download vast.ai's root certificate in order to make pyworker requests

lib/backend.py

@@ -126,7 +126,7 @@ class Backend:
         async def cancel_api_call_if_disconnected() -> web.Response:
             await request.wait_for_disconnection()
             log.debug(f"request with reqnum: {auth_data.reqnum} was canceled")
-            self.metrics._request_canceled(workload=workload)
+            self.metrics._request_canceled(workload=workload, reqnum=auth_data.reqnum)
             return web.Response(status=500)
 
         async def make_request() -> Union[web.Response, web.StreamResponse]:
@@ -141,6 +141,7 @@ class Backend:
             else:
                 log.debug(f"Starting request for reqnum:{auth_data.reqnum}")
             try:
+                start_time = time.time()
                 response = await self.__call_api(handler=handler, payload=payload)
                 status_code = response.status
                 log.debug(
@@ -152,17 +153,19 @@ class Backend:
                     )
                 )
                 res = await handler.generate_client_response(request, response)
-                self.metrics._request_success(workload=workload)
+                self.metrics._request_end(
+                    workload=workload,
+                    req_response_time=time.time() - start_time,
+                    reqnum=auth_data.reqnum,
+                )
                 return res
             except requests.exceptions.RequestException as e:
                 log.debug(f"[backend] Request error: {e}")
-                self.metrics._request_errored(workload=workload)
+                self.metrics._request_errored(
+                    workload=workload, reqnum=auth_data.reqnum
+                )
                 return web.Response(status=500)
             finally:
-                self.metrics._request_end(
-                    workload=workload,
-                    reqnum=auth_data.reqnum,
-                )
                 self.sem.release()
 
         ###########
@@ -183,6 +186,12 @@ class Backend:
         except Exception as e:
             log.debug(f"Exception in main handler loop {e}")
             return web.Response(status=500)
+        finally:
+            if request.task.cancelled():
+                log.debug(f"request with reqnum: {auth_data.reqnum} was canceled")
+                self.metrics._request_canceled(
+                    workload=workload, reqnum=auth_data.reqnum
+                )
 
     async def __healthcheck(self):
         health_check_url = self.benchmark_handler.healthcheck_endpoint
@@ -280,52 +289,41 @@ class Backend:
                     return float(f.readline())
             except FileNotFoundError:
                 pass
-
-            log.debug("Initial run to trigger model loading...")
-            payload = self.benchmark_handler.make_benchmark_payload()
-            await self.__call_api(handler=self.benchmark_handler, payload=payload)
-
             max_throughput = 0
+            last_throughput = 0
             sum_throughput = 0
-            concurrent_requests = 10 if self.allow_parallel_requests else 1
-
-            for run in range(1, self.benchmark_handler.benchmark_runs + 1):
+            for run in range(self.benchmark_handler.benchmark_runs + 1):
                 start = time.time()
-                tasks = []
-                total_workload = 0
-
-                for _ in range(concurrent_requests):
-                    payload = self.benchmark_handler.make_benchmark_payload()
-                    total_workload += payload.count_workload()
-                    tasks.append(
-                        self.__call_api(handler=self.benchmark_handler, payload=payload)
-                    )
-
-                responses = await gather(*tasks)
-                time_elapsed = time.time() - start
-
-                throughput = total_workload / time_elapsed
-                sum_throughput += throughput
-                max_throughput = max(max_throughput, throughput)
-
-                # Log results for debugging
-                log.debug(
-                    "\n".join(
-                        [
-                            "#" * 60,
-                            f"Run: {run}, concurrent_requests: {concurrent_requests}",
-                            f"Total workload: {total_workload}, time_elapsed: {time_elapsed}s",
-                            f"Throughput: {throughput} workload/s",
-                            f"Successful responses: {len([r for r in responses if r.status == 200])}",
-                            "#" * 60,
-                        ]
-                    )
+                payload = self.benchmark_handler.make_benchmark_payload()
+                res = await self.__call_api(
+                    handler=self.benchmark_handler, payload=payload
                 )
-
+                data = await res.json()
+                time_elapsed = time.time() - start
+                # first run triggers one-time loading of the model which is very slow, so we skip counting it
+                if run == 0:
+                    continue
+                else:
+                    workload = payload.count_workload()
+                    last_throughput = workload / time_elapsed
+                    sum_throughput += last_throughput
+                    max_throughput = max(max_throughput, last_throughput)
+                    log.debug(
+                        "\n".join(
+                            [
+                                "#" * 60,
+                                f"Run: {run}, workload: {workload} time_elapsed: {time_elapsed}, throughput: {last_throughput}",
+                                "",
+                                f"response: {data}",
+                                "#" * 60,
+                            ]
+                        )
+                    )
             average_throughput = sum_throughput / self.benchmark_handler.benchmark_runs
             log.debug(
                 f"benchmark result: avg {average_throughput} workload per second, max {max_throughput}"
             )
+            # save max_throughput so we don't have to run benchmark again on restart of cold instances
             with open(BENCHMARK_INDICATOR_FILE, "w") as f:
                 f.write(str(max_throughput))
             return max_throughput

lib/data_types.py

@@ -8,6 +8,7 @@ from aiohttp import web, ClientResponse
 import inspect
 
 import psutil
+import requests
 
 
 """
@@ -205,13 +206,13 @@ class ModelMetrics:
     workload_received: float
     workload_cancelled: float
     workload_errored: float
-    # these are not
     workload_pending: float
+    # these are not
+    cur_perf: float
     error_msg: Optional[str]
     max_throughput: float
     requests_recieved: Set[int] = field(default_factory=set)
     requests_working: Set[int] = field(default_factory=set)
-    last_update: float = field(default_factory=time.time)
 
     @classmethod
     def empty(cls):
@@ -220,15 +221,12 @@ class ModelMetrics:
             workload_served=0.0,
             workload_cancelled=0.0,
             workload_errored=0.0,
+            cur_perf=0.0,
             workload_received=0.0,
             error_msg=None,
             max_throughput=0.0,
         )
 
-    @property
-    def cur_perf(self) -> float:
-        return max(self.workload_served / (time.time() - self.last_update), 0.0)
-
     @property
     def workload_processing(self) -> float:
         return max(self.workload_received - self.workload_cancelled, 0.0)
@@ -242,7 +240,6 @@ class ModelMetrics:
         self.workload_received = 0
         self.workload_cancelled = 0
         self.workload_errored = 0
-        self.last_update = time.time()
 
 
 @dataclass

lib/metrics.py

@@ -46,31 +46,33 @@ class Metrics:
         self.model_metrics.requests_recieved.add(reqnum)
         self.model_metrics.requests_working.add(reqnum)
 
-    def _request_end(self, workload: float, reqnum: int) -> None:
+    def _request_end(
+        self, workload: float, req_response_time: float, reqnum: int
+    ) -> None:
         """
-        this function is called after handling of a request ends, regardless of the outcome
-        """
-        self.model_metrics.workload_pending -= workload
-        self.model_metrics.requests_working.discard(reqnum)
-
-    def _request_success(self, workload: float) -> None:
-        """
-        this function is called after a response from model API is received and forwarded.
+        this function is called after a response from model API is received.
         """
         self.model_metrics.workload_served += workload
+        self.model_metrics.workload_pending -= workload
+        self.model_metrics.requests_working.discard(reqnum)
+        self.model_metrics.cur_perf = workload / req_response_time
         self.update_pending = True
 
-    def _request_errored(self, workload: float) -> None:
+    def _request_errored(self, workload: float, reqnum: int) -> None:
         """
         this function is called if model API returns an error
         """
+        self.model_metrics.workload_pending -= workload
         self.model_metrics.workload_errored += workload
+        self.model_metrics.requests_working.discard(reqnum)
 
-    def _request_canceled(self, workload: float) -> None:
+    def _request_canceled(self, workload: float, reqnum: int) -> None:
         """
         this function is called if client drops connection before model API has responded
         """
+        self.model_metrics.workload_pending -= workload
         self.model_metrics.workload_cancelled += workload
+        self.model_metrics.requests_working.discard(reqnum)
 
     async def _send_metrics_loop(self) -> Awaitable[NoReturn]:
         while True:

requirements.txt

@@ -1,4 +1,4 @@
-aiohttp[speedups]==3.10.1
+aiohttp==3.10.1
 anyio~=4.4
 lib~=4.0
 nltk~=3.9
@@ -6,5 +6,5 @@ psutil~=6.0
 pycryptodome~=3.20
 Requests~=2.32
 transformers~=4.52
-utils==1.0.*
+utils~=1.0
 hf_transfer>=0.1.9

utils/endpoint_util.py

@@ -30,12 +30,7 @@ class Endpoint:
         Returns:
             Endpoint API key if successful, None otherwise
         """
-        endpoints = {
-            "alpha": "alpha",
-            "candidate": "candidate",
-            "prod": "console",
-        }
-        vast_console_url = f"https://{endpoints[instance]}.vast.ai/api/v0/endptjobs/"
+        vast_console_url = "https://console.vast.ai/api/v0/endptjobs/"
         headers = {"Authorization": f"Bearer {account_api_key}"}
 
         try: