PyTorch MNIST Tutorial

This tutorial describes how to port an existing PyTorch model to Determined. We will port a simple image classification model for the MNIST dataset. This tutorial is based on the official PyTorch MNIST example.

Prerequisites

• Access to a Determined cluster. If you have not yet installed Determined, refer to the installation instructions.

• The Determined CLI should be installed on your local machine. For installation instructions, see here. After installing the CLI, configure it to connect to your Determined cluster by setting the DET_MASTER environment variable to the hostname or IP address where Determined is running.

Overview

To use a PyTorch model in Determined, you need to port the model to Determined’s API. For most models, this porting process is straightforward, and once the model has been ported, all of the features of Determined will then be available: for example, you can do distributed training or hyperparameter search without changing your model code, and Determined will store and visualize your model metrics automatically.

When training a PyTorch model, Determined provides a built-in training loop that feeds batches of data into your forward pass, performs backpropagation, and computes training metrics. Determined also handles checkpointing, log management, and device initialization. To plug your model code into the Determined training loop, you define methods to perform the following tasks:

• initialization

• build the model

• define the optimizer

• define the forward pass

• load the training data set

• load the validation data set

The Determined training loop will then invoke these functions automatically. These methods should be organized into a trial class, which is a user-defined Python class that inherits from determined.pytorch.PyTorchTrial. The following sections walk through how to write your first trial class and then how to run a training job with Determined.

The complete code for this tutorial can be found here: mnist_pytorch. We suggest you follow along with the code as you read through this tutorial.

Building a PyTorchTrial Class

Here is what the skeleton of our trial class looks like:

import torch.nn as nn
from determined.pytorch import DataLoader, PyTorchTrial

class MNISTTrial(PyTorchTrial):
    def __init__(self, context: det.TrialContext):
        # Initialize the trial class.
        pass

    def build_model(self):
        # Build the model.
        pass

    def optimizer(self, model: nn.Module):
        # Define the optimizer.
        pass

    def train_batch(self, batch: TorchData, model: nn.Module, epoch_idx: int, batch_idx: int):
        # Define the training forward pass and calculate loss and other metrics
        # for a batch of training data.
        pass

    def evaluate_batch(self, batch: TorchData, model: nn.Module):
        # Define how to evaluate the model by calculating loss and other metrics
        # for a batch of validation data.
        pass

    def build_training_data_loader(self):
        # Create the training data loader.
        # This should return a determined.pytorch.Dataset.
        pass

    def build_validation_data_loader(self):
        # Create the validation data loader.
        # This should return a determined.pytorch.Dataset.
        pass

We now discuss how to implement each of these methods in more detail.

Initialization

As with any Python class, the __init__ method is invoked to construct our trial class. Determined passes this method a single parameter, TrialContext. The trial context contains information about the trial, such as the values of the hyperparameters to use for training. For the time being, we don’t need to access any properties from the trial context object, but we assign it to an instance variable so that we can use it later:

def __init__(self, context: PyTorchTrialContext):
    # Store trial context for later use.
    self.context = context

Building the Model

The build_model method returns a torch.Module or torch.Sequential object. The MNIST model code uses the Torch Sequential API and we can continue to use that API in our implementation of build_model. The current values of the model’s hyperparameters can be accessed via the get_hparam method of the trial context.

from determined.pytorch import reset_parameters
...

def build_model(self):
    model = nn.Sequential(
        nn.Conv2d(1, self.context.get_hparam("n_filters1"), 3, 1),
        nn.ReLU(),
        nn.Conv2d(
            self.context.get_hparam("n_filters1"), self.context.get_hparam("n_filters2"), 3,
        ),
        nn.ReLU(),
        nn.MaxPool2d(2),
        nn.Dropout2d(self.context.get_hparam("dropout1")),
        Flatten(),
        nn.Linear(144 * self.context.get_hparam("n_filters2"), 128),
        nn.ReLU(),
        nn.Dropout2d(self.context.get_hparam("dropout2")),
        nn.Linear(128, 10),
        nn.LogSoftmax(),
    )

    reset_parameters(model)
    return model

Defining the Optimizer

The optimizer method returns a torch.optim object. The MNIST model code uses torch.optim.Adadelta and we can continue to use that in our implementation of optimizer.

def optimizer(self, model: nn.Module):
    return torch.optim.Adadelta(model.parameters(), lr=self.context.get_hparam("learning_rate"))

Loading Data

The last two methods we need to define are build_training_data_loader and build_validation_data_loader. Determined uses these methods to load the training and validation datasets, respectively. This method should return a determined.pytorch.DataLoader, which is very similar to torch.utils.data.DataLoader.

def build_training_data_loader(self):
    if not self.data_downloaded:
        self.download_directory = data.download_dataset(
            download_directory=self.download_directory,
            data_config=self.context.get_data_config(),
        )
        self.data_downloaded = True

    train_data = data.get_dataset(self.download_directory, train=True)
    return DataLoader(train_data, batch_size=self.context.get_per_slot_batch_size())

def build_validation_data_loader(self):
    if not self.data_downloaded:
        self.download_directory = data.download_dataset(
            download_directory=self.download_directory,
            data_config=self.context.get_data_config(),
        )
        self.data_downloaded = True

    validation_data = data.get_dataset(self.download_directory, train=False)
    return DataLoader(validation_data, batch_size=self.context.get_per_slot_batch_size())

Defining the Forward Pass

The train_batch computes the forward pass and calculates the loss. Determined expects a dictionary with the calculated loss and other user defined metrics and will automatically average all the metrics. Since Determined handles the backprop call, we do not need to call loss.backwards() or optim.zero_grad().

def train_batch(self, batch: TorchData, model: nn.Module, epoch_idx: int, batch_idx: int):
    batch = cast(Tuple[torch.Tensor, torch.Tensor], batch)
    data, labels = batch

    output = model(data)
    loss = torch.nn.functional.nll_loss(output, labels)

    return {"loss": loss}

def evaluate_batch(self, batch: TorchData, model: nn.Module):
    batch = cast(Tuple[torch.Tensor, torch.Tensor], batch)
    data, labels = batch

    output = model(data)
    validation_loss = torch.nn.functional.nll_loss(output, labels).item()

    pred = output.argmax(dim=1, keepdim=True)
    accuracy = pred.eq(labels.view_as(pred)).sum().item() / len(data)

    return {"validation_loss": validation_loss, "accuracy": accuracy}

Training the Model

Now that we have ported our model code to the trial API, we can use Determined to train a single instance of the model or to do a hyperparameter search. In Determined, a trial is a training task that consists of a dataset, a deep learning model, and values for all of the model’s hyperparameters. An experiment is a collection of one or more trials: an experiment can either train a single model (with a single trial), or can define a search over a user-defined hyperparameter space.

To create an experiment, we start by writing a configuration file which defines the kind of experiment we want to run. In this case, we want to train a single model for a fixed number of batches, using fixed values for the model’s hyperparameters:

description: mnist_pytorch_const
data:
    url: https://s3-us-west-2.amazonaws.com/determined-ai-test-data/pytorch_mnist.tar.gz
hyperparameters:
    learning_rate: 1.0
    global_batch_size: 64
    n_filters1: 32
    n_filters2: 64
    dropout1: 0.25
    dropout2: 0.5
searcher:
    name: single
    metric: validation_loss
    max_steps: 9 # 9 steps is ~ one epoch
    smaller_is_better: true
entrypoint: model_def:MNistTrial

Rather than specifying the number of batches to train for directly, we instead specify the number of steps. By default, a step consists of 100 batches, so the config file above specifies that the model should be trained on 900 batches of data.

The entrypoint specifies the name of the trial class to use. This is useful if our model code contains more than one trial class. In this case, we use an entrypoint of model_def:MNistTrial because our trial class is named MNistTrial and it is defined in a Python file named model_def.py.

For more information on experiment configuration, see the experiment configuration reference.

Running an Experiment

The Determined CLI can be used to create a new experiment, which will immediately start running on the cluster. To do this, we run:

det experiment create const.yaml .

Here, the first argument (const.yaml) is the name of the experiment configuration file and the second argument (.) is the location of the directory that contains our model definition files. You may need to configure the CLI with the network address where the Determined master is running, via the -m flag or the DET_MASTER environment variable. For more information, see the CLI reference page.

Once the experiment is started, you will see a notification:

Preparing files (../mnist_pytorch) to send to master... 2.5KB and 4 files
Created experiment xxx
Activated experiment xxx

Evaluating the Model

Model evaluation is done automatically for you by Determined. To access information on both training and validation performance, simply go to the WebUI by entering the address of the Determined master in your web browser.

Once you are on the Determined landing page, you can find your experiment either via the experiment ID (xxx) or via its description.

Next Steps

• TensorFlow Keras CNN Tutorial

• Experiment Configuration

• Command Configuration

• YAML Topic Guide

• Benefits of Determined

• Terminology and Concepts