verbose logging in tensorflow serving via docker

Question

Is there a way to set log level in tf serving via docker? I see these params but do not see anything about logging there

    --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=48           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.)
    --per_process_gpu_memory_fraction=0.000000  float   Fraction that each process occupies of the GPU memory space the value is between 0.0 and 1.0 (with 0.0 as the default) If 1.0, the server will allocate all the memory when the server starts, If 0.0, Tensorflow will automatically select a value.
    --saved_model_tags="serve"          string  Comma-separated set of tags corresponding to the meta graph def to load from SavedModel.
    --grpc_channel_arguments=""         string  A comma separated list of arguments to be passed to the grpc server. (e.g. grpc.max_connection_age_ms=2000)
    --enable_model_warmup=true          bool    Enables model warmup, which triggers lazy initializations (such as TF optimizations) at load time, to reduce first request latency.
    --version=false                     bool    Display version
    --monitoring_config_file=""         string  If non-empty, read an ascii MonitoringConfig protobuf from the supplied file name
    --remove_unused_fields_from_bundle_metagraph=true   bool    Removes unused fields from MetaGraphDef proto message to save memory.
    --use_tflite_model=false            bool    EXPERIMENTAL; CAN BE REMOVED ANYTIME! Load and use TensorFlow Lite model from `model.tflite` file in SavedModel directory instead of the TensorFlow model from `saved_model.pb` file.

There are some related SO questions, like this one but they talk about serving via bazel.

Specifically, I want to log the incoming requests.

Answer 1

I'm not sure this is going to give you exactly what you want, but I have had luck getting more verbose logging out of TensorFlow Serving by setting the environment variable TF_CPP_MIN_VLOG_LEVEL, where the bigger the value, the more verbose the logging.

E.g., TF_CPP_MIN_VLOG_LEVEL=4 will be extremely verbose.

Answer 2

This can help you :

docker ... -e TF_CPP_MIN_VLOG_LEVEL=4 ...

Add -e TF_CPP_MIN_VLOG_LEVEL=4 or -e TF_CPP_VMODULE=http_server=1 to docker command