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Serving TensorFlow models with custom ops

TensorFlow comes pre-built with an extensive library of ops and op kernels (implementations) fine-tuned for different hardware types (CPU, GPU, etc.). These operations are automatically linked into the TensorFlow Serving ModelServer binary with no additional work required by the user. However, there are two use cases that require the user to link in ops into the ModelServer explicitly:

  • You have written your own custom op (ex. using this guide)
  • You are using an already implemented op that is not shipped with TensorFlow

Note: Starting in version 2.0, TensorFlow no longer distributes the contrib module; if you are serving a TensorFlow program using contrib ops, use this guide to link these ops into ModelServer explicitly.

Regardless of whether you implemented the op or not, in order to serve a model with custom ops, you need access to the source of the op. This guide walks you through the steps of using the source to make custom ops available for serving. For guidance on implementation of custom ops, please refer to the tensorflow/custom-op repo.

Prerequisite: With Docker installed, you have cloned the TensorFlow Serving repository and your current working directory is the root of the repo.

Copy over op source into Serving project

In order to build TensorFlow Serving with your custom ops, you will first need to copy over the op source into your serving project. For this example, you will use tensorflow_zero_out from the custom-op repository mentioned above.

Wihin the serving repo, create a custom_ops directory, which will house all your custom ops. For this example, you will only have the tensorflow_zero_out code.

mkdir tensorflow_serving/custom_ops
cp -r <custom_ops_repo_root>/tensorflow_zero_out tensorflow_serving/custom_ops

Build static library for the op

In tensorflow_zero_out's BUILD file, you see a target producing a shared object file (.so), which you would load into python in order to create and train your model. TensorFlow Serving, however, statically links ops at build time, and requires a .a file. So you will add a build rule that produces this file to tensorflow_serving/custom_ops/tensorflow_zero_out/BUILD:

cc_library(
    name = 'zero_out_ops',
    srcs = [
        "cc/kernels/zero_out_kernels.cc",
        "cc/ops/zero_out_ops.cc",
    ],
    alwayslink = 1,
    deps = [
        "@org_tensorflow//tensorflow/core:framework",
    ]
)

Build ModelServer with the op linked in

To serve a model that uses a custom op, you have to build the ModelServer binary with that op linked in. Specifically, you add the zero_out_ops build target created above to the ModelServer's BUILD file.

Edit tensorflow_serving/model_servers/BUILD to add your custom op build target to SUPPORTED_TENSORFLOW_OPS which is inluded in the server_lib target:

SUPPORTED_TENSORFLOW_OPS = [
    ...
    "//tensorflow_serving/custom_ops/tensorflow_zero_out:zero_out_ops"
]

Then use the Docker environment to build the ModelServer:

tools/run_in_docker.sh bazel build tensorflow_serving/model_servers:tensorflow_model_server

Serve a model containing your custom op

You can now run the ModelServer binary and start serving a model that contains this custom op:

tools/run_in_docker.sh -o "-p 8501:8501" \
bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server \
--rest_api_port=8501 --model_name=<model_name> --model_base_path=<model_base_path>

Send an inference request to test op manually

You can now send an inference request to the model server to test your custom op:

curl http://localhost:8501/v1/models/<model_name>:predict -X POST \
-d '{"inputs": [[1,2], [3,4]]}'

This page contains a more complete API for sending REST requests to the model server.