Train an MNIST model with PyTorch

This notebook’s CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.

MNIST is a widely used dataset for handwritten digit classification. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). This tutorial shows how to train and test an MNIST model on SageMaker using PyTorch.

Runtime

This notebook takes approximately 5 minutes to run.

Contents

PyTorch Estimator
Implement the entry point for training
Set hyperparameters
Set up channels for the training and testing data
Run the training script on SageMaker
Inspect and store model data
Test and debug the entry point before executing the training container
Conclusion

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import os
import json

import sagemaker
from sagemaker.pytorch import PyTorch
from sagemaker import get_execution_role


sess = sagemaker.Session()
region = sess.boto_region_name

role = get_execution_role()

output_path = "s3://" + sess.default_bucket() + "/DEMO-mnist"

PyTorch Estimator

The PyTorch class allows you to run your training script on SageMaker infrastracture in a containerized environment. In this notebook, we refer to this container as training container.

You need to configure it with the following parameters to set up the environment:

entry_point: A user-defined Python file used by the training container as the instructions for training. We further discuss this file in the next subsection.
role: An IAM role to make AWS service requests
instance_type: The type of SageMaker instance to run your training script. Set it to local if you want to run the training job on the SageMaker instance you are using to run this notebook
instance_count: The number of instances to run your training job on. Multiple instances are needed for distributed training.
output_path: S3 bucket URI to save training output (model artifacts and output files)
framework_version: The version of PyTorch to use
py_version: The Python version to use

For more information, see the EstimatorBase API reference

Implement the entry point for training

The entry point for training is a Python script that provides all the code for training a PyTorch model. It is used by the SageMaker PyTorch Estimator (PyTorch class above) as the entry point for running the training job.

Under the hood, SageMaker PyTorch Estimator creates a docker image with runtime environemnts specified by the parameters you provide to initiate the estimator class, and it injects the training script into the docker image as the entry point to run the container.

In the rest of the notebook, we use training image to refer to the docker image specified by the PyTorch Estimator and training container to refer to the container that runs the training image.

This means your training script is very similar to a training script you might run outside Amazon SageMaker, but it can access the useful environment variables provided by the training image. See the complete list of environment variables for a complete description of all environment variables your training script can access.

In this example, we use the training script code/train.py as the entry point for our PyTorch Estimator.

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!pygmentize 'code/train.py'

Set hyperparameters

In addition, the PyTorch estimator allows you to parse command line arguments to your training script via hyperparameters.

Note: local mode is not supported in SageMaker Studio.

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# Set local_mode to True to run the training script on the machine that runs this notebook

local_mode = False

if local_mode:
    instance_type = "local"
else:
    instance_type = "ml.c4.xlarge"

est = PyTorch(
    entry_point="train.py",
    source_dir="code",  # directory of your training script
    role=role,
    framework_version="1.5.0",
    py_version="py3",
    instance_type=instance_type,
    instance_count=1,
    volume_size=250,
    output_path=output_path,
    hyperparameters={"batch-size": 128, "epochs": 1, "learning-rate": 1e-3, "log-interval": 100},
)

The training container executes your training script like:

python train.py --batch-size 100 --epochs 1 --learning-rate 1e-3 --log-interval 100

Set up channels for the training and testing data

Tell the PyTorch estimator where to find the training and testing data. It can be a path to an S3 bucket, or a path in your local file system if you use local mode. In this example, we download the MNIST data from a public S3 bucket and upload it to your default bucket.

[ ]:

import logging
import boto3
from botocore.exceptions import ClientError

# Download training and testing data from a public S3 bucket


def download_from_s3(data_dir="./data", train=True):
    """Download MNIST dataset and convert it to numpy array

    Args:
        data_dir (str): directory to save the data
        train (bool): download training set

    Returns:
        None
    """

    if not os.path.exists(data_dir):
        os.makedirs(data_dir)

    if train:
        images_file = "train-images-idx3-ubyte.gz"
        labels_file = "train-labels-idx1-ubyte.gz"
    else:
        images_file = "t10k-images-idx3-ubyte.gz"
        labels_file = "t10k-labels-idx1-ubyte.gz"

    # download objects
    s3 = boto3.client("s3")
    bucket = f"sagemaker-example-files-prod-{region}"
    for obj in [images_file, labels_file]:
        key = os.path.join("datasets/image/MNIST", obj)
        dest = os.path.join(data_dir, obj)
        if not os.path.exists(dest):
            s3.download_file(bucket, key, dest)
    return


download_from_s3("./data", True)
download_from_s3("./data", False)

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# Upload to the default bucket

prefix = "DEMO-mnist"
bucket = sess.default_bucket()
loc = sess.upload_data(path="./data", bucket=bucket, key_prefix=prefix)

channels = {"training": loc, "testing": loc}

The keys of the channels dictionary are passed to the training image, and it creates the environment variable SM_CHANNEL_<key name>.

In this example, SM_CHANNEL_TRAINING and SM_CHANNEL_TESTING are created in the training image (see how code/train.py accesses these variables). For more information, see: SM_CHANNEL_{channel_name}.

If you want, you can create a channel for validation:

channels = {
    'training': train_data_loc,
    'validation': val_data_loc,
    'test': test_data_loc
}

You can then access this channel within your training script via SM_CHANNEL_VALIDATION.

Run the training script on SageMaker

Now, the training container has everything to execute your training script. Start the container by calling the fit() method.

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est.fit(inputs=channels)

Inspect and store model data

Now, the training is finished, and the model artifact has been saved in the output_path.

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pt_mnist_model_data = est.model_data
print("Model artifact saved at:\n", pt_mnist_model_data)

We store the variable pt_mnist_model_data in the current notebook kernel.

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%store pt_mnist_model_data

Test and debug the entry point before executing the training container

The entry point code/train.py can be executed in the training container. When you develop your own training script, it is a good practice to simulate the container environment in the local shell and test it before sending it to SageMaker, because debugging in a containerized environment is rather cumbersome. The following script shows how you can test your training script:

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!pygmentize code/test_train.py

Conclusion

In this notebook, we trained a PyTorch model on the MNIST dataset by fitting a SageMaker estimator. For next steps on how to deploy the trained model and perform inference, see Deploy a Trained PyTorch Model.

Notebook CI Test Results

This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.