Docker

LightlyTrain is available as a Docker image on Docker Hub for containerized deployment.

Installation

Install Docker with NVIDIA GPU support (docs). Then, pull the latest version of the LightlyTrain Docker image from Docker Hub:

docker pull lightly/train:latest

You can verify that the image is working correctly by running the following command:

docker run --rm --gpus=all lightly/train:latest lightly-train --help

This should print the LightlyTrain help message.

Usage

Start a LightlyTrain Docker container in interactive mode:

docker run -it --rm --gpus=all --shm-size=4gb --user $(id -u):$(id -g) -v /my_output_dir:/out -v /my_data_dir:/data lightly/train:latest

Flags:

• -it: Starts the container in interactive mode.

• --rm: Removes the container after it has been stopped.

• --gpus=all: Enables GPU support.

• --shm-size=4gb: Sets the shared memory size to 4 GB. Increase this for large datasets.

• --user $(id -u):$(id -g): Run the container with the same user as the host. This makes sure that all files created by the container (checkpoints, logs, etc.) have the same permissions as the user running the container.

• -v /my_output_dir:/out: Mounts the host directory /my_output_dir to the container directory /out. All files created by the container (checkpoints, logs, etc.) will be saved in this directory.

• -v /my_data_dir:/data: Mounts the host directory /my_data_dir to the container directory /data. This directory must contain your training data. See the Data docs for more information on how to structure your data.

Once the container is running, you can run LightlyTrain commands inside the container as if you had installed it locally. The only difference is that paths must be specified relative to the mounted directories /out and /data. For example, to train a model, run the following command inside the container:

Command Line

lightly-train train out="/out/my_experiment" data="/data" model="torchvision/resnet50"