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Build your Docker for SPATL

This image is built based on Docker Pytorch, thanks very much for their great efforts.

Requirements

In order to use this image you must have Docker Engine installed. Instructions for setting up Docker Engine are available on the Docker website.

CUDA requirements

If you have a CUDA-compatible NVIDIA graphics card, you can use a CUDA-enabled version of the PyTorch image to enable hardware acceleration.

Firstly, ensure that you install the appropriate NVIDIA drivers. On Ubuntu, I've found that the easiest way of ensuring that you have the right version of the drivers set up is by installing a version of CUDA at least as new as the image you intend to use via the official NVIDIA CUDA download page. As an example, if you intend on using the cuda-10.1 image then setting up CUDA 10.1 or CUDA 10.2 should ensure that you have the correct graphics drivers.

You will also need to install the NVIDIA Container Toolkit to enable GPU device access within Docker containers. This can be found at NVIDIA/nvidia-docker.

Build your images for SPATL

Go to where the Dockerfile located Docker/Dockerfile. The Docker configuration is written in the Dockerfile.

To build your local image, you could simply run the following command:

$  docker build --tag spatl .

To see a list of images we have on our local machine, simply run the docker images command.

Usage

Running PyTorch scripts

It is possible to run PyTorch programs inside a container using the python3 command. Go to the directory where the spatl_federated_learning.py located.

To conduct the experiment shown in the paper, you could run it with the following command:

docker run --rm -it --init \
  --gpus=all \
  --ipc=host \
  --user="$(id -u):$(id -g)" \
  --volume="$PWD:/app" \
  spatl python3 spatl_federated_learning.py --model=vgg --dataset=cifar10 --alg=gradient_control --lr=0.01 --batch-size=64 --epochs=5 --n_parties=30 --beta=0.1 --device='cuda' --datadir='./data/' --logdir='./logs/'  --noise=0 --sample=0.7 --rho=0.9 --comm_round=200 --init_seed=0

Here's a description of the Docker command-line options shown above:

  • --gpus=all: Required if using CUDA, optional otherwise. Passes the graphics cards from the host to the container. You can also more precisely control which graphics cards are exposed using this option (see documentation at https://github.com/NVIDIA/nvidia-docker).
  • --ipc=host: Required if using multiprocessing, as explained at https://github.com/pytorch/pytorch#docker-image.
  • --user="$(id -u):$(id -g)": Sets the user inside the container to match your user and group ID. Optional, but is useful for writing files with correct ownership.
  • --volume="$PWD:/app": Mounts the current working directory into the container. The default working directory inside the container is /app. Optional.

Running different experiment

If you are going to reproduce different experiment results shown in the paper, you can change the hyper-parameter arguments, you can find all the listed arguments in the utils/parameter.py. To run different experiment, you can change the argument in python3 spatl_federated_learning.py --xxx xxx