Tutorial shows how to train self supervised learning model using Grid, PyTorch Lightning


This example covers the Self supervised learning deep learning task.

Tutorial time: 5 minutes

  1. Start New Run with 1 GPU

  2. Visualize Experiments

  3. Download artifacts

  4. Update to run with 8 GPUs


In Self supervised learning (SSL), input data is not provided with labels. Rather, input data is divided into parts where some parts are suppressed with a mask and then model is trained to predict the data that is missing. Self supervised methods for image classification are becoming popular, this tutorial shows an example

We will be using this repository: an implementation in PyTorch Lightning framework


The dataset used is CIFAR-10, collection of images in 10 different classes. However there is no need to upload data for this example, the repository includes functions to download


A Simple Framework for Contrastive Learning of Visual Representations (SimCLR) is used for this example. If you are interested to see the code it is here

Step 1: Start a new Run

Use the Web Interface, select New Run and choose this script:

Add script arguments to set batch size, number of workers etc

--batch_size 256 \
--num_workers 16 \
--exclude_bn_bias \
--max_epochs 800

Model starts to train.

Step 2: Visualize the experiments

Visualize training on the Web interface, see metrics and download checkpoints from the run

Step 3: Download artifacts

Navigate to experiment details and download the artifacts

Bonus: Run on CLI with 8 GPUs

Run same example with 8 V100, notice how fast model trains

grid run \
--instance_type 1_v100_16gb \
--gpus 8 \
src/ \
--gpus 8 \
--batch_size 256 \
--num_workers 16 \
--exclude_bn_bias \
--max_epochs 800