Suppress matplot debug logs

lib/metrics.py

@@ -45,7 +45,6 @@ class Metrics:
         self.model_metrics.workload_received += workload
         self.model_metrics.requests_recieved.add(reqnum)
         self.model_metrics.requests_working.add(reqnum)
-        self.update_pending = True
 
     def _request_end(self, workload: float, reqnum: int) -> None:
         """
@@ -79,10 +78,10 @@ class Metrics:
             elapsed = time.time() - self.last_metric_update
             if self.system_metrics.model_is_loaded is False and elapsed >= 10:
                 log.debug(f"sending loading model metrics after {int(elapsed)}s wait")
-                self.__send_metrics_and_reset()
+                self.__send_metrics_and_reset(elapsed)
             elif self.update_pending or elapsed > 10:
                 log.debug(f"sending loaded model metrics after {int(elapsed)}s wait")
-                self.__send_metrics_and_reset()
+                self.__send_metrics_and_reset(elapsed)
 
     def _model_loaded(self, max_throughput: float) -> None:
         self.system_metrics.model_loading_time = (
@@ -97,13 +96,13 @@ class Metrics:
 
     #######################################Private#######################################
 
-    def __send_metrics_and_reset(self):
+    def __send_metrics_and_reset(self, elapsed):
 
         def compute_autoscaler_data() -> AutoScalaerData:
             return AutoScalaerData(
                 id=self.id,
                 loadtime=(self.system_metrics.model_loading_time or 0.0),
-                cur_load=(self.model_metrics.workload_processing),
+                cur_load=(self.model_metrics.workload_processing / elapsed),
                 max_perf=self.model_metrics.max_throughput,
                 cur_perf=self.model_metrics.cur_perf,
                 error_msg=self.model_metrics.error_msg or "",

lib/test_utils.py

@@ -292,12 +292,12 @@ def test_load_cmd(
     args = arg_parser.parse_args()
     if hasattr(args, "comfy_model"):
         os.environ["COMFY_MODEL"] = args.comfy_model
-    server_url = dict(
-        prod="https://run.vast.ai",
-        alpha="https://run-alpha.vast.ai",
-        candidate="https://run-candidate.vast.ai",
-        local="http://localhost:8080",
-    )[args.instance]
+    server_url = {
+        "prod": "https://run.vast.ai",
+        "alpha": "https://run-alpha.vast.ai",
+        "candidate": "https://run-candidate.vast.ai",
+        "local": "http://localhost:8080",
+    }.get(args.instance, "http://localhost:8080")
     run_test(
         num_requests=args.num_requests,
         requests_per_second=args.requests_per_second,

start_server.sh

@@ -59,12 +59,12 @@ then
     fi
 
     # Fork testing
-    [[ ! -d $SERVER_DIR ]] && git clone "${PYWORKER_REPO:-https://github.com/vast-ai/pyworker}" "$SERVER_DIR"
+    git clone "${PYWORKER_REPO:-https://github.com/vast-ai/pyworker}" "$SERVER_DIR"
     if [[ -n ${PYWORKER_REF:-} ]]; then
         (cd "$SERVER_DIR" && git checkout "$PYWORKER_REF")
     fi
 
-    uv venv --python-preference only-managed "$ENV_PATH" -p 3.10
+    uv venv --managed-python "$ENV_PATH" -p 3.10
     source "$ENV_PATH/bin/activate"
 
     uv pip install -r "${SERVER_DIR}/requirements.txt"

utils/endpoint_util.py

@@ -1,5 +1,6 @@
 import logging
-from typing import Any, Dict, Optional
+import time
+from typing import Any, Dict, Optional, Tuple
 
 import requests
 
@@ -16,6 +17,38 @@ class Endpoint:
     Utility class for handling endpoint operations.
     """
 
+    @staticmethod
+    def get_endpoint_info(
+        endpoint_name: str, account_api_key: str, instance: str
+    ) -> Optional[Dict[str, Any]]:
+        headers = {"Authorization": f"Bearer {account_api_key}"}
+        url = f"{Endpoint.get_server_url(instance)}?autoscaler_instance={instance}"
+        # Retry a few times to smooth over transient propagation/network delays
+        for attempt in range(4):
+            try:
+                response = requests.get(url, headers=headers, timeout=8)
+                if response.status_code != 200:
+                    # brief backoff and retry
+                    time.sleep(0.3 * (attempt + 1))
+                    continue
+                try:
+                    data = response.json()
+                except Exception:
+                    # JSON parse failed; backoff and retry
+                    time.sleep(0.3 * (attempt + 1))
+                    continue
+                result = data.get("results", []) if isinstance(data, dict) else []
+                endpoint = next(
+                    (item for item in result if item.get("endpoint_name") == endpoint_name),
+                    None,
+                )
+                if endpoint and endpoint.get("id") and endpoint.get("api_key"):
+                    return {"id": endpoint.get("id"), "api_key": endpoint.get("api_key")}
+            except Exception:
+                # network or other transient error; retry
+                time.sleep(0.3 * (attempt + 1))
+        return None
+
     @staticmethod
     def get_autoscaler_server_url(instance: str) -> str:
         endpoints = {
@@ -23,7 +56,10 @@ class Endpoint:
             "candidate": "run-candidate",
             "prod": "run",
         }
-        return f"https://{endpoints[instance]}.vast.ai/"
+        host = endpoints.get(instance)
+        if host:
+            return f"https://{host}.vast.ai/"
+        return "http://localhost:8080"
 
     @staticmethod
     def get_server_url(instance: str) -> str:
@@ -32,7 +68,8 @@ class Endpoint:
             "candidate": "candidate",
             "prod": "console",
         }
-        return f"https://{endpoints[instance]}.vast.ai/api/v0/endptjobs/"
+        host = endpoints.get(instance, "alpha")
+        return f"https://{host}.vast.ai/api/v0/endptjobs/"
 
     @staticmethod
     def get_endpoint_api_key(
@@ -55,6 +92,7 @@ class Endpoint:
             response = requests.get(
                 f"{Endpoint.get_server_url(instance)}?autoscaler_instance={instance}",
                 headers=headers,
+                timeout=8,
             )
 
             if response.status_code != 200:
@@ -64,14 +102,14 @@ class Endpoint:
 
             try:
                 data = response.json()
-            except requests.exceptions.JSONDecodeError as e:
+            except Exception as e:
                 log.debug(f"Failed to parse JSON response: {e}")
                 return None
 
             result = data.get("results", [])
 
             endpoint: Optional[Dict[str, Any]] = next(
-                (item for item in result if item["endpoint_name"] == endpoint_name),
+                (item for item in result if item.get("endpoint_name") == endpoint_name),
                 None,
             )
             if not endpoint:

workers/comfyui-json/README.md

@@ -12,21 +12,9 @@ A docker image is provided but you may use any if the above requirements are met
 
 ## Benchmarking
 
-### Custom Benchmark Workflows
+A simple image generation benchmark runs when each worker initializes to validate GPU performance and identify underperforming machines.
 
-You can provide a custom ComfyUI workflow for benchmarking by creating `workers/comfyui-json/misc/benchmark.json`. This allows you to test performance using your preferred models and workflow complexity.
-
-**Ways to provide the benchmark file:**
-- Fork this repository and add your `benchmark.json` file
-- Write the file during worker provisioning (onstart script or setup phase)
-
-An example file is provided in the repository. To ensure varied generations, use the placeholder `__RANDOM_INT__` in place of static seed values - it will be replaced with a random integer for each benchmark run.
-
-### Default Benchmark (Fallback)
-
-If `benchmark.json` is not available, a simple image generation benchmark runs when each worker initializes. This validates GPU performance and helps identify underperforming machines.
-
-The default benchmark uses Stable Diffusion v1.5 with ComfyUI's standard text-to-image workflow. Configure it using these environment variables:
+The benchmark uses Stable Diffusion v1.5 with ComfyUI's default text-to-image workflow. Configure the benchmark complexity and duration using these variables:
 
 | Environment Variable | Default Value | Description |
 | -------------------- | ------------- | ----------- |
@@ -36,7 +24,7 @@ The default benchmark uses Stable Diffusion v1.5 with ComfyUI's standard text-to
 
 Each benchmark run uses a random prompt from `misc/test_prompts.txt` and a random seed to ensure consistent GPU load patterns.
 
-#### Calibrating Fallback Benchmark Duration
+### Calibrating Benchmark Duration
 
 To screen for underperforming hardware, set `BENCHMARK_TEST_STEPS` to match your expected production workflow duration. This allows you to identify machines that won't meet performance requirements.
 

workers/comfyui-json/client.py

@@ -98,7 +98,6 @@ def call_text2image_workflow(
         endpoint=route_response["endpoint"],
         reqnum=route_response["reqnum"],
         url=route_response["url"],
-        request_idx=route_response["request_idx"],
     )
     
     # Build the payload for the worker request

workers/comfyui-json/data_types.py

@@ -5,13 +5,12 @@ import dataclasses
 from typing import Dict, Any
 from functools import cache
 from math import ceil
-from pathlib import Path
-import json
-import logging
 
 from lib.data_types import ApiPayload, JsonDataException
 
-log = logging.getLogger(__file__)
+
+with open("workers/comfyui/misc/test_prompts.txt", "r") as f:
+    test_prompts = f.readlines()
 
 def count_workload() -> float:
     # Always 100.0 where there is a single instance of ComfyUI handling requests
@@ -25,32 +24,9 @@ class ComfyWorkflowData(ApiPayload):
     @classmethod
     def for_test(cls):
         """
-        If the user has provided a benchmark workflow we can use it here to properly gauge performance.
-        Otherwise, use the variables available to simulate workflows of the required running time
+        Use the variables available to simulate workflows of the required running time
         Example: SD1.5, simple image gen 10000 steps, 512px x 512px will run for approximately 9 minutes @ ~18 it/s (RTX 4090)
         """
-        # Try to load benchmark.json
-        benchmark_file = Path("workers/comfyui-json/misc/benchmark.json")
-        
-        if benchmark_file.exists():
-            try:
-                with open(benchmark_file, "r") as f:
-                    benchmark_workflow = json.load(f)
-                return cls(
-                    input={
-                        "request_id": f"test-{random.randint(1000, 99999)}",
-                        "workflow_json": benchmark_workflow
-                    }
-                )
-            except (json.JSONDecodeError, IOError):
-                # JSON is malformed or file can't be read, fall through to default
-                log.error(f"Failed to benchmark using {benchmark_file}")
-        
-        # Fallback: read prompts and construct payload
-        log.info("Using fallback method for benchmarking")
-        with open("workers/comfyui-json/misc/test_prompts.txt", "r") as f:
-            test_prompts = f.readlines()
-        
         test_prompt = random.choice(test_prompts).rstrip()
         return cls(
             input={

workers/comfyui-json/misc/benchmark.json.example

@@ -1,107 +0,0 @@
-{
-    "3": {
-        "inputs": {
-            "seed": "__RANDOM_INT__",
-            "steps": 20,
-            "cfg": 8,
-            "sampler_name": "euler",
-            "scheduler": "normal",
-            "denoise": 1,
-            "model": [
-            "4",
-            0
-            ],
-            "positive": [
-            "6",
-            0
-            ],
-            "negative": [
-            "7",
-            0
-            ],
-            "latent_image": [
-            "5",
-            0
-            ]
-        },
-        "class_type": "KSampler",
-        "_meta": {
-            "title": "KSampler"
-        }
-    },
-    "4": {
-        "inputs": {
-            "ckpt_name": "v1-5-pruned-emaonly-fp16.safetensors"
-        },
-        "class_type": "CheckpointLoaderSimple",
-        "_meta": {
-            "title": "Load Checkpoint"
-        }
-    },
-    "5": {
-        "inputs": {
-            "width": 512,
-            "height": 512,
-            "batch_size": 1
-        },
-        "class_type": "EmptyLatentImage",
-        "_meta": {
-            "title": "Empty Latent Image"
-        }
-    },
-    "6": {
-        "inputs": {
-            "text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
-            "clip": [
-            "4",
-            1
-            ]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-            "title": "CLIP Text Encode (Prompt)"
-        }
-    },
-    "7": {
-        "inputs": {
-            "text": "text, watermark",
-            "clip": [
-            "4",
-            1
-            ]
-        },
-        "class_type": "CLIPTextEncode",
-        "_meta": {
-            "title": "CLIP Text Encode (Prompt)"
-        }
-    },
-    "8": {
-        "inputs": {
-            "samples": [
-            "3",
-            0
-            ],
-            "vae": [
-            "4",
-            2
-            ]
-        },
-        "class_type": "VAEDecode",
-        "_meta": {
-            "title": "VAE Decode"
-        }
-    },
-    "9": {
-        "inputs": {
-            "filename_prefix": "ComfyUI",
-            "images": [
-            "8",
-            0
-            ]
-        },
-        "class_type": "SaveImage",
-        "_meta": {
-            "title": "Save Image"
-        }
-    }
-}

workers/comfyui-json/server.py

@@ -19,7 +19,6 @@ MODEL_SERVER_START_LOG_MSG = "To see the GUI go to: "
 MODEL_SERVER_ERROR_LOG_MSGS = [
     "MetadataIncompleteBuffer",  # This error is emitted when the downloaded model is corrupted
     "Value not in list: ",  # This error is emitted when the model file is not there at all
-    "[ERROR] Provisioning Script failed", # Error inserted by provisioning script if models/nodes fail to download
 ]
 
 
@@ -71,7 +70,7 @@ class ComfyWorkflowHandler(EndpointHandler[ComfyWorkflowData]):
 
     @property
     def healthcheck_endpoint(self) -> Optional[str]:
-        return f"{MODEL_SERVER_URL}/health"
+        return "/health"
 
     @classmethod
     def payload_cls(cls) -> Type[ComfyWorkflowData]:

workers/comfyui/client.py

@@ -82,7 +82,6 @@ def call_custom_workflow_for_sd3(
         endpoint=message["endpoint"],
         reqnum=message["reqnum"],
         url=message["url"],
-        request_idx=message["request_idx"],
     )
     workflow = {
         "3": {

workers/openai/test_load.py

@@ -1,8 +1,349 @@
-from lib.test_utils import test_load_cmd, test_args
+from lib.test_utils import test_args
+from utils.endpoint_util import Endpoint
+from utils.ssl import get_cert_file_path
+from lib.data_types import AuthData
 from .data_types.server import CompletionsData
-import os
 
-WORKER_ENDPOINT = "/v1/completions"
+import os
+import time
+import threading
+import requests
+from dataclasses import dataclass
+from collections import Counter
+from urllib.parse import urljoin, urlparse
+import re
+
+# Headless plotting
+import matplotlib
+matplotlib.use("Agg")
+import logging
+logging.getLogger("matplotlib.font_manager").setLevel(logging.WARNING)
+import matplotlib.pyplot as plt
+import numpy as np
+from concurrent.futures import ThreadPoolExecutor, wait, FIRST_COMPLETED
+from requests.adapters import HTTPAdapter
+
+def get_incremented_path(path: str) -> str:
+    base, ext = os.path.splitext(path)
+    if not os.path.exists(path):
+        return path
+    i = 1
+    while os.path.exists(f"{base}-{i}{ext}"):
+        i += 1
+    return f"{base}-{i}{ext}"
+
+WORKER_ENDPOINT = "/v1/completions" # This will return the full text output at once. Latency metrics reflect that (ie not measuring TTFT)
+
+@dataclass
+class ReqResult:
+    worker_url: str
+    route_ms: float
+    worker_ms: float
+    total_ms: float
+    ok: bool
+    error: str = ""
+    t_start: float = 0.0
+    t_end: float = 0.0
+    workload: float = 0.0
+
+def do_one(endpoint_name: str,
+           endpoint_id: int,
+           endpoint_api_key: str,
+           server_url: str,
+           worker_endpoint: str,
+           payload,
+           results_list,
+           t0,
+           status_samples,
+           route_session,
+           worker_session):
+    try:
+        u = payload.count_workload()
+        route_payload = {"endpoint": endpoint_name, "api_key": endpoint_api_key, "cost": u}
+        headers = {"Authorization": f"Bearer {endpoint_api_key}"}
+        start = time.time()
+        r0 = route_session.post(urljoin(server_url, "/route/"), json=route_payload, headers=headers, timeout=4)
+        t_after_route = time.time()
+        if r0.status_code != 200:
+            results_list.append(ReqResult("", (t_after_route - start) * 1000.0, 0.0, (t_after_route - start) * 1000.0, False,
+                                          f"route {r0.status_code} {r0.text}"))
+            return
+        msg = r0.json()
+
+        # 1) "Status" is in the response when no worker is ready
+        worker_sampled = True
+        status = msg.get("status", "")
+        if status:
+            m = re.search(r"total workers:\s*(\d+).*loading workers:\s*(\d+).*standby workers:\s*(\d+).*error workers:\s*(\d+)", status, re.I | re.S)
+            if m:
+                tot, loading, standby, err = map(int, m.groups())
+                idle = max(tot - loading - standby - err, 0)
+                status_samples.append((time.time() - t0, idle))
+                worker_sampled = False
+
+        # 2) Otherwise (successful request), sample via /get_endpoint_workers/ for eligible (idle) worker tracking
+        if worker_sampled:
+            try:
+                r_status = route_session.post(
+                    urljoin(server_url, "/get_endpoint_workers/"),
+                    json={"id": endpoint_id},
+                    headers={"Authorization": f"Bearer {endpoint_api_key}"},
+                    timeout=3,
+                )
+                if r_status.status_code == 200:
+                    workers = r_status.json()
+                    idle = 0
+                    for w in workers:
+                        st = str(w.get("status", "")).lower()
+                        if (st in ("idle")):
+                            idle += 1
+                    status_samples.append((time.time() - t0, idle))
+            except Exception:
+                pass
+
+        # 3) Send the request
+        worker_address = msg["url"]
+        req = dict(payload=payload.__dict__, auth_data=AuthData.from_json_msg(msg).__dict__)
+        t1 = time.time()
+        # Use explicit connect/read timeouts to avoid long hangs
+        r1 = worker_session.post(
+            urljoin(worker_address, worker_endpoint),
+            json=req,
+            verify=get_cert_file_path(),
+            timeout=(4, 120),
+        )
+        t2 = time.time()
+        if r1.status_code != 200:
+            results_list.append(ReqResult(worker_address, (t_after_route - start) * 1000.0, (t2 - t1) * 1000.0,
+                                          (t2 - start) * 1000.0, False,
+                                          f"infer {r1.status_code} {r1.text}"))
+            return
+        results_list.append(ReqResult(worker_address, (t_after_route - start) * 1000.0, (t2 - t1) * 1000.0, (t2 - start) * 1000.0,
+                                        True, "", t_start=start - t0, t_end=t2 - t0, workload=u))
+    except Exception as e:
+        t = time.time()
+        results_list.append(ReqResult("", (t - start) * 1000.0, 0.0, (t - start) * 1000.0, False, str(e)))
+
+def run_load_with_metrics(num_requests: int,
+                          requests_per_second: float,
+                          endpoint_group_name: str,
+                          account_api_key: str,
+                          server_url: str,
+                          worker_endpoint: str,
+                          instance: str,
+                          out_path: str):
+    # Resolve endpoint id + endpoint-scoped API key
+    ep_info = Endpoint.get_endpoint_info(endpoint_name=endpoint_group_name,
+                                         account_api_key=account_api_key,
+                                         instance=instance)
+    if not ep_info or not ep_info.get("api_key") or not ep_info.get("id"):
+        print(f"Endpoint {endpoint_group_name} not found for API key")
+        return
+    endpoint_id = int(ep_info["id"])
+    endpoint_api_key = ep_info["api_key"]
+
+    t0 = time.time()
+    results = []
+    status_samples = []
+    # Concurrency control
+    max_concurrency = int(os.environ.get("MAX_CONCURRENCY", "1024"))
+    submit_queue_factor = 2  # cap queued tasks to reduce memory
+
+    # Shared HTTP sessions with connection pooling (persistent connections)
+    def make_session(pool_connections: int, pool_maxsize: int) -> requests.Session:
+        sess = requests.Session()
+        adapter = HTTPAdapter(pool_connections=pool_connections, pool_maxsize=pool_maxsize, max_retries=0)
+        sess.mount("https://", adapter)
+        sess.mount("http://", adapter)
+        return sess
+
+    # Router: mostly single host, small connection pool is sufficient
+    route_session = make_session(pool_connections=8, pool_maxsize=max_concurrency)
+    # Workers: many hosts; allow many pools and per-host concurrency up to max_concurrency
+    worker_session = make_session(pool_connections=max(256, max_concurrency), pool_maxsize=max_concurrency)
+
+    # Fire requests using a thread pool, scheduling at requested RPS
+    inflight = set()
+    with ThreadPoolExecutor(max_workers=max_concurrency) as executor:
+        for i in range(num_requests):
+            # Pace submissions to RPS
+            target_time = t0 + i / max(requests_per_second, 1e-9)
+            sleep_s = target_time - time.time()
+            if sleep_s > 0:
+                time.sleep(min(sleep_s, 0.5))  # sleep in chunks to stay responsive
+
+            payload = CompletionsData.for_test()
+            fut = executor.submit(
+                do_one,
+                endpoint_group_name,
+                endpoint_id,
+                endpoint_api_key,
+                server_url,
+                worker_endpoint,
+                payload,
+                results,
+                t0,
+                status_samples,
+                route_session,
+                worker_session,
+            )
+            inflight.add(fut)
+            # Prevent unbounded queue growth
+            if len(inflight) >= max_concurrency * submit_queue_factor:
+                done, not_done = wait(inflight, return_when=FIRST_COMPLETED)
+                inflight = not_done
+        # Wait for all outstanding tasks
+        if inflight:
+            wait(inflight)
+    # Close sessions
+    try:
+        route_session.close()
+    finally:
+        worker_session.close()
+
+    # Aggregate results
+    oks = [r for r in results if r.ok]
+    errs = [r for r in results if not r.ok]
+    total_reqs = len(results)
+    succ = len(oks)
+
+    total_ms = np.array([r.total_ms for r in oks]) if succ else np.array([])
+    worker_ms = np.array([r.worker_ms for r in oks]) if succ else np.array([])
+    #route_ms = np.array([r.route_ms for r in oks]) if succ else np.array([])
+
+    avg_total = float(np.mean(total_ms)) if succ else 0.0
+    p50_total, p95_total = (float(np.percentile(total_ms, 50)), float(np.percentile(total_ms, 95))) if succ else (0.0, 0.0)
+    total_compute_time_ms = float(np.sum(worker_ms)) if succ else 0.0
+
+    # Distribution over workers (by host:port)
+    hosts = [urlparse(r.worker_url).netloc for r in oks if r.worker_url]
+    dist = Counter(hosts)
+
+    # Idle over time (mode per second)
+    idle_ts, idle_vals = [], []
+    if status_samples:
+        buckets = {}
+        for ts, idle in status_samples:
+            k = int(ts)
+            buckets.setdefault(k, []).append(idle)
+        keys = sorted(buckets.keys())
+        idle_ts = keys
+        # Use the most frequent sampled value per second (mode) to keep integer counts
+        idle_vals = []
+        for k in keys:
+            vals_k = [int(v) for v in buckets[k]]
+            if vals_k:
+                cnt = Counter(vals_k)
+                idle_vals.append(cnt.most_common(1)[0][0])
+            else:
+                idle_vals.append(0)
+
+    print(f"\nResults: total={total_reqs} success={succ} errors={len(errs)}")
+    print(f"Avg latency (ms): {avg_total:.1f}  p50: {p50_total:.1f}  p95: {p95_total:.1f}")
+    print(f"Total compute time (sum worker latency, s): {total_compute_time_ms/1000.0:.2f}")
+    if errs:
+        print("Sample errors:")
+        for e in errs[:5]:
+            print(f"  {e.error}")
+
+    # Plot: 2x3 grid
+    fig, axes = plt.subplots(2, 3, figsize=(15, 8))
+    fig.suptitle(f"Load test: {endpoint_group_name}  n={total_reqs}, rps={requests_per_second}, success={succ}")
+
+    # Dist per worker
+    ax0 = axes[0, 0]
+    if dist:
+        items = sorted(dist.items(), key=lambda kv: kv[1], reverse=True)
+        labels, counts = zip(*items)
+        ax0.bar(range(len(labels)), counts)
+        ax0.set_xticks(range(len(labels)))
+        ax0.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
+    ax0.set_title("Request distribution over workers")
+    ax0.set_ylabel("count")
+
+    # Latency histogram (total)
+    ax1 = axes[0, 1]
+    if succ:
+        ax1.hist(total_ms, bins=30, color="#4e79a7")
+    ax1.set_title("Total latency (ms)")
+    ax1.set_xlabel("ms")
+    ax1.set_ylabel("freq")
+
+    # Eligible workers over time
+    ax_idle = axes[0, 2]
+    if idle_ts:
+        ax_idle.plot(idle_ts, idle_vals, "-o", ms=3)
+    ax_idle.set_title("Eligible workers over time")
+    ax_idle.set_xlabel("time (s)")
+    ax_idle.set_ylabel("eligible count")
+
+    # Throughput over time (completions/sec)
+    ax_idle = axes[1, 0]
+    ax_idle.clear()
+    if succ:
+        per_sec = {}
+        for r in oks:
+            s = int(r.t_end)
+            per_sec[s] = per_sec.get(s, 0) + 1
+        ts = sorted(per_sec.keys())
+        vals = [per_sec[t] for t in ts]
+        ax_idle.plot(ts, vals, "-o", ms=3)
+    ax_idle.set_title("Completions per second")
+    ax_idle.set_xlabel("time (s)")
+    ax_idle.set_ylabel("req/s")
+
+    # Summary text
+    ax3 = axes[1, 1]
+    ax3.axis("off")
+    text = (
+        f"Total requests: {total_reqs}\n"
+        f"Success: {succ}  Errors: {len(errs)}\n"
+        f"Avg latency: {avg_total:.1f} ms\n"
+        f"p50: {p50_total:.1f} ms  p95: {p95_total:.1f} ms\n"
+        f"Total compute time: {total_compute_time_ms/1000.0:.2f} s"
+    )
+    ax3.set_title("Summary")
+    ax3.text(0.02, 0.98, text, va="top", ha="left", fontsize=11, transform=ax3.transAxes)
+
+    # Latency CDF (total_ms)
+    ax_cdf = axes[1, 2]
+    if succ:
+        x = np.sort(total_ms)
+        y = np.linspace(0, 1, len(x), endpoint=True)
+        ax_cdf.plot(x, y)
+    ax_cdf.set_title("Latency CDF")
+    ax_cdf.set_xlabel("ms")
+    ax_cdf.set_ylabel("fraction ≤ x")
+
+    # Ensure unique output path and create directory if needed
+    final_out_path = get_incremented_path(out_path)
+    out_dir = os.path.dirname(final_out_path)
+    if out_dir:
+        os.makedirs(out_dir, exist_ok=True)
+
+    plt.tight_layout(rect=[0, 0, 1, 0.96])
+    plt.savefig(final_out_path, dpi=120)
+    print(f"Saved report to: {final_out_path}")
+
+    # Per-worker latency boxplot (top 12 by volume)
+    groups = {}
+    for r in oks:
+        host = urlparse(r.worker_url).netloc
+        groups.setdefault(host, []).append(r.total_ms)
+    items = sorted(groups.items(), key=lambda kv: len(kv[1]), reverse=True)[:12]
+    if items:
+        labels, data = zip(*items)
+        fig2, axb = plt.subplots(1, 1, figsize=(12, 5))
+        axb.boxplot(data, showfliers=False)
+        axb.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
+        axb.set_title("Per-worker latency (ms)")
+        axb.set_ylabel("ms")
+        plt.tight_layout()
+        extra_out = get_incremented_path(os.path.splitext(out_path)[0] + "-workers.png")
+        plt.savefig(extra_out, dpi=120)
+        fig2.tight_layout()
+        fig2.savefig(extra_out, dpi=120)
+        print(f"Saved worker latency plot to: {extra_out}")
 
 if __name__ == "__main__":
     # Check if MODEL_NAME environment variable is set
@@ -16,13 +357,32 @@ if __name__ == "__main__":
         help="Model to use for completions request (required if MODEL_NAME env var not set)",
     )
 
-    # Parse known args to get model early, before test_load_cmd adds its args
+    # Parse known args to get model early, before adding load args
     known_args, _ = test_args.parse_known_args()
-
-    # Set environment variable if model was provided
     if hasattr(known_args, "model") and known_args.model:
         os.environ["MODEL_NAME"] = known_args.model
         print(f"Set MODEL_NAME environment variable to: {known_args.model}")
 
-    # Now call test_load_cmd normally - it will add its own args and re-parse
-    test_load_cmd(CompletionsData, WORKER_ENDPOINT, arg_parser=test_args)
+    # Load test args
+    test_args.add_argument("-n", dest="num_requests", type=int, required=True, help="total number of requests")
+    test_args.add_argument("-rps", dest="requests_per_second", type=float, required=True, help="requests per second")
+    test_args.add_argument("--out", dest="out_path", type=str, default="load_test_report.png", help="path to save the report image")
+    args = test_args.parse_args()
+
+    server_url = {
+        "prod": "https://run.vast.ai",
+        "alpha": "https://run-alpha.vast.ai",
+        "candidate": "https://run-candidate.vast.ai",
+        "local": "http://localhost:8080"
+    }.get(args.instance, "http://localhost:8080")
+
+    run_load_with_metrics(
+        num_requests=args.num_requests,
+        requests_per_second=args.requests_per_second,
+        endpoint_group_name=args.endpoint_group_name,
+        account_api_key=args.api_key,
+        server_url=server_url,
+        worker_endpoint=WORKER_ENDPOINT,
+        instance=args.instance,
+        out_path=args.out_path,
+    )