tensorflow/tensorflow
tensorflow
TensorFlow（官网：[]
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TensorFlow Runtime Docker Images
These containers are a quick way to run or try TensorFlow. The source is available https://github.com/tensorflow/build/tree/master/tensorflow_runtime_dockerfiles. For building TensorFlow or extensions for TensorFlow, please see https://hub.docker.com/r/tensorflow/build.
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/***
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. See the https://hub.docker.com/r/tensorflow/tensorflow/tags/ for the available images.
Base Image Tags
Images built after Sept 2021 are based on Ubuntu 20.04. Earlier images are based on Ubuntu 18.04 or 16.04.
1.xx-,latest-, andnightly-tags come with TensorFlow pre-installed. Versioned tags contain their version, thelatest-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.develandcustom-optags are no longer supported. Please use https://hub.docker.com/r/tensorflow/build instead.
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; 3.8 for Ubuntu 20-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
-py3are deprecated. -gputags are based on https://hub.docker.com/r/nvidia/cuda/. You need https://github.com/NVIDIA/nvidia-docker to run them. NOTE: GPU versions of TensorFlow 1.13 and above (this includes thelatest-tags) require an NVidia driver that supports CUDA 10. See https://docs.nvidia.com/deploy/cuda-compatibility/index.html#binary-compatibility__table-toolkit-driver.-jupytertags include Jupyter and some TensorFlow tutorial notebooks.. They start a Jupyter notebook server on boot. Mount a volume to/tf/notebooksto work on your own notebooks.
Running Containers
bash$ 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.
bash$ 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.
更多相关 Docker 镜像与资源
以下是 tensorflow/tensorflow 相关的常用 Docker 镜像，适用于 不同场景 等不同场景：
- pytorch/pytorch Docker 镜像说明
- bitnami/pytorch Docker 镜像说明（PyTorch 深度学习框架，Bitnami 企业级配置）
- rocm/pytorch Docker 镜像说明（PyTorch 机器学习框架，ROCm GPU 加速版本）
- bitnamicharts/pytorch Docker 镜像说明（Bitnami PyTorch 图表栈，适合深度学习训练与推理）
- ascendai/pytorch Docker 镜像说明（Pytorch 镜像变体，适合 Pytorch、Tensorflow、Vllm 相关部署场景）
镜像拉取方式
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问 标签列表页面。
轩辕镜像加速拉取命令点我查看更多 tensorflow 镜像标签
docker pull docker.xuanyuan.run/tensorflow/tensorflow:<标签>
DockerHub 原生拉取命令
docker pull tensorflow/tensorflow:<标签>
镜像拉取常见问题
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来自真实用户的反馈，见证轩辕镜像的优质服务
oldzhang
运维工程师
Linux服务器
5
"Docker访问体验非常流畅，大镜像也能快速完成下载。"