Update log action configuration to specify model loading message

Update docker-in-docker feature version to 3.0.1 in devcontainer configuration
Remove commented out info log actions from log configuration
2026-06-09 12:08:10 +03:00 · 2026-05-25 08:13:15 +00:00 · 2026-05-21 19:55:13 +00:00 · 2026-05-21 19:50:21 +00:00 · 2026-05-21 19:47:16 +00:00 · 2026-05-21 19:40:47 +00:00
55 changed files with 1906 additions and 4682 deletions
@@ -0,0 +1,37 @@
 // .devcontainer/devcontainer.json
 // Dev container for the Vast.ai serverless Ollama template.
 // Includes Docker-in-Docker so you can build and test images from inside the container.
 {
  "name": "vast.ai-serverless-ollama",
  "image": "mcr.microsoft.com/devcontainers/base:trixie",
  "features": {
    "ghcr.io/devcontainers/features/python:1": {
      "installTools": true,
      "version": "3.12"
    },
    "ghcr.io/devcontainers/features/docker-in-docker:3.0.1": {
      "moby": false,
      "version": "latest",
      "installDockerBuildx": true,
      "dockerDashComposeVersion": "v2"
    }
  },
  "runArgs": ["--privileged"],
  "containerEnv": {
    "DOCKER_BUILDKIT": "1"
  },
  "postCreateCommand": "python3 -m pip install --user --upgrade pip && python3 -m pip install --user -r requirements.txt pyyaml",
  "customizations": {
    "vscode": {
      "extensions": [
        "ms-python.python",
        "ms-azuretools.vscode-docker"
      ],
      "settings": {
        "python.defaultInterpreterPath": "/usr/bin/python3",
        "terminal.integrated.defaultProfile.linux": "bash",
        "docker.showStartPage": false
      }
    }
  }
 }
@@ -1,90 +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:
-*   **vLLM:** [Vast.ai Template](https://cloud.vast.ai?ref_id=62897&template_id=63ae93902bf3978bea033782592b784d)
+```python
-*   **TGI (Text Generation Inference):** [Vast.ai Template](https://cloud.vast.ai?ref_id=62897&template_id=6fa6bd5bdf5f0df63db80e40b086037d)
+from vastai import (
-*   **ComfyUI:** [Vast.ai Template](https://cloud.vast.ai?ref_id=62897&template_id=e6748878ba688e765e3e9fca29541938)
+    Worker,
    WorkerConfig,
    HandlerConfig,
    BenchmarkConfig,
    LogActionConfig,
 )
-Currently available workers:
+worker_config = WorkerConfig(
-*   `openai`: A simple example worker for a basic vLLM 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/)
@@ -1,434 +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 asyncio
 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,
    RequestMetrics,
    BenchmarkResult
 )
 VERSION = "0.2.1"
 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]]
    max_wait_time: float = 10.0
    reqnum = -1
    version = VERSION
    msg_history = []
    sem: Semaphore = dataclasses.field(default_factory=Semaphore)
    unsecured: bool = dataclasses.field(
        default_factory=lambda: bool(strtobool(os.environ.get("UNSECURED", "false"))),
    )
    report_addr: str = dataclasses.field(
        default_factory=lambda: os.environ.get("REPORT_ADDR", "https://run.vast.ai")
    )
    mtoken: str = dataclasses.field(
        default_factory=lambda: os.environ.get("MASTER_TOKEN", "")
    )
    def __post_init__(self):
        self.metrics = Metrics()
        self.metrics._set_version(self.version)
        self.metrics._set_mtoken(self.mtoken)
        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):
        report_addr = self.report_addr.rstrip("/")
        command = ["curl", "-X", "GET", f"{report_addr}/pubkey/"]
        try:
            result = subprocess.check_output(command, universal_newlines=True)
            log.debug("public key:")
            log.debug(result)
            key = RSA.import_key(result)
            if key is not None:
                return key
        except (ValueError , subprocess.CalledProcessError) as e:
            log.debug(f"Error downloading key: {e}")
        self.backend_errored("Failed to get autoscaler pubkey")
    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()
        request_metrics: RequestMetrics = RequestMetrics(request_idx=auth_data.request_idx, reqnum=auth_data.reqnum, workload=workload, status="Created")
        async def cancel_api_call_if_disconnected() -> web.Response:
            await request.wait_for_disconnection()
            log.debug(f"request with reqnum: {request_metrics.reqnum} was canceled")
            self.metrics._request_canceled(request_metrics)
            raise asyncio.CancelledError
        async def make_request() -> Union[web.Response, web.StreamResponse]:
            try:
                response = await self.__call_api(handler=handler, payload=payload)
                status_code = response.status
                log.debug(
                    " ".join(
                        [
                            f"request with reqnum:{request_metrics.reqnum}",
                            f"returned status code: {status_code},",
                        ]
                    )
                )
                res = await handler.generate_client_response(request, response)
                self.metrics._request_success(request_metrics)
                return res
            except requests.exceptions.RequestException as e:
                log.debug(f"[backend] Request error: {e}")
                self.metrics._request_errored(request_metrics)
                return web.Response(status=500)
        ###########
        if self.__check_signature(auth_data) is False:
            self.metrics._request_reject(request_metrics)
            return web.Response(status=401)
        if self.metrics.model_metrics.wait_time > self.max_wait_time:
            self.metrics._request_reject(request_metrics)
            return web.Response(status=429)
        acquired = False
        try:
            self.metrics._request_start(request_metrics)
            if self.allow_parallel_requests is False:
                log.debug(f"Waiting to aquire Sem for reqnum:{request_metrics.reqnum}")
                await self.sem.acquire()
                acquired = True
                log.debug(
                    f"Sem acquired for reqnum:{request_metrics.reqnum}, starting request..."
                )
            else:
                log.debug(f"Starting request for reqnum:{request_metrics.reqnum}")
            done, pending = await wait(
                [
                    create_task(make_request()),
                    create_task(cancel_api_call_if_disconnected()),
                ],
                return_when=FIRST_COMPLETED,
            )
            for t in pending:
                t.cancel()
            await asyncio.gather(*pending, return_exceptions=True)
            done_task = done.pop()
            try:
                return done_task.result()
            except Exception as e:
                log.debug(f"Request task raised exception: {e}")
                return web.Response(status=500)
        except asyncio.CancelledError:
            # Client is gone. Do not write a response; just unwind.
            return web.Response(status=499) 
        except Exception as e:
            log.debug(f"Exception in main handler loop {e}")
            return web.Response(status=500)
        finally:
            # Always release the semaphore if it was acquired
            if acquired:
                self.sem.release()
            self.metrics._request_end(request_metrics)
    @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(), self.metrics._send_delete_requests_loop()
        )
    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" and key != "__request_id"
        }
        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, sort_keys=True), 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()
                benchmark_requests = []
                for i in range(concurrent_requests):
                    payload = self.benchmark_handler.make_benchmark_payload()
                    workload = payload.count_workload()
                    task = self.__call_api(handler=self.benchmark_handler, payload=payload)
                    benchmark_requests.append(
                        BenchmarkResult(request_idx=i, workload=workload, task=task)
                    )
                responses = await gather(*[br.task for br in benchmark_requests])
                for br, response in zip(benchmark_requests, responses):
                    br.response = response
                total_workload = sum(br.workload for br in benchmark_requests if br.is_successful)
                time_elapsed = time.time() - start
                successful_responses = sum([1 for br in benchmark_requests if br.is_successful])
                if successful_responses == 0:
                    self.backend_errored("No successful responses from benchmark")
                    log.debug(f"benchmark failed: {successful_responses}/{concurrent_requests} successful responses")
                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: {successful_responses}/{concurrent_requests}",
                            "#" * 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, encoding='utf-8', errors='ignore') as f:
                while True:
                    line = await f.readline()
                    if line:
                        await handle_log_line(line.rstrip())
                    else:
                        await asyncio.sleep(LOG_POLL_INTERVAL)
        ###########
        while True:
            if os.path.isfile(self.model_log_file) is True:
                return await tail_log()
            else:
                await sleep(1)
@@ -1,324 +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, Awaitable
 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"""
    cost: str
    endpoint_id: int
    reqnum: int
    request_idx: int
    signature: str
    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, expected: float | None) -> None:
        # 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
        if self.model_loading_time == expected:
            self.model_loading_time = None
@dataclass
 class RequestMetrics:
    """Tracks metrics for an active request."""
    request_idx: int
    reqnum: int
    workload: float
    status: str
    success: bool = False
@dataclass
 class BenchmarkResult:
    request_idx: int
    workload: float
    task: Awaitable[ClientResponse]
    response: Optional[ClientResponse] = None
    @property
    def is_successful(self) -> bool:
        return self.response is not None and self.response.status == 200
@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
    workload_rejected: float
    # these are not
    workload_pending: float
    error_msg: Optional[str]
    max_throughput: float
    requests_recieved: Set[int] = field(default_factory=set)
    requests_working: dict[int, RequestMetrics] = field(default_factory=dict)
    requests_deleting: list[RequestMetrics] = field(default_factory=list)
    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_rejected=0.0,
            workload_received=0.0,
            error_msg=None,
            max_throughput=0.0,
        )
    @property
    def workload_processing(self) -> float:
        return max(self.workload_received - self.workload_cancelled, 0.0)
    @property
    def wait_time(self) -> float:
        if (len(self.requests_working) == 0):
            return 0.0
        return sum([request.workload for request in self.requests_working.values()]) / max(self.max_throughput, 0.00001)
    @property
    def cur_load(self) -> float:
        return sum([request.workload for request in self.requests_working.values()])
    @property
    def working_request_idxs(self) -> list[int]:
        return [req.request_idx for req in self.requests_working.values()]
    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.workload_rejected = 0
        self.last_update = time.time()
@dataclass
 class AutoScalerData:
    """Data that is reported to autoscaler"""
    id: int
    mtoken: str
    version: str
    loadtime: float
    cur_load: float
    rej_load: float
    new_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
    working_request_idxs: list[int]
    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
@@ -1,286 +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 asyncio
 from aiohttp import ClientSession, ClientTimeout, TCPConnector, ClientResponseError
 from lib.data_types import AutoScalerData, SystemMetrics, ModelMetrics, RequestMetrics
 from typing import Awaitable, NoReturn, List
 METRICS_UPDATE_INTERVAL = 1
 DELETE_REQUESTS_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:
    version: str = "0"
    mtoken: str = ""
    last_metric_update: float = 0.0
    last_request_served: 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)
    _session: ClientSession | None = field(default=None, init=False, repr=False)
    async def http(self) -> ClientSession:
        if self._session is None:
            self._session = ClientSession(
                timeout=ClientTimeout(total=10),
                connector=TCPConnector(limit=8, limit_per_host=4, force_close=True, enable_cleanup_closed=True)
            )
        return self._session
    async def aclose(self) -> None:
        if self._session is not None:
            await self._session.close()
            self._session = None
    def _request_start(self, request: RequestMetrics) -> None:
        """
        this function is called prior to forwarding a request to a model API.
        """
        log.debug("request start")
        request.status = "Started"
        self.model_metrics.workload_pending += request.workload
        self.model_metrics.workload_received += request.workload
        self.model_metrics.requests_recieved.add(request.reqnum)
        self.model_metrics.requests_working[request.reqnum] = request
        self.update_pending = True
    def _request_end(self, request: RequestMetrics) -> None:
        """
        this function is called after handling of a request ends, regardless of the outcome
        """
        self.model_metrics.workload_pending -= request.workload
        self.model_metrics.requests_working.pop(request.reqnum, None)
        self.model_metrics.requests_deleting.append(request)
        self.last_request_served = time.time()
    def _request_success(self, request: RequestMetrics) -> None:
        """
        this function is called after a response from model API is received and forwarded.
        """
        self.model_metrics.workload_served += request.workload
        request.status = "Success"
        request.success = True
        self.update_pending = True
    def _request_errored(self, request: RequestMetrics) -> None:
        """
        this function is called if model API returns an error
        """
        self.model_metrics.workload_errored += request.workload
        request.status = "Error"
        request.success = False
        self.update_pending = True
    def _request_canceled(self, request: RequestMetrics) -> None:
        """
        this function is called if client drops connection before model API has responded
        """
        self.model_metrics.workload_cancelled += request.workload
        request.success = True
        request.status = "Cancelled"
    def _request_reject(self, request: RequestMetrics):
        """
        this function is called if the current wait time for the model is above max_wait_time
        """
        self.model_metrics.requests_recieved.add(request.reqnum)
        self.model_metrics.requests_deleting.append(request)
        self.model_metrics.workload_rejected += request.workload
        request.success = False
        request.status = "Rejected"
        self.update_pending = True
    async def _send_delete_requests_loop(self) -> Awaitable[NoReturn]:
        while True:
            await sleep(DELETE_REQUESTS_INTERVAL)
            if len(self.model_metrics.requests_deleting) > 0:
                await self.__send_delete_requests_and_reset()
    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")
                await self.__send_metrics_and_reset()
            elif self.update_pending or elapsed > 10:
                log.debug(f"sending loaded model metrics after {int(elapsed)}s wait")
                await 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
    def _set_version(self, version: str) -> None:
        self.version = version
    def _set_mtoken(self, mtoken: str) -> None:
        self.mtoken = mtoken
    #######################################Private#######################################
    async def __send_delete_requests_and_reset(self):
        async def post(report_addr: str, idxs: list[int], success_flag: bool) -> bool:
            data = {
                "worker_id": self.id,
                "mtoken": self.mtoken,
                "request_idxs": idxs,
                "success": success_flag,
            }
            log.debug(
                f"Deleting requests that {'succeeded' if success_flag else 'failed'}: {data['request_idxs']}"
            )
            full_path = report_addr.rstrip("/") + "/delete_requests/"
            for attempt in range(1, 4):
                try:
                    session = await self.http()
                    async with session.post(full_path, json=data) as res:
                        log.debug(f"delete_requests response: {res.status}")
                        res.raise_for_status()
                    return True
                except asyncio.TimeoutError:
                    log.debug("delete_requests timed out")
                except (ClientResponseError, Exception) as e:
                    log.debug(f"delete_requests failed with error: {e}")
                await asyncio.sleep(2)
                log.debug(f"retrying delete_request, attempt: {attempt}")
            return False
        # Take a snapshot of what we plan to send this tick.
        # New arrivals after this snapshot will remain in the queue for the next tick.
        snapshot = list(self.model_metrics.requests_deleting)
        success_idxs = [r.request_idx for r in snapshot if r.success is True]
        failed_idxs  = [r.request_idx for r in snapshot if r.success is False]
        if not success_idxs and not failed_idxs:
            return  # nothing to do
        for report_addr in self.report_addr:
            # TODO: Add a Redis subscriber queue for delete_requests
            if report_addr == "https://cloud.vast.ai/api/v0":
                # Patch: ignore the Redis API report_addr
                continue
            sent_success = True
            sent_failed  = True
            if success_idxs:
                sent_success = await post(report_addr, success_idxs, True)
            if failed_idxs:
                sent_failed = await post(report_addr, failed_idxs, False)
            if sent_success and sent_failed:
                # Remove only the items we actually sent from the live queue.
                sent_set = set(success_idxs) | set(failed_idxs)
                self.model_metrics.requests_deleting[:] = [
                    r for r in self.model_metrics.requests_deleting
                    if r.request_idx not in sent_set
                ]
                break
    async def __send_metrics_and_reset(self):
        loadtime_snapshot = self.system_metrics.model_loading_time
        def compute_autoscaler_data() -> AutoScalerData:
            return AutoScalerData(
                id=self.id,
                mtoken=self.mtoken,
                version=self.version,
                loadtime=(loadtime_snapshot or 0.0), 
                new_load=self.model_metrics.workload_processing,
                cur_load=self.model_metrics.cur_load,
                rej_load=self.model_metrics.workload_rejected,
                max_perf=self.model_metrics.max_throughput,
                cur_perf=self.model_metrics.workload_served,
                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,
                working_request_idxs=self.model_metrics.working_request_idxs,
                cur_capacity=0,
                max_capacity=0,
                url=self.url,
            )
        async def send_data(report_addr: str) -> bool:
            data = compute_autoscaler_data()
            log_data = asdict(data)
            def obfuscate(secret: str) -> str:
                if secret is None:
                    return ""
                return secret[:7] + "..." if len(secret) > 7 else ("*" * len(secret))
            log_data["mtoken"] = obfuscate(log_data.get("mtoken"))
            log.debug(
                "\n".join(
                    [
                        "#" * 60,
                        f"sending data to autoscaler",
                        f"{json.dumps(log_data, indent=2)}",
                        "#" * 60,
                    ]
                )
            )
            full_path = report_addr.rstrip("/") + "/worker_status/"
            for attempt in range(1, 4):
                try:
                    session = await self.http()
                    async with session.post(full_path, json=asdict(data)) as res:
                        res.raise_for_status()
                    return True
                except asyncio.TimeoutError:
                    log.debug(f"autoscaler status update timed out")
                except (ClientResponseError, Exception)  as e:
                    log.debug(f"autoscaler status update failed with error: {e}")
                await asyncio.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()
        sent = False
        for report_addr in self.report_addr:
            if await send_data(report_addr):
                sent = True
                break
        if sent:
            # clear the one-shot loadtime only if we actually sent *this* value
            self.system_metrics.reset(expected=loadtime_snapshot)
            self.update_pending = False
            self.model_metrics.reset()
            self.last_metric_update = time.time()
@@ -1,60 +0,0 @@
 import os
 import logging
 from typing import List
 import ssl
 from asyncio import run, gather
 import asyncio
 from lib.backend import Backend
 from lib.metrics import Metrics
 from aiohttp import web
 log = logging.getLogger(__file__)
 def start_server(backend: Backend, routes: List[web.RouteDef], **kwargs):
    try:
        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())
    except Exception as e:
        err_msg = f"PyWorker failed to launch: {e}"
        log.error(err_msg)
        async def beacon():
            metrics = Metrics()
            metrics._set_version(getattr(backend, "version", "0"))
            metrics._set_mtoken(getattr(backend, "mtoken", ""))
            try:
                while True:
                    metrics._model_errored(err_msg)
                    await metrics._Metrics__send_metrics_and_reset()
                    await asyncio.sleep(10)
            finally:
                await metrics.aclose()
        run(beacon())
@@ -1,314 +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
    endpoint_id: int
    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_id": self.endpoint_id,
            "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_info = Endpoint.get_endpoint_info(
        endpoint_name=endpoint_group_name, account_api_key=api_key, instance=instance
    )
    if not endpoint_info:
        log.debug(f"Endpoint {endpoint_group_name} not found for API key")
        return
    endpoint_id = endpoint_info["id"]
    endpoint_api_key = endpoint_info["api_key"]
    try:
        for _ in range(num_requests):
            client = ClientState(
                endpoint_group_name=endpoint_group_name,
                endpoint_id=endpoint_id,
                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 = {
        "prod": "https://run.vast.ai",
        "alpha": "https://run-alpha.vast.ai",
        "candidate": "https://run-candidate.vast.ai",
        "local": "http://localhost:8080",
    }.get(args.instance, "http://localhost:8080")
    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,
    )
@@ -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.
@@ -1,11 +1 @@
-aiohttp[speedups]==3.10.1
+vastai-sdk
 anyio~=4.4
 lib~=4.0
 nltk~=3.9
 psutil~=6.0
 pycryptodome~=3.20
 Requests~=2.32
 transformers~=4.52
 utils==1.0.*
 hf_transfer>=0.1.9
 vastai-sdk>=0.2.0
@@ -2,10 +2,17 @@
 set -e -o pipefail
 # Check for force update flag
 FORCE_UPDATE=false
 if [ -f "/.force_update" ]; then
    echo "Force update flag detected at /.force_update"
    FORCE_UPDATE=true
 fi
 WORKSPACE_DIR="${WORKSPACE_DIR:-/workspace}"
 SERVER_DIR="$WORKSPACE_DIR/vast-pyworker"
-ENV_PATH="$WORKSPACE_DIR/worker-env"
+ENV_PATH="${ENV_PATH:-$WORKSPACE_DIR/worker-env}"
 DEBUG_LOG="$WORKSPACE_DIR/debug.log"
 PYWORKER_LOG="$WORKSPACE_DIR/pyworker.log"
@@ -15,18 +22,76 @@ WORKER_PORT="${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
 }
 function install_vastai_sdk() {
    local uv_flags=()
    if [ "${USE_SYSTEM_PYTHON:-}" = "true" ]; then
        uv_flags+=(--system --break-system-packages)
    fi
    if [ "$FORCE_UPDATE" = true ]; then
        uv_flags+=(--force-reinstall)
        echo "Force reinstalling vastai"
    fi
    # If SDK_BRANCH is set, install vastai from the vast-cli repo at that branch/tag/commit.
    if [ -n "${SDK_BRANCH:-}" ]; then
        if [ -n "${SDK_VERSION:-}" ]; then
            echo "WARNING: Both SDK_BRANCH and SDK_VERSION are set; using SDK_BRANCH=${SDK_BRANCH}"
        fi
        echo "Installing vastai from https://github.com/vast-ai/vast-cli/ @ ${SDK_BRANCH}"
        if ! uv pip install "${uv_flags[@]}" "vastai @ git+https://github.com/vast-ai/vast-cli.git@${SDK_BRANCH}"; then
            report_error_and_exit "Failed to install vastai from vast-ai/vast-cli@${SDK_BRANCH}"
        fi
        return 0
    fi
    if [ -n "${SDK_VERSION:-}" ]; then
        echo "Installing vastai version ${SDK_VERSION}"
        if ! uv pip install "${uv_flags[@]}" "vastai==${SDK_VERSION}"; then
            report_error_and_exit "Failed to install vastai==${SDK_VERSION}"
        fi
        return 0
    fi
    echo "Installing default vastai"
    if ! uv pip install "${uv_flags[@]}" vastai; then
        report_error_and_exit "Failed to install vastai"
    fi
 }
 [ -n "$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
@@ -41,55 +106,151 @@ echo_var DEBUG_LOG
 echo_var PYWORKER_LOG
 echo_var MODEL_LOG
-# if instance is rebooted, we want to clear out the log file so pyworker doesn't read lines
+ROTATE_MODEL_LOG="${ROTATE_MODEL_LOG:-false}"
-# from the run prior to reboot. past logs are saved in $MODEL_LOG.old for debugging only
+if [ "$ROTATE_MODEL_LOG" = "true" ] && [ -e "$MODEL_LOG" ]; then
 if [ -e "$MODEL_LOG" ]; then
    echo "Rotating model log at $MODEL_LOG to $MODEL_LOG.old"
-    cat "$MODEL_LOG" >> "$MODEL_LOG.old" 
+    if ! cat "$MODEL_LOG" >> "$MODEL_LOG.old"; then
-    : > "$MODEL_LOG"
+        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" ]
+if [ "${USE_SYSTEM_PYTHON:-}" = "true" ]; then
-then
+    echo "Using system Python: $(which python3)"
    if ! which uv > /dev/null 2>&1; then
        if ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
            report_error_and_exit "Failed to install uv package manager"
        fi
        if [[ -f ~/.local/bin/env ]]; then
            if ! source ~/.local/bin/env; then
                report_error_and_exit "Failed to source uv environment"
            fi
        fi
    fi
    install_vastai_sdk
    touch ~/.no_auto_tmux
 elif [ ! -d "$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}" "$SERVER_DIR"; then
            report_error_and_exit "Failed to clone pyworker repository"
        fi
    elif [ "$FORCE_UPDATE" = true ]; then
        echo "Force updating pyworker repository"
        if ! (cd "$SERVER_DIR" && git fetch --all); then
            report_error_and_exit "Failed to fetch pyworker repository updates"
        fi
    fi
    if [[ -n ${PYWORKER_REF:-} ]]; then
-        (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF")
+        if [ "$FORCE_UPDATE" = true ]; then
            echo "Force updating to pyworker reference: $PYWORKER_REF"
            if ! (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF" && git pull); then
                report_error_and_exit "Failed to force update pyworker reference: $PYWORKER_REF"
            fi
        else
            if ! (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF"); then
                report_error_and_exit "Failed to checkout pyworker reference: $PYWORKER_REF"
            fi
        fi
    elif [ "$FORCE_UPDATE" = true ]; then
        echo "Force updating pyworker to latest"
        if ! (cd "$SERVER_DIR" && git pull); then
            report_error_and_exit "Failed to pull latest pyworker changes"
        fi
    fi
-    uv venv --python-preference only-managed "$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 "${SERVER_DIR}/requirements.txt"; then
        report_error_and_exit "Failed to install Python requirements"
    fi
    install_vastai_sdk
    if ! touch ~/.no_auto_tmux; then
        report_error_and_exit "Failed to create ~/.no_auto_tmux"
    fi
 else
-    [[ -f ~/.local/bin/env ]] && source ~/.local/bin/env
+    if [[ -f ~/.local/bin/env ]]; then
-    source "$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 "$ENV_PATH/bin/activate"; then
        report_error_and_exit "Failed to activate existing virtual environment"
    fi
    echo "environment activated"
    echo "venv: $VIRTUAL_ENV"
    # Handle force update for existing environment
    if [ "$FORCE_UPDATE" = true ]; then
        echo "Performing force update on existing environment"
        if [[ -d $SERVER_DIR ]]; then
            echo "Force updating pyworker repository"
            if ! (cd "$SERVER_DIR" && git fetch --all); then
                report_error_and_exit "Failed to fetch pyworker repository updates"
            fi
            if [[ -n ${PYWORKER_REF:-} ]]; then
                echo "Force updating to pyworker reference: $PYWORKER_REF"
                if ! (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF" && git pull); then
                    report_error_and_exit "Failed to force update pyworker reference: $PYWORKER_REF"
                fi
            else
                echo "Force updating pyworker to latest"
                if ! (cd "$SERVER_DIR" && git pull); then
                    report_error_and_exit "Failed to pull latest pyworker changes"
                fi
            fi
        fi
        install_vastai_sdk
    fi
 fi
-[ ! -d "$SERVER_DIR/workers/$BACKEND" ] && echo "$BACKEND not supported!" && exit 1
+# Remove force update flag after successful update
 if [ "$FORCE_UPDATE" = true ]; then
    echo "Removing force update flag"
    rm -f "/.force_update"
    echo "Force update completed successfully"
 fi
 if [ "$USE_SSL" = true ]; then
-    cat << EOF > /etc/openssl-san.cnf
+    if ! cat << EOF > /etc/openssl-san.cnf
    [req]
    default_bits       = 2048
    distinguished_name = req_distinguished_name
@@ -109,66 +270,99 @@ if [ "$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' \
+    max_retries=5
-        --data-binary @//etc/instance.csr \
+    retry_delay=2
-        -X \
+    for attempt in $(seq 1 "$max_retries"); do
-        POST "https://console.vast.ai/api/v0/sign_cert/?instance_id=$CONTAINER_ID" > /etc/instance.crt;
+        http_code=$(curl -sS -o /etc/instance.crt -w '%{http_code}' \
            --header 'Content-Type: application/octet-stream' \
            --data-binary @/etc/instance.csr \
            -X POST "https://console.vast.ai/api/v0/sign_cert/?instance_id=$CONTAINER_ID")
        if [ "$http_code" -ge 200 ] && [ "$http_code" -lt 300 ]; then
            break
        fi
        echo "SSL cert signing attempt $attempt/$max_retries failed (HTTP $http_code)"
        if [ "$attempt" -eq "$max_retries" ]; then
            report_error_and_exit "Failed to sign SSL certificate after $max_retries attempts (HTTP $http_code)"
        fi
        sleep "$retry_delay"
        retry_delay=$((retry_delay * 2))
    done
 fi
 export REPORT_ADDR WORKER_PORT USE_SSL UNSECURED
-cd "$SERVER_DIR"
+# ─── SDK Deployment Mode ───────────────────────────────────────────────
 if [ "$IS_DEPLOYMENT" = "true" ]; then
    echo "=== SDK Deployment Mode ==="
    echo "DEPLOYMENT_ID: $DEPLOYMENT_ID"
    DEPLOY_DIR="/workspace/deployment"
    mkdir -p "$DEPLOY_DIR"
    VAST_API_BASE="${VAST_API_BASE:-https://console.vast.ai}"
    # Download deployment code, retrying until the blob is available on S3.
    # The s3_key exists in the DB as soon as the deployment is created, but the
    # actual upload may still be in flight from the client side.
    # Install SDK (uses the install_vastai_sdk function which supports SDK_BRANCH/SDK_VERSION)
    install_vastai_sdk
    # Run deployment in serve mode
    export VAST_DEPLOYMENT_MODE=serve
    echo "Starting deployment: python3 $DEPLOY_DIR/deployment.py"
    serve-vast-deployment
    exit $?
 fi
 # ─── End SDK Deployment Mode ───────────────────────────────────────────
 if ! cd "$SERVER_DIR"; then
    report_error_and_exit "Failed to cd into SERVER_DIR: $SERVER_DIR"
 fi
 echo "launching PyWorker server"
 set +e
-python3 -m "workers.$BACKEND.server" |& tee -a "$PYWORKER_LOG"
+
-PY_STATUS=${PIPESTATUS[0]}
+PY_STATUS=1
 if [ -f "$SERVER_DIR/worker.py" ]; then
    echo "Running worker.py"
    python3 -m "worker" |& tee -a "$PYWORKER_LOG"
    PY_STATUS=${PIPESTATUS[0]}
 fi
 if [ "${PY_STATUS}" -ne 0 ] && [ -f "$SERVER_DIR/workers/$BACKEND/worker.py" ]; then
    echo "Running 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/$BACKEND/server.py" ]; then
    echo "Running workers.${BACKEND}.server"
    python3 -m "workers.${BACKEND}.server" |& tee -a "$PYWORKER_LOG"
    PY_STATUS=${PIPESTATUS[0]}
 fi
 set -e
 if [ "${PY_STATUS}" -ne 0 ]; then
-  echo "PyWorker exited with status ${PY_STATUS}; notifying autoscaler..."
+    if [ ! -f "$SERVER_DIR/worker.py" ] && [ ! -f "$SERVER_DIR/workers/$BACKEND/worker.py" ] && [ ! -f "$SERVER_DIR/workers/$BACKEND/server.py" ]; then
-  ERROR_MSG="PyWorker exited: code ${PY_STATUS}"
+        report_error_and_exit "Failed to find PyWorker"
-  MTOKEN="${MASTER_TOKEN:-}"
+    fi
-  VERSION="${PYWORKER_VERSION:-0}"
+    report_error_and_exit "PyWorker exited with status ${PY_STATUS}"
  IFS=',' read -r -a REPORT_ADDRS <<< "${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": "${VERSION}",
  "loadtime": 0,
  "new_load": 0,
  "cur_load": 0,
  "rej_load": 0,
  "max_perf": 0,
  "cur_perf": 0,
  "error_msg": "${ERROR_MSG}",
  "num_requests_working": 0,
  "num_requests_recieved": 0,
  "additional_disk_usage": 0,
  "working_request_idxs": [],
  "cur_capacity": 0,
  "max_capacity": 0,
  "url": "${URL}"
 }
 JSON
 )" "${addr%/}/worker_status/" || true
  done
 fi
-echo "launching PyWorker server done"
+echo "PyWorker bootstrap complete"
@@ -1,136 +0,0 @@
 import logging
 import time
 from typing import Any, Dict, Optional, Tuple
 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_endpoint_info(
        endpoint_name: str, account_api_key: str, instance: str
    ) -> Optional[Dict[str, Any]]:
        headers = {"Authorization": f"Bearer {account_api_key}"}
        url = f"{Endpoint.get_server_url(instance)}?autoscaler_instance={instance}"
        # Retry a few times to smooth over transient propagation/network delays
        for attempt in range(4):
            try:
                response = requests.get(url, headers=headers, timeout=8)
                if response.status_code != 200:
                    # brief backoff and retry
                    time.sleep(0.3 * (attempt + 1))
                    continue
                try:
                    data = response.json()
                except Exception:
                    # JSON parse failed; backoff and retry
                    time.sleep(0.3 * (attempt + 1))
                    continue
                result = data.get("results", []) if isinstance(data, dict) else []
                endpoint = next(
                    (item for item in result if item.get("endpoint_name") == endpoint_name),
                    None,
                )
                if endpoint and endpoint.get("id") and endpoint.get("api_key"):
                    return {"id": endpoint.get("id"), "api_key": endpoint.get("api_key")}
            except Exception:
                # network or other transient error; retry
                time.sleep(0.3 * (attempt + 1))
        return None
    @staticmethod
    def get_autoscaler_server_url(instance: str) -> str:
        endpoints = {
            "alpha": "run-alpha",
            "candidate": "run-candidate",
            "prod": "run",
        }
        host = endpoints.get(instance)
        if host:
            return f"https://{host}.vast.ai/"
        return "http://localhost:8080"
    @staticmethod
    def get_server_url(instance: str) -> str:
        endpoints = {
            "alpha": "alpha",
            "candidate": "candidate",
            "prod": "console",
        }
        host = endpoints.get(instance, "alpha")
        return f"https://{host}.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,
                timeout=8,
            )
            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 Exception 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.get("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
@@ -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
@@ -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 `1000`. 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
@@ -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())
@@ -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()
@@ -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. See the [Serverless documentation](https://docs.vast.ai/serverless) for guides and how-to's.
-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.
 ## Instance Setup
@@ -302,3 +302,11 @@ WEBHOOK_TIMEOUT=30                   # Webhook timeout in seconds
  }
 }
 ```
 ## Client Libraries
 See the client example for implementation details on how to integrate with the ComfyUI worker.
 ---
 See Vast's serverless documentation for more details on how to use ComfyUI with autoscaler.
@@ -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"]
        )
@@ -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"
        }
    }
 }
@@ -0,0 +1 @@
 # This folder is required for the provisioning scripts of ace and wan to complete.
@@ -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.
@@ -1,150 +0,0 @@
 import os
 import logging
 import dataclasses
 import base64
 from typing import Optional, Union, Type
 import aiohttp
 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")
 COMFYUI_URL = os.getenv("COMFYUI_URL", "http://127.0.0.1:18188")  # Raw ComfyUI server
 # 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")
 async def handle_view(request: web.Request) -> web.Response:
    """Proxy /view requests to raw ComfyUI server to fetch generated images"""
    # Forward query params to raw ComfyUI (not the API wrapper)
    query_string = request.query_string
    url = f"{COMFYUI_URL}/view?{query_string}"
    log.debug(f"Proxying /view request to: {url}")
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get(url) as resp:
                if resp.status == 200:
                    content = await resp.read()
                    return web.Response(
                        body=content,
                        status=200,
                        content_type=resp.content_type or "image/png"
                    )
                else:
                    text = await resp.text()
                    return web.Response(
                        text=text,
                        status=resp.status,
                        content_type="text/plain"
                    )
    except Exception as e:
        log.error(f"Error proxying /view: {e}")
        return web.Response(text=str(e), status=500)
 routes = [
    web.post("/generate/sync", backend.create_handler(ComfyWorkflowHandler())),
    web.get("/view", handle_view),
    web.get("/ping", handle_ping),
 ]
 if __name__ == "__main__":
    start_server(backend, routes)
@@ -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)
@@ -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()
@@ -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
@@ -1,173 +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
 from vastai import Serverless
 ENDPOINT_NAME = "my-comfyui-endpoint"
 COST = 100 # Use a constant cost for image generation
 def call_default_workflow(endpoint_id: int, api_key: str, server_url: str) -> None:
    WORKER_ENDPOINT = "/prompt"
    COST = 100
    route_payload = {
        "endpoint_id": endpoint_id,
        "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_id=message["endpoint_id"],
        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_id: int, api_key: str, server_url: str
 ) -> None:
    WORKER_ENDPOINT = "/custom-workflow"
    COST = 100
    route_payload = {
        "endpoint_id": endpoint_id,
        "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_id=message["endpoint_id"],
        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
    log = logging.getLogger(__name__)
    args = test_args.parse_args()
    endpoint_info = Endpoint.get_endpoint_info(
        endpoint_name=args.endpoint_group_name,
        account_api_key=args.api_key,
        instance=args.instance,
    )
    if endpoint_info:
        endpoint_id = endpoint_info["id"]
        endpoint_api_key = endpoint_info["api_key"]
        try:
            call_default_workflow(
                endpoint_id=endpoint_id,
                api_key=endpoint_api_key,
                server_url=args.server_url,
            )
            call_custom_workflow_for_sd3(
                endpoint_id=endpoint_id,
                api_key=endpoint_api_key,
                server_url=args.server_url,
            )
        except Exception as e:
            log.error(f"Error during API call: {e}")
    else:
        log.error(f"Failed to get endpoint info for {args.endpoint_group_name}")
@@ -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
            }
        )
@@ -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"
        }
      }
    }
  }
 }
@@ -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"
        }
      }
    }
  }
 }
@@ -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.
@@ -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)
@@ -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)
@@ -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"
 ```
@@ -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
            }
        )
@@ -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)
@@ -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)
@@ -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>
 ```
@@ -102,15 +102,13 @@ async def call_completions(client: Serverless, *, model: str, prompt: str, endpo
    endpoint = await client.get_endpoint(name=endpoint_name)
    payload = {
-        "input": {
+        "model": model,
-            "model": model,
+        "prompt": prompt,
-            "prompt": prompt,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
-            "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
            "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["input"]["max_tokens"])
+    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]], endpoint_name: str, **kwargs) -> Dict[str, Any]:
@@ -118,17 +116,15 @@ async def call_chat_completions(client: Serverless, *, model: str, messages: Lis
    endpoint = await client.get_endpoint(name=endpoint_name)
    payload = {
-        "input": {
+        "model": model,
-            "model": model,
+        "messages": messages,
-            "messages": messages,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
-            "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
-            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+        **({"tools": kwargs["tools"]} if "tools" in kwargs else {}),
-            **({"tools": kwargs["tools"]} if "tools" in kwargs else {}),
+        **({"tool_choice": kwargs["tool_choice"]} if "tool_choice" 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["input"]["max_tokens"])
+    resp = await endpoint.request("/v1/chat/completions", payload, cost=payload["max_tokens"])
    return resp["response"]
 # ---- Streaming variants ----
@@ -137,17 +133,15 @@ async def stream_completions(client: Serverless, *, model: str, prompt: str, end
    endpoint = await client.get_endpoint(name=endpoint_name)
    payload = {
-        "input": {
+        "model": model,
-            "model": model,
+        "prompt": prompt,
-            "prompt": prompt,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
-            "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
-            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+        "stream": True,
-            "stream": True,
+        **({"stop": kwargs["stop"]} if "stop" in kwargs else {}),
            **({"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["input"]["max_tokens"], stream=True)
+    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]], endpoint_name: str, **kwargs):
@@ -155,18 +149,16 @@ async def stream_chat_completions(client: Serverless, *, model: str, messages: L
    endpoint = await client.get_endpoint(name=endpoint_name)
    payload = {
-        "input": {
+        "model": model,
-            "model": model,
+        "messages": messages,
-            "messages": messages,
+        "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
-            "max_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+        "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
-            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+        "stream": True,
-            "stream": True,
+        **({"tools": kwargs["tools"]} if "tools" in kwargs else {}),
-            **({"tools": kwargs["tools"]} if "tools" in kwargs else {}),
+        **({"tool_choice": kwargs["tool_choice"]} if "tool_choice" 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["input"]["max_tokens"], stream=True)
+    resp = await endpoint.request("/v1/chat/completions", payload, cost=payload["max_tokens"], stream=True)
    return resp["response"]  # async generator
@@ -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"}]
@@ -1,207 +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":
        system_prompt = """You are a helpful AI assistant. You have access to the following knowledge base:
        Zebras (US: /ˈziːbrəz/, UK: /ˈzɛbrəz, ˈziː-/)[2] (subgenus Hippotigris) are African equines 
        with distinctive black-and-white striped coats. There are three living species: Grévy's zebra 
        (Equus grevyi), the plains zebra (E. quagga), and the mountain zebra (E. zebra). Zebras share the 
        genus Equus with horses and asses, the three groups being the only living members of the family 
        Equidae. Zebra stripes come in different patterns, unique to each individual. Zebras inhabit eastern 
        and southern Africa and can be found in a variety of habitats such as savannahs, grasslands, 
        woodlands, shrublands, and mountainous areas.
        Please answer the following question based on the above context."""
        unique_question = " ".join(random.choices(WORD_LIST, k=int(100)))
        model = os.environ.get("MODEL_NAME")
        if not model:
            raise ValueError("MODEL_NAME environment variable not set")
        test_input = {
            "model": model,
            "prompt": f"{system_prompt}\n\n{unique_question}",
            "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":
        system_prompt = """You are a helpful AI assistant. You have access to the following knowledge base:
        Zebras (US: /ˈziːbrəz/, UK: /ˈzɛbrəz, ˈziː-/)[2] (subgenus Hippotigris) are African equines 
        with distinctive black-and-white striped coats. There are three living species: Grévy's zebra 
        (Equus grevyi), the plains zebra (E. quagga), and the mountain zebra (E. zebra). Zebras share the 
        genus Equus with horses and asses, the three groups being the only living members of the family 
        Equidae. Zebra stripes come in different patterns, unique to each individual. Zebras inhabit eastern 
        and southern Africa and can be found in a variety of habitats such as savannahs, grasslands, 
        woodlands, shrublands, and mountainous areas.
        Please answer the following question based on the above context."""
        unique_question = " ".join(random.choices(WORD_LIST, k=int(100)))
        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": "system", "content": system_prompt},  # Shared prefix
                {"role": "user", "content": unique_question}   # Unique per request
            ],
            "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()
@@ -1,62 +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
    "main: model loaded" # llama.cpp
 ]
 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,
    max_wait_time=600.0,
    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)
@@ -1,434 +0,0 @@
 from lib.test_utils import test_args
 from utils.endpoint_util import Endpoint
 from utils.ssl import get_cert_file_path
 from lib.data_types import AuthData
 from .data_types.server import CompletionsData
 import os
 import time
 import threading
 import requests
 from dataclasses import dataclass
 from collections import Counter
 from urllib.parse import urljoin, urlparse
 import re
 # Headless plotting
 import matplotlib
 matplotlib.use("Agg")
 import logging
 logging.getLogger("matplotlib.font_manager").setLevel(logging.WARNING)
 import matplotlib.pyplot as plt
 import numpy as np
 from concurrent.futures import ThreadPoolExecutor, wait, FIRST_COMPLETED
 from requests.adapters import HTTPAdapter
 def get_incremented_path(path: str) -> str:
    base, ext = os.path.splitext(path)
    if not os.path.exists(path):
        return path
    i = 1
    while os.path.exists(f"{base}-{i}{ext}"):
        i += 1
    return f"{base}-{i}{ext}"
 WORKER_ENDPOINT = "/v1/completions" # This will return the full text output at once. Latency metrics reflect that (ie not measuring TTFT)
@dataclass
 class ReqResult:
    worker_url: str
    route_ms: float
    worker_ms: float
    total_ms: float
    ok: bool
    error: str = ""
    status_code: int = 0
    t_start: float = 0.0
    t_end: float = 0.0
    workload: float = 0.0
 def do_one(endpoint_name: str,
           endpoint_id: int,
           endpoint_api_key: str,
           server_url: str,
           worker_endpoint: str,
           payload,
           results_list,
           t0,
           status_samples,
           route_session,
           worker_session):
    try:
        workload = payload.count_workload()
        route_payload = {"endpoint_id": endpoint_id, "api_key": endpoint_api_key, "cost": workload}
        headers = {"Authorization": f"Bearer {endpoint_api_key}"}
        start = time.time()
        r0 = route_session.post(urljoin(server_url, "/route/"), json=route_payload, headers=headers, timeout=4)
        t_after_route = time.time()
        if r0.status_code != 200:
            results_list.append(ReqResult(worker_url="", 
                                            route_ms=(t_after_route - start) * 1000.0, 
                                            worker_ms=0.0, 
                                            total_ms=(t_after_route - start) * 1000.0, 
                                            ok=False, 
                                            error=f"route error {r0.reason} {r0.text}",
                                            status_code=r0.status_code,
                                            t_start=start - t0, 
                                            t_end=t_after_route - t0, 
                                            workload=workload))
            return
        msg = r0.json()
        # 1) Check if we got a worker back from route
        worker_url = msg.get("url", "")
        if not worker_url:
            status = msg.get("status", "")
            m = re.search(r"total workers:\s*(\d+).*loading workers:\s*(\d+).*standby workers:\s*(\d+).*error workers:\s*(\d+)", status, re.I | re.S)
            if m:
                tot, loading, standby, err = map(int, m.groups())
                idle = max(tot - loading - standby - err, 0)
                status_samples.append((time.time() - t0, idle))
        # 2) If we got a worker, send the request
        if worker_url:
            req = dict(payload=payload.__dict__, auth_data=AuthData.from_json_msg(msg).__dict__)
            t_before_worker = time.time()
            r1 = worker_session.post(
                urljoin(worker_url, worker_endpoint),
                json=req,
                verify=get_cert_file_path(),
                timeout=(4, 120),
            )
            t_after_worker = time.time()
            if r1.status_code != 200:
                results_list.append(ReqResult(worker_url=worker_url, 
                                                route_ms=(t_after_route - start) * 1000.0, 
                                                worker_ms=(t_after_worker - t_before_worker) * 1000.0,
                                                total_ms=(t_after_worker - start) * 1000.0, 
                                                ok=False,
                                                error=f"worker inference error {r1.reason} {r1.text}",
                                                status_code=r1.status_code,
                                                t_start=start - t0, 
                                                t_end=t_after_worker - t0, 
                                                workload=workload))
                return
            # Success case
            results_list.append(ReqResult(worker_url=worker_url, 
                                            route_ms=(t_after_route - start) * 1000.0, 
                                            worker_ms=(t_after_worker - t_before_worker) * 1000.0, 
                                            total_ms=(t_after_worker - start) * 1000.0,
                                            ok=True,
                                            error="",
                                            status_code=200,
                                            t_start=start - t0, 
                                            t_end=t_after_worker - t0, 
                                            workload=workload))
        # 3) If so, sample via /get_endpoint_workers/ for eligible (idle) worker tracking
        if worker_url:
            try:
                r_status = route_session.post(
                    urljoin(server_url, "/get_endpoint_workers/"),
                    json={"id": endpoint_id},
                    headers={"Authorization": f"Bearer {endpoint_api_key}"},
                    timeout=3,
                )
                if r_status.status_code == 200:
                    workers = r_status.json()
                    idle = 0
                    for w in workers:
                        st = str(w.get("status", "")).lower()
                        if (st in ("idle")):
                            idle += 1
                    status_samples.append((time.time() - t0, idle))
            except Exception:
                pass
    except Exception as e:
        t = time.time()
        results_list.append(ReqResult(worker_url="", 
                                        route_ms=0.0, 
                                        worker_ms=0.0, 
                                        total_ms=0.0, 
                                        ok=False, 
                                        error=f"unknown error {e}",
                                        status_code=0,
                                        t_start=t - t0, 
                                        t_end=t - t0, 
                                        workload=0.0))
 def run_load_with_metrics(num_requests: int,
                          requests_per_second: float,
                          endpoint_group_name: str,
                          account_api_key: str,
                          server_url: str,
                          worker_endpoint: str,
                          instance: str,
                          out_path: str):
    ep_info = Endpoint.get_endpoint_info(endpoint_name=endpoint_group_name,
                                         account_api_key=account_api_key,
                                         instance=instance)
    if not ep_info or not ep_info.get("api_key") or not ep_info.get("id"):
        print(f"Endpoint {endpoint_group_name} not found for API key")
        return
    endpoint_id = int(ep_info["id"])
    endpoint_api_key = ep_info["api_key"]
    t0 = time.time()
    results = []
    status_samples = []
    max_concurrency = int(os.environ.get("MAX_CONCURRENCY", "8192"))
    submit_queue_factor = 2  # cap queued tasks to reduce memory
    # Shared HTTP sessions with connection pooling (persistent connections)
    def make_session(pool_connections: int, pool_maxsize: int) -> requests.Session:
        sess = requests.Session()
        adapter = HTTPAdapter(pool_connections=pool_connections, pool_maxsize=pool_maxsize, max_retries=0)
        sess.mount("https://", adapter)
        sess.mount("http://", adapter)
        return sess
    # Router: mostly single host, small connection pool is sufficient
    route_session = make_session(pool_connections=1, pool_maxsize=max_concurrency)
    # Workers: many hosts; allow many pools and per-host concurrency up to max_concurrency
    worker_session = make_session(pool_connections=64, pool_maxsize=max_concurrency // 8)
    # Fire requests using a thread pool, scheduling at requested RPS
    inflight = set()
    with ThreadPoolExecutor(max_workers=max_concurrency) as executor:
        for i in range(num_requests):
            # Pace submissions to RPS
            target_time = t0 + i / max(requests_per_second, 1e-9)
            sleep_s = target_time - time.time()
            if sleep_s > 0:
                time.sleep(min(sleep_s, 0.5))  # sleep in chunks to stay responsive
            payload = CompletionsData.for_test()
            fut = executor.submit(
                do_one,
                endpoint_group_name,
                endpoint_id,
                endpoint_api_key,
                server_url,
                worker_endpoint,
                payload,
                results,
                t0,
                status_samples,
                route_session,
                worker_session,
            )
            inflight.add(fut)
            # Prevent unbounded queue growth
            if len(inflight) >= max_concurrency * submit_queue_factor:
                done, not_done = wait(inflight, return_when=FIRST_COMPLETED)
                inflight = not_done
        # Wait for all outstanding tasks
        if inflight:
            wait(inflight)
    # Close sessions
    try:
        route_session.close()
    finally:
        worker_session.close()
    # Aggregate results
    oks = [r for r in results if r.ok]
    errs = [r for r in results if not r.ok]
    total_reqs = len(results)
    succ = len(oks)
    total_ms = np.array([r.total_ms for r in oks]) if succ else np.array([])
    worker_ms = np.array([r.worker_ms for r in oks]) if succ else np.array([])
    route_ms = np.array([r.route_ms for r in oks]) if succ else np.array([])
    avg_total = float(np.mean(total_ms)) if succ else 0.0
    avg_worker = float(np.mean(worker_ms)) if succ else 0.0
    avg_route = float(np.mean(route_ms)) if succ else 0.0
    p50_total, p95_total = (float(np.percentile(total_ms, 50)), float(np.percentile(total_ms, 95))) if succ else (0.0, 0.0)
    # Distribution over workers (by host:port)
    hosts = [urlparse(r.worker_url).netloc for r in oks if r.worker_url]
    dist = Counter(hosts)
    # Idle over time (mode per second)
    idle_ts, idle_vals = [], []
    if status_samples:
        buckets = {}
        for ts, idle in status_samples:
            k = int(ts)
            buckets.setdefault(k, []).append(idle)
        keys = sorted(buckets.keys())
        idle_ts = keys
        # Use the most frequent sampled value per second (mode) to keep integer counts
        idle_vals = []
        for k in keys:
            vals_k = [int(v) for v in buckets[k]]
            if vals_k:
                cnt = Counter(vals_k)
                idle_vals.append(cnt.most_common(1)[0][0])
            else:
                idle_vals.append(0)
    print(f"\nResults: total={total_reqs} success={succ} errors={len(errs)}")
    print(f"Avg latency (ms): {avg_total:.1f}  p50: {p50_total:.1f}  p95: {p95_total:.1f}")
    print(f"Avg route latency (ms): {avg_route:.1f}  Avg worker latency (ms): {avg_worker:.1f}")
    if errs:
        print("Sample errors:")
        for e in errs[:5]:
            print(f"  {e.status_code} {e.error}")
    # Plot: 2x3 grid
    fig, axes = plt.subplots(2, 3, figsize=(15, 8))
    fig.suptitle(f"Load test: {endpoint_group_name}  n={total_reqs}, rps={requests_per_second}, success={succ}")
    # Dist per worker
    ax0 = axes[0, 0]
    if dist:
        items = sorted(dist.items(), key=lambda kv: kv[1], reverse=True)
        labels, counts = zip(*items)
        ax0.bar(range(len(labels)), counts)
        ax0.set_xticks(range(len(labels)))
        ax0.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
    ax0.set_title("Request distribution over workers")
    ax0.set_ylabel("count")
    # Latency histogram (total)
    ax1 = axes[0, 1]
    if succ:
        ax1.hist(total_ms, bins=30)
    ax1.set_title("Total latency (ms)")
    ax1.set_xlabel("ms")
    ax1.set_ylabel("freq")
    # Eligible workers over time
    ax_idle = axes[0, 2]
    if idle_ts:
        ax_idle.plot(idle_ts, idle_vals, "-o", ms=3)
    ax_idle.set_title("Eligible workers over time")
    ax_idle.set_xlabel("time (s)")
    ax_idle.set_ylabel("eligible count")
    # Throughput over time (completions/sec)
    ax_idle = axes[1, 0]
    ax_idle.clear()
    if succ:
        per_sec = {}
        for r in oks:
            s = int(r.t_end)
            per_sec[s] = per_sec.get(s, 0) + 1
        ts = sorted(per_sec.keys())
        vals = [per_sec[t] for t in ts]
        ax_idle.plot(ts, vals, "-o", ms=3)
    ax_idle.set_title("Completions per second")
    ax_idle.set_xlabel("time (s)")
    ax_idle.set_ylabel("completions / sec")
    # Summary text
    ax3 = axes[1, 1]
    ax3.axis("off")
    text = (
        f"Total requests: {total_reqs}\n"
        f"Success: {succ}  Errors: {len(errs)}\n"
        f"Avg total latency: {avg_total:.1f} ms\n"
        f"p50: {p50_total:.1f} ms  p95: {p95_total:.1f} ms\n"
        f"Avg route latency: {avg_route:.1f} ms\n"  
        f"Avg worker latency: {avg_worker:.1f} ms\n"
        f"300 errors: {len([r for r in errs if r.status_code >= 300 and r.status_code < 400])}\n"
        f"429 errors: {len([r for r in errs if r.status_code == 429])}\n"
        f"500 errors: {len([r for r in errs if r.status_code >= 500])}\n"
        f"Other errors: {len([r for r in errs if r.status_code not in [300, 429, 500]])}\n"
    )
    ax3.set_title("Summary")
    ax3.text(0.02, 0.98, text, va="top", ha="left", fontsize=11, transform=ax3.transAxes)
    # Error count over time
    ax_errors = axes[1, 2]
    all_end_times = [int(r.t_end) for r in results if r.t_end > 0]
    if all_end_times:
        min_second = min(all_end_times)
        max_second = max(all_end_times)
        # Count errors per second
        errors_per_second = {}
        for result in errs:
            second = int(result.t_end)
            errors_per_second[second] = errors_per_second.get(second, 0) + 1
        # Create complete timeline including zeros
        time_seconds = list(range(min_second, max_second + 1))
        error_counts = [errors_per_second.get(sec, 0) for sec in time_seconds]
        ax_errors.plot(time_seconds, error_counts, "-o", ms=3)
    ax_errors.set_title("Errors per second")
    ax_errors.set_xlabel("time (s)")
    ax_errors.set_ylabel("errors / sec")
    # Ensure unique output path and create directory if needed
    final_out_path = get_incremented_path(out_path)
    out_dir = os.path.dirname(final_out_path)
    if out_dir:
        os.makedirs(out_dir, exist_ok=True)
    plt.tight_layout(rect=[0, 0, 1, 0.96])
    plt.savefig(final_out_path, dpi=120)
    print(f"Saved report to: {final_out_path}")
    # Per-worker latency boxplot (top 12 by volume)
    groups = {}
    for r in oks:
        host = urlparse(r.worker_url).netloc
        groups.setdefault(host, []).append(r.total_ms)
    items = sorted(groups.items(), key=lambda kv: len(kv[1]), reverse=True)[:12]
    if items:
        labels, data = zip(*items)
        fig2, axb = plt.subplots(1, 1, figsize=(12, 5))
        axb.boxplot(data, showfliers=False)
        axb.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
        axb.set_title("Per-worker latency (ms)")
        axb.set_ylabel("ms")
        plt.tight_layout()
        extra_out = get_incremented_path(os.path.splitext(out_path)[0] + "-workers.png")
        plt.savefig(extra_out, dpi=120)
        fig2.tight_layout()
        fig2.savefig(extra_out, dpi=120)
        print(f"Saved worker latency plot to: {extra_out}")
 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 adding load args
    known_args, _ = test_args.parse_known_args()
    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}")
    # Load test args
    test_args.add_argument("-n", dest="num_requests", type=int, required=True, help="total number of requests")
    test_args.add_argument("-rps", dest="requests_per_second", type=float, required=True, help="requests per second")
    test_args.add_argument("--out", dest="out_path", type=str, default="load_test_report.png", help="path to save the report image")
    args = test_args.parse_args()
    server_url = {
        "prod": "https://run.vast.ai",
        "alpha": "https://run-alpha.vast.ai",
        "candidate": "https://run-candidate.vast.ai",
        "local": "http://localhost:8080"
    }.get(args.instance, "http://localhost:8080")
    run_load_with_metrics(
        num_requests=args.num_requests,
        requests_per_second=args.requests_per_second,
        endpoint_group_name=args.endpoint_group_name,
        account_api_key=args.api_key,
        server_url=server_url,
        worker_endpoint=WORKER_ENDPOINT,
        instance=args.instance,
        out_path=args.out_path,
    )
@@ -0,0 +1,88 @@
 import random
 import os
 import re
 import logging
 from vastai import Worker, WorkerConfig, HandlerConfig, LogActionConfig, BenchmarkConfig
 logging.getLogger().setLevel(logging.WARNING)  # Only show warnings and errors
 # Ollama model configuration
 MODEL_SERVER_URL           = 'http://127.0.0.1'
 MODEL_SERVER_PORT          = 11434
 MODEL_LOG_FILE             = '/var/log/onstart.log'
 MODEL_HEALTHCHECK_ENDPOINT = "/"
 # Ollama-specific log messages
 def request_parser(request):
    data = request
    if request.get("input") is not None:
        data = request.get("input")
    return data
 def completions_benchmark_generator() -> dict:
    # extract words from the python source code of the worker to create a list of words for generating prompts
    WORD_LIST = []
    # Try to load from perl copyright file first
    try:
        with open("/usr/share/doc/perl/copyright", 'r') as f:
            source_code = f.read()
            WORD_LIST = re.findall(r'\b\w+\b', source_code)
    except (FileNotFoundError, IOError):
        # Fallback to loading from python file
        with open(__file__, 'r') as f:
            source_code = f.read()
            WORD_LIST = re.findall(r'\b\w+\b', source_code)
    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,
            request_parser=request_parser,
            max_queue_time=600.0,
            benchmark_config=BenchmarkConfig(
                generator=completions_benchmark_generator,
                concurrency=10,
                runs=3
            )
        ),
        HandlerConfig(
            route="/v1/chat/completions",
            workload_calculator= lambda data: data.get("max_tokens", 0),
            allow_parallel_requests=True,
            request_parser=request_parser,
            max_queue_time=600.0,
        )
    ],
    log_action_config=LogActionConfig(
        on_load=["llama_server: model loaded"],
        on_error=["Traceback (most recent call last):","Error:"],
        #on_info=["load_tensors:","llama_context:","print_info:","llama_model_loader:"]
    )
 )
 Worker(worker_config).run()
@@ -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)
@@ -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)
@@ -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)
@@ -0,0 +1,77 @@
 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": 500,
            "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=600.0,
            benchmark_config=BenchmarkConfig(
                generator=benchmark_generator,
                concurrency=10,
                runs=3
            ),
            workload_calculator= lambda x: x["parameters"]["max_new_tokens"]
        ),
        HandlerConfig(
            route="/generate_stream",
            allow_parallel_requests=True,
            max_queue_time=600.0,
            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()
@@ -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 `10000`. 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
@@ -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())
@@ -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()
Author	SHA1	Message	Date
Mikhail Yevchenko	7df01c41b4	Update log action configuration to specify model loading message	2026-06-09 12:08:10 +03:00
Mikhail Yevchenko	885064fba6	Update docker-in-docker feature version to 3.0.1 in devcontainer configuration	2026-05-25 08:13:15 +00:00
Mikhail Yevchenko	e8e04fe8bc	Remove commented out info log actions from log configuration	2026-05-21 19:55:13 +00:00
Mikhail Yevchenko	6cb3acdd64	Add logging import and set logger level to WARNING	2026-05-21 19:50:21 +00:00
Mikhail Yevchenko	586ccbff1b	Update log action configuration to specify detailed error and info messages	2026-05-21 19:47:16 +00:00
Mikhail Yevchenko	bcc6b62277	Update log action configuration to enable error and info logging	2026-05-21 19:40:47 +00:00
Mikhail Yevchenko	3285d9118f	Enhance completions benchmark generator to extract words from a fallback Perl copyright file	2026-05-21 19:33:41 +00:00
Mikhail Yevchenko	f77d943d79	Refactor log message handling and improve word extraction in completions benchmark	2026-05-21 19:25:09 +00:00
Mikhail Yevchenko	976622a594	Remove nltk dep	2026-05-21 19:11:53 +00:00
Mikhail Yevchenko	0b47ef80fb	Remove version specifications for vastai-sdk and nltk in requirements.txt	2026-05-21 19:10:13 +00:00
Mikhail Yevchenko	3898a8a651	Update model server URL to remove port specification	2026-05-21 18:50:41 +00:00
Mikhail Yevchenko	170571714f	Add log message for model load event in Ollama configuration	2026-05-21 15:27:28 +00:00
Mikhail Yevchenko	81347ab8a0	Remove placeholder log messages for model load, error, and info	2026-05-21 15:11:30 +00:00
Mikhail Yevchenko	6bb0097829	Refactor model configuration and update log messages for Ollama	2026-05-21 15:11:25 +00:00
Mikhail Yevchenko	1cea6fbd2d	Update model server URL and port configuration	2026-05-20 13:34:45 +00:00
Mikhail Yevchenko	40db98915f	Add devcontainer configuration for Vast.ai serverless Ollama template	2026-05-18 20:38:26 +00:00
Mikhail Yevchenko	94926b74b6	Add log message for server listening status	2026-05-18 19:42:40 +00:00
Mikhail Yevchenko	d0347b0755	Update log file path and enhance load log messages	2026-05-18 18:41:14 +00:00
Lucas Armand	9bc9ba11c5	Increase TGI benchmark tokens to 500	2026-04-30 14:04:39 -07:00
LucasArmandVast	48fdc65e3d	Update to vastai package (#84 )	2026-04-14 10:41:31 -07:00
LucasArmandVast	2cd97315cd	Add nltk requirement for openai worker (#83 ) * Add nltk requirement for openai worker * pin version	2026-04-13 11:30:06 -07:00
Lucas Armand	83c31e25a9	Add force update detection	2026-03-31 13:46:22 -07:00
Lucas Armand	fbe1dca6fa	more env_path fixes	2026-03-30 16:28:51 -07:00
Lucas Armand	4c3120dbc5	allow override env_path	2026-03-30 16:25:01 -07:00
Lucas Armand	d7d9b915f6	allow break system packages	2026-03-30 16:09:17 -07:00
Lucas Armand	4660b337fb	Check for USE_SYSTEM_PYTHON	2026-03-30 14:46:38 -07:00
edgaratvast	7506ecb6b5	directly invoke one stop shop setup executable exported by vastai pip package for deployments (#82 )	2026-03-26 10:59:49 -07:00
LucasArmandVast	50633c5003	Update deployments script with retries. (#81 )	2026-03-23 14:58:32 -07:00
LucasArmandVast	2e8f18276f	Add beta deployments script (#80 )	2026-03-23 14:14:06 -07:00
Scott Darden	eba9c480eb	Merge pull request #79 from vast-ai/update-requirements Updated requirements to only require vastai-sdk	2026-01-14 12:07:33 -08:00
Lucas Armand	aaca1c9645	Updated requirements to only require vastai-sdk	2026-01-14 10:47:07 -08:00
LucasArmandVast	f319db6bd5	flag for model log rotate (#78 )	2026-01-12 17:03:18 -08:00
LucasArmandVast	4d786b4d17	SDK Versioning Improvements (#77 ) * Add SDK_BRANCH	2026-01-02 10:23:07 -08:00
LucasArmandVast	bd3e0032a1	Add SDK version checking (#76 )	2025-12-17 21:01:52 -08:00
Lucas Armand	e02f4bc943	Lowered concurrency of vLLM and TGI benchmarks	2025-12-17 11:55:33 -08:00
Lucas Armand	bcb04b9a32	add missing comma	2025-12-17 11:40:40 -08:00
Lucas Armand	9daf171487	Increase queue limits for vLLM and TGI	2025-12-17 11:38:55 -08:00
LucasArmandVast	29f836eb1a	Backwards compatible vLLM payload (#75 ) * Support old vLLM payloads	2025-12-15 19:58:02 -08:00
LucasArmandVast	4380d98c01	Use PyWorker SDK (#67 ) * Change PyWorker to Worker SDK * Moved /lib to vast-sdk (https://github.com/vast-ai/vast-sdk)	2025-12-15 19:33:03 -08:00
Abiola Akinnubi	2ce741a8b7	Merge pull request #74 from vast-ai/AUTO-912 Mark pyworkers as "Error" if startup script fails. to avoid silent fail that waits for autoscaler.	2025-12-11 17:05:13 -08:00
Abiola Akinnubi	4ecc07032f	Mark pyworkers as "Error" if startup script fails. to avoid silent fail that waits for autoscaler.	2025-12-11 12:51:56 -08:00
edgaratvast	df61e6e946	correct version pin for aiohttp (#73 ) Co-authored-by: Edgar Lin <edgarlin2000@gmail.com>	2025-12-10 19:34:52 -08:00
LucasArmandVast	70f8a8f534	Merge pull request #72 from vast-ai/hotfix-pin-pycares Hotfix: pin pycares	2025-12-10 20:41:44 -05:00
Lucas Armand	7be8aa6397	pin pycares	2025-12-10 17:38:03 -08:00
		`@@ -0,0 +1 @@`
							`# This folder is required for the provisioning scripts of ace and wan to complete.`