Simple example¶
Installation and setup¶
In this tutorial you will run a very simple MNIST example in pytorch using Track.
First, install Track,
then install pytorch
, torchvision
and clone the
PyTorch examples repository:
$ pip3 install torch torchvision
$ git clone git@github.com:pytorch/examples.git
Adapting the code of MNIST example¶
After cloning pytorch examples repository, cd to mnist folder:
$ cd examples/mnist
In main, just after parsing the arguments, you can initialize the track client and create a trial. The client specifies how will the data be saved on your computer, different methods are supported. Once the client is initialized, you can create a new trial.
A trial is a set of data retrieved for a set of arguments.
$ ....
$ args = parser.parse_args()
$ client = TrackClient('file:mnist_example.json')
$ trial = client.new_trial(arguments=args)
Then you can store any kind of data that you think will be useful. In our example we decided to save the error rate on the test set
$ def test(args, model, device, test_loader, trial):
$ ...
$ trial.log_metrics(error_rate=1 - (correct / len(test_loader.dataset)))
At the end of training file mnist_example.json will be generated holding all the data you saved during training.