Included Prometheus interceptor support for gRPC streaming

docs/examples/streaming/README.ipynb

@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -138,8 +138,7 @@
     "{\n",
     "  \"debug\": false,\n",
     "  \"parallel_workers\": 0,\n",
-    "  \"gzip_enabled\": false,\n",
-    "  \"metrics_endpoint\": null\n",
+    "  \"gzip_enabled\": false\n",
     "}\n"
    ]
   },
@@ -150,8 +149,7 @@
     "Note the currently there are three main limitations of the streaming support in MLServer:\n",
     "\n",
     "- distributed workers are not supported (i.e., the `parallel_workers` setting should be set to `0`)\n",
-    "- `gzip` middleware is not supported for REST (i.e., `gzip_enabled` setting should be set to `false`)\n",
-    "- metrics endpoint is not available (i.e. `metrics_endpoint` is also disabled for streaming for gRPC)"
+    "- `gzip` middleware is not supported for REST (i.e., `gzip_enabled` setting should be set to `false`)"
    ]
   },
   {
@@ -163,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -227,14 +225,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing generate-request.json\n"
+      "Overwriting generate-request.json\n"
      ]
     }
    ],
@@ -272,9 +270,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['What']\n",
+      "[' is']\n",
+      "[' the']\n",
+      "[' capital']\n",
+      "[' of']\n",
+      "[' France?']\n"
+     ]
+    }
+   ],
    "source": [
     "import httpx\n",
     "from httpx_sse import connect_sse\n",
@@ -301,9 +312,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['What']\n",
+      "[' is']\n",
+      "[' the']\n",
+      "[' capital']\n",
+      "[' of']\n",
+      "[' France?']\n"
+     ]
+    }
+   ],
    "source": [
     "import grpc\n",
     "import mlserver.types as types\n",
@@ -315,7 +339,7 @@
     "inference_request = types.InferenceRequest.parse_file(\"./generate-request.json\")\n",
     "\n",
     "# need to convert from string to bytes for grpc\n",
-    "inference_request.inputs[0] = StringCodec.encode_input(\"prompt\", inference_request.inputs[0].data.__root__)\n",
+    "inference_request.inputs[0] = StringCodec.encode_input(\"prompt\", inference_request.inputs[0].data.root)\n",
     "inference_request_g = converters.ModelInferRequestConverter.from_types(\n",
     "    inference_request, model_name=\"text-model\", model_version=None\n",
     ")\n",
@@ -338,11 +362,6 @@
    "source": [
     "Note that for gRPC, the request is transformed into an async generator which is then passed to the `ModelStreamInfer` method. The response is also an async generator which can be iterated over to get the response."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
@@ -361,7 +380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/examples/streaming/README.md

@@ -78,8 +78,7 @@ The next step will be to create 2 configuration files:
 {
   "debug": false,
   "parallel_workers": 0,
-  "gzip_enabled": false,
-  "metrics_endpoint": null
+  "gzip_enabled": false
 }
 
 ```
@@ -88,7 +87,6 @@ Note the currently there are three main limitations of the streaming support in
 
 - distributed workers are not supported (i.e., the `parallel_workers` setting should be set to `0`)
 - `gzip` middleware is not supported for REST (i.e., `gzip_enabled` setting should be set to `false`)
-- metrics endpoint is not available (i.e. `metrics_endpoint` is also disabled for streaming for gRPC)
 
 #### model-settings.json
 
@@ -195,7 +193,7 @@ import mlserver.grpc.dataplane_pb2_grpc as dataplane
 inference_request = types.InferenceRequest.parse_file("./generate-request.json")
 
 # need to convert from string to bytes for grpc
-inference_request.inputs[0] = StringCodec.encode_input("prompt", inference_request.inputs[0].data.__root__)
+inference_request.inputs[0] = StringCodec.encode_input("prompt", inference_request.inputs[0].data.root)
 inference_request_g = converters.ModelInferRequestConverter.from_types(
     inference_request, model_name="text-model", model_version=None
 )
@@ -213,5 +211,3 @@ async with grpc.aio.insecure_channel("localhost:8081") as grpc_channel:
 ```
 
 Note that for gRPC, the request is transformed into an async generator which is then passed to the `ModelStreamInfer` method. The response is also an async generator which can be iterated over to get the response.
-
-

docs/examples/streaming/settings.json

@@ -2,6 +2,5 @@
 {
   "debug": false,
   "parallel_workers": 0,
-  "gzip_enabled": false,
-  "metrics_endpoint": null
+  "gzip_enabled": false
 }

docs/examples/streaming/text_model.py

@@ -7,19 +7,6 @@
 
 class TextModel(MLModel):
 
-    async def predict(self, payload: InferenceRequest) -> InferenceResponse:
-        text = StringCodec.decode_input(payload.inputs[0])[0]
-        return InferenceResponse(
-            model_name=self._settings.name,
-            outputs=[
-                StringCodec.encode_output(
-                    name="output",
-                    payload=[text],
-                    use_bytes=True,
-                ),
-            ],
-        )
-
     async def predict_stream(
         self, payloads: AsyncIterator[InferenceRequest]
     ) -> AsyncIterator[InferenceResponse]:

docs/user-guide/streaming.md

@@ -32,4 +32,3 @@ There are three main limitations of the streaming support in MLServer:
 
 - the `parallel_workers` setting should be set to `0` to disable distributed workers (to be addressed in future releases)
 - for REST, the `gzip_enabled` setting should be set to `false` to disable GZIP compression, as streaming is not compatible with GZIP compression (see issue [here]( https://github.com/encode/starlette/issues/20#issuecomment-704106436))
-- `metrics_endpoint` is also disabled for streaming for gRPC (to be addressed in future releases)