文件列表:

Dockerfile (890, 2019-01-17)
LICENSE (1063, 2019-01-17)
Makefile (355, 2019-01-17)
models (0, 2019-01-17)
models\bar (0, 2019-01-17)
models\bar\1 (0, 2019-01-17)
models\baz (0, 2019-01-17)
models\baz\0 (0, 2019-01-17)
models\baz\1 (0, 2019-01-17)
models\config.tf (478, 2019-01-17)
models\foo (0, 2019-01-17)
models\foo\1 (0, 2019-01-17)

# TensorFlow Model Server [](https://hub.docker.com/r/heycar/tensorflow-model-server/) [](https://github.com/hey-car/tensorflow-model-server) [](https://github.com/hey-car/tensorflow-model-server) > Docker image with a tensor model server running ## Motivation In order to run TensorFlow predictions in production, it is recommended to use [TensorFlow Serving](https://www.tensorflow.org/serving/). The problem is that there are no official docker images, standard ways of running it. Also, their documentation on "how to run" doesn't work properly. ## Models ### Exporting the model In order to make TFS work, the models have to be exported with the [SavedModelBuilder](https://www.tensorflow.org/serving/serving_basic). That will create a folder strucure similar to: ``` models/variables/ # variables folder models/saved_model.pb # graph file ``` ### Folder structure The folder strucure needs to follow this pattern: ``` models/config.tf # config file models/foo/ # model models/foo/1/ # version models/foo/1/variables/ # variables folder exported from the SavedModelBuilder models/foo/1/saved_model.pb # graph file exported from the SavedModelBuilder ... ``` [](https://www.youtube.com/watch?v=ACmydtFDTGs) ### Config file A basic map of the models folder with the name, path and platform of the models. **Important**: The root path of the Docker image is `/tensorflow/`. Therefore, you need to prepend that to the models path, as the example: ``` model_config_list: { config: { name: "foo", base_path: "/tensorflow/models/foo", model_platform: "tensorflow" model_version_policy: {latest{}}, }, config: { name: "bar", base_path: "/tensorflow/models/bar", model_platform: "tensorflow" model_version_policy: {all{}} }, config: { name: "baz", base_path: "/tensorflow/models/baz", model_platform: "tensorflow" model_version_policy: {specific{versions:0,versions:1}} } } ``` ### Showtime In order to run locally, you can: ``` make run ``` Check the `Makefile` for more info: * `$MODELS_PATH` is the folder where you want to get the models from * `$TAG` is the desired release of this image ## Contributing Remember to update the tag in the `Makefile`, use the same tag for git. Then, run: `make release`, which will `build` and `push` the new docker image to dockerhub. ## TODO Open to contributors - Move the port to an environment variable - Add tests (Run a container with the inception or mnist server) - Relase a 1.0 version

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