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Serving Tensorflow Estimator with Tensorflow Serving Examples

In this repository, I would like to show examples to server tensorflow estimator with tensorflow serving. The goals of this repository are to understand:

  1. how to create tensorflow estimators to serve with tensorflow serving,
  2. how to server a model with tensorflow, and
  3. how to implement a gRPC client for tensorflow serving.

At the time when I am writing this, there are less documentation about serving custom tensorflow estimator. That's why I made this for myself. I hope it would help someone with learning something similar.

Requirements

  • Python 2.7
  • Docker
  • Anaconda

How to run the examples

1. You clone the repository.

git clone [email protected]:yu-iskw/tensorflow-serving-example.git

2. You create an anaconda environment.

At the moment when I am creating this, versions of main components are the following.

  • tensorflow: 1.12.0
  • tensorflow-serving-api: 1.12.0
  • tensorflow-serving: 1.12.0
conda env create -f environment.yml

Once you create the environment, you need to activate this using the following command:

source activate tensorflow-serving-example

3. You train a model.

You train a model with python/train/mnist_custom_estimator.py. It is used for training a model to the MNIST task.

python python/train/mnist_custom_estimator.py \
    --steps 100 \
    --saved_dir ./models/ \
    --model_dir /tmp/mnist_custom_estimator

Where --steps is the number of steps to train a model, --saved_dir is a path to save the model for tensorflow serving and --model_dir is a path to save traditional checkpoints, meta graph and so on.

When you finish the trained model, the saved model exists under ./models directory like below. Here, 1548714304 is the unix timestamp when the model was saved. This is different from what you get. It depends on when you run the python code.

We can server saved_model.pb with tensorflow serving.

./models
└── 1548714304
    ├── saved_model.pb
        └── variables
                ├── variables.data-00000-of-00001
                        └── variables.index

4. You build a docker image for tensorflow serving.

As you can see ./Dockerfile, it just installs the pre-built tensorflow serving package. This is because building it takes a little long time to compile tensorflow serving. If you want to make a docker image from the tensorflow serving source, those docker files would help.

docker build --rm -f Dockerfile -t tensorflow-serving-example:0.6 .

At the time when I am writing this, there is something wrong with tensorflow-serving-universal. If you are interested in the issue, please track Package recently broken on ubuntu 16.04 ? · Issue #819 · tensorflow/serving.

5. You run the docker container with the trained model.

Before running a docker container, you must prepare for the served model.

# Prepare for the served model.
mkdir -p ./models_for_serving/mnist/1
cp -R ./models/mnist_custom_estimator/pb/1548714304/* ./models_for_serving/mnist/1

# As a result of copying the files, the directory should be like following.
models_for_serving/
└── mnist
    └── 1
        ├── saved_model.pb
        └── variables
            ├── variables.data-00000-of-00001
            └── variables.index

As you probably know, tenwoflow can handle multiple versions of served models. 1 at the tail of ./models_for_serving/mnist/1/ means the served model version.

We have prepared for the served model. Now let's move on to running a docker container to server the model. The model serving supports not only gRPC API, but HTTP/REST API. The port 8500 is used for gRPC. Meanwhile, the port 8501 is used for RESTful API.

# Run a docker container.
docker run --rm -it -v /Users/yuishikawa/local/src/github/tensorflow-serving-example/models_for_serving:/models \
    -e MODEL_NAME=mnist \
    -e MODEL_PATH=/models/mnist \
    -p 8500:8500  \
    -p 8501:8501  \
    --name tensorflow-serving-example \
    tensorflow-serving-example:0.6

Where MODEL_NAME is an environment variable to identify the served model when a gRPC client requests, MODEL_PATH is an environment variable to identify the path to the saved model. Besides, we share ./models_for_serving between docker host and guest.

6. You create a gRPC client for tensorflow serving.

./python/grpc_mnist_client.py is an example of gRPC client for tensorflow serving.

TENSORFLOW_SERVER_HOST="..."
python python/grpc_mnist_client.py \
  --image ./data/0.png \
  --model mnist \
  --host $TENSORFLOW_SERVER_HOST

You can pass one with --image option. I already prepared for some sample MNIST images in ./data/. If you use docker-machine, you can get the host with docker-machine ip. --model mnist is defined, when running a docker container with -e MODEL_NAME='mnist'.

Appendix: Serving premodeled tensorflow estimator

./python/train/mnist_premodeled_estimator.py is an example to save a trained model with a premodeled tensorflow estimator. One of the differences from a custom tensorflow estimator is the model spec signature name. When saving a model with a custom tensorflow estimator, the signature name is serving_default by default. On the other hand, when saving a model with a pre-modeled tensorflow estimator, the signature name is predict by default.

TENSORFLOW_SERVER_HOST="..."
python python/grpc_mnist_client.py \
  --image ./data/0.png \
  --model mnist \
  --host $TENSORFLOW_SERVER_HOST \
  --signature_name predict