Collapse null pyworker client to a single mode parameterized by --count

Now that the session model means no HTTP connection is held during the
reservation, the dichotomy between "single reserve" and "trapezoid demo"
collapses — both are "open N sessions, each held for H seconds, started
I seconds apart, close." Replace --reserve/--demo/--duration/--plateau
with --count/--hold/--interval. --session-cost becomes --cost.

Client is now 64 lines (down from 120).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

requirements.txt

@@ -1,11 +1,2 @@
-aiohttp[speedups]==3.10.1
+vastai-sdk>=0.3.0
-anyio~=4.4
+nltk==3.9.4 
-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
-git+https://github.com/vast-ai/vast-sdk.git@session

start_server.sh

@@ -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"
 
@@ -46,6 +53,42 @@ JSON
     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!"
@@ -63,7 +106,8 @@ echo_var DEBUG_LOG
 echo_var PYWORKER_LOG
 echo_var MODEL_LOG
 
-if [ -e "$MODEL_LOG" ]; then
+ROTATE_MODEL_LOG="${ROTATE_MODEL_LOG:-false}"
+if [ "$ROTATE_MODEL_LOG" = "true" ] && [ -e "$MODEL_LOG" ]; then
     echo "Rotating model log at $MODEL_LOG to $MODEL_LOG.old"
     if ! cat "$MODEL_LOG" >> "$MODEL_LOG.old"; then
         report_error_and_exit "Failed to rotate model log"
@@ -84,8 +128,21 @@ if ! grep -q "VAST" /etc/environment; then
     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
         if ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
@@ -104,17 +161,34 @@ then
         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 ${PYWORKER_REF:-} ]]; then
-        if ! (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF"); then
+        if [ "$FORCE_UPDATE" = true ]; then
-            report_error_and_exit "Failed to checkout pyworker reference: $PYWORKER_REF"
+            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
 
     if ! uv venv --python-preference only-managed "$ENV_PATH" -p 3.10; then
         report_error_and_exit "Failed to create virtual environment"
     fi
-
+    
     if ! source "$ENV_PATH/bin/activate"; then
         report_error_and_exit "Failed to activate virtual environment"
     fi
@@ -123,6 +197,8 @@ 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
@@ -132,11 +208,44 @@ else
             report_error_and_exit "Failed to source uv environment"
         fi
     fi
-    if ! source "$WORKSPACE_DIR/worker-env/bin/activate"; then
+    if ! source "$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
+
+# 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
@@ -174,16 +283,51 @@ EOF
         report_error_and_exit "Failed to generate SSL certificate request"
     fi
 
-    if ! 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=$CONTAINER_ID" > /etc/instance.crt; then
+        http_code=$(curl -sS -o /etc/instance.crt -w '%{http_code}' \
-        report_error_and_exit "Failed to sign SSL certificate"
+            --header 'Content-Type: application/octet-stream' \
-    fi
+            --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
 
+# ─── 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
@@ -195,19 +339,19 @@ set +e
 PY_STATUS=1
 
 if [ -f "$SERVER_DIR/worker.py" ]; then
-    echo "trying worker.py"
+    echo "Running worker.py"
     python3 -m "worker" |& tee -a "$PYWORKER_LOG"
     PY_STATUS=${PIPESTATUS[0]}
 fi
 
 if [ "${PY_STATUS}" -ne 0 ] && [ -f "$SERVER_DIR/workers/$BACKEND/worker.py" ]; then
-    echo "trying workers.${BACKEND}.worker"
+    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/$BACKEND/server.py" ]; then
-    echo "trying workers.${BACKEND}.server"
+    echo "Running workers.${BACKEND}.server"
     python3 -m "workers.${BACKEND}.server" |& tee -a "$PYWORKER_LOG"
     PY_STATUS=${PIPESTATUS[0]}
 fi
@@ -221,4 +365,4 @@ if [ "${PY_STATUS}" -ne 0 ]; then
     report_error_and_exit "PyWorker exited with status ${PY_STATUS}"
 fi
 
-echo "launching PyWorker server done"
+echo "PyWorker bootstrap complete"

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 `100`. 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 `1000`. 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

@@ -1,8 +1,16 @@
 # ComfyUI PyWorker
 
-This is the base PyWorker for ComfyUI. It provides a unified interface for running any ComfyUI workflow through a proxy-based architecture.
+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 `100`.  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
+
+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
 
@@ -10,6 +18,137 @@ This worker requires both [ComfyUI](https://github.com/comfyanonymous/ComfyUI) a
 
 A docker image is provided but you may use any if the above requirements are met.
 
+## 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:
@@ -170,4 +309,4 @@ See the client example for implementation details on how to integrate with the C
 
 ---
 
-See Vast's serverless documentation for more details on how to use ComfyUI with autoscaler.
+See Vast's serverless documentation for more details on how to use ComfyUI with autoscaler.

workers/comfyui-json/client.py

@@ -1,34 +1,312 @@
+import os
+import sys
+import json
 import uuid
 import random
 import asyncio
-import random
+import logging
+import argparse
+import aiohttp
 
 from vastai import Serverless
 
-async def main():
+# ---------------------- Config ----------------------
-    async with Serverless() as client:
+DEFAULT_PROMPT = "a beautiful sunset over mountains, digital art, highly detailed"
-        endpoint = await client.get_endpoint(name="my-comfy-endpoint") # Change this to your endpoint name
+ENDPOINT_NAME = "my-comfyui-endpoint"
+DEFAULT_WIDTH = 512
+DEFAULT_HEIGHT = 512
+DEFAULT_STEPS = 20
+COST = 100  # Fixed cost for ComfyUI requests
 
-        payload = {
+# Optional S3 Configuration (from environment variables)
-            "input": {
+S3_ENDPOINT_URL = os.getenv("S3_ENDPOINT_URL")
-                "request_id": str(uuid.uuid4()),
+S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME")
-                "modifier": "Text2Image",
+S3_ACCESS_KEY_ID = os.getenv("S3_ACCESS_KEY_ID")
-                "modifications": {
+S3_SECRET_ACCESS_KEY = os.getenv("S3_SECRET_ACCESS_KEY")
-                    "prompt": "a beautiful landscape with mountains and lakes",
+
-                    "width": 1024,
+logging.basicConfig(level=logging.INFO, format="%(levelname)s - %(message)s")
-                    "height": 1024,
+log = logging.getLogger(__name__)
-                    "steps": 20,
+
-                    "seed": random.randint(0, 2**32 - 1)
+
-                },
+def get_s3_client():
-                "workflow_json": {}  # Empty since using modifier approach
+    """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,
+                "width": width,
+                "height": height,
+                "steps": steps,
+                "seed": seed,
+            },
         }
-        
+    }
-        response = await endpoint.request("/generate/sync", payload, cost=100)
+    return await endpoint.request("/generate/sync", payload, cost=COST)
+
+
+async def call_generate_workflow(
+    client: Serverless,
+    *,
+    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)
 
-        # Get the file from the path on the local machine using SCP or SFTP
-        # or configure S3 to upload to cloud storage.
-        print(response["response"]["output"][0]["local_path"])
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main_async())

workers/null/README.md

@@ -0,0 +1,88 @@
+# Null PyWorker
+
+Holds Vast Serverless reservations open without forwarding any work to a
+model. Use it when your real workload (a queue consumer in any language)
+runs as a separate process on the instance and you just want to drive
+Vast autoscaling: **one POST reserves a worker, one POST releases it.**
+
+## Use case
+
+You have a job queue on your own infrastructure (Redis, SQS, NATS, etc.)
+and a consumer (node, golang, python, a binary — anything) that pulls
+from it. You want one Vast worker per unit of in-flight work, scaling
+elastically from zero. The null PyWorker is the autoscaling driver; your
+consumer does the work.
+
+## How it works
+
+Reservations use the framework's session API. The SDK's
+`endpoint.session(...)` POSTs `/session/create` to reserve a worker;
+`session.close()` POSTs `/session/end` to release it. `max_sessions=1`
+means each worker holds exactly one reservation — the next reservation
+either lands on a free worker or triggers a scale-up.
+
+The PyWorker itself does nothing functional:
+
+- One trivial `/ping` route to satisfy the framework's benchmark
+  requirement (its `max_perf` is pinned to 100).
+- An internal `/release` endpoint on `127.0.0.1:18999` for the local
+  consumer to end the session without needing `session_auth`.
+
+## Endpoint parameters
+
+Tested working configuration:
+
+| Parameter | Value | Why |
+|---|---|---|
+| `target_util` | `1.0` | One session = one worker. Default `0.9` rounds up to an extra worker. |
+| `min_load` | `0` | Scale-to-zero floor. |
+| `max_queue_time` | `1` | Stop routing to an occupied worker after ~1s of implied queue. |
+| `target_queue_time` | `0.5` | Trigger scale-up promptly once anything queues. |
+| `inactivity_timeout` | `10` (seconds) | Permit scale-to-zero after 10s idle. |
+
+## API
+
+| Route | Where | Use |
+|---|---|---|
+| `POST /session/create` | endpoint, signed | Reserve a worker (`endpoint.session(...)`) |
+| `POST /session/end` | endpoint, signed | Release (`session.close()`) |
+| `POST /release` | `127.0.0.1:18999`, no auth | Local consumer release, no `session_auth` needed |
+
+## Healthcheck
+
+Default: stub on `127.0.0.1:18999/health` returning `200`. Set
+`BACKEND_HEALTH_URL=http://127.0.0.1:9090/health` (absolute URL) to point
+the framework at your queue consumer's health endpoint instead — if the
+consumer dies, the autoscaler sees the worker as broken.
+
+## Deploying
+
+1. Point `PYWORKER_REPO` at this repo (or your fork).
+2. Set `BACKEND=null` in the template.
+3. Run your queue consumer alongside the PyWorker. When it's done with
+   a unit of work:
+   ```bash
+   curl -X POST http://127.0.0.1:18999/release
+   ```
+
+## Client demo
+
+```bash
+# Single reservation, hold 180s
+python -m workers.null.client --endpoint <NAME> --instance alpha
+
+# Three concurrent reservations, started 30s apart, each held 360s
+python -m workers.null.client --endpoint <NAME> --instance alpha --count 3 --hold 360
+```
+
+Flags: `--count` (number of concurrent sessions, default 1), `--hold`
+(seconds each session is held, default 180), `--interval` (seconds
+between starts when `--count > 1`, default 30), `--cost` (cost reported
+at session-create, default 100 = `max_perf`), `--instance` (`prod` |
+`alpha` | `candidate` | `local`).
+
+## Environment variables
+
+- `BACKEND_HEALTH_URL` — absolute URL the framework healthchecks. Stub
+  is used when unset.
+- `NULL_CONTROL_PORT` — internal control server port. Defaults to `18999`.

workers/null/client.py

@@ -0,0 +1,64 @@
+import argparse
+import asyncio
+import logging
+import os
+import sys
+
+from vastai import Serverless
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s[%(levelname)-5s] %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+log = logging.getLogger(__file__)
+
+
+async def reserve(client: Serverless, endpoint_name: str, hold: float, cost: int, label: str):
+    endpoint = await client.get_endpoint(name=endpoint_name)
+    async with await endpoint.session(cost=cost, lifetime=hold + 60) as s:
+        sid = s.session_id
+        log.info("[%s] %s open, holding %.0fs", label, sid, hold)
+        await asyncio.sleep(hold)
+    log.info("[%s] %s closed", label, sid)
+
+
+async def main_async():
+    p = argparse.ArgumentParser(description="Vast Null PyWorker demo client")
+    p.add_argument("--endpoint", default=os.environ.get("VAST_ENDPOINT", "null-prod"))
+    p.add_argument("--instance", choices=("prod", "alpha", "candidate", "local"),
+                   default=os.environ.get("VAST_INSTANCE", "prod"))
+    p.add_argument("--count", type=int, default=1,
+                   help="concurrent sessions to open (default: 1)")
+    p.add_argument("--interval", type=float, default=30.0,
+                   help="seconds between session starts when count>1 (default: 30)")
+    p.add_argument("--hold", type=float, default=180.0,
+                   help="seconds to hold each session (default: 180)")
+    p.add_argument("--cost", type=int, default=100,
+                   help="cost reported at session-create (default: 100)")
+    args = p.parse_args()
+
+    print(f"endpoint={args.endpoint} instance={args.instance} "
+          f"count={args.count} hold={args.hold}s cost={args.cost}")
+
+    try:
+        async with Serverless(instance=args.instance) as client:
+            tasks = []
+            for i in range(args.count):
+                label = f"res-{i+1}" if args.count > 1 else "reservation"
+                tasks.append(asyncio.create_task(
+                    reserve(client, args.endpoint, args.hold, args.cost, label),
+                    name=label,
+                ))
+                if i + 1 < args.count:
+                    await asyncio.sleep(args.interval)
+            await asyncio.gather(*tasks, return_exceptions=True)
+    except KeyboardInterrupt:
+        log.info("Interrupted")
+    except Exception as e:
+        log.error("Error: %s", e, exc_info=True)
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    asyncio.run(main_async())

workers/null/worker.py

@@ -0,0 +1,143 @@
+import asyncio
+import logging
+import os
+from contextlib import asynccontextmanager
+from urllib.parse import urlsplit
+
+from aiohttp import web
+
+from vastai import (
+    Worker,
+    WorkerConfig,
+    HandlerConfig,
+    BenchmarkConfig,
+    LogActionConfig,
+)
+
+log = logging.getLogger(__file__)
+
+TARGET_PERF = 100.0
+BENCHMARK_SENTINEL = "__null_worker_benchmark__"
+
+INTERNAL_HOST = "127.0.0.1"
+INTERNAL_PORT = int(os.environ.get("NULL_CONTROL_PORT", 18999))
+STUB_HEALTH_PATH = "/health"
+
+BACKEND_HEALTH_URL = os.environ.get("BACKEND_HEALTH_URL", "").strip()
+if BACKEND_HEALTH_URL:
+    _p = urlsplit(BACKEND_HEALTH_URL)
+    if not _p.scheme or not _p.hostname:
+        raise ValueError(f"BACKEND_HEALTH_URL must be absolute, got: {BACKEND_HEALTH_URL!r}")
+    HEALTH_BASE_URL = f"{_p.scheme}://{_p.hostname}"
+    HEALTH_PORT = _p.port or (443 if _p.scheme == "https" else 80)
+    HEALTH_PATH = _p.path or "/"
+    USE_STUB_HEALTH = False
+else:
+    HEALTH_BASE_URL = f"http://{INTERNAL_HOST}"
+    HEALTH_PORT = INTERNAL_PORT
+    HEALTH_PATH = STUB_HEALTH_PATH
+    USE_STUB_HEALTH = True
+
+
+_backend_ref: dict = {"backend": None}
+
+
+def _build_internal_app() -> web.Application:
+    app = web.Application()
+
+    async def release_handler(_request: web.Request) -> web.Response:
+        # Closes the singleton session. Uses name-mangled __close_session
+        # to bypass the session_auth check — safe because this server is
+        # bound to 127.0.0.1, and it spares the consumer from threading
+        # session_auth through its queue.
+        backend = _backend_ref.get("backend")
+        if backend is None:
+            return web.json_response({"released": False, "reason": "backend not ready"}, status=503)
+        sids = list(backend.sessions.keys())
+        if not sids:
+            return web.json_response({"released": False, "reason": "no active session"}, status=200)
+        closed = []
+        for sid in sids:
+            try:
+                if await backend._Backend__close_session(sid):
+                    closed.append(sid)
+            except Exception as e:
+                log.warning(f"Error closing session {sid}: {e}")
+        return web.json_response({"released": bool(closed), "session_ids": closed}, status=200)
+
+    app.router.add_post("/release", release_handler)
+
+    if USE_STUB_HEALTH:
+        async def stub_health(_request: web.Request) -> web.Response:
+            return web.Response(status=200, text="ok")
+        app.router.add_get(STUB_HEALTH_PATH, stub_health)
+
+    return app
+
+
+@asynccontextmanager
+async def null_lifecycle():
+    # Pin max_throughput to TARGET_PERF exactly — the framework's
+    # __run_benchmark short-circuits to float(file_contents) if this exists.
+    try:
+        with open(".has_benchmark", "w") as fh:
+            fh.write(str(int(TARGET_PERF)))
+    except OSError as e:
+        log.warning(f"Could not pin benchmark cache: {e}")
+
+    runner = web.AppRunner(_build_internal_app())
+    await runner.setup()
+    await web.TCPSite(runner, INTERNAL_HOST, INTERNAL_PORT).start()
+
+    log.info(
+        "Null pyworker control server: http://%s:%d (POST /release%s)",
+        INTERNAL_HOST,
+        INTERNAL_PORT,
+        f", GET {STUB_HEALTH_PATH}" if USE_STUB_HEALTH else "",
+    )
+    if not USE_STUB_HEALTH:
+        log.info("Framework healthcheck → %s", BACKEND_HEALTH_URL)
+
+    try:
+        yield
+    finally:
+        await runner.cleanup()
+
+
+async def ping(**params: object) -> dict:
+    # Exists only to satisfy the framework's "at least one handler with a
+    # BenchmarkConfig" requirement. Sleep 1s on the benchmark path as a
+    # fallback in case the .has_benchmark cache pin failed; otherwise the
+    # benchmark cache short-circuits and this never runs.
+    if params.get(BENCHMARK_SENTINEL):
+        await asyncio.sleep(1.0)
+        return {"ok": True, "benchmark": True}
+    return {"ok": True}
+
+
+worker_config = WorkerConfig(
+    model_server_url=HEALTH_BASE_URL,
+    model_server_port=HEALTH_PORT,
+    model_healthcheck_url=HEALTH_PATH,
+    lifecycle=null_lifecycle(),
+    max_sessions=1,
+    handlers=[
+        HandlerConfig(
+            route="/ping",
+            allow_parallel_requests=True,
+            remote_function=ping,
+            workload_calculator=lambda _payload: TARGET_PERF,
+            benchmark_config=BenchmarkConfig(
+                generator=lambda: {BENCHMARK_SENTINEL: True},
+                runs=1,
+                concurrency=1,
+                do_warmup=False,
+            ),
+        ),
+    ],
+    log_action_config=LogActionConfig(),
+)
+
+_worker = Worker(worker_config)
+_backend_ref["backend"] = _worker.backend
+_worker.run()

workers/openai/README.md

@@ -8,14 +8,13 @@ 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%2B%20Qwen%2FQwen3-8B%20(Serverless)) (recommended)
+- [vLLM](https://cloud.vast.ai/?ref_id=62897&creator_id=62897&name=vLLM%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/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.
+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)
 
@@ -34,38 +33,20 @@ uv pip install -r requirements.txt
 
 Several examples have been provided in the client to help you get started with your own implementation.
 
-### Completions
+First, set your API key as an environment variable:
-
-Call to `/v1/completions` with json response
 
 ```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --completion --model <MODEL_NAME>
+export VAST_API_KEY=<your_api_key>
 ```
 
-### Chat Completion (json)
+The `--model` and `--endpoint` flags are optional. If not provided, they default to `Qwen/Qwen3-8B` and `my-vllm-endpoint` respectively.
-
-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>
+python -m workers.openai.client --chat-stream --endpoint <ENDPOINT_NAME> --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)
@@ -75,6 +56,32 @@ 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>
+python -m workers.openai.client --interactive --endpoint <ENDPOINT_NAME> --model <MODEL_NAME>
+```
+
+### Chat Completion (json)
+
+Call to `/v1/chat/completions` with json response
+
+```bash
+python -m workers.openai.client --chat --endpoint <ENDPOINT_NAME> --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 --tools --endpoint <ENDPOINT_NAME> --model <MODEL_NAME>
+```
+
+### Completions
+
+Call to `/v1/completions` with json response
+
+```bash
+python -m workers.openai.client --completion --endpoint <ENDPOINT_NAME> --model <MODEL_NAME>
 ```
 

workers/openai/client.py

@@ -18,7 +18,7 @@ logging.basicConfig(
 log = logging.getLogger(__file__)
 
 # ---------------------- Prompts ----------------------
-COMPLETIONS_PROMPT = "the capital of USA is"
+COMPLETIONS_PROMPT = "Zebras are primarily grazers and can subsist on lower-quality vegetation. They are preyed on mainly by"
 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, **kwargs) -> Dict[str, Any]:
+async def call_completions(client: Serverless, *, model: str, prompt: str, endpoint_name: 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, **kwa
     resp = await endpoint.request("/v1/completions", payload, cost=payload["max_tokens"])
     return resp["response"]
 
-async def call_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], **kwargs) -> Dict[str, Any]:
+async def call_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], endpoint_name: str, **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, **kwargs):
+async def stream_completions(client: Serverless, *, model: str, prompt: str, endpoint_name: 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, **k
     resp = await endpoint.request("/v1/completions", payload, cost=payload["max_tokens"], stream=True)
     return resp["response"]  # async generator
 
-async def stream_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], **kwargs):
+async def stream_chat_completions(client: Serverless, *, model: str, messages: List[Dict[str, Any]], endpoint_name: str, **kwargs):
 
-    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+    endpoint = await client.get_endpoint(name=endpoint_name)
 
     payload = {
         "model": model,
@@ -166,9 +166,10 @@ 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, tool_manager: Optional[ToolManager] = None):
+    def __init__(self, client: Serverless, model: str, endpoint_name: 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 -----
@@ -177,10 +178,15 @@ 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")
@@ -211,6 +217,8 @@ 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
     
@@ -223,6 +231,7 @@ class APIDemo:
             client=self.client,
             model=self.model,
             prompt=COMPLETIONS_PROMPT,
+            endpoint_name=self.endpoint_name,
             max_tokens=MAX_TOKENS,
             temperature=DEFAULT_TEMPERATURE,
         )
@@ -241,6 +250,7 @@ class APIDemo:
                 client=self.client,
                 model=self.model, 
                 messages=messages,
+                endpoint_name=self.endpoint_name,
                 max_tokens=MAX_TOKENS,
                 temperature=DEFAULT_TEMPERATURE
             )
@@ -253,6 +263,7 @@ class APIDemo:
                 client=self.client,
                 model=self.model, 
                 messages=messages,
+                endpoint_name=self.endpoint_name,
                 max_tokens=MAX_TOKENS,
                 temperature=DEFAULT_TEMPERATURE
             )
@@ -279,6 +290,7 @@ class APIDemo:
                 client=self.client,
                 model=self.model,
                 messages=messages,
+                endpoint_name=self.endpoint_name,
                 tools=minimal_tool,
                 tool_choice="none",
                 max_tokens=10
@@ -304,6 +316,7 @@ 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,
@@ -381,6 +394,7 @@ class APIDemo:
             client=self.client,
             model=self.model,
             messages=messages,
+            endpoint_name=self.endpoint_name,
             max_tokens=MAX_TOKENS,
             temperature=DEFAULT_TEMPERATURE,
         )
@@ -419,7 +433,6 @@ 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()
 
@@ -445,7 +458,8 @@ class APIDemo:
                 stream = await stream_chat_completions(
                     client=self.client,
                     model=self.model, 
-                    messages=messages, 
+                    messages=messages,
+                    endpoint_name=self.endpoint_name,
                     max_tokens=MAX_TOKENS, 
                     temperature=0.7
                 )
@@ -465,8 +479,8 @@ class APIDemo:
 # ---------------------- CLI ----------------------
 def build_arg_parser() -> argparse.ArgumentParser:
     p = argparse.ArgumentParser(description="Vast vLLM Demo (Serverless SDK)")
-    p.add_argument("--model", required=True, help="Model to use for requests (required)")
+    p.add_argument("--model", default=DEFAULT_MODEL, help=f"Model to use for requests (default: {DEFAULT_MODEL})")
-    p.add_argument("--endpoint", default="my-vllm-endpoint", help="Vast endpoint name (default: my-vllm-endpoint)")
+    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("--completion", action="store_true", help="Test completions endpoint")
@@ -494,12 +508,14 @@ async def main_async():
         print("Please specify exactly one test mode")
         sys.exit(1)
 
-    print(f"Using model: {args.model}")
     print("=" * 60)
+    print(f"Using model: {args.model}")
+    print(f"Using endpoint: {args.endpoint}")
+
 
     try:
         async with Serverless() as client:
-            demo = APIDemo(client, args.model, ToolManager())
+            demo = APIDemo(client, args.model, args.endpoint, ToolManager())
 
             if args.completion:
                 await demo.demo_completions()

workers/openai/worker.py

@@ -28,6 +28,12 @@ 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)))
@@ -54,18 +60,20 @@ worker_config = WorkerConfig(
             route="/v1/completions",
             workload_calculator= lambda data: data.get("max_tokens", 0),
             allow_parallel_requests=True,
-            max_queue_time=60.0,
+            request_parser=request_parser,
+            max_queue_time=600.0,
             benchmark_config=BenchmarkConfig(
                 generator=completions_benchmark_generator,
-                concurrency=100,
+                concurrency=10,
-                runs=2
+                runs=3
             )
         ),
         HandlerConfig(
             route="/v1/chat/completions",
             workload_calculator= lambda data: data.get("max_tokens", 0),
             allow_parallel_requests=True,
-            max_queue_time=60.0,
+            request_parser=request_parser,
+            max_queue_time=600.0,
         )
     ],
     log_action_config=LogActionConfig(

workers/tgi/README.md

@@ -1,19 +1,103 @@
-This is the base PyWorker for TGI, designed to create PyWorkers that can utilize various LLMs. It offers two primary endpoints:
+# HuggingFace TGI PyWorker
 
-1. `generate`: Generates the LLM's response to a given prompt in a single request.
+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.
-2. `generate_stream`: Streams the LLM's response token by token.
 
-Both endpoints use the following API payload format:
+## Instance Setup
+
+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": "PROMPT",
+  "inputs": "Your prompt here",
   "parameters": {
-    "max_new_tokens": 250
+    "max_new_tokens": 1024,
+    "temperature": 0.7,
+    "return_full_text": false
   }
 }
 ```
 
-Note that the max_new_tokens parameter, rather than the prompt size, impacts performance. For example, if an
+### Generate Stream (Streaming)
-instance is benchmarked to process 100 tokens per second, a request with max_new_tokens = 200 will take
+
-approximately 2 seconds to complete.
+`/generate_stream` - Streams the response token by token.
+
+```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

@@ -1,61 +1,222 @@
+import logging
+import json
+import os
+import sys
+import argparse
+
 from vastai import Serverless
 import asyncio
 
-ENDPOINT_NAME = "my-tgi-endpoint" # Change this to match your endpoint name
+# ---------------------- Logging ----------------------
-MAX_TOKENS = 1024
+logging.basicConfig(
-PROMPT = "Think step by step: Tell me about the Python programming language."
+    level=logging.DEBUG,
+    format="%(asctime)s[%(levelname)-5s] %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+log = logging.getLogger(__file__)
 
-async def call_generate(client: Serverless) -> None:
+# ---------------------- Defaults ----------------------
-    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+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
+
+
+# ---------------------- API Calls ----------------------
+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": MAX_TOKENS,
+            "max_new_tokens": kwargs.get("max_tokens", MAX_TOKENS),
-            "temperature": 0.7,
+            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
-            "return_full_text": False
+            "return_full_text": False,
         }
     }
-
+    log.debug("POST /generate %s", json.dumps(payload)[:500])
-    resp = await endpoint.request("/generate", payload, cost=MAX_TOKENS)
+    resp = await endpoint.request("/generate", payload, cost=payload["parameters"]["max_new_tokens"])
-
+    return resp["response"]
-    print(resp["response"]["generated_text"])
 
 
-async def call_generate_stream(client: Serverless) -> None:
+async def call_generate_stream(client: Serverless, *, endpoint_name: str, prompt: str, **kwargs):
-    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+    """Streaming generation via /generate_stream endpoint"""
+    endpoint = await client.get_endpoint(name=endpoint_name)
 
     payload = {
-        "inputs": PROMPT,
+        "inputs": prompt,
         "parameters": {
-            "max_new_tokens": MAX_TOKENS,
+            "max_new_tokens": kwargs.get("max_tokens", MAX_TOKENS),
-            "temperature": 0.7,
+            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
             "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=MAX_TOKENS,
+        cost=payload["parameters"]["max_new_tokens"],
         stream=True,
     )
-    stream = resp["response"]
+    return resp["response"]  # async generator
 
-    printed_answer = False
-    async for event in stream:
-        tok = (event.get("token") or {}).get("text")
-        if tok:
-            if not printed_answer:
-                printed_answer = True
-                print("Answer:\n", end="", flush=True)
-            print(tok, end="", flush=True)
 
-async def main():
+# ---------------------- Demo Runner ----------------------
-    async with Serverless() as client:
+class APIDemo:
-        await call_generate(client)
+    """Demo and testing functionality for the TGI API client"""
-        await call_generate_stream(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(tok, end="", flush=True)
+                full_response += tok
+
+        print()  # newline
+        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)
+
+            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())
+    asyncio.run(main_async())

workers/tgi/worker.py

@@ -35,7 +35,7 @@ def benchmark_generator() -> dict:
     benchmark_data = {
         "inputs": prompt,
         "parameters": {
-            "max_new_tokens": 128,
+            "max_new_tokens": 500,
             "temperature": 0.7,
             "return_full_text": False
         }
@@ -52,17 +52,18 @@ worker_config = WorkerConfig(
         HandlerConfig(
             route="/generate",
             allow_parallel_requests=True,
-            max_queue_time=60.0,
+            max_queue_time=600.0,
             benchmark_config=BenchmarkConfig(
                 generator=benchmark_generator,
-                concurrency=50
+                concurrency=10,
+                runs=3
             ),
             workload_calculator= lambda x: x["parameters"]["max_new_tokens"]
         ),
         HandlerConfig(
             route="/generate_stream",
             allow_parallel_requests=True,
-            max_queue_time=60.0,
+            max_queue_time=600.0,
             workload_calculator= lambda x: x["parameters"]["max_new_tokens"]
         )
     ],

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 `100`. 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 `10000`. ComfyUI does not support concurrent workloads, and there is no provision to run multiple ComfyUI instances per worker node.
 
 ## Requirements