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Running with Docker

Establishing TensorSpace-Converter runtime environment is a tedious topic? Dockerize it!

Here is a TensorSpace-Converter Dockerfile, you can use it to build a out-of-box tensorspacejs image. We also provide some easy to use scripts to init (init_docker_converter.sh) and run (run_docker_converter.sh) tensorspacejs docker image.

Step 1: Init

To init tensorspacejs Docker image (make sure start Docker daemon before init the image):

bash init_docker_converter.sh

Step 2: Run

To run docker image. Put TensorSpace-Converter script and model assets in a work_dir, and execute run_docker_converter.sh to run tensorspacejs image:

bash run_docker_converter.sh --work_dir ./example

Parameters Setting

  • work_dir

work_dir is where to place converter.sh and input model assets, meanwhile, tensorspacejs Docker image will save generated model files into this directory. Configure work_dir when run the tensorspacejs Docker image through run_docker_converter.sh. In this example, configure work_dir to be ./example

  • converter.sh

converter.sh contains TensorSpace-Converter conversion code, tensorspacejs image will execute this script. Place converter.sh at the root of work_dir, make sure the file name is converter.sh (should not be renamed).

  • input and output

Place input model under work_dir, the path of input model and output folder are relative to work_dir. In this example, set input_path to be ./input/keras_model.h5 and output_path to be ./output in converter.sh.