TensorFlow - 要使用哪个 Docker 映像? [英] TensorFlow - which Docker image to use?

问题描述

来自 TensorFlow 下载和设置

Docker 安装我看到了:

Docker installation I see:

1. b.gcr.io/tensorflow/tensorflow 最新 4ac133eed955 653.1 MB
2. b.gcr.io/tensorflow/tensorflow latest-devel 6a90f0a0e005 2.111 GB
3. b.gcr.io/tensorflow/tensorflow-full 最新 edc3d721078b 2.284 GB

1. b.gcr.io/tensorflow/tensorflow latest 4ac133eed955 653.1 MB
2. b.gcr.io/tensorflow/tensorflow latest-devel 6a90f0a0e005 2.111 GB
3. b.gcr.io/tensorflow/tensorflow-full latest edc3d721078b 2.284 GB

我知道 2. &3. 有源代码，我现在使用 2..

I know 2. & 3. are with source code and I am using 2. for now.

2. & 和有什么不一样?3. ?推荐哪一种用于正常"使用?

What is the difference between 2. & 3. ? Which one is recommended for "normal" use?

TLDR:

首先 - 感谢 Docker 镜像！它们是开始 TF 的最简单、最干净的方式.

First of all - thanks for Docker images! They are the easiest and cleanest way to start with TF.

关于图片的一些事情

• 没有 PIL
• 没有nano(但是有vi)并且apt-get 找不到它.是的，我可能可以为它配置存储库，但为什么不开箱即用

推荐答案

有四张图:

1. b.gcr.io/tensorflow/tensorflow:TensorFlow CPU 二进制图像.
2. b.gcr.io/tensorflow/tensorflow:latest-devel:CPU 二进制图像加上源代码.
3. b.gcr.io/tensorflow/tensorflow:latest-gpu:TensorFlow GPU 二进制图像.
4. gcr.io/tensorflow/tensorflow:latest-devel-gpu:GPU 二进制图像加源代码.

关注的两个属性是:
1. CPU 或 GPU
2.无源或加源

And the two properties of concern are:
1. CPU or GPU
2. no source or plus source

CPU 或 GPU:CPU

对于第一次使用的用户，强烈建议避免使用 GPU 版本，因为它们可能是任何难以使用或无法使用的地方.原因是并非所有机器都有符合要求的NVidia图形芯片.您应该首先让 TensorFlow 工作以了解它，然后根据需要继续使用 GPU 版本.

For a first time user it is highly recommended to avoid the GPU version as they can be any where from difficult to impossible to use. The reason is that not all machines have an NVidia graphic chip that meet the requirements. You should first get TensorFlow working to understand it then move onto using the GPU version if you want/need.

来自 TensorFlow 构建说明

From TensorFlow Build Instructions

可选:安装 CUDA(Linux 上的 GPU)

为了构建或运行具有 GPU 支持的 TensorFlow，Cuda需要安装NVIDIA的Toolkit 7.0和CUDNN 6.5 V2.

In order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and CUDNN 6.5 V2 from NVIDIA need to be installed.

TensorFlow GPU 支持需要具有 GPU 卡NVidia 计算能力 >= 3.5.支持的卡包括但不限于:

TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.5. Supported cards include but are not limited to:

英伟达泰坦
NVidia Titan X
英伟达 K20
英伟达 K40

NVidia Titan
NVidia Titan X
NVidia K20
NVidia K40

no source or plus source: no source

docker 图像无需源代码即可工作.如果您出于某种原因需要重建 TensorFlow，例如添加 新操作.

The docker images will work without needing the source. You should only want or need the source if you need to rebuild TensorFlow for some reason such as adding a new OP.

对于刚开始使用 TensorFlow 的人的标准建议是从没有源的 CPU 版本开始.

The standard recommendation for someone new to using TensorFlow is to start with the CPU version without the source.

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