Loadsim272

Docker uses containers tocreate virtual environments that isolate a TensorFlow installation from the restof the system. TensorFlow programs are run within this virtual environment thatcan share resources with its host machine (access directories, use the GPU,connect to the Internet, etc.). TheTensorFlow Docker images are tested for each release.

Jul 31, 2018 Docker uses the raw format on Macs running the Apple Filesystem (APFS). APFS supports sparse files, which compress long runs of zeroes representing unused space. The output of ls is misleading, because it lists the logical size of the file rather than its physical size. Buildx comes with a default builder that you can observe by typing docker buildx ls into your terminal. However, we want to make and use a builder ourselves. You will need a new builder, so make one with docker buildx create -name m1builder. Now you should see the new builder when running docker buildx ls. Use and bootstrap.

Docker is the easiest way to enable TensorFlow GPU support on Linux since only theNVIDIA® GPU driver is required on the host machine (the NVIDIA® CUDA® Toolkit does not need tobe installed).

TensorFlow Docker requirements

1. Install Docker onyour local host machine.
2. For GPU support on Linux, install NVIDIA Docker support.
   • Take note of your Docker version with docker -v. Versions earlier than 19.03 require nvidia-docker2 and the --runtime=nvidia flag. On versions including and after 19.03, you will use the nvidia-container-toolkit package and the --gpus all flag. Both options are documented on the page linked above.

Download a TensorFlow Docker image

The official TensorFlow Docker images are located in the tensorflow/tensorflow Docker Hub repository. Image releases are tagged using the following format:

Each base tag has variants that add or change functionality:

You can use multiple variants at once. For example, the following downloadsTensorFlow release images to your machine:

Start a TensorFlow Docker container

To start a TensorFlow-configured container, use the following command form:

For details, see the docker run reference.

Examples using CPU-only images

Let's verify the TensorFlow installation using the latest tagged image. Dockerdownloads a new TensorFlow image the first time it is run:

Let's demonstrate some more TensorFlow Docker recipes. Start a bash shellsession within a TensorFlow-configured container:

Docker For Mac Download

Within the container, you can start a python session and import TensorFlow.

To run a TensorFlow program developed on the host machine within a container,mount the host directory and change the container's working directory(-v hostDir:containerDir -w workDir):

Permission issues can arise when files created within a container are exposed tothe host. It's usually best to edit files on the host system.

Start a Jupyter Notebook server usingTensorFlow's nightly build:

Follow the instructions and open the URL in your host web browser:http://127.0.0.1:8888/?token=...

GPU support

Docker is the easiest way to run TensorFlow on a GPU since the host machineonly requires the NVIDIA® driver (the NVIDIA® CUDA® Toolkit is not required).

Install the Nvidia Container Toolkit to add NVIDIA® GPU support to Docker. nvidia-container-runtime is onlyavailable for Linux. See the nvidia-container-runtimeplatform support FAQ for details.

Check if a GPU is available:

Verify your nvidia-docker installation:

Docker For Mac Edge

Examples using GPU-enabled images

Download and run a GPU-enabled TensorFlow image (may take a few minutes):

It can take a while to set up the GPU-enabled image. If repeatedly runningGPU-based scripts, you can use docker exec to reuse a container.

Use the latest TensorFlow GPU image to start a bash shell session in the container: