---
title: Docker Images for TensorFlow
---

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## How to create docker images to run Tensorflow on YARN

Dockerfile to run Tensorflow on YARN need two part:

**Base libraries which Tensorflow depends on**

1) OS base image, for example ```ubuntu:18.04```

2) Tensorflow depended libraries and packages. For example ```python```, ```scipy```. For GPU support, need ```cuda```, ```cudnn```, etc.

3) Tensorflow package.

**Libraries to access HDFS**

1) JDK

2) Hadoop

Here's an example of a base image (w/o GPU support) to install Tensorflow:
```shell
FROM ubuntu:18.04

# Pick up some TF dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
        build-essential \
        curl \
        libfreetype6-dev \
        libpng-dev \
        libzmq3-dev \
        pkg-config \
        python \
        python-dev \
        rsync \
        software-properties-common \
        unzip \
        && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

RUN export DEBIAN_FRONTEND=noninteractive && apt-get update && apt-get install -yq krb5-user libpam-krb5 && apt-get clean

RUN curl -O https://bootstrap.pypa.io/get-pip.py && \
    python get-pip.py && \
    rm get-pip.py

RUN pip --no-cache-dir install \
        Pillow \
        h5py \
        ipykernel \
        jupyter \
        matplotlib \
        numpy \
        pandas \
        scipy \
        sklearn \
        && \
    python -m ipykernel.kernelspec

RUN pip --no-cache-dir install \
    http://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.13.1-cp27-none-linux_x86_64.whl
```

On top of above image, add files, install packages to access HDFS
```shell
RUN apt-get update && apt-get install -y openjdk-8-jdk wget
# Install hadoop
ENV HADOOP_VERSION="2.9.2"
RUN wget http://mirrors.hust.edu.cn/apache/hadoop/common/hadoop-${HADOOP_VERSION}/hadoop-${HADOOP_VERSION}.tar.gz
RUN tar zxf hadoop-${HADOOP_VERSION}.tar.gz
RUN ln -s hadoop-${HADOOP_VERSION} hadoop-current
RUN rm hadoop-${HADOOP_VERSION}.tar.gz
```

Build and push to your own docker registry: Use ```docker build ... ``` and ```docker push ...``` to finish this step.

## Use examples to build your own Tensorflow docker images

We provided following examples for you to build tensorflow docker images.

For Tensorflow 1.13.1 (Precompiled to CUDA 10.x)

- *docker/tensorflow/base/ubuntu-18.04/Dockerfile.cpu.tf_1.13.1*: Tensorflow 1.13.1 supports CPU only.
- *docker/tensorflow/with-cifar10-models/ubuntu-18.04/Dockerfile.cpu.tf_1.13.1*: Tensorflow 1.13.1 supports CPU only, and included models
- *docker/tensorflow/base/ubuntu-18.04/Dockerfile.gpu.tf_1.13.1*: Tensorflow 1.13.1 supports GPU, which is prebuilt to CUDA10.
- *docker/tensorflow/with-cifar10-models/ubuntu-18.04/Dockerfile.gpu.tf_1.13.1*: Tensorflow 1.13.1 supports GPU, which is prebuilt to CUDA10, with models.

## Build Docker images

### Manually build Docker image:

Under `docker/` directory, run `build-all.sh` to build Docker images. It will build following images:

- `tf-1.13.1-gpu-base:0.0.1` for base Docker image which includes Hadoop, Tensorflow, GPU base libraries.
- `tf-1.13.1-gpu-base:0.0.1` for base Docker image which includes Hadoop. Tensorflow.
- `tf-1.13.1-gpu:0.0.1` which includes cifar10 model
- `tf-1.13.1-cpu:0.0.1` which inclues cifar10 model (cpu only).

### Use prebuilt images

(No liability)
You can also use prebuilt images for convenience:

- hadoopsubmarine/tf-1.13.1-gpu:0.0.1
- hadoopsubmarine/tf-1.13.1-cpu:0.0.1
