update tgi client

defaults to ENDPOINT_NAME and DEFAULT_MODEL but uses the flag first

lib/backend.py

@@ -30,7 +30,7 @@ from lib.data_types import (
     BenchmarkResult
 )
 
-VERSION = "0.2.0"
+VERSION = "0.2.1"
 
 MSG_HISTORY_LEN = 100
 log = logging.getLogger(__file__)
@@ -235,14 +235,10 @@ class Backend:
             log.debug("No healthcheck endpoint defined, skipping healthcheck")
             return
 
-        first_healthcheck = True
         while True:
             await sleep(10)
             if self.__start_healthcheck is False:
                 continue
-            if first_healthcheck:
-                log.info(f"[healthcheck] First healthcheck starting (model is now loaded)")
-                first_healthcheck = False
             try:
                 log.debug(f"Performing healthcheck on {health_check_url}")
                 async with self.healthcheck_session.get(health_check_url) as response:
@@ -260,22 +256,9 @@ class Backend:
                 self.backend_errored(str(e))
 
     async def _start_tracking(self) -> None:
-        log.info("Starting tracking tasks (read_logs, send_metrics_loop, healthcheck, send_delete_requests_loop)")
-        task_names = ["read_logs", "send_metrics_loop", "healthcheck", "send_delete_requests_loop"]
-        results = await gather(
-            self.__read_logs(), 
-            self.metrics._send_metrics_loop(), 
-            self.__healthcheck(), 
-            self.metrics._send_delete_requests_loop(),
-            return_exceptions=True
+        await gather(
+            self.__read_logs(), self.metrics._send_metrics_loop(), self.__healthcheck(), self.metrics._send_delete_requests_loop()
         )
-        # If we get here, one or more tasks exited (they should run forever)
-        log.error(f"CRITICAL: _start_tracking gather returned! This should never happen. Results: {results}")
-        for name, result in zip(task_names, results):
-            if isinstance(result, Exception):
-                log.error(f"Tracking task '{name}' crashed with exception: {result}", exc_info=result)
-            elif result is not None:
-                log.warning(f"Tracking task '{name}' exited unexpectedly with result: {result}")
 
     def backend_errored(self, msg: str) -> None:
         self.metrics._model_errored(msg)
@@ -416,20 +399,15 @@ class Backend:
                         # await sleep(5)
                         try:
                             max_throughput = await run_benchmark()
-                            log.info(f"[benchmark] Benchmark complete, max_throughput={max_throughput}, setting healthcheck=True")
                             self.__start_healthcheck = True
                             self.metrics._model_loaded(
                                 max_throughput=max_throughput,
                             )
-                            log.info(f"[benchmark] _model_loaded() called, returning from handle_log_line")
                         except ClientConnectorError as e:
                             log.debug(
-                                f"failed to connect to model api during benchmark"
+                                f"failed to connect to comfyui api during benchmark"
                             )
                             self.backend_errored(str(e))
-                        except Exception as e:
-                            log.error(f"Unexpected error during benchmark: {e}", exc_info=True)
-                            self.backend_errored(f"Benchmark failed: {e}")
                     case LogAction.ModelError if msg in log_line:
                         log.debug(f"Got log line indicating error: {log_line}")
                         self.backend_errored(msg)
@@ -441,14 +419,10 @@ class Backend:
             log.debug(f"tailing file: {self.model_log_file}")
             async with await open_file(self.model_log_file, encoding='utf-8', errors='ignore') as f:
                 while True:
-                    try:
-                        line = await f.readline()
-                        if line:
-                            await handle_log_line(line.rstrip())
-                        else:
-                            await asyncio.sleep(LOG_POLL_INTERVAL)
-                    except Exception as e:
-                        log.error(f"Error processing log line: {e}", exc_info=True)
+                    line = await f.readline()
+                    if line:
+                        await handle_log_line(line.rstrip())
+                    else:
                         await asyncio.sleep(LOG_POLL_INTERVAL)
 
         ###########

lib/metrics.py

@@ -1,5 +1,4 @@
 import os
-import sys
 import time
 import logging
 import json
@@ -18,14 +17,6 @@ DELETE_REQUESTS_INTERVAL = 1
 log = logging.getLogger(__file__)
 
 
-def _flush_logs():
-    """Force flush all log handlers and stdout/stderr."""
-    for handler in logging.root.handlers:
-        handler.flush()
-    sys.stdout.flush()
-    sys.stderr.flush()
-
-
 @cache
 def get_url() -> str:
     use_ssl = os.environ.get("USE_SSL", "false") == "true"
@@ -128,41 +119,22 @@ class Metrics:
                 await self.__send_delete_requests_and_reset()
 
     async def _send_metrics_loop(self) -> Awaitable[NoReturn]:
-        loop_count = 0
-        first_loaded_send_done = False
         while True:
             await sleep(METRICS_UPDATE_INTERVAL)
-            loop_count += 1
             elapsed = time.time() - self.last_metric_update
-            # Log heartbeat every 30 seconds to confirm loop is running
-            if loop_count % 30 == 0:
-                log.debug(f"[heartbeat] metrics loop alive, loop_count={loop_count}, model_loaded={self.system_metrics.model_is_loaded}")
-                _flush_logs()
-            # Extra logging for first few iterations after model loads
-            if self.system_metrics.model_is_loaded and not first_loaded_send_done:
-                log.info(f"[transition] First iteration with model_loaded=True, loop_count={loop_count}, elapsed={elapsed:.1f}")
-                _flush_logs()
             if self.system_metrics.model_is_loaded is False and elapsed >= 10:
                 log.debug(f"sending loading model metrics after {int(elapsed)}s wait")
                 await self.__send_metrics_and_reset()
             elif self.update_pending or elapsed > 10:
                 log.debug(f"sending loaded model metrics after {int(elapsed)}s wait")
                 await self.__send_metrics_and_reset()
-                if self.system_metrics.model_is_loaded and not first_loaded_send_done:
-                    first_loaded_send_done = True
-                    log.info(f"[transition] First loaded metrics send complete, continuing to next iteration...")
-                    _flush_logs()
 
     def _model_loaded(self, max_throughput: float) -> None:
-        log.info(f"MODEL LOADED: Setting model_is_loaded=True, max_throughput={max_throughput}")
-        _flush_logs()
         self.system_metrics.model_loading_time = (
             time.time() - self.system_metrics.model_loading_start
         )
         self.system_metrics.model_is_loaded = True
         self.model_metrics.max_throughput = max_throughput
-        log.info(f"MODEL LOADED: model_loading_time={self.system_metrics.model_loading_time}")
-        _flush_logs()
 
     def _model_errored(self, error_msg: str) -> None:
         self.model_metrics.set_errored(error_msg)
@@ -299,7 +271,6 @@ class Metrics:
         ###########
 
         self.system_metrics.update_disk_usage()
-        had_loadtime = loadtime_snapshot is not None and loadtime_snapshot > 0
 
         sent = False
         for report_addr in self.report_addr:
@@ -308,14 +279,8 @@ class Metrics:
                 break
 
         if sent:
-            if had_loadtime:
-                log.info(f"FIRST LOADTIME METRICS SENT SUCCESSFULLY! loadtime={loadtime_snapshot}")
-                _flush_logs()
             # clear the one-shot loadtime only if we actually sent *this* value
             self.system_metrics.reset(expected=loadtime_snapshot)
             self.update_pending = False
             self.model_metrics.reset()
             self.last_metric_update = time.time()
-            if had_loadtime:
-                log.info(f"POST-SEND: reset complete, last_metric_update={self.last_metric_update}, continuing loop...")
-                _flush_logs()

lib/server.py

@@ -1,7 +1,5 @@
 import os
 import logging
-import signal
-import sys
 from typing import List
 import ssl
 from asyncio import run, gather
@@ -14,25 +12,7 @@ from aiohttp import web
 log = logging.getLogger(__file__)
 
 
-def _setup_signal_handlers():
-    """Setup signal handlers to log when process receives termination signals."""
-    def signal_handler(signum, frame):
-        sig_name = signal.Signals(signum).name
-        log.error(f"SIGNAL RECEIVED: {sig_name} ({signum}) - process is being terminated")
-        sys.stdout.flush()
-        sys.stderr.flush()
-        sys.exit(128 + signum)
-    
-    # Handle common termination signals
-    for sig in [signal.SIGTERM, signal.SIGINT, signal.SIGHUP]:
-        try:
-            signal.signal(sig, signal_handler)
-        except (OSError, ValueError):
-            pass  # Some signals may not be available
-
-
 def start_server(backend: Backend, routes: List[web.RouteDef], **kwargs):
-    _setup_signal_handlers()
     try:
         log.debug("getting certificate...")
         use_ssl = os.environ.get("USE_SSL", "false") == "true"

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
-
-Call to `/v1/completions` with json response
+First, set your API key as an environment variable:
 
 ```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)
-
-Call to `/v1/chat/completions` with json response
-
-```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --chat --model <MODEL_NAME>
-```
+The `--model` and `--endpoint` flags are optional. If not provided, they default to `Qwen/Qwen3-8B` and `my-vllm-endpoint` respectively.
 
 ### Chat Completion (streaming)
 
 Call to `/v1/chat/completions` with streaming response
 
 ```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --chat-stream --model <MODEL_NAME>
-```
-
-### Tool Use (json)
-
-Call to `/v1/chat/completions` with tool and json response.
-
-This test defines a simple tool which will list the contents of the local pyworker directory.  The output is then analysed by the model.
-
-```bash
-python -m workers.openai.client -k <API_KEY> -e <ENDPOINT_NAME> --tools --model <MODEL_NAME>
+python -m workers.openai.client --chat-stream --endpoint <ENDPOINT_NAME> --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("--endpoint", default="my-vllm-endpoint", help="Vast endpoint name (default: my-vllm-endpoint)")
+    p.add_argument("--model", default=DEFAULT_MODEL, help=f"Model to use for requests (default: {DEFAULT_MODEL})")
+    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

@@ -11,6 +11,7 @@ MODEL_SERVER_START_LOG_MSG = [
     "llama runner started",  # Ollama
     '"message":"Connected","target":"text_generation_router"',  # TGI
     '"message":"Connected","target":"text_generation_router::server"',  # TGI
+    "main: model loaded" # llama.cpp
 ]
 
 MODEL_SERVER_ERROR_LOG_MSGS = [
@@ -34,6 +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/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.
-2. `generate_stream`: Streams the LLM's response token by token.
+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.
 
-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
-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 (Streaming)
+
+`/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
-MAX_TOKENS = 1024
-PROMPT = "Think step by step: Tell me about the Python programming language."
+# ---------------------- Logging ----------------------
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s[%(levelname)-5s] %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+log = logging.getLogger(__file__)
 
-async def call_generate(client: Serverless) -> None:
-    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+# ---------------------- Defaults ----------------------
+DEFAULT_PROMPT = "Think step by step: Tell me about the Python programming language."
+
+ENDPOINT_NAME = "TGI-Prod2"       # change this to your TGI endpoint name
+MAX_TOKENS = 1024
+DEFAULT_TEMPERATURE = 0.7
+
+
+# ---------------------- 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,
-            "temperature": 0.7,
-            "return_full_text": False
+            "max_new_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+            "temperature": kwargs.get("temperature", DEFAULT_TEMPERATURE),
+            "return_full_text": False,
         }
     }
-
-    resp = await endpoint.request("/generate", payload, cost=MAX_TOKENS)
-
-    print(resp["response"]["generated_text"])
+    log.debug("POST /generate %s", json.dumps(payload)[:500])
+    resp = await endpoint.request("/generate", payload, cost=payload["parameters"]["max_new_tokens"])
+    return resp["response"]
 
 
-async def call_generate_stream(client: Serverless) -> None:
-    endpoint = await client.get_endpoint(name=ENDPOINT_NAME)
+async def call_generate_stream(client: Serverless, *, endpoint_name: str, prompt: str, **kwargs):
+    """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,
-            "temperature": 0.7,
+            "max_new_tokens": kwargs.get("max_tokens", MAX_TOKENS),
+            "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():
-    async with Serverless() as client:
-        await call_generate(client)
-        await call_generate_stream(client)
+# ---------------------- Demo Runner ----------------------
+class APIDemo:
+    """Demo and testing functionality for the TGI API client"""
+
+    def __init__(self, client: Serverless, endpoint_name: str):
+        self.client = client
+        self.endpoint_name = endpoint_name
+
+    async def handle_streaming_response(self, stream) -> str:
+        """Process streaming response and print tokens"""
+        full_response = ""
+        printed_answer = False
+
+        async for event in stream:
+            tok = (event.get("token") or {}).get("text")
+            if tok:
+                if not printed_answer:
+                    printed_answer = True
+                    print("\n💬 Response: ", end="", flush=True)
+                print(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())