Invoke deployed endpoint on port 8080 with gRPC

/kind bug

I have deployed a model using tensorflow savedmodel and it is listening on both http port:8080 and grpc port:9000 by default, I tested http one with url and headers, in headers we have to write host name other wise it doesn’t work, it was working fine. Then I deployed same model with http listening on 9000 and grpc listening on 8080 with custom yaml file, it deployed successfully. But I dont know how to invoke endpoint with grpc, I have ip address and port which is 80 as it is mapped on 8080 port of our model. Another thing is that is it necessary to pass host name in grpc like it is in rest api to mention host in headers while invoking rest api.

Here is my yaml file

apiVersion: "serving.kubeflow.org/v1alpha2"
kind: "InferenceService"
metadata:
  annotations:
  name: "<model-name>"
spec:
  default:
    predictor:
      serviceAccountName: sa
      custom:
        container:
          name: "kfserving-container"
          args:
          - tensorflow_model_server
          - --model_base_path=/mnt/models
          - --model_name=<model>
          - --port=8080
          - --rest_api_port=9000
          env:
          - name: STORAGE_URI
            value: "<s3 path>"
          image: tensorflow/serving:latest-gpu
        resources:
          limits:
            cpu: "1"
            memory: 16Gi
          requests:
            cpu: "1"
            memory: 16Gi

Here are the logs of my pod

usage: tensorflow_model_server
2020-11-25 07:15:05.248383: I tensorflow_serving/model_servers/server.cc:87] Building single TensorFlow model file config:  model_name: <model-name> model_base_path: /mnt/models
Flags:
	--port=8500                      	int32	Port to listen on for gRPC API
	--grpc_socket_path=""            	string	If non-empty, listen to a UNIX socket for gRPC API on the given path. Can be either relative or absolute path.
	--rest_api_port=0                	int32	Port to listen on for HTTP/REST API. If set to zero HTTP/REST API will not be exported. This port must be different than the one specified in --port.
	--rest_api_num_threads=16        	int32	Number of threads for HTTP/REST API processing. If not set, will be auto set based on number of CPUs.
	--rest_api_timeout_in_ms=30000   	int32	Timeout for HTTP/REST API calls.
	--enable_batching=false          	bool	enable batching
	--allow_version_labels_for_unavailable_models=false	bool	If true, allows assigning unused version labels to models that are not available yet.
	--batching_parameters_file=""    	string	If non-empty, read an ascii BatchingParameters protobuf from the supplied file name and use the contained values instead of the defaults.
	--model_config_file=""           	string	If non-empty, read an ascii ModelServerConfig protobuf from the supplied file name, and serve the models in that file. This config file can be used to specify multiple models to serve and other advanced parameters including non-default version policy. (If used, --model_name, --model_base_path are ignored.)
	--model_config_file_poll_wait_seconds=0	int32	Interval in seconds between each poll of the filesystemfor model_config_file. If unset or set to zero, poll will be done exactly once and not periodically. Setting this to negative is reserved for testing purposes only.
	--model_name="default"           	string	name of model (ignored if --model_config_file flag is set)
	--model_base_path=""             	string	path to export (ignored if --model_config_file flag is set, otherwise required)
	--max_num_load_retries=5         	int32	maximum number of times it retries loading a model after the first failure, before giving up. If set to 0, a load is attempted only once. Default: 5
	--load_retry_interval_micros=60000000	int64	The interval, in microseconds, between each servable load retry. If set negative, it doesn't wait. Default: 1 minute
	--file_system_poll_wait_seconds=1	int32	Interval in seconds between each poll of the filesystem for new model version. If set to zero poll will be exactly done once and not periodically. Setting this to negative value will disable polling entirely causing ModelServer to indefinitely wait for a new model at startup. Negative values are reserved for testing purposes only.
	--flush_filesystem_caches=true   	bool	If true (the default), filesystem caches will be flushed after the initial load of all servables, and after each subsequent individual servable reload (if the number of load threads is 1). This reduces memory consumption of the model server, at the potential cost of cache misses if model files are accessed after servables are loaded.
	--tensorflow_session_parallelism=0	int64	Number of threads to use for running a Tensorflow session. Auto-configured by default.Note that this option is ignored if --platform_config_file is non-empty.
	--tensorflow_intra_op_parallelism=0	int64	Number of threads to use to parallelize the executionof an individual op. Auto-configured by default.Note that this option is ignored if --platform_config_file is non-empty.
	--tensorflow_inter_op_parallelism=0	int64	Controls the number of operators that can be executed simultaneously. Auto-configured by default.Note that this option is ignored if --platform_config_file is non-empty.
	--ssl_config_file=""             	string	If non-empty, read an ascii SSLConfig protobuf from the supplied file name and set up a secure gRPC channel
	--platform_config_file=""        	string	If non-empty, read an ascii PlatformConfigMap protobuf from the supplied file name, and use that platform config instead of the Tensorflow platform. (If used, --enable_batching is ignored.)
2020-11-25 07:15:05.248564: I tensorflow_serving/model_servers/server_core.cc:464] Adding/updating models.
2020-11-25 07:15:05.248577: I tensorflow_serving/model_servers/server_core.cc:575]  (Re-)adding model: tracking
2020-11-25 07:15:05.348793: I tensorflow_serving/core/basic_manager.cc:739] Successfully reserved resources to load servable {name: tracking version: 1}
2020-11-25 07:15:05.348814: I tensorflow_serving/core/loader_harness.cc:66] Approving load for servable version {name: tracking version: 1}
2020-11-25 07:15:05.348822: I tensorflow_serving/core/loader_harness.cc:74] Loading servable version {name: tracking version: 1}
2020-11-25 07:15:05.348850: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:31] Reading SavedModel from: /mnt/models/1
2020-11-25 07:15:05.357807: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:54] Reading meta graph with tags { serve }
2020-11-25 07:15:05.357836: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:234] Reading SavedModel debug info (if present) from: /mnt/models/1
2020-11-25 07:15:05.357919: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-11-25 07:15:05.359175: I external/org_tensorflow/tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-11-25 07:15:05.362778: I external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-25 07:15:05.363092: I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:00:1e.0 name: Tesla T4 computeCapability: 7.5
coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s
2020-11-25 07:15:05.363104: I external/org_tensorflow/tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2020-11-25 07:15:05.363147: I external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-25 07:15:05.363447: I external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-25 07:15:05.363720: I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-11-25 07:15:06.234968: I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-25 07:15:06.234994: I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
2020-11-25 07:15:06.234999: I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
2020-11-25 07:15:06.235103: I external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-25 07:15:06.235413: I external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-25 07:15:06.235701: I external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-25 07:15:06.236017: I external/org_tensorflow/tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 249 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)
2020-11-25 07:15:06.253277: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:199] Restoring SavedModel bundle.
2020-11-25 07:15:06.253319: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:209] The specified SavedModel has no variables; no checkpoints were restored. File does not exist: /mnt/models/1/variables/variables.index
2020-11-25 07:15:06.253339: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:303] SavedModel load for tags { serve }; Status: success: OK. Took 904488 microseconds.
2020-11-25 07:15:06.253753: I tensorflow_serving/servables/tensorflow/saved_model_warmup_util.cc:59] No warmup data file found at /mnt/models/1/assets.extra/tf_serving_warmup_requests
2020-11-25 07:15:06.253878: I tensorflow_serving/core/loader_harness.cc:87] Successfully loaded servable version {name: <model-name> version: 1}
2020-11-25 07:15:06.255357: I tensorflow_serving/model_servers/server.cc:367] Running gRPC ModelServer at 0.0.0.0:8080 ...
[warn] getaddrinfo: address family for nodename not supported
[evhttp_server.cc : 238] NET_LOG: Entering the event loop ...
2020-11-25 07:15:06.256714: I tensorflow_serving/model_servers/server.cc:387] Exporting HTTP/REST API at:localhost:9000 ...

[yuzisun]

@daganida88 @akash-harijan Please check the tensorflow gRPC example

[daganida88]

I am also using v1alpha2 and not v1beta1… it says inference service resource does not exists in v1beta1.