changed to session

requirements.txt

@@ -1,6 +1,4 @@
-aiohttp==3.10.1
+aiohttp[speedups]==3.10.1
-aiodns~=3.6.0
-pycares~=4.11.0
 anyio~=4.4
 lib~=4.0
 nltk~=3.9
@@ -10,4 +8,4 @@ Requests~=2.32
 transformers~=4.52
 utils==1.0.*
 hf_transfer>=0.1.9
-vastai-sdk>=0.3.0
+git+https://github.com/vast-ai/vast-sdk.git@session

workers/ace/README.md

@@ -2,7 +2,7 @@
 
 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.
+Each request has a static cost of `100`. ComfyUI does not support concurrent workloads, and there is no provision to run multiple ComfyUI instances per worker node.
 
 ## Requirements
 

workers/comfyui-json/README.md

@@ -1,154 +1,15 @@
 # ComfyUI PyWorker
 
-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.
+This is the base PyWorker for ComfyUI. It provides a unified interface for running any ComfyUI workflow through a proxy-based architecture.
 
 The cost for each request has a static value of `100`.  ComfyUI does not handle concurrent workloads and there is no current provision to load multiple instances of ComfyUI per worker node.
 
-## Instance Setup
-
-1. Pick a template
-
-- [ComfyUI (Serverless)](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=ComfyUI%20(Serverless))
-
-2. Follow the [getting started guide](https://docs.vast.ai/documentation/serverless/quickstart) for help with configuring your serverless setup. For testing, we recommend that you use the default options presented by the web interface.
-
 ## Requirements
 
 This worker requires both [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [ComfyUI API Wrapper](https://github.com/ai-dock/comfyui-api-wrapper).
 
 A docker image is provided but you may use any if the above requirements are met.
 
-## Client
-
-The client demonstrates how to use the Vast Serverless SDK to generate images, save them locally, and optionally upload to S3-compatible storage.
-
-### Setup
-
-1. 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
-```
-
-2. Set your API key:
-
-```bash
-export VAST_API_KEY=<your_api_key>
-```
-
-### Usage
-
-```bash
-# Default prompt
-python -m workers.comfyui-json.client
-
-# Custom prompt
-python -m workers.comfyui-json.client --prompt "a cat sitting on a rainbow"
-
-# With options
-python -m workers.comfyui-json.client --prompt "sunset" --width 1024 --height 1024 --steps 30
-
-# Using a custom workflow file
-python -m workers.comfyui-json.client --workflow my_workflow.json
-
-# With S3 upload
-python -m workers.comfyui-json.client --s3
-```
-
-### CLI Flags
-
-| Flag | Default | Description |
-|------|---------|-------------|
-| `--endpoint` | `my-comfyui-endpoint` | Vast endpoint name |
-| `--prompt` | (default) | Text prompt for image generation |
-| `--workflow` | (none) | Path to custom workflow JSON file |
-| `--width` | 512 | Image width in pixels |
-| `--height` | 512 | Image height in pixels |
-| `--steps` | 20 | Number of denoising steps |
-| `--seed` | (random) | Random seed for reproducibility |
-| `--s3` | (disabled) | Upload generated images to S3 |
-
-### Output
-
-Images are saved to `./generated_images/comfy_{seed}.png`.
-
-### S3 Upload (Optional)
-
-You can optionally upload generated images to an S3-compatible storage service (AWS S3, Cloudflare R2, Backblaze B2, etc.) by using the `--s3` flag.
-
-**1. Set environment variables:**
-
-```bash
-export S3_ENDPOINT_URL="https://your-account.r2.cloudflarestorage.com"
-export S3_BUCKET_NAME="my-bucket"
-export S3_ACCESS_KEY_ID="your-access-key-id"
-export S3_SECRET_ACCESS_KEY="your-secret-access-key"
-```
-
-**2. Run with S3 upload enabled:**
-
-```bash
-python -m workers.comfyui-json.client --prompt "a beautiful landscape" --s3
-```
-
-Images will be saved locally AND uploaded to `s3://{bucket}/comfyui/{filename}`.
-
-**Note:** Requires `boto3` (`pip install boto3`).
-
-## Benchmarking
-
-### Custom Benchmark Workflows
-
-You can provide a custom ComfyUI workflow for benchmarking by creating `workers/comfyui-json/misc/benchmark.json`. This allows you to test performance using your preferred models and workflow complexity.
-
-**Ways to provide the benchmark file:**
-- Fork this repository and add your `benchmark.json` file
-- Write the file during worker provisioning (onstart script or setup phase)
-
-An example file is provided in the repository. To ensure varied generations, use the placeholder `__RANDOM_INT__` in place of static seed values - it will be replaced with a random integer for each benchmark run.
-
-### Default Benchmark (Fallback)
-
-If `benchmark.json` is not available, a simple image generation benchmark runs when each worker initializes. This validates GPU performance and helps identify underperforming machines.
-
-The default benchmark uses Stable Diffusion v1.5 with ComfyUI's standard text-to-image workflow. Configure it using these environment variables:
-
-| Environment Variable | Default Value | Description |
-| -------------------- | ------------- | ----------- |
-| BENCHMARK_TEST_WIDTH | 512 | Image width (pixels) |
-| BENCHMARK_TEST_HEIGHT | 512 | Image height (pixels) |
-| BENCHMARK_TEST_STEPS | 20 | Number of denoising steps |
-
-Each benchmark run uses a random prompt from `misc/test_prompts.txt` and a random seed to ensure consistent GPU load patterns.
-
-#### Calibrating Fallback Benchmark Duration
-
-To screen for underperforming hardware, set `BENCHMARK_TEST_STEPS` to match your expected production workflow duration. This allows you to identify machines that won't meet performance requirements.
-
-**Example:** If your typical workflow should complete in 90 seconds on acceptable hardware:
-
-```bash
-# 1. Measure it/sec on your reference machine
-# RTX 4090 typically achieves ~43 it/sec with SD1.5
-
-# 2. Calculate required steps
-# 90 seconds × 43 it/sec = 3870 steps
-
-# 3. Configure benchmark
-export BENCHMARK_TEST_STEPS=3870
-
-# 4. Machines completing significantly slower than 90s indicate hardware issues
-```
-
-**Performance expectations:**
-- Benchmark duration should remain consistent across identical GPU models
-- Significant variation (>20%) may indicate thermal, power, or configuration issues
-
 ## Endpoint
 
 The worker provides a single endpoint:

workers/comfyui-json/client.py

@@ -1,312 +1,34 @@
-import os
-import sys
-import json
 import uuid
 import random
 import asyncio
-import logging
+import random
-import argparse
-import aiohttp
 
 from vastai import Serverless
 
-# ---------------------- Config ----------------------
+async def main():
-DEFAULT_PROMPT = "a beautiful sunset over mountains, digital art, highly detailed"
+    async with Serverless() as client:
-ENDPOINT_NAME = "my-comfyui-endpoint"
+        endpoint = await client.get_endpoint(name="my-comfy-endpoint") # Change this to your endpoint name
-DEFAULT_WIDTH = 512
-DEFAULT_HEIGHT = 512
-DEFAULT_STEPS = 20
-COST = 100  # Fixed cost for ComfyUI requests
 
-# Optional S3 Configuration (from environment variables)
-S3_ENDPOINT_URL = os.getenv("S3_ENDPOINT_URL")
-S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME")
-S3_ACCESS_KEY_ID = os.getenv("S3_ACCESS_KEY_ID")
-S3_SECRET_ACCESS_KEY = os.getenv("S3_SECRET_ACCESS_KEY")
-
-logging.basicConfig(level=logging.INFO, format="%(levelname)s - %(message)s")
-log = logging.getLogger(__name__)
-
-
-def get_s3_client():
-    """Create and return an S3 client configured for the S3-compatible endpoint"""
-    try:
-        import boto3
-        from botocore.config import Config
-    except ImportError:
-        log.error("boto3 is required for S3 uploads. Install with: pip install boto3")
-        return None
-
-    if not all([S3_ENDPOINT_URL, S3_BUCKET_NAME, S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY]):
-        log.error("S3 environment variables not fully configured. Required:")
-        log.error("  S3_ENDPOINT_URL, S3_BUCKET_NAME, S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY")
-        return None
-
-    return boto3.client(
-        "s3",
-        endpoint_url=S3_ENDPOINT_URL,
-        aws_access_key_id=S3_ACCESS_KEY_ID,
-        aws_secret_access_key=S3_SECRET_ACCESS_KEY,
-        config=Config(signature_version="s3v4"),
-    )
-
-
-# ---------------------- API Functions ----------------------
-async def call_generate(
-    client: Serverless,
-    *,
-    endpoint_name: str,
-    prompt: str,
-    width: int,
-    height: int,
-    steps: int,
-    seed: int,
-) -> dict:
-    """Generate image using Text2Image modifier"""
-    endpoint = await client.get_endpoint(name=endpoint_name)
         payload = {
             "input": {
                 "request_id": str(uuid.uuid4()),
                 "modifier": "Text2Image",
                 "modifications": {
-                "prompt": prompt,
+                    "prompt": "a beautiful landscape with mountains and lakes",
-                "width": width,
+                    "width": 1024,
-                "height": height,
+                    "height": 1024,
-                "steps": steps,
+                    "steps": 20,
-                "seed": seed,
+                    "seed": random.randint(0, 2**32 - 1)
                 },
+                "workflow_json": {}  # Empty since using modifier approach
             }
         }
-    return await endpoint.request("/generate/sync", payload, cost=COST)
         
+        response = await endpoint.request("/generate/sync", payload, cost=100)
 
-async def call_generate_workflow(
+        # Get the file from the path on the local machine using SCP or SFTP
-    client: Serverless,
+        # or configure S3 to upload to cloud storage.
-    *,
+        print(response["response"]["output"][0]["local_path"])
-    endpoint_name: str,
-    workflow_json: dict,
-) -> dict:
-    """Generate using custom workflow JSON"""
-    endpoint = await client.get_endpoint(name=endpoint_name)
-    payload = {
-        "input": {
-            "request_id": str(uuid.uuid4()),
-            "workflow_json": workflow_json,
-        }
-    }
-    return await endpoint.request("/generate/sync", payload, cost=COST)
-
-
-# ---------------------- Demo Class ----------------------
-class APIDemo:
-    def __init__(self, client: Serverless, endpoint_name: str, upload_s3: bool = False):
-        self.client = client
-        self.endpoint_name = endpoint_name
-        self.upload_s3 = upload_s3
-        self.s3_client = get_s3_client() if upload_s3 else None
-        
-        if upload_s3 and not self.s3_client:
-            log.warning("S3 upload requested but client creation failed. Images will only be saved locally.")
-
-    def extract_filename(self, response: dict) -> str | None:
-        """Extract the generated image filename from ComfyUI response"""
-        if "comfyui_response" in response:
-            for data in response["comfyui_response"].values():
-                if isinstance(data, dict) and "outputs" in data:
-                    for node_output in data["outputs"].values():
-                        if "images" in node_output and node_output["images"]:
-                            return node_output["images"][0].get("filename")
-        return None
-
-    async def save_image(self, worker_url: str, filename: str, local_name: str) -> str | None:
-        """Fetch and save image locally from the worker, optionally upload to S3"""
-        os.makedirs("generated_images", exist_ok=True)
-        return await self._fetch_image(worker_url, filename, local_name)
-
-    def _upload_to_s3(self, local_path: str, s3_key: str) -> str | None:
-        """Upload a local file to S3 and return the S3 URL"""
-        if not self.s3_client:
-            return None
-        
-        try:
-            self.s3_client.upload_file(
-                local_path,
-                S3_BUCKET_NAME,
-                s3_key,
-                ExtraArgs={"ContentType": "image/png"}
-            )
-            s3_url = f"{S3_ENDPOINT_URL}/{S3_BUCKET_NAME}/{s3_key}"
-            print(f"  ☁️  Uploaded to S3: {s3_key}")
-            return s3_url
-        except Exception as e:
-            log.error(f"Failed to upload to S3: {e}")
-            return None
-
-    async def _fetch_image(self, worker_url: str, filename: str, local_name: str) -> str | None:
-        """Fetch image from worker's /view endpoint and save locally"""
-        if not worker_url:
-            return None
-            
-        try:
-            url = f"{worker_url}/view"
-            params = {"filename": filename, "type": "output"}
-            
-            async with aiohttp.ClientSession() as session:
-                async with session.get(url, params=params, ssl=False) as resp:
-                    if resp.status == 200:
-                        path = f"generated_images/{local_name}"
-                        image_data = await resp.read()
-                        with open(path, "wb") as f:
-                            f.write(image_data)
-                        print(f"  💾 Saved: {path}")
-                        
-                        # Upload to S3 if enabled
-                        if self.upload_s3 and self.s3_client:
-                            s3_key = f"comfyui/{local_name}"
-                            self._upload_to_s3(path, s3_key)
-                        
-                        return path
-            return None
-        except Exception:
-            return None
-
-    async def demo_prompt(
-        self,
-        prompt: str,
-        width: int,
-        height: int,
-        steps: int,
-        seed: int | None,
-    ):
-        """Demo: Generate image from text prompt"""
-        print("=" * 60)
-        print("COMFYUI TEXT-TO-IMAGE DEMO")
-        print("=" * 60)
-
-        if seed is None:
-            seed = random.randint(0, 2**32 - 1)
-
-        print(f"Prompt: {prompt[:100]}..." if len(prompt) > 100 else f"Prompt: {prompt}")
-        print(f"Size: {width}x{height}, Steps: {steps}, Seed: {seed}")
-        print("\n🎨 Generating image...")
-
-        response = await call_generate(
-            self.client,
-            endpoint_name=self.endpoint_name,
-            prompt=prompt,
-            width=width,
-            height=height,
-            steps=steps,
-            seed=seed,
-        )
-
-        print("\n✅ Generation complete!")
-
-        # Get worker URL for fetching images
-        worker_url = response.get("url", "")
-        print(f"Worker URL: {worker_url}")
-
-        # Fetch and save image
-        if "response" in response:
-            filename = self.extract_filename(response["response"])
-            if filename:
-                path = await self.save_image(worker_url, filename, f"comfy_{seed}.png")
-                if not path:
-                    print(f"❌ Failed to fetch image")
-            else:
-                print("❌ No image in response")
-        else:
-            print("❌ Unexpected response format")
-
-    async def demo_workflow(self, workflow_file: str):
-        """Demo: Generate using custom workflow file"""
-        print("=" * 60)
-        print("COMFYUI CUSTOM WORKFLOW DEMO")
-        print("=" * 60)
-
-        if not os.path.exists(workflow_file):
-            log.error(f"Workflow file not found: {workflow_file}")
-            return
-
-        with open(workflow_file, "r") as f:
-            workflow_json = json.load(f)
-
-        print(f"Workflow: {workflow_file}")
-        print("\n🎨 Generating...")
-
-        response = await call_generate_workflow(
-            self.client,
-            endpoint_name=self.endpoint_name,
-            workflow_json=workflow_json,
-        )
-
-        print("\n✅ Generation complete!")
-
-        worker_url = response.get("url", "")
-
-        if "response" in response:
-            filename = self.extract_filename(response["response"])
-            if filename:
-                path = await self.save_image(worker_url, filename, "workflow.png")
-                if not path:
-                    print(f"❌ Failed to fetch image")
-            else:
-                print("❌ No image in response")
-        else:
-            print("❌ Unexpected response format")
-
-
-# ---------------------- CLI ----------------------
-def build_arg_parser() -> argparse.ArgumentParser:
-    p = argparse.ArgumentParser(description="Vast ComfyUI-JSON Demo (Serverless SDK)")
-    p.add_argument("--endpoint", default=ENDPOINT_NAME, help=f"Vast endpoint name (default: {ENDPOINT_NAME})")
-    p.add_argument("--prompt", type=str, default=DEFAULT_PROMPT, metavar="TEXT",
-                   help=f"Prompt text (default: '{DEFAULT_PROMPT[:30]}...')")
-    p.add_argument("--workflow", type=str, metavar="FILE", help="Use custom workflow JSON file instead")
-    p.add_argument("--width", type=int, default=DEFAULT_WIDTH, help=f"Image width (default: {DEFAULT_WIDTH})")
-    p.add_argument("--height", type=int, default=DEFAULT_HEIGHT, help=f"Image height (default: {DEFAULT_HEIGHT})")
-    p.add_argument("--steps", type=int, default=DEFAULT_STEPS, help=f"Steps (default: {DEFAULT_STEPS})")
-    p.add_argument("--seed", type=int, default=None, help="Seed (default: random)")
-    p.add_argument("--s3", action="store_true", 
-                   help="Upload generated images to S3 (requires S3_ENDPOINT_URL, S3_BUCKET_NAME, S3_ACCESS_KEY_ID, S3_SECRET_ACCESS_KEY env vars)")
-    return p
-
-
-async def main_async():
-    args = build_arg_parser().parse_args()
-
-    print("=" * 60)
-    print(f"Using endpoint: {args.endpoint}")
-    if args.s3:
-        print(f"S3 upload: enabled (bucket: {S3_BUCKET_NAME})")
-
-    try:
-        async with Serverless() as client:
-            demo = APIDemo(client, args.endpoint, upload_s3=args.s3)
-
-            if args.workflow:
-                await demo.demo_workflow(workflow_file=args.workflow)
-            else:
-                await demo.demo_prompt(
-                    prompt=args.prompt,
-                    width=args.width,
-                    height=args.height,
-                    steps=args.steps,
-                    seed=args.seed,
-                )
-
-    except AttributeError as e:
-        if "API key" in str(e):
-            log.error("API key missing. Set VAST_API_KEY environment variable.")
-        else:
-            log.error(f"Error: {e}")
-        sys.exit(1)
-    except Exception as e:
-        log.error(f"Error: {e}")
-        import traceback
-        traceback.print_exc()
-        sys.exit(1)
-
 
 if __name__ == "__main__":
-    asyncio.run(main_async())
+    asyncio.run(main())

workers/openai/README.md

@@ -8,13 +8,14 @@ This is the base PyWorker for OpenAI compatible inference servers.  See the [Ser
 
 This worker is compatible with any backend API that properly implements the `/v1/completions` and `/v1/chat/completions` endpoints.  We currently have three templates you can choose from but you can also create your own without having to modify the PyWorker.
 
-- [vLLM](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=vLLM%20(Serverless)) (recommended)
+- [vLLM](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=vLLM%20%2B%20Qwen%2FQwen3-8B%20(Serverless)) (recommended)
 - [Ollama](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=Ollama%20%2B%20Qwen3%3A32b%20(Serverless))
+- [HuggingFace TGI](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=TGI%20%2B%20Qwen3-8B%20(Serverless))
 
 
 All of these templates can be configured via the template interface.  You may want to change the model or startup arguments, depending on the template you selected.
 
-2. Follow the [getting started guide](https://docs.vast.ai/documentation/serverless/quickstart) for help with configuring your serverless setup.  For testing, we recommend that you use the default options presented by the web interface.
+2. Follow the [getting started guide](https://docs.vast.ai/serverless/getting-started) for help with configuring your serverless setup.  For testing, we recommend that you use the default options presented by the web interface.
 
 ## Client Setup (Demo)
 
@@ -33,30 +34,12 @@ uv pip install -r requirements.txt
 
 Several examples have been provided in the client to help you get started with your own implementation.
 
-First, set your API key as an environment variable:
+### Completions
+
+Call to `/v1/completions` with json response
 
 ```bash
-export VAST_API_KEY=<your_api_key>
+python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --completion --model <MODEL_NAME>
-```
-
-The `--model` and `--endpoint` flags are optional. If not provided, they default to `Qwen/Qwen3-8B` and `my-vllm-endpoint` respectively.
-
-### Chat Completion (streaming)
-
-Call to `/v1/chat/completions` with streaming response
-
-```bash
-python -m workers.openai.client --chat-stream --endpoint <ENDPOINT_NAME> --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 --interactive --endpoint <ENDPOINT_NAME> --model <MODEL_NAME>
 ```
 
 ### Chat Completion (json)
@@ -64,7 +47,15 @@ python -m workers.openai.client --interactive --endpoint <ENDPOINT_NAME> --model
 Call to `/v1/chat/completions` with json response
 
 ```bash
-python -m workers.openai.client --chat --endpoint <ENDPOINT_NAME> --model <MODEL_NAME>
+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)
@@ -74,14 +65,16 @@ 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 --tools --endpoint <ENDPOINT_NAME> --model <MODEL_NAME>
+python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --tools --model <MODEL_NAME>
 ```
 
-### Completions
+### Interactive Chat (streaming)
 
-Call to `/v1/completions` with json response
+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 --completion --endpoint <ENDPOINT_NAME> --model <MODEL_NAME>
+python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --interactive --model <MODEL_NAME>
 ```
 

workers/openai/client.py

@@ -18,7 +18,7 @@ logging.basicConfig(
 log = logging.getLogger(__file__)
 
 # ---------------------- Prompts ----------------------
-COMPLETIONS_PROMPT = "Zebras are primarily grazers and can subsist on lower-quality vegetation. They are preyed on mainly by"
+COMPLETIONS_PROMPT = "the capital of USA is"
 CHAT_PROMPT = "Think step by step: Tell me about the Python programming language."
 TOOLS_PROMPT = (
     "Can you list the files in the current working directory and tell me what you see? "
@@ -97,9 +97,9 @@ def _tool_state_to_message_tool_calls(state: Dict[int, Dict[str, Any]]) -> List[
 
 
 # ---- OpenAI-compatible calls (non-streaming) ----
-async def call_completions(client: Serverless, *, model: str, prompt: str, endpoint_name: str, **kwargs) -> Dict[str, Any]:
+async def call_completions(client: Serverless, *, model: str, prompt: str, **kwargs) -> Dict[str, Any]:
 
-    endpoint = await client.get_endpoint(name=endpoint_name)
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
 
     payload = {
         "model": model,
@@ -111,9 +111,9 @@ async def call_completions(client: Serverless, *, model: str, prompt: str, endpo
     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]:
+async def call_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], **kwargs) -> Dict[str, Any]:
 
-    endpoint = await client.get_endpoint(name=endpoint_name)
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
 
     payload = {
         "model": model,
@@ -128,9 +128,9 @@ async def call_chat_completions(client: Serverless, *, model: str, messages: Lis
     return resp["response"]
 
 # ---- Streaming variants ----
-async def stream_completions(client: Serverless, *, model: str, prompt: str, endpoint_name: str, **kwargs):
+async def stream_completions(client: Serverless, *, model: str, prompt: str, **kwargs):
 
-    endpoint = await client.get_endpoint(name=endpoint_name)
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
 
     payload = {
         "model": model,
@@ -144,9 +144,9 @@ async def stream_completions(client: Serverless, *, model: str, prompt: str, end
     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):
+async def stream_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], **kwargs):
 
-    endpoint = await client.get_endpoint(name=endpoint_name)
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
 
     payload = {
         "model": model,
@@ -166,10 +166,9 @@ async def stream_chat_completions(client: Serverless, *, model: str, messages: L
 class APIDemo:
     """Demo and testing functionality for the API client"""
 
-    def __init__(self, client: Serverless, model: str, endpoint_name: str, tool_manager: Optional[ToolManager] = None):
+    def __init__(self, client: Serverless, model: str, tool_manager: Optional[ToolManager] = None):
         self.client = client
         self.model = model
-        self.endpoint_name = endpoint_name
         self.tool_manager = tool_manager or ToolManager()
 
     # ----- Streaming handler -----
@@ -178,16 +177,11 @@ class APIDemo:
         reasoning_content = ""
         printed_reasoning = False
         printed_answer = False
-        finish_reason = None
 
         async for chunk in stream:
             choice = (chunk.get("choices") or [{}])[0]
             delta = choice.get("delta", {})
 
-            # Track finish reason
-            if choice.get("finish_reason"):
-                finish_reason = choice.get("finish_reason")
-
             # reasoning tokens
             rc = delta.get("reasoning_content")
             if rc and show_reasoning:
@@ -217,8 +211,6 @@ class APIDemo:
                 print(f"Reasoning tokens: {len(reasoning_content.split())}")
             if printed_answer:
                 print(f"Response tokens: {len(full_response.split())}")
-            if finish_reason:
-                print(f"Finish reason: {finish_reason}")
 
         return full_response
     
@@ -231,7 +223,6 @@ class APIDemo:
             client=self.client,
             model=self.model,
             prompt=COMPLETIONS_PROMPT,
-            endpoint_name=self.endpoint_name,
             max_tokens=MAX_TOKENS,
             temperature=DEFAULT_TEMPERATURE,
         )
@@ -250,7 +241,6 @@ class APIDemo:
                 client=self.client,
                 model=self.model, 
                 messages=messages,
-                endpoint_name=self.endpoint_name,
                 max_tokens=MAX_TOKENS,
                 temperature=DEFAULT_TEMPERATURE
             )
@@ -263,7 +253,6 @@ class APIDemo:
                 client=self.client,
                 model=self.model, 
                 messages=messages,
-                endpoint_name=self.endpoint_name,
                 max_tokens=MAX_TOKENS,
                 temperature=DEFAULT_TEMPERATURE
             )
@@ -290,7 +279,6 @@ class APIDemo:
                 client=self.client,
                 model=self.model,
                 messages=messages,
-                endpoint_name=self.endpoint_name,
                 tools=minimal_tool,
                 tool_choice="none",
                 max_tokens=10
@@ -316,7 +304,6 @@ class APIDemo:
             client=self.client,
             model=self.model,
             messages=messages,
-            endpoint_name=self.endpoint_name,
             tools=self.tool_manager.get_ls_tool_definition(),
             tool_choice="auto",
             max_tokens=MAX_TOKENS,
@@ -394,7 +381,6 @@ class APIDemo:
             client=self.client,
             model=self.model,
             messages=messages,
-            endpoint_name=self.endpoint_name,
             max_tokens=MAX_TOKENS,
             temperature=DEFAULT_TEMPERATURE,
         )
@@ -433,6 +419,7 @@ class APIDemo:
         print("=" * 60)
         print("INTERACTIVE STREAMING CHAT")
         print("=" * 60)
+        print(f"Using model: {self.model}")
         print("Type 'quit' to exit, 'clear' to clear history")
         print()
 
@@ -459,7 +446,6 @@ class APIDemo:
                     client=self.client,
                     model=self.model, 
                     messages=messages, 
-                    endpoint_name=self.endpoint_name,
                     max_tokens=MAX_TOKENS, 
                     temperature=0.7
                 )
@@ -479,8 +465,8 @@ class APIDemo:
 # ---------------------- CLI ----------------------
 def build_arg_parser() -> argparse.ArgumentParser:
     p = argparse.ArgumentParser(description="Vast vLLM Demo (Serverless SDK)")
-    p.add_argument("--model", default=DEFAULT_MODEL, help=f"Model to use for requests (default: {DEFAULT_MODEL})")
+    p.add_argument("--model", required=True, help="Model to use for requests (required)")
-    p.add_argument("--endpoint", default=ENDPOINT_NAME, help=f"Vast endpoint name (default: {ENDPOINT_NAME})")
+    p.add_argument("--endpoint", default="my-vllm-endpoint", help="Vast endpoint name (default: my-vllm-endpoint)")
 
     modes = p.add_mutually_exclusive_group(required=False)
     modes.add_argument("--completion", action="store_true", help="Test completions endpoint")
@@ -508,14 +494,12 @@ async def main_async():
         print("Please specify exactly one test mode")
         sys.exit(1)
 
-    print("=" * 60)
     print(f"Using model: {args.model}")
-    print(f"Using endpoint: {args.endpoint}")
+    print("=" * 60)
-
 
     try:
         async with Serverless() as client:
-            demo = APIDemo(client, args.model, args.endpoint, ToolManager())
+            demo = APIDemo(client, args.model, ToolManager())
 
             if args.completion:
                 await demo.demo_completions()

workers/openai/worker.py

@@ -28,12 +28,6 @@ MODEL_INFO_LOG_MSGS = [
 nltk.download("words")
 WORD_LIST = nltk.corpus.words.words()
 
-def request_parser(request):
-    data = request
-    if request.get("input") is not None:
-        data = request.get("input")
-    return data
-
 
 def completions_benchmark_generator() -> dict:
     prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
@@ -61,7 +55,6 @@ worker_config = WorkerConfig(
             workload_calculator= lambda data: data.get("max_tokens", 0),
             allow_parallel_requests=True,
             max_queue_time=60.0,
-            request_parser=request_parser,
             benchmark_config=BenchmarkConfig(
                 generator=completions_benchmark_generator,
                 concurrency=100,
@@ -73,7 +66,6 @@ worker_config = WorkerConfig(
             workload_calculator= lambda data: data.get("max_tokens", 0),
             allow_parallel_requests=True,
             max_queue_time=60.0,
-            request_parser=request_parser
         )
     ],
     log_action_config=LogActionConfig(

workers/tgi/README.md

@@ -1,103 +1,19 @@
-# HuggingFace TGI PyWorker
+This is the base PyWorker for TGI, designed to create PyWorkers that can utilize various LLMs. It offers two primary endpoints:
 
-This is the base PyWorker for HuggingFace Text Generation Inference (TGI) servers. See the [Serverless documentation](https://docs.vast.ai/serverless) for guides and how-to's.
+1. `generate`: Generates the LLM's response to a given prompt in a single request.
+2. `generate_stream`: Streams the LLM's response token by token.
 
-## Instance Setup
+Both endpoints use the following API payload format:
-
-1. Pick a template
-
-This worker is compatible with any TGI backend. We have a template you can use or you can create your own.
-
-- [HuggingFace TGI](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=TGI%20(Serverless))
-
-The template can be configured via the template interface. You may want to change the model or startup arguments.
-
-2. Follow the [getting started guide](https://docs.vast.ai/documentation/serverless/quickstart) for help with configuring your serverless setup. For testing, we recommend that you use the default options presented by the web interface.
-
-## Client Setup (Demo)
-
-1. 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
-```
-
-## Using the Test Client
-
-The test client demonstrates both streaming and non-streaming generation using TGI's native API.
-
-First, set your API key as an environment variable:
-
-```bash
-export VAST_API_KEY=<your_api_key>
-```
-
-The `--endpoint` flag is optional. If not provided, it defaults to `my-tgi-endpoint`.
-
-### Generate (Streaming)
-
-Call to `/generate_stream` with streaming response:
-
-```bash
-python -m workers.tgi.client --generate-stream --endpoint <ENDPOINT_NAME>
-```
-
-### Generate (Non-Streaming)
-
-Call to `/generate` with json response:
-
-```bash
-python -m workers.tgi.client --generate --endpoint <ENDPOINT_NAME>
-```
-
-### Interactive Session (Streaming)
-
-Interactive session with streaming responses. Type `quit` to exit.
-
-```bash
-python -m workers.tgi.client --interactive --endpoint <ENDPOINT_NAME>
-```
-
-## API Endpoints
-
-TGI provides two primary endpoints:
-
-### Generate (Non-Streaming)
-
-`/generate` - Returns the complete response in a single request.
 
 ```json
 {
-  "inputs": "Your prompt here",
+  "inputs": "PROMPT",
   "parameters": {
-    "max_new_tokens": 1024,
+    "max_new_tokens": 250
-    "temperature": 0.7,
-    "return_full_text": false
   }
 }
 ```
 
-### Generate Stream (Streaming)
+Note that the max_new_tokens parameter, rather than the prompt size, impacts performance. For example, if an
-
+instance is benchmarked to process 100 tokens per second, a request with max_new_tokens = 200 will take
-`/generate_stream` - Streams the response token by token.
+approximately 2 seconds to complete.
-
-```json
-{
-  "inputs": "Your prompt here",
-  "parameters": {
-    "max_new_tokens": 1024,
-    "temperature": 0.7,
-    "do_sample": true,
-    "return_full_text": false
-  }
-}
-```
-
-## Performance Notes
-
-The `max_new_tokens` parameter (not the prompt size) primarily impacts performance. For example, if an instance is benchmarked to process 100 tokens per second, a request with `max_new_tokens = 200` will take approximately 2 seconds to complete.

workers/tgi/client.py

@@ -1,222 +1,61 @@
-import logging
-import json
-import os
-import sys
-import argparse
-
 from vastai import Serverless
 import asyncio
 
-# ---------------------- Logging ----------------------
+ENDPOINT_NAME = "my-tgi-endpoint" # Change this to match your endpoint name
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s[%(levelname)-5s] %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
-log = logging.getLogger(__file__)
-
-# ---------------------- Defaults ----------------------
-DEFAULT_PROMPT = "Think step by step: Tell me about the Python programming language."
-
-ENDPOINT_NAME = "TGI-Prod2"       # change this to your TGI endpoint name
 MAX_TOKENS = 1024
-DEFAULT_TEMPERATURE = 0.7
+PROMPT = "Think step by step: Tell me about the Python programming language."
 
-
+async def call_generate(client: Serverless) -> None:
-# ---------------------- API Calls ----------------------
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
-async def call_generate(client: Serverless, *, endpoint_name: str, prompt: str, **kwargs) -> dict:
-    """Non-streaming generation via /generate endpoint"""
-    endpoint = await client.get_endpoint(name=endpoint_name)
 
     payload = {
-        "inputs": prompt,
+        "inputs": PROMPT,
         "parameters": {
-            "max_new_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+            "max_new_tokens": MAX_TOKENS,
-            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+            "temperature": 0.7,
-            "return_full_text": False,
+            "return_full_text": False
         }
     }
-    log.debug("POST /generate %s", json.dumps(payload)[:500])
+
-    resp = await endpoint.request("/generate", payload, cost=payload["parameters"]["max_new_tokens"])
+    resp = await endpoint.request("/generate", payload, cost=MAX_TOKENS)
-    return resp["response"]
+
+    print(resp["response"]["generated_text"])
 
 
-async def call_generate_stream(client: Serverless, *, endpoint_name: str, prompt: str, **kwargs):
+async def call_generate_stream(client: Serverless) -> None:
-    """Streaming generation via /generate_stream endpoint"""
+    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
-    endpoint = await client.get_endpoint(name=endpoint_name)
 
     payload = {
-        "inputs": prompt,
+        "inputs": PROMPT,
         "parameters": {
-            "max_new_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+            "max_new_tokens": MAX_TOKENS,
-            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+            "temperature": 0.7,
             "do_sample": True,
             "return_full_text": False,
         }
     }
-    log.debug("STREAM /generate_stream %s", json.dumps(payload)[:500])
+
     resp = await endpoint.request(
         "/generate_stream",
         payload,
-        cost=payload["parameters"]["max_new_tokens"],
+        cost=MAX_TOKENS,
         stream=True,
     )
-    return resp["response"]  # async generator
+    stream = resp["response"]
 
-
-# ---------------------- Demo Runner ----------------------
-class APIDemo:
-    """Demo and testing functionality for the TGI API client"""
-
-    def __init__(self, client: Serverless, endpoint_name: str):
-        self.client = client
-        self.endpoint_name = endpoint_name
-
-    async def handle_streaming_response(self, stream) -> str:
-        """Process streaming response and print tokens"""
-        full_response = ""
     printed_answer = False
-
     async for event in stream:
         tok = (event.get("token") or {}).get("text")
         if tok:
             if not printed_answer:
                 printed_answer = True
-                    print("\n💬 Response: ", end="", flush=True)
+                print("Answer:\n", end="", flush=True)
             print(tok, end="", flush=True)
-                full_response += tok
 
-        print()  # newline
+async def main():
-        if printed_answer:
-            print(f"\nStreaming completed. Response tokens: {len(full_response.split())}")
-
-        return full_response
-
-    async def demo_generate(self) -> None:
-        """Demo non-streaming generation"""
-        print("=" * 60)
-        print("GENERATE DEMO (NON-STREAMING)")
-        print("=" * 60)
-
-        response = await call_generate(
-            client=self.client,
-            endpoint_name=self.endpoint_name,
-            prompt=DEFAULT_PROMPT,
-            max_tokens=MAX_TOKENS,
-            temperature=DEFAULT_TEMPERATURE,
-        )
-        
-        print(f"\n💬 Response: {response.get('generated_text', '')}")
-        print(f"\nFull Response:\n{json.dumps(response, indent=2)}")
-
-    async def demo_generate_stream(self) -> None:
-        """Demo streaming generation"""
-        print("=" * 60)
-        print("GENERATE DEMO (STREAMING)")
-        print("=" * 60)
-
-        stream = await call_generate_stream(
-            client=self.client,
-            endpoint_name=self.endpoint_name,
-            prompt=DEFAULT_PROMPT,
-            max_tokens=MAX_TOKENS,
-            temperature=DEFAULT_TEMPERATURE,
-        )
-
-        try:
-            await self.handle_streaming_response(stream)
-        except Exception as e:
-            log.error("\nError during streaming: %s", e, exc_info=True)
-
-    async def interactive_chat(self) -> None:
-        """Interactive session with streaming generation"""
-        print("=" * 60)
-        print("INTERACTIVE STREAMING SESSION")
-        print("=" * 60)
-        print(f"Using endpoint: {self.endpoint_name}")
-        print("Type 'quit' to exit")
-        print()
-
-        while True:
-            try:
-                user_input = input("You: ").strip()
-
-                if user_input.lower() == "quit":
-                    print("👋 Goodbye!")
-                    break
-                elif not user_input:
-                    continue
-
-                print("Assistant: ", end="", flush=True)
-                stream = await call_generate_stream(
-                    client=self.client,
-                    endpoint_name=self.endpoint_name,
-                    prompt=user_input,
-                    max_tokens=MAX_TOKENS,
-                    temperature=DEFAULT_TEMPERATURE,
-                )
-
-                full_response = ""
-                async for event in stream:
-                    tok = (event.get("token") or {}).get("text")
-                    if tok:
-                        print(tok, end="", flush=True)
-                        full_response += tok
-                print()  # newline
-
-            except KeyboardInterrupt:
-                print("\n👋 Session interrupted. Goodbye!")
-                break
-            except Exception as e:
-                log.error("\nError: %s", e)
-                continue
-
-
-# ---------------------- CLI ----------------------
-def build_arg_parser() -> argparse.ArgumentParser:
-    p = argparse.ArgumentParser(description="Vast TGI Demo (Serverless SDK)")
-    p.add_argument("--endpoint", default=ENDPOINT_NAME, help=f"Vast endpoint name (default: {ENDPOINT_NAME})")
-
-    modes = p.add_mutually_exclusive_group(required=False)
-    modes.add_argument("--generate", action="store_true", help="Test generate endpoint (non-streaming)")
-    modes.add_argument("--generate-stream", action="store_true", help="Test generate endpoint with streaming")
-    modes.add_argument("--interactive", action="store_true", help="Start interactive streaming session")
-    return p
-
-
-async def main_async():
-    args = build_arg_parser().parse_args()
-
-    selected = sum([args.generate, args.generate_stream, args.interactive])
-    if selected == 0:
-        print("Please specify exactly one test mode:")
-        print("  --generate        : Test generate endpoint (non-streaming)")
-        print("  --generate-stream : Test generate endpoint with streaming")
-        print("  --interactive     : Start interactive streaming session")
-        print(f"\nExample: python {os.path.basename(sys.argv[0])} --generate-stream --endpoint my-tgi-endpoint")
-        sys.exit(1)
-    elif selected > 1:
-        print("Please specify exactly one test mode")
-        sys.exit(1)
-
-    print("=" * 60)
-    print(f"Using endpoint: {args.endpoint}")
-
-    try:
     async with Serverless() as client:
-            demo = APIDemo(client, args.endpoint)
+        await call_generate(client)
-
+        await call_generate_stream(client)
-            if args.generate:
-                await demo.demo_generate()
-            elif args.generate_stream:
-                await demo.demo_generate_stream()
-            elif args.interactive:
-                await demo.interactive_chat()
-
-    except Exception as e:
-        log.error("Error during test: %s", e, exc_info=True)
-        sys.exit(1)
-
 
 if __name__ == "__main__":
-    asyncio.run(main_async())
+    asyncio.run(main())

workers/wan/README.md

@@ -2,7 +2,7 @@
 
 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.
+Each request has a static cost of `100`. ComfyUI does not support concurrent workloads, and there is no provision to run multiple ComfyUI instances per worker node.
 
 ## Requirements