add_argument("--completion", action="store_true", help="Test completions endpoint")
-        "--model", required=True, help="Model to use for requests (required)"
+    modes.add_argument("--chat", action="store_true", help="Test chat completions endpoint (non-streaming)")
-    )
+    modes.add_argument("--chat-stream", action="store_true", help="Test chat completions endpoint with streaming")
+    modes.add_argument("--tools", action="store_true", help="Test function calling with ls tool (non-streaming+streamed phases)")
+    modes.add_argument("--interactive", action="store_true", help="Start interactive streaming chat session")
+    return p
 
-    # Add test mode arguments
-    test_args.add_argument(
-        "--completion", action="store_true", help="Test completions endpoint"
-    )
-    test_args.add_argument(
-        "--chat",
-        action="store_true",
-        help="Test chat completions endpoint (non-streaming)",
-    )
-    test_args.add_argument(
-        "--chat-stream",
-        action="store_true",
-        help="Test chat completions endpoint with streaming",
-    )
-    test_args.add_argument(
-        "--tools",
-        action="store_true",
-        help="Test function calling with ls tool (non-streaming)",
-    )
-    test_args.add_argument(
-        "--interactive",
-        action="store_true",
-        help="Start interactive streaming chat session",
-    )
 
-    args = test_args.parse_args()
+async def main_async():
+    args = build_arg_parser().parse_args()
 
-    # Check that only one test mode is selected
+    selected = sum([args.completion, args.chat, args.chat_stream, args.tools, args.interactive])
-    test_modes = [
+    if selected == 0:
-        args.completion,
-        args.chat,
-        args.chat_stream,
-        args.tools,
-        args.interactive,
-    ]
-    selected_count = sum(test_modes)
-
-    if selected_count == 0:
         print("Please specify exactly one test mode:")
         print("  --completion    : Test completions endpoint")
         print("  --chat          : Test chat completions endpoint (non-streaming)")
         print("  --chat-stream   : Test chat completions endpoint with streaming")
-        print("  --tools         : Test function calling with ls tool (non-streaming)")
+        print("  --tools         : Test function calling with ls tool")
         print("  --interactive   : Start interactive streaming chat session")
-        print(
+        print(f"\nExample: python {os.path.basename(sys.argv[0])} --model Qwen/Qwen3-8B --chat-stream --endpoint my-vllm-endpoint")
-            f"\nExample: python {sys.argv[0]} --model Qwen/Qwen3-8B --chat-stream -k YOUR_KEY -e YOUR_ENDPOINT"
-        )
         sys.exit(1)
-    elif selected_count > 1:
+    elif selected > 1:
         print("Please specify exactly one test mode")
         sys.exit(1)
 
+    print(f"Using model: {args.model}")
+    print("=" * 60)
+
     try:
-        endpoint_api_key = Endpoint.get_endpoint_api_key(
+        async with Serverless() as client:
-            endpoint_name=args.endpoint_group_name,
+            demo = APIDemo(client, args.model, ToolManager())
-            account_api_key=args.api_key,
-            instance=args.instance,
-        )
 
-        if not endpoint_api_key:
+            if args.completion:
-            log.error(
+                await demo.demo_completions()
-                f"Could not retrieve API key for endpoint '{args.endpoint_group_name}'. Exiting."
+            elif args.chat:
-            )
+                await demo.demo_chat(use_streaming=False)
-            sys.exit(1)
+            elif args.chat_stream:
-
+                await demo.demo_chat(use_streaming=True)
-        # Create the core API client
+            elif args.tools:
-        client = APIClient(
+                await demo.demo_ls_tool()
-            endpoint_group_name=args.endpoint_group_name,
+            elif args.interactive:
-            api_key=args.api_key,
+                await demo.interactive_chat()
-            server_url=Endpoint.get_autoscaler_server_url(args.instance),
-            endpoint_api_key=endpoint_api_key,
-        )
-
-        # Create tool manager and demo (passing the model parameter)
-        tool_manager = ToolManager()
-        demo = APIDemo(client, args.model, tool_manager)
-
-        print(f"Using model: {args.model}")
-        print("=" * 60)
-
-        # Run the selected test
-        if args.completion:
-            demo.demo_completions()
-        elif args.chat:
-            demo.demo_chat(use_streaming=False)
-        elif args.chat_stream:
-            demo.demo_chat(use_streaming=True)
-        elif args.tools:
-            demo.demo_ls_tool()
-        elif args.interactive:
-            demo.interactive_chat()
 
     except Exception as e:
-        log.error(f"Error during test: {e}", exc_info=True)
+        log.error("Error during test: %s", e, exc_info=True)
         sys.exit(1)
 
 
 if __name__ == "__main__":
-    main()
+    asyncio.run(main_async())

workers/openai/data_types/client.py

@@ -1,58 +0,0 @@
-import json
-from dataclasses import dataclass, field, fields, is_dataclass
-from typing import Optional, List, Dict, Any
-
-
-class SerializableDataclass:
-    def _serialize_recursive(self, obj: Any) -> Any:
-        if is_dataclass(obj):
-            return {
-                field.name: self._serialize_recursive(getattr(obj, field.name))
-                for field in fields(obj)
-            }
-        elif isinstance(obj, dict):
-            return {key: self._serialize_recursive(value) for key, value in obj.items()}
-        elif isinstance(obj, (list, tuple)):
-            return [self._serialize_recursive(item) for item in obj]
-        elif isinstance(obj, set):
-            return [self._serialize_recursive(item) for item in obj]
-        else:
-            return obj
-
-    def to_dict(self) -> Dict[str, Any]:
-        return self._serialize_recursive(self)
-
-    def to_json(self, indent: int = 2) -> str:
-        return json.dumps(self.to_dict(), indent=indent)
-
-
-@dataclass
-class CompletionConfig(SerializableDataclass):
-    """Configuration for completion requests"""
-
-    model: str
-    prompt: str = "Hello"
-    max_tokens: int = 256
-    temperature: float = 0.7
-    top_k: int = 20
-    top_p: float = 0.4
-    stream: bool = False
-
-
-@dataclass
-class ChatCompletionConfig(SerializableDataclass):
-    """Configuration for chat completion requests"""
-
-    model: str
-    messages: list = field(default_factory=list)
-    max_tokens: int = 2096
-    temperature: float = 0.7
-    top_k: int = 20
-    top_p: float = 0.4
-    stream: bool = False
-    tools: Optional[List[Dict[str, Any]]] = field(default_factory=list)
-    tool_choice: str = "auto"
-
-    def __post_init__(self):
-        if self.messages is None:
-            self.messages = [{"role": "user", "content": "Hello"}]

workers/openai/data_types/server.py

@@ -1,182 +0,0 @@
-import os, json, random
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from lib.data_types import EndpointHandler, ApiPayload, JsonDataException
-from typing import Union, Type, Dict, Any, Optional
-from aiohttp import web, ClientResponse
-import nltk
-import logging
-
-nltk.download("words")
-WORD_LIST = nltk.corpus.words.words()
-log = logging.getLogger(__name__)
-
-"""
-Generic dataclass accepts any dictionary in input.
-"""
-
-
-@dataclass
-class GenericData(ApiPayload, ABC):
-    input: Dict[str, Any]
-
-    @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "GenericData":
-        return cls(input=data["input"])
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "GenericData":
-        errors = {}
-
-        # Validate required parameters
-        required_params = ["input"]
-        for param in required_params:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-
-        if errors:
-            raise JsonDataException(errors)
-
-        try:
-            # Create clean data dict and delegate to from_dict
-            clean_data = {"input": json_msg["input"]}
-
-            return cls.from_dict(clean_data)
-
-        except (json.JSONDecodeError, JsonDataException) as e:
-            errors["parameters"] = str(e)
-            raise JsonDataException(errors)
-
-    @classmethod
-    @abstractmethod
-    def for_test(cls) -> "GenericData":
-        pass
-
-    def generate_payload_json(self) -> Dict[str, Any]:
-        return self.input
-
-    def count_workload(self) -> int:
-        return self.input.get("max_tokens", 0)
-
-
-@dataclass
-class GenericHandler(EndpointHandler[GenericData], ABC):
-
-    @property
-    @abstractmethod
-    def endpoint(self) -> str:
-        pass
-
-    @property
-    def healthcheck_endpoint(self) -> Optional[str]:
-        return os.environ.get("MODEL_HEALTH_ENDPOINT")
-
-    @classmethod
-    def payload_cls(cls) -> Type[GenericData]:
-        return GenericData
-
-    @abstractmethod
-    def make_benchmark_payload(self) -> GenericData:
-        pass
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        match model_response.status:
-            case 200:
-                # Check if the response is actually streaming based on response headers/content-type
-                is_streaming_response = (
-                    model_response.content_type == "text/event-stream"
-                    or model_response.content_type == "application/x-ndjson"
-                    or model_response.headers.get("Transfer-Encoding") == "chunked"
-                    or "stream" in model_response.content_type.lower()
-                )
-
-                if is_streaming_response:
-                    log.debug("Detected streaming response...")
-                    res = web.StreamResponse()
-                    res.content_type = model_response.content_type
-                    await res.prepare(client_request)
-                    async for chunk in model_response.content:
-                        await res.write(chunk)
-                    await res.write_eof()
-                    log.debug("Done streaming response")
-                    return res
-                else:
-                    log.debug("Detected non-streaming response...")
-                    content = await model_response.read()
-                    return web.Response(
-                        body=content,
-                        status=200,
-                        content_type=model_response.content_type,
-                    )
-            case code:
-                log.debug("SENDING RESPONSE: ERROR: unknown code")
-                return web.Response(status=code)
-
-
-@dataclass
-class CompletionsData(GenericData):
-    @classmethod
-    def for_test(cls) -> "CompletionsData":
-        prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
-        model = os.environ.get("MODEL_NAME")
-        if not model:
-            raise ValueError("MODEL_NAME environment variable not set")
-
-        test_input = {
-            "model": model,
-            "prompt": prompt,
-            "temperature": 0.7,
-            "max_tokens": 500,
-        }
-        return cls(input=test_input)
-
-
-@dataclass
-class CompletionsHandler(GenericHandler):
-    @property
-    def endpoint(self) -> str:
-        return "/v1/completions"
-
-    @classmethod
-    def payload_cls(cls) -> Type[CompletionsData]:
-        return CompletionsData
-
-    def make_benchmark_payload(self) -> CompletionsData:
-        return CompletionsData.for_test()
-
-
-@dataclass
-class ChatCompletionsData(GenericData):
-    """Chat completions-specific data implementation"""
-
-    @classmethod
-    def for_test(cls) -> "ChatCompletionsData":
-        prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
-        model = os.environ.get("MODEL_NAME")
-        if not model:
-            raise ValueError("MODEL_NAME environment variable not set")
-
-        # Chat completions use messages format instead of prompt
-        test_input = {
-            "model": model,
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0.7,
-            "max_tokens": 500,
-        }
-        return cls(input=test_input)
-
-
-@dataclass
-class ChatCompletionsHandler(GenericHandler):
-    @property
-    def endpoint(self) -> str:
-        return "/v1/chat/completions"
-
-    @classmethod
-    def payload_cls(cls) -> Type[ChatCompletionsData]:
-        return ChatCompletionsData
-
-    def make_benchmark_payload(self) -> ChatCompletionsData:
-        return ChatCompletionsData.for_test()

workers/openai/server.py

@@ -1,60 +0,0 @@
-import os
-import logging
-from .data_types.server import CompletionsHandler, ChatCompletionsHandler
-from aiohttp import web
-from lib.backend import Backend, LogAction
-from lib.server import start_server
-
-# This line indicates that the inference server is listening
-MODEL_SERVER_START_LOG_MSG = [
-    "Application startup complete.",  # vLLM
-    "llama runner started",  # Ollama
-    '"message":"Connected","target":"text_generation_router"',  # TGI
-    '"message":"Connected","target":"text_generation_router::server"',  # TGI
-]
-
-MODEL_SERVER_ERROR_LOG_MSGS = [
-    "INFO exited: vllm",  # vLLM
-    "RuntimeError: Engine",  # vLLM
-    "Error: pull model manifest:",  # Ollama
-    "stalled; retrying",  # Ollama
-    "Error: WebserverFailed",  # TGI
-    "Error: DownloadError",  # TGI
-    "Error: ShardCannotStart",  # TGI
-]
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-backend = Backend(
-    model_server_url=os.environ["MODEL_SERVER_URL"],
-    model_log_file=os.environ["MODEL_LOG"],
-    allow_parallel_requests=True,
-    benchmark_handler=CompletionsHandler(benchmark_runs=3, benchmark_words=256),
-    log_actions=[
-        *[(LogAction.ModelLoaded, info_msg) for info_msg in MODEL_SERVER_START_LOG_MSG],
-        (LogAction.Info, '"message":"Download'),
-        *[
-            (LogAction.ModelError, error_msg)
-            for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
-        ],
-    ],
-)
-
-
-async def handle_ping(_):
-    return web.Response(body="pong")
-
-
-routes = [
-    web.post("/v1/completions", backend.create_handler(CompletionsHandler())),
-    web.post("/v1/chat/completions", backend.create_handler(ChatCompletionsHandler())),
-    web.get("/ping", handle_ping),
-]
-
-if __name__ == "__main__":
-    start_server(backend, routes)

workers/openai/test_load.py

@@ -1,28 +0,0 @@
-from lib.test_utils import test_load_cmd, test_args
-from .data_types.server import CompletionsData
-import os
-
-WORKER_ENDPOINT = "/v1/completions"
-
-if __name__ == "__main__":
-    # Check if MODEL_NAME environment variable is set
-    model_name_set = os.environ.get("MODEL_NAME") is not None
-
-    # Add model argument - required only if MODEL_NAME is not set
-    test_args.add_argument(
-        "--model",
-        dest="model",
-        required=not model_name_set,
-        help="Model to use for completions request (required if MODEL_NAME env var not set)",
-    )
-
-    # Parse known args to get model early, before test_load_cmd adds its args
-    known_args, _ = test_args.parse_known_args()
-
-    # Set environment variable if model was provided
-    if hasattr(known_args, "model") and known_args.model:
-        os.environ["MODEL_NAME"] = known_args.model
-        print(f"Set MODEL_NAME environment variable to: {known_args.model}")
-
-    # Now call test_load_cmd normally - it will add its own args and re-parse
-    test_load_cmd(CompletionsData, WORKER_ENDPOINT, arg_parser=test_args)

workers/openai/worker.py

@@ -0,0 +1,78 @@
+import nltk
+import random
+import os
+
+from vastai import Worker, WorkerConfig, HandlerConfig, LogActionConfig, BenchmarkConfig
+
+# vLLM model configuration
+MODEL_SERVER_URL           = 'http://127.0.0.1'
+MODEL_SERVER_PORT          = 18000
+MODEL_LOG_FILE             = '/var/log/portal/vllm.log'
+MODEL_HEALTHCHECK_ENDPOINT = "/health"
+
+# vLLM-specific log messages
+MODEL_LOAD_LOG_MSG = [
+    "Application startup complete.",
+]
+
+MODEL_ERROR_LOG_MSGS = [
+    "INFO exited: vllm",
+    "RuntimeError: Engine",
+    "Traceback (most recent call last):"
+]
+
+MODEL_INFO_LOG_MSGS = [
+    '"message":"Download'
+]
+
+nltk.download("words")
+WORD_LIST = nltk.corpus.words.words()
+
+
+def completions_benchmark_generator() -> dict:
+    prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
+    model = os.environ.get("MODEL_NAME")
+    if not model:
+        raise ValueError("MODEL_NAME environment variable not set")
+
+    benchmark_data = {
+        "model": model,
+        "prompt": prompt,
+        "temperature": 0.7,
+        "max_tokens": 500,
+    }
+
+    return benchmark_data
+
+worker_config = WorkerConfig(
+    model_server_url=MODEL_SERVER_URL,
+    model_server_port=MODEL_SERVER_PORT,
+    model_log_file=MODEL_LOG_FILE,
+    model_healthcheck_url=MODEL_HEALTHCHECK_ENDPOINT,
+    handlers=[
+        HandlerConfig(
+            route="/v1/completions",
+            workload_calculator= lambda data: data.get("max_tokens", 0),
+            allow_parallel_requests=True,
+            max_queue_time=60.0,
+            benchmark_config=BenchmarkConfig(
+                generator=completions_benchmark_generator,
+                concurrency=100,
+                runs=2
+            )
+        ),
+        HandlerConfig(
+            route="/v1/chat/completions",
+            workload_calculator= lambda data: data.get("max_tokens", 0),
+            allow_parallel_requests=True,
+            max_queue_time=60.0,
+        )
+    ],
+    log_action_config=LogActionConfig(
+        on_load=MODEL_LOAD_LOG_MSG,
+        on_error=MODEL_ERROR_LOG_MSGS,
+        on_info=MODEL_INFO_LOG_MSGS
+    )
+)
+
+Worker(worker_config).run()

workers/tgi/client.py

@@ -1,125 +1,61 @@
-import logging
+from vastai import Serverless
-import sys
+import asyncio
-import json
-from urllib.parse import urljoin
-import requests
-from utils.endpoint_util import Endpoint
-from utils.ssl import get_cert_file_path
 
-logging.basicConfig(
+ENDPOINT_NAME = "my-tgi-endpoint" # Change this to match your endpoint name
-    level=logging.DEBUG,
+MAX_TOKENS = 1024
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
+PROMPT = "Think step by step: Tell me about the Python programming language."
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
 
+async def call_generate(client: Serverless) -> None:
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
 
-def call_generate(endpoint_group_name: str, api_key: str, server_url: str) -> None:
+    payload = {
-    WORKER_ENDPOINT = "/generate"
+        "inputs": PROMPT,
-    COST = 100
+        "parameters": {
-    route_payload = {
+            "max_new_tokens": MAX_TOKENS,
-        "endpoint": endpoint_group_name,
+            "temperature": 0.7,
-        "api_key": api_key,
+            "return_full_text": False
-        "cost": COST,
+        }
     }
-    response = requests.post(
-        urljoin(server_url, "/route/"),
-        json=route_payload,
-        timeout=4,
-    )
-    response.raise_for_status()  # Raise an exception for bad status codes
-    message = response.json()
-    url = message["url"]
 
-    auth_data = dict(
+    resp = await endpoint.request("/generate", payload, cost=MAX_TOKENS)
-        signature=message["signature"],
-        cost=message["cost"],
-        endpoint=message["endpoint"],
-        reqnum=message["reqnum"],
-        url=url,
-    )
 
-    payload = dict(inputs="tell me about cats", parameters=dict(max_new_tokens=500))
+    print(resp["response"]["generated_text"])
-    req_data = dict(payload=payload, auth_data=auth_data)
-    url = urljoin(url, WORKER_ENDPOINT)
-    print(f"url: {url}")
-    response = requests.post(
-        url,
-        json=req_data,
-        verify=get_cert_file_path(),
-    )
-    response.raise_for_status()
-    res = response.json()
-    print(res)
 
 
-def call_generate_stream(
+async def call_generate_stream(client: Serverless) -> None:
-    endpoint_group_name: str, api_key: str, server_url: str
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
-) -> None:
+
-    WORKER_ENDPOINT = "/generate_stream"
+    payload = {
-    COST = 100
+        "inputs": PROMPT,
-    route_payload = {
+        "parameters": {
-        "endpoint": endpoint_group_name,
+            "max_new_tokens": MAX_TOKENS,
-        "api_key": api_key,
+            "temperature": 0.7,
-        "cost": COST,
+            "do_sample": True,
+            "return_full_text": False,
+        }
     }
-    response = requests.post(
-        urljoin(server_url, "/route/"),
-        json=route_payload,
-        timeout=4,
-    )
-    response.raise_for_status()  # Raise an exception for bad status codes
-    message = response.json()
-    url = message["url"]
-    print(f"url: {url}")
-    auth_data = dict(
-        signature=message["signature"],
-        cost=message["cost"],
-        endpoint=message["endpoint"],
-        reqnum=message["reqnum"],
-        url=message["url"],
-    )
-    payload = dict(inputs="tell me about dogs", parameters=dict(max_new_tokens=500))
-    req_data = dict(payload=payload, auth_data=auth_data)
-    url = urljoin(url, WORKER_ENDPOINT)
-    response = requests.post(url, json=req_data, stream=True)
-    response.raise_for_status()  # Raise an exception for bad status codes
-    for line in response.iter_lines():
-        payload = line.decode().lstrip("data:").rstrip()
-        if payload:
-            try:
-                data = json.loads(payload)
-                print(data["token"]["text"], end="")
-                sys.stdout.flush()
-            except (json.JSONDecodeError, KeyError) as e:
-                log.warning(f"Failed to parse streaming response: {e}")
-                continue
-    print()
 
+    resp = await endpoint.request(
+        "/generate_stream",
+        payload,
+        cost=MAX_TOKENS,
+        stream=True,
+    )
+    stream = resp["response"]
+
+    printed_answer = False
+    async for event in stream:
+        tok = (event.get("token") or {}).get("text")
+        if tok:
+            if not printed_answer:
+                printed_answer = True
+                print("Answer:\n", end="", flush=True)
+            print(tok, end="", flush=True)
+
+async def main():
+    async with Serverless() as client:
+        await call_generate(client)
+        await call_generate_stream(client)
 
 if __name__ == "__main__":
-    from lib.test_utils import test_args
+    asyncio.run(main())
-
-    args = test_args.parse_args()
-
-    endpoint_api_key = Endpoint.get_endpoint_api_key(
-        endpoint_name=args.endpoint_group_name,
-        account_api_key=args.api_key,
-        instance=args.instance,
-    )
-    if endpoint_api_key:
-        try:
-            call_generate(
-                api_key=endpoint_api_key,
-                endpoint_group_name=args.endpoint_group_name,
-                server_url=args.server_url,
-            )
-            call_generate_stream(
-                api_key=endpoint_api_key,
-                endpoint_group_name=args.endpoint_group_name,
-                server_url=args.server_url,
-            )
-        except Exception as e:
-            log.error(f"Error during API call: {e}")
-    else:
-        log.error(f"Failed to get API key for endpoint {args.endpoint_group_name} ")

workers/tgi/data_types.py

@@ -1,73 +0,0 @@
-import dataclasses
-import random
-import inspect
-from typing import Dict, Any
-
-from transformers import OpenAIGPTTokenizer
-import nltk
-
-from lib.data_types import ApiPayload, JsonDataException
-
-nltk.download("words")
-WORD_LIST = nltk.corpus.words.words()
-
-tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
-
-
-@dataclasses.dataclass
-class InputParameters:
-    max_new_tokens: int = 256
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputParameters":
-        errors = {}
-        for param in inspect.signature(cls).parameters:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-        if errors:
-            raise JsonDataException(errors)
-        return cls(
-            **{
-                k: v
-                for k, v in json_msg.items()
-                if k in inspect.signature(cls).parameters
-            }
-        )
-
-
-@dataclasses.dataclass
-class InputData(ApiPayload):
-    inputs: str
-    parameters: InputParameters
-
-    @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "InputData":
-        return cls(
-            inputs=data["inputs"], parameters=InputParameters(**data["parameters"])
-        )
-
-    @classmethod
-    def for_test(cls) -> "InputData":
-        prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
-        return cls(inputs=prompt, parameters=InputParameters())
-
-    def generate_payload_json(self) -> Dict[str, Any]:
-        return dataclasses.asdict(self)
-
-    def count_workload(self) -> int:
-        return self.parameters.max_new_tokens
-
-    @classmethod
-    def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputData":
-        errors = {}
-        for param in inspect.signature(cls).parameters:
-            if param not in json_msg:
-                errors[param] = "missing parameter"
-        if errors:
-            raise JsonDataException(errors)
-        try:
-            parameters = InputParameters.from_json_msg(json_msg["parameters"])
-            return cls(inputs=json_msg["inputs"], parameters=parameters)
-        except JsonDataException as e:
-            errors["parameters"] = e.message
-            raise JsonDataException(errors)

workers/tgi/server.py

@@ -1,130 +0,0 @@
-import os
-import logging
-from typing import Union, Type
-import dataclasses
-
-from aiohttp import web, ClientResponse
-
-from lib.backend import Backend, LogAction
-from lib.data_types import EndpointHandler
-from lib.server import start_server
-from .data_types import InputData
-
-
-MODEL_SERVER_URL = "http://0.0.0.0:5001"
-
-# This is the last log line that gets emitted once comfyui+extensions have been fully loaded
-MODEL_SERVER_START_LOG_MSG = [
-    '"message":"Connected","target":"text_generation_router"',
-    '"message":"Connected","target":"text_generation_router::server"',
-]
-MODEL_SERVER_ERROR_LOG_MSGS = [
-    "Error: WebserverFailed",
-    "Error: DownloadError",
-    "Error: ShardCannotStart",
-]
-
-
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-
-@dataclasses.dataclass
-class GenerateHandler(EndpointHandler[InputData]):
-
-    @property
-    def endpoint(self) -> str:
-        return "/generate"
-
-    @property
-    def healthcheck_endpoint(self) -> str:
-        return f"{MODEL_SERVER_URL}/health"
-
-    @classmethod
-    def payload_cls(cls) -> Type[InputData]:
-        return InputData
-
-    def make_benchmark_payload(self) -> InputData:
-        return InputData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        _ = client_request
-        match model_response.status:
-            case 200:
-                log.debug("SUCCESS")
-                data = await model_response.json()
-                return web.json_response(data=data)
-            case code:
-                log.debug("SENDING RESPONSE: ERROR: unknown code")
-                return web.Response(status=code)
-
-
-class GenerateStreamHandler(EndpointHandler[InputData]):
-    @property
-    def endpoint(self) -> str:
-        return "/generate_stream"
-
-    @property
-    def healthcheck_endpoint(self) -> str:
-        return f"{MODEL_SERVER_URL}/health"
-
-    @classmethod
-    def payload_cls(cls) -> Type[InputData]:
-        return InputData
-
-    def make_benchmark_payload(self) -> InputData:
-        return InputData.for_test()
-
-    async def generate_client_response(
-        self, client_request: web.Request, model_response: ClientResponse
-    ) -> Union[web.Response, web.StreamResponse]:
-        match model_response.status:
-            case 200:
-                log.debug("Streaming response...")
-                res = web.StreamResponse()
-                res.content_type = "text/event-stream"
-                await res.prepare(client_request)
-                async for chunk in model_response.content:
-                    await res.write(chunk)
-                await res.write_eof()
-                log.debug("Done streaming response")
-                return res
-            case code:
-                log.debug("SENDING RESPONSE: ERROR: unknown code")
-                return web.Response(status=code)
-
-
-backend = Backend(
-    model_server_url=MODEL_SERVER_URL,
-    model_log_file=os.environ["MODEL_LOG"],
-    allow_parallel_requests=True,
-    benchmark_handler=GenerateHandler(benchmark_runs=3, benchmark_words=256),
-    log_actions=[
-        *[(LogAction.ModelLoaded, info_msg) for info_msg in MODEL_SERVER_START_LOG_MSG],
-        (LogAction.Info, '"message":"Download'),
-        *[
-            (LogAction.ModelError, error_msg)
-            for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
-        ],
-    ],
-)
-
-
-async def handle_ping(_):
-    return web.Response(body="pong")
-
-
-routes = [
-    web.post("/generate", backend.create_handler(GenerateHandler())),
-    web.post("/generate_stream", backend.create_handler(GenerateStreamHandler())),
-    web.get("/ping", handle_ping),
-]
-
-if __name__ == "__main__":
-    start_server(backend, routes)

workers/tgi/test_load.py

@@ -1,7 +0,0 @@
-from lib.test_utils import test_load_cmd, test_args
-from .data_types import InputData
-
-WORKER_ENDPOINT = "/generate"
-
-if __name__ == "__main__":
-    test_load_cmd(InputData, WORKER_ENDPOINT, arg_parser=test_args)

workers/tgi/worker.py

@@ -0,0 +1,76 @@
+import nltk
+import random
+
+from vastai import Worker, WorkerConfig, HandlerConfig, LogActionConfig, BenchmarkConfig
+
+# TGI model configuration
+MODEL_SERVER_URL           = 'http://0.0.0.0'
+MODEL_SERVER_PORT          = 5001
+MODEL_LOG_FILE             = "/workspace/infer.log"
+MODEL_HEALTHCHECK_ENDPOINT = "/health"
+
+# TGI-specific log messages
+MODEL_LOAD_LOG_MSG = [
+    '"message":"Connected","target":"text_generation_router"',
+    '"message":"Connected","target":"text_generation_router::server"',
+]
+
+MODEL_ERROR_LOG_MSGS = [
+    "Error: WebserverFailed",
+    "Error: DownloadError",
+    "Error: ShardCannotStart",
+]
+
+MODEL_INFO_LOG_MSGS = [
+    '"message":"Download'
+]
+
+nltk.download("words")
+WORD_LIST = nltk.corpus.words.words()
+
+
+def benchmark_generator() -> dict:
+    prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
+
+    benchmark_data = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": 128,
+            "temperature": 0.7,
+            "return_full_text": False
+        }
+    }
+
+    return benchmark_data
+
+worker_config = WorkerConfig(
+    model_server_url=MODEL_SERVER_URL,
+    model_server_port=MODEL_SERVER_PORT,
+    model_log_file=MODEL_LOG_FILE,
+    model_healthcheck_url=MODEL_HEALTHCHECK_ENDPOINT,
+    handlers=[
+        HandlerConfig(
+            route="/generate",
+            allow_parallel_requests=True,
+            max_queue_time=60.0,
+            benchmark_config=BenchmarkConfig(
+                generator=benchmark_generator,
+                concurrency=50
+            ),
+            workload_calculator= lambda x: x["parameters"]["max_new_tokens"]
+        ),
+        HandlerConfig(
+            route="/generate_stream",
+            allow_parallel_requests=True,
+            max_queue_time=60.0,
+            workload_calculator= lambda x: x["parameters"]["max_new_tokens"]
+        )
+    ],
+    log_action_config=LogActionConfig(
+        on_load=MODEL_LOAD_LOG_MSG,
+        on_error=MODEL_ERROR_LOG_MSGS,
+        on_info=MODEL_INFO_LOG_MSGS
+    )
+)
+
+Worker(worker_config).run()

workers/wan/README.md

@@ -0,0 +1,170 @@
+# ComfyUI Wan 2.2 PyWorker
+
+This is the PyWorker implementation for running **Wan 2.2 T2V A14B** text-to-video workflows in ComfyUI. It provides a unified interface for executing complete ComfyUI video-generation workflows through a proxy-based architecture and returning generated video assets.
+
+Each request has a static cost of `100`. ComfyUI does not support concurrent workloads, and there is no provision to run multiple ComfyUI instances per worker node.
+
+## Requirements
+
+This worker requires the following components:
+
+- ComfyUI (https://github.com/comfyanonymous/ComfyUI)
+- ComfyUI API Wrapper (https://github.com/ai-dock/comfyui-api-wrapper)
+- Wan 2.2 T2V A14B models and required custom nodes
+
+A Docker image is provided with all required Wan 2.2 models pre-installed, but any image may be used if the above requirements are met.
+
+## Endpoint
+
+The worker exposes a single synchronous endpoint:
+
+- `/generate/sync`: Processes a complete ComfyUI workflow JSON and generates video output
+
+## Request Format
+
+The Wan 2.2 worker **only supports custom workflow mode**. Modifier-based workflows are not supported.
+
+```json
+{
+  "input": {
+    "request_id": "uuid-string",
+    "workflow_json": {
+      // Complete ComfyUI Wan 2.2 workflow JSON
+    },
+    "s3": { },
+    "webhook": { }
+  }
+}
+```
+
+## Request Fields
+
+### Required Fields
+
+- `input`: Container for all request parameters
+- `input.workflow_json`: Complete ComfyUI workflow graph for Wan 2.2 video generation
+
+### Optional Fields
+
+- `input.request_id`: Client-defined request identifier
+- `input.s3`: S3-compatible storage configuration
+- `input.webhook`: Webhook configuration for completion notifications
+
+The special string `"__RANDOM_INT__"` may be used in the workflow JSON and will be replaced with a random integer before submission to ComfyUI.
+
+## S3 Configuration
+
+Generated video assets can be automatically uploaded to S3-compatible storage. Configuration can be supplied per request or via environment variables. Request-level values take precedence.
+
+### Via Request JSON
+
+```json
+"s3": {
+  "access_key_id": "your-s3-access-key",
+  "secret_access_key": "your-s3-secret-access-key",
+  "endpoint_url": "https://s3.amazonaws.com",
+  "bucket_name": "your-bucket",
+  "region": "us-east-1"
+}
+```
+
+### Via Environment Variables
+
+```bash
+S3_ACCESS_KEY_ID=your-key
+S3_SECRET_ACCESS_KEY=your-secret
+S3_BUCKET_NAME=your-bucket
+S3_ENDPOINT_URL=https://s3.amazonaws.com
+S3_REGION=us-east-1
+```
+
+## Webhook Configuration
+
+Webhooks are triggered on request completion or failure.
+
+### Via Request JSON
+
+```json
+"webhook": {
+  "url": "https://your-webhook-url",
+  "extra_params": {
+    "custom_field": "value"
+  }
+}
+```
+
+### Via Environment Variables
+
+```bash
+WEBHOOK_URL=https://your-webhook-url
+WEBHOOK_TIMEOUT=30
+```
+
+## Example Request
+
+### Wan 2.2 Text-to-Video Workflow
+
+```json
+{
+  "input": {
+    "workflow_json": {
+      "90": {
+        "inputs": {
+          "clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+          "type": "wan",
+          "device": "default"
+        },
+        "class_type": "CLIPLoader"
+      },
+      "99": {
+        "inputs": {
+          "text": "A cinematic slow-motion portrait of a woman turning her head",
+          "clip": ["90", 0]
+        },
+        "class_type": "CLIPTextEncode"
+      },
+      "104": {
+        "inputs": {
+          "width": 640,
+          "height": 640,
+          "length": 81,
+          "batch_size": 1
+        },
+        "class_type": "EmptyHunyuanLatentVideo"
+      }
+    }
+  }
+}
+```
+
+## Response Format
+
+A successful response includes execution metadata, ComfyUI output details, and generated video assets.
+
+### Response Fields
+
+- `id`: Unique request identifier
+- `status`: `completed`, `failed`, `processing`, `generating`, or `queued`
+- `message`: Human-readable status message
+- `comfyui_response`: Raw response from ComfyUI, including execution status and progress
+- `output`: Array of generated outputs
+- `timings`: Timing information for the request
+
+### Output Object
+
+Each entry in `output` includes:
+
+- `filename`: Generated file name (e.g., `.mp4`)
+- `local_path`: File path on the worker
+- `url`: Pre-signed download URL (if S3 is configured)
+- `type`: Output type (`output`)
+- `subfolder`: Output directory (e.g., `video`)
+- `node_id`: ComfyUI node that produced the output
+- `output_type`: Output category (e.g., `images`)
+
+## Notes and Limitations
+
+- Only full ComfyUI workflow JSONs are supported
+- Concurrent requests are not supported per worker
+- Wan 2.2 models must be installed before processing requests
+- Video generation workflows may take several minutes depending on resolution, length, and GPU performance

workers/wan/client.py

@@ -0,0 +1,205 @@
+from vastai import Serverless
+import asyncio
+
+async def main():
+    async with Serverless() as client:
+        endpoint = await client.get_endpoint(name="my-wan-endpoint")
+
+        # ComfyUI API compatible json workflow for Wan 2.2 T2V
+        workflow = {
+          "90": {
+            "inputs": {
+              "clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+              "type": "wan",
+              "device": "default"
+            },
+            "class_type": "CLIPLoader",
+            "_meta": {
+              "title": "Load CLIP"
+            }
+          },
+          "91": {
+            "inputs": {
+              "text": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走，裸露，NSFW",
+              "clip": ["90", 0]
+            },
+            "class_type": "CLIPTextEncode",
+            "_meta": {
+              "title": "CLIP Text Encode (Negative Prompt)"
+            }
+          },
+          "92": {
+            "inputs": {
+              "vae_name": "wan_2.1_vae.safetensors"
+            },
+            "class_type": "VAELoader",
+            "_meta": {
+              "title": "Load VAE"
+            }
+          },
+          "93": {
+            "inputs": {
+              "shift": 8.000000000000002,
+              "model": ["101", 0]
+            },
+            "class_type": "ModelSamplingSD3",
+            "_meta": {
+              "title": "ModelSamplingSD3"
+            }
+          },
+          "94": {
+            "inputs": {
+              "shift": 8,
+              "model": ["102", 0]
+            },
+            "class_type": "ModelSamplingSD3",
+            "_meta": {
+              "title": "ModelSamplingSD3"
+            }
+          },
+          "95": {
+            "inputs": {
+              "add_noise": "disable",
+              "noise_seed": 0,
+              "steps": 20,
+              "cfg": 3.5,
+              "sampler_name": "euler",
+              "scheduler": "simple",
+              "start_at_step": 10,
+              "end_at_step": 10000,
+              "return_with_leftover_noise": "disable",
+              "model": ["94", 0],
+              "positive": ["99", 0],
+              "negative": ["91", 0],
+              "latent_image": ["96", 0]
+            },
+            "class_type": "KSamplerAdvanced",
+            "_meta": {
+              "title": "KSampler (Advanced)"
+            }
+          },
+          "96": {
+            "inputs": {
+              "add_noise": "enable",
+              "noise_seed": "__RANDOM_INT__",
+              "steps": 20,
+              "cfg": 3.5,
+              "sampler_name": "euler",
+              "scheduler": "simple",
+              "start_at_step": 0,
+              "end_at_step": 10,
+              "return_with_leftover_noise": "enable",
+              "model": ["93", 0],
+              "positive": ["99", 0],
+              "negative": ["91", 0],
+              "latent_image": ["104", 0]
+            },
+            "class_type": "KSamplerAdvanced",
+            "_meta": {
+              "title": "KSampler (Advanced)"
+            }
+          },
+          "97": {
+            "inputs": {
+              "samples": ["95", 0],
+              "vae": ["92", 0]
+            },
+            "class_type": "VAEDecode",
+            "_meta": {
+              "title": "VAE Decode"
+            }
+          },
+          "98": {
+            "inputs": {
+              "filename_prefix": "video/ComfyUI",
+              "format": "auto",
+              "codec": "auto",
+              "video": ["100", 0]
+            },
+            "class_type": "SaveVideo",
+            "_meta": {
+              "title": "Save Video"
+            }
+          },
+          "99": {
+            "inputs": {
+              "text": "Beautiful young European woman with honey blonde hair gracefully turning her head back over shoulder, gentle smile, bright eyes looking at camera. Hair flowing in slow motion as she turns. Soft natural lighting, clean background, cinematic portrait.",
+              "clip": ["90", 0]
+            },
+            "class_type": "CLIPTextEncode",
+            "_meta": {
+              "title": "CLIP Text Encode (Positive Prompt)"
+            }
+          },
+          "100": {
+            "inputs": {
+              "fps": 16,
+              "images": ["97", 0]
+            },
+            "class_type": "CreateVideo",
+            "_meta": {
+              "title": "Create Video"
+            }
+          },
+          "101": {
+            "inputs": {
+              "unet_name": "wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors",
+              "weight_dtype": "default"
+            },
+            "class_type": "UNETLoader",
+            "_meta": {
+              "title": "Load Diffusion Model"
+            }
+          },
+          "102": {
+            "inputs": {
+              "unet_name": "wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors",
+              "weight_dtype": "default"
+            },
+            "class_type": "UNETLoader",
+            "_meta": {
+              "title": "Load Diffusion Model"
+            }
+          },
+          "104": {
+            "inputs": {
+              "width": 640,
+              "height": 640,
+              "length": 81,
+              "batch_size": 1
+            },
+            "class_type": "EmptyHunyuanLatentVideo",
+            "_meta": {
+              "title": "EmptyHunyuanLatentVideo"
+            }
+          }
+        }
+
+        payload = {
+          "input": {
+            "request_id": "",
+            "workflow_json": workflow,
+            "s3": {
+              "access_key_id": "",
+              "secret_access_key": "",
+              "endpoint_url": "",
+              "bucket_name": "",
+              "region": ""
+            },
+            "webhook": {
+              "url": "",
+              "extra_params": {
+                "user_id": "12345",
+                "project_id": "abc-def"
+              }
+            }
+          }
+        }
+
+        response = await endpoint.request("/generate/sync", payload)
+
+        # Response contains status, output, and any errors
+        print(response["response"])
+
+if __name__ == "__main__":
+    asyncio.run(main())

workers/wan/worker.py

@@ -0,0 +1,288 @@
+import random
+import sys
+
+from vastai import Worker, WorkerConfig, HandlerConfig, LogActionConfig, BenchmarkConfig
+
+# ComyUI model configuration
+MODEL_SERVER_URL           = 'http://127.0.0.1'
+MODEL_SERVER_PORT          = 18288
+MODEL_LOG_FILE             = '/var/log/portal/comfyui.log'
+MODEL_HEALTHCHECK_ENDPOINT = "/health"
+
+# ComyUI-specific log messages
+MODEL_LOAD_LOG_MSG = [
+    "To see the GUI go to: "
+]
+
+MODEL_ERROR_LOG_MSGS = [
+    "MetadataIncompleteBuffer",
+    "Value not in list: ",
+    "[ERROR] Provisioning Script failed"
+]
+
+MODEL_INFO_LOG_MSGS = [
+    '"message":"Downloading'
+]
+
+benchmark_prompts = [
+    "Cartoon hoodie hero; orc, anime cat, bunny; black goo; buff; vector on white.",
+    "Cozy farming-game scene with fine details.",
+    "2D vector child with soccer ball; airbrush chrome; swagger; antique copper.",
+    "Realistic futuristic downtown of low buildings at sunset.",
+    "Perfect wave front view; sunny seascape; ultra-detailed water; artful feel.",
+    "Clear cup with ice, fruit, mint; creamy swirls; fluid-sim CGI; warm glow.",
+    "Male biker with backpack on motorcycle; oilpunk; award-worthy magazine cover.",
+    "Collage for textile; surreal cartoon cat in cap/jeans before poster; crisp.",
+    "Medieval village inside glass sphere; volumetric light; macro focus.",
+    "Iron Man with glowing axe; mecha sci-fi; jungle scene; dynamic light.",
+    "Pope Francis DJ in leather jacket, mixing on giant console; dramatic.",
+]
+
+benchmark_dataset = [
+    {
+        "input": {
+            "workflow_json": {
+                "90": {
+                    "inputs": {
+                    "clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+                    "type": "wan",
+                    "device": "default"
+                    },
+                    "class_type": "CLIPLoader",
+                    "_meta": {
+                    "title": "Load CLIP"
+                    }
+                },
+                "91": {
+                    "inputs": {
+                    "text": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走，裸露，NSFW",
+                    "clip": [
+                        "90",
+                        0
+                    ]
+                    },
+                    "class_type": "CLIPTextEncode",
+                    "_meta": {
+                    "title": "CLIP Text Encode (Negative Prompt)"
+                    }
+                },
+                "92": {
+                    "inputs": {
+                    "vae_name": "wan_2.1_vae.safetensors"
+                    },
+                    "class_type": "VAELoader",
+                    "_meta": {
+                    "title": "Load VAE"
+                    }
+                },
+                "93": {
+                    "inputs": {
+                    "shift": 8.000000000000002,
+                    "model": [
+                        "101",
+                        0
+                    ]
+                    },
+                    "class_type": "ModelSamplingSD3",
+                    "_meta": {
+                    "title": "ModelSamplingSD3"
+                    }
+                },
+                "94": {
+                    "inputs": {
+                    "shift": 8,
+                    "model": [
+                        "102",
+                        0
+                    ]
+                    },
+                    "class_type": "ModelSamplingSD3",
+                    "_meta": {
+                    "title": "ModelSamplingSD3"
+                    }
+                },
+                "95": {
+                    "inputs": {
+                    "add_noise": "disable",
+                    "noise_seed": 0,
+                    "steps": 20,
+                    "cfg": 3.5,
+                    "sampler_name": "euler",
+                    "scheduler": "simple",
+                    "start_at_step": 10,
+                    "end_at_step": 10000,
+                    "return_with_leftover_noise": "disable",
+                    "model": [
+                        "94",
+                        0
+                    ],
+                    "positive": [
+                        "99",
+                        0
+                    ],
+                    "negative": [
+                        "91",
+                        0
+                    ],
+                    "latent_image": [
+                        "96",
+                        0
+                    ]
+                    },
+                    "class_type": "KSamplerAdvanced",
+                    "_meta": {
+                    "title": "KSampler (Advanced)"
+                    }
+                },
+                "96": {
+                    "inputs": {
+                    "add_noise": "enable",
+                    "noise_seed": "__RANDOM_INT__",
+                    "steps": 20,
+                    "cfg": 3.5,
+                    "sampler_name": "euler",
+                    "scheduler": "simple",
+                    "start_at_step": 0,
+                    "end_at_step": 10,
+                    "return_with_leftover_noise": "enable",
+                    "model": [
+                        "93",
+                        0
+                    ],
+                    "positive": [
+                        "99",
+                        0
+                    ],
+                    "negative": [
+                        "91",
+                        0
+                    ],
+                    "latent_image": [
+                        "104",
+                        0
+                    ]
+                    },
+                    "class_type": "KSamplerAdvanced",
+                    "_meta": {
+                    "title": "KSampler (Advanced)"
+                    }
+                },
+                "97": {
+                    "inputs": {
+                    "samples": [
+                        "95",
+                        0
+                    ],
+                    "vae": [
+                        "92",
+                        0
+                    ]
+                    },
+                    "class_type": "VAEDecode",
+                    "_meta": {
+                    "title": "VAE Decode"
+                    }
+                },
+                "98": {
+                    "inputs": {
+                    "filename_prefix": "video/ComfyUI",
+                    "format": "auto",
+                    "codec": "auto",
+                    "video": [
+                        "100",
+                        0
+                    ]
+                    },
+                    "class_type": "SaveVideo",
+                    "_meta": {
+                    "title": "Save Video"
+                    }
+                },
+                "99": {
+                    "inputs": {
+                    "text":prompt,
+                    "clip": [
+                        "90",
+                        0
+                    ]
+                    },
+                    "class_type": "CLIPTextEncode",
+                    "_meta": {
+                    "title": "CLIP Text Encode (Positive Prompt)"
+                    }
+                },
+                "100": {
+                    "inputs": {
+                    "fps": 16,
+                    "images": [
+                        "97",
+                        0
+                    ]
+                    },
+                    "class_type": "CreateVideo",
+                    "_meta": {
+                    "title": "Create Video"
+                    }
+                },
+                "101": {
+                    "inputs": {
+                    "unet_name": "wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors",
+                    "weight_dtype": "default"
+                    },
+                    "class_type": "UNETLoader",
+                    "_meta": {
+                    "title": "Load Diffusion Model"
+                    }
+                },
+                "102": {
+                    "inputs": {
+                    "unet_name": "wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors",
+                    "weight_dtype": "default"
+                    },
+                    "class_type": "UNETLoader",
+                    "_meta": {
+                    "title": "Load Diffusion Model"
+                    }
+                },
+                "104": {
+                    "inputs": {
+                    "width": 640,
+                    "height": 640,
+                    "length": 81,
+                    "batch_size": 1
+                    },
+                    "class_type": "EmptyHunyuanLatentVideo",
+                    "_meta": {
+                    "title": "EmptyHunyuanLatentVideo"
+                    }
+                }
+            }
+        }
+    } for prompt in benchmark_prompts
+]
+
+worker_config = WorkerConfig(
+    model_server_url=MODEL_SERVER_URL,
+    model_server_port=MODEL_SERVER_PORT,
+    model_log_file=MODEL_LOG_FILE,
+    model_healthcheck_url=MODEL_HEALTHCHECK_ENDPOINT,
+    handlers=[
+        HandlerConfig(
+            route="/generate/sync",
+            allow_parallel_requests=False,
+            max_queue_time=10.0,
+            benchmark_config=BenchmarkConfig(
+                dataset=benchmark_dataset,
+                runs=1
+            ),
+            workload_calculator= lambda _ : 10000.0
+        )
+    ],
+    log_action_config=LogActionConfig(
+        on_load=MODEL_LOAD_LOG_MSG,
+        on_error=MODEL_ERROR_LOG_MSGS,
+        on_info=MODEL_INFO_LOG_MSGS
+    )
+)
+
+Worker(worker_config).run()