docker

TensorFlow Docker Images

This directory contains example Dockerfiles to run TensorFlow on cluster managers.

• Dockerfile is the most basic example, which just adds a Python training program on top of the tensorflow/tensorflow Docker image.
• Dockerfile.hdfs installs Hadoop libraries and sets the appropriate environment variables to enable reading from HDFS.
• mnist.py demonstrates the programmatic setup for distributed TensorFlow training.

Best Practices

• Always pin the TensorFlow version with the Docker image tag. This ensures that TensorFlow updates don't adversely impact your training program for future runs.
• When creating an image, specify version tags (see below). If you make code changes, increment the version. Cluster managers will not pull an updated Docker image if they have them cached. Also, versions ensure that you have a single copy of the code running for each job.

Building the Docker Files

First, pick an image name for the job. When running on a cluster manager, you will want to push your images to a container registry. Note that both the Google Container Registry and the Amazon EC2 Container Registry require special paths. We append :v1 to version our images. Versioning images is strongly recommended for reasons described in the best practices section.

docker build -t <image_name>:v1 -f Dockerfile .
# Use gcloud docker push instead if on Google Container Registry.
docker push <image_name>:v1

If you make any updates to the code, increment the version and rerun the above commands with the new version.

Running the mnist Example

The mnist.py example reads the mnist data in the TFRecords format. You can run the convert_to_records.py program to convert mnist data to TFRecords.

When running distributed TensorFlow, you should upload the converted data to a common location on distributed storage, such as GCS or HDFS.