TensorFlow - which Docker image to use?

Question

From TensorFlow Download and Setup under

Docker installation I see:

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

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

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

TLDR:

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

Few aside things about images

• there is no PIL
• there is no nano (but there is vi) and apt-get cannot find it. yes i probable can configure repos for it, but why not out of the box

Answer 1

There are four images:

1. b.gcr.io/tensorflow/tensorflow: TensorFlow CPU binary image.
2. b.gcr.io/tensorflow/tensorflow:latest-devel: CPU Binary image plus source code.
3. b.gcr.io/tensorflow/tensorflow:latest-gpu: TensorFlow GPU binary image.
4. gcr.io/tensorflow/tensorflow:latest-devel-gpu: GPU Binary image plus source code.

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

CPU or GPU: CPU

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.

From TensorFlow Build Instructions

Optional: Install CUDA (GPUs on Linux)

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

NVidia Titan
NVidia Titan X
NVidia K20
NVidia K40

no source or plus source: no source

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.

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