default endpoint name

update tgi client

requirements.txt

@@ -8,4 +8,4 @@ Requests~=2.32
 transformers~=4.52
 utils==1.0.*
 hf_transfer>=0.1.9
-git+https://github.com/vast-ai/vast-sdk.git@worker-sdk
+vastai-sdk>=0.2.0

start_server.sh

@@ -133,19 +133,8 @@ cd "$SERVER_DIR"
 echo "launching PyWorker server"
 
 set +e
-
+python3 -m "workers.$BACKEND.server" |& tee -a "$PYWORKER_LOG"
-# Try worker entrypoint first
-echo "trying workers.${BACKEND}.worker"
-python3 -m "workers.${BACKEND}.worker" |& tee -a "$PYWORKER_LOG"
 PY_STATUS=${PIPESTATUS[0]}
-
-# If that fails, fall back to server
-if [ "${PY_STATUS}" -ne 0 ]; then
-  echo "workers.${BACKEND}.worker failed with status ${PY_STATUS}, trying workers.${BACKEND}.server"
-  python3 -m "workers.${BACKEND}.server" |& tee -a "$PYWORKER_LOG"
-  PY_STATUS=${PIPESTATUS[0]}
-fi
-
 set -e
 
 if [ "${PY_STATUS}" -ne 0 ]; then
@@ -182,4 +171,4 @@ JSON
   done
 fi
 
-echo "launching PyWorker server done"
+echo "launching PyWorker server done"

workers/ace/worker.py

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

workers/comfyui-json/README.md

@@ -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 `1`.  ComfyUI does not handle concurrent workloads and there is no current provision to load multiple instances of ComfyUI per worker node.
+The cost for each request has a static value of `1`. 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 +18,88 @@ 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
@@ -212,11 +302,3 @@ WEBHOOK_TIMEOUT=30                   # Webhook timeout in seconds
   }
 }
 ```
-
-## Client Libraries
-
-See the test client examples for implementation details on how to integrate with the ComfyUI worker.
-
----
-
-See Vast's serverless documentation for more details on how to use ComfyUI with autoscaler.

workers/comfyui-json/client.py

@@ -1,35 +1,312 @@
-from .data_types import count_workload
+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=count_workload())
+    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/comfyui-json/server.py

@@ -4,6 +4,7 @@ import dataclasses
 import base64
 from typing import Optional, Union, Type
 
+import aiohttp
 from aiohttp import web, ClientResponse
 
 from lib.backend import Backend, LogAction
@@ -13,6 +14,7 @@ from .data_types import ComfyWorkflowData
 
 
 MODEL_SERVER_URL = os.getenv("MODEL_SERVER_URL", "http://127.0.0.1:18288")
+COMFYUI_URL = os.getenv("COMFYUI_URL", "http://127.0.0.1:18188")  # Raw ComfyUI server
 
 # This is the last log line that gets emitted once comfyui+extensions have been fully loaded
 MODEL_SERVER_START_LOG_MSG = "To see the GUI go to: "
@@ -108,8 +110,39 @@ async def handle_ping(_):
     return web.Response(body="pong")
 
 
+async def handle_view(request: web.Request) -> web.Response:
+    """Proxy /view requests to raw ComfyUI server to fetch generated images"""
+    # Forward query params to raw ComfyUI (not the API wrapper)
+    query_string = request.query_string
+    url = f"{COMFYUI_URL}/view?{query_string}"
+    
+    log.debug(f"Proxying /view request to: {url}")
+    
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url) as resp:
+                if resp.status == 200:
+                    content = await resp.read()
+                    return web.Response(
+                        body=content,
+                        status=200,
+                        content_type=resp.content_type or "image/png"
+                    )
+                else:
+                    text = await resp.text()
+                    return web.Response(
+                        text=text,
+                        status=resp.status,
+                        content_type="text/plain"
+                    )
+    except Exception as e:
+        log.error(f"Error proxying /view: {e}")
+        return web.Response(text=str(e), status=500)
+
+
 routes = [
     web.post("/generate/sync", backend.create_handler(ComfyWorkflowHandler())),
+    web.get("/view", handle_view),
     web.get("/ping", handle_ping),
 ]
 

workers/comfyui-json/worker.py

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

workers/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 = {
         "input": {
@@ -113,9 +113,9 @@ async def call_completions(client: Serverless, *, model: str, prompt: str, **kwa
     resp = await endpoint.request("/v1/completions", payload, cost=payload["input"]["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 = {
         "input": {
@@ -132,9 +132,9 @@ 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 = {
         "input": {
@@ -150,9 +150,9 @@ async def stream_completions(client: Serverless, *, model: str, prompt: str, **k
     resp = await endpoint.request("/v1/completions", payload, cost=payload["input"]["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 = {
         "input": {
@@ -174,9 +174,10 @@ 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 -----
@@ -185,10 +186,15 @@ 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")
@@ -219,6 +225,8 @@ 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
     
@@ -231,6 +239,7 @@ class APIDemo:
             client=self.client,
             model=self.model,
             prompt=COMPLETIONS_PROMPT,
+            endpoint_name=self.endpoint_name,
             max_tokens=MAX_TOKENS,
             temperature=DEFAULT_TEMPERATURE,
         )
@@ -249,6 +258,7 @@ class APIDemo:
                 client=self.client,
                 model=self.model, 
                 messages=messages,
+                endpoint_name=self.endpoint_name,
                 max_tokens=MAX_TOKENS,
                 temperature=DEFAULT_TEMPERATURE
             )
@@ -261,6 +271,7 @@ class APIDemo:
                 client=self.client,
                 model=self.model, 
                 messages=messages,
+                endpoint_name=self.endpoint_name,
                 max_tokens=MAX_TOKENS,
                 temperature=DEFAULT_TEMPERATURE
             )
@@ -287,6 +298,7 @@ class APIDemo:
                 client=self.client,
                 model=self.model,
                 messages=messages,
+                endpoint_name=self.endpoint_name,
                 tools=minimal_tool,
                 tool_choice="none",
                 max_tokens=10
@@ -312,6 +324,7 @@ 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,
@@ -389,6 +402,7 @@ class APIDemo:
             client=self.client,
             model=self.model,
             messages=messages,
+            endpoint_name=self.endpoint_name,
             max_tokens=MAX_TOKENS,
             temperature=DEFAULT_TEMPERATURE,
         )
@@ -427,7 +441,6 @@ 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()
 
@@ -453,7 +466,8 @@ 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
                 )
@@ -473,8 +487,8 @@ 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")
@@ -502,12 +516,14 @@ 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/server.py

@@ -35,6 +35,7 @@ backend = Backend(
     model_server_url=os.environ["MODEL_SERVER_URL"],
     model_log_file=os.environ["MODEL_LOG"],
     allow_parallel_requests=True,
+    max_wait_time=600.0,
     benchmark_handler=CompletionsHandler(benchmark_runs=3, benchmark_words=256),
     log_actions=[
         *[(LogAction.ModelLoaded, info_msg) for info_msg in MODEL_SERVER_START_LOG_MSG],

workers/openai/worker.py

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

workers/tgi/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

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

workers/wan/worker.py

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