Grid Demo | cats-and-dogs

In this demo example, you'll train a classifier on the classic cats and dogs dataset.

If you haven't already set up the Grid CLI, follow this 1 minute guide on how to install the Grid CLI.

TLDR: pip install lightning-grid --upgrade

grid login

Overview

This example involves three steps:

1. Downloading the dataset from Kaggle
2. Uploading the data to Grid using datastores
3. Training the run.py script on the cats_and_dogs dataset

Download the datatset

First, you'll need to download the cats and dogs dataset and remove the 1590 corrupt images that unfortunately are shipped with the dataset from Microsoft.

python get_data.py

Upload data to Grid

Now that you have some data, let's upload that as a Grid Datastore

grid datastores create --source cats_and_dogs_data/PetImages --name cats-and-dogs-ds

Here the --source represents the path to the data which will be uploaded. The --name will be used as the datastore name in Grid.

Submit a training run with Grid**

Training Parameters Here are the parameters we'll specify to grid train:

Grid flags:

1. --grid_name: specifies a name for your training run
2. --grid_instance_type: defines number of GPUs and memory
3. --grid_gpus: the number of GPUs per experiment
4. --grid_datastore_name: the name of the datastore (created above) that you'd like to attach to this training run
5. --grid_datastore_version: the version of the datatstore to attach to this training run (defaults to 1)

Then we'll specify the script we're using to train our model followed by the script arguments.

Script: run.py

These are the arguments defined by the run.py script:

Script arguments:

1. data_dir
2. gpus
3. precision
4. max_epochs

Cool! Now we can spin up a Grid Train run.

Submit the command below:

grid train  \
  --grid_name cats-v-dogs \
  --grid_instance_type g3.4xlarge  \
  --grid_gpus 1  \
  --grid_datastore_name cats-and-dogs-ds\
  --grid_datastore_version 1 \
  run.py \
  --data_dir /opt/datastore \
  --gpus 1 \
  --precision 16 \
  --max_epochs 10

You can use the grid status command to check on the status of the run. To view progess in the Grid UI, use grid view.