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tensorflow/tensorflow docker image 镜像

 3 years ago
source link: https://hub.docker.com/r/tensorflow/tensorflow
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 docker pull tensorflow/tensorflow


## TensorFlow Docker Images

TensorFlow's many tags are defined on GitHub, where you can also find extra Dockerfiles. See the full list of tags for the available images.

These images are based on TensorFlow's official Python binaries, which require a CPU with AVX support. Most modern CPUs do support AVX, so it's unlikely that you will have a problem with this. See also https://github.com/tensorflow/tensorflow/issues/19584

The tags described below are accurate for all releases starting with TF 1.13. Older releases are still tagged using the older format and images.

Base Image Tags

Images built after May 20 2019 (TF nightly, plus TF versions 1.14 and onward) are based on Ubuntu 18.04. Earlier images are based on Ubuntu 16.04.

  • 1.xx-, latest-, and nightly- tags come with TensorFlow pre-installed. Versioned tags contain their version, the latest- tags contain the latest release (excluding pre-releases like release candidates, alphas, and betas), and the nightly images come with the latest TensorFlow nightly Python package.
  • devel images come with Bazel and are ideal for developing changes to TensorFlow at master. /tensorflow_src includes the TensorFlow source tree at the latest nightly commit where the Pip package built successfully in the container. We no longer provide images for developing on top of older versions of TF (1.12.0 was the last release where this was the case). latest-devel and devel are identical; nightly-devel was renamed to devel.
  • custom-op is a special experimental image for developing TF custom ops.

Optional Features

  • Versioned images <= 1.15.0 (1.x) and <= 2.1.0 (2.x) have Python 3 (3.5 for Ubuntu 16-based images; 3.6 for Ubuntu 18-based images) in images tagged "-py3" and Python 2.7 in images without "py" in the tag. All newer images are Python 3 only. Tags containing -py3 are deprecated.
  • -gpu tags are based on Nvidia CUDA. You need nvidia-docker to run them. NOTE: GPU versions of TensorFlow 1.13 and above (this includes the latest- tags) require an NVidia driver that supports CUDA 10. See NVidia's support matrix.
  • -jupyter tags include Jupyter and some TensorFlow tutorial notebooks.. They start a Jupyter notebook server on boot. Mount a volume to /tf/notebooks to work on your own notebooks.

Running Containers

$ docker run -it --rm tensorflow/tensorflow bash

Start a CPU-only container

$ docker run -it --rm --runtime=nvidia tensorflow/tensorflow:latest-gpu python

Start a GPU container, using the Python interpreter.

$ docker run -it --rm -v $(realpath ~/notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-jupyter

Run a Jupyter notebook server with your own notebook directory (assumed here to be ~/notebooks). To use it, navigate to localhost:8888 in your browser.


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