MNIST Training using PyTorch


  1. Background

  2. Setup

  3. Data

  4. Train

  5. Host


Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and MXNet. This notebook example shows how to use Horovod with PyTorch in SageMaker using MNIST dataset.

For more information about the PyTorch in SageMaker, please visit sagemaker-pytorch-containers and sagemaker-python-sdk github repositories.


This notebook was created and tested on an ml.p2.xlarge notebook instance.

Let’s start by creating a SageMaker session and specifying:

  • The S3 bucket and prefix that you want to use for training and model data. This should be within the same region as the Notebook Instance, training, and hosting.

  • The IAM role arn used to give training and hosting access to your data. See the Amazon SageMaker Roles for how to create these. Note, if more than one role is required for notebook instances, training, and/or hosting, please replace the sagemaker.get_execution_role() with the appropriate full IAM role arn string(s).

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import sagemaker

sagemaker_session = sagemaker.Session()

bucket = sagemaker_session.default_bucket()
prefix = "sagemaker/DEMO-pytorch-mnist"

role = sagemaker.get_execution_role()


Getting the data

In this example, we will ues MNIST dataset. 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).

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import torchvision
from torchvision import datasets, transforms
from packaging.version import Version

# Set the source to download MNIST data from
if Version(torchvision.__version__) < Version(TORCHVISION_VERSION):
    # Set path to data source and include checksum to make sure data isn't corrupted
    datasets.MNIST.resources = [
    # Set path to data source
    datasets.MNIST.mirrors = [

        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]

Uploading the data to S3

We are going to use the sagemaker.Session.upload_data function to upload our datasets to an S3 location. The return value inputs identifies the location – we will use later when we start the training job.

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inputs = sagemaker_session.upload_data(path="data", bucket=bucket, key_prefix=prefix)
print("input spec (in this case, just an S3 path): {}".format(inputs))


Training script

The script provides the code we need for training a SageMaker model. The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables, such as:

  • SM_MODEL_DIR: A string representing the path to the directory to write model artifacts to. These artifacts are uploaded to S3 for model hosting.

  • SM_NUM_GPUS: The number of gpus available in the current container.

  • SM_CURRENT_HOST: The name of the current container on the container network.

  • SM_HOSTS: JSON encoded list containing all the hosts .

Supposing one input channel, ‘training’, was used in the call to the PyTorch estimator’s fit() method, the following will be set, following the format SM_CHANNEL_[channel_name]:

  • SM_CHANNEL_TRAINING: A string representing the path to the directory containing data in the ‘training’ channel.

For more information about training environment variables, please visit SageMaker Containers.

A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to model_dir so that it can be hosted later. Hyperparameters are passed to your script as arguments and can be retrieved with an argparse.ArgumentParser instance.

This script uses Horovod framework for distributed training where Horovod-related lines are commented with Horovod:. For example, hvd.broadcast_parameters, hvd.DistributedOptimizer and etc.

For example, the script run by this notebook:

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!pygmentize code/

Run training in SageMaker

The PyTorch class allows us to run our training function as a training job on SageMaker infrastructure. We need to configure it with our training script, an IAM role, the number of training instances, the training instance type, and hyperparameters. In this case we are going to run our training job on 2 ml.p2.xlarge instances. But this example can be ran on one or multiple, cpu or gpu instances (full list of available instances). The hyperparameters parameter is a dict of values that will be passed to your training script – you can see how to access these values in the script above.

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from sagemaker.pytorch import PyTorch

estimator = PyTorch(
    hyperparameters={"epochs": 6, "backend": "gloo"},

After we’ve constructed our PyTorch object, we can fit it using the data we uploaded to S3. SageMaker makes sure our data is available in the local filesystem, so our training script can simply read the data from disk.

[ ]:{"training": inputs})


Create endpoint

After training, we need to use the PyTorch estimator object to create a PyTorchModel object and set a different entry_point, otherwise, the training script will be used for inference. (Note that the new entry_point must be under the same source_dir as Then we use the PyTorchModel object to deploy a PyTorchPredictor. This creates a Sagemaker Endpoint – a hosted prediction service that we can use to perform inference.

An implementation of model_fn is required for inference script. We are going to use default implementations of input_fn, predict_fn, output_fn and transform_fm defined in sagemaker-pytorch-containers.

Here’s an example of the inference script:

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!pygmentize code/

The arguments to the deploy function allow us to set the number and type of instances that will be used for the Endpoint. These do not need to be the same as the values we used for the training job. Here we will deploy the model to a single ml.p2.xlarge instance.

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# Create a PyTorchModel object with a different entry_point
model = estimator.create_model(entry_point="", source_dir="code")

# Deploy the model to a ml.m4.xlarge instance
predictor = model.deploy(initial_instance_count=1, instance_type="ml.p2.xlarge")


We can now use this predictor to classify hand-written digits. We take examples from the MNIST test dataset and run inference on them.

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import numpy as np
from ast import literal_eval

# Load and view test image to perform inference on
data_file = open("input.txt", "r")
data =
print("input image: ", data)

# Run inference on image
data = literal_eval(data)
image = np.array([data], dtype=np.float32)
response = predictor.predict(image)
prediction = response.argmax(axis=1)[0]


After you have finished with this example, remember to delete the prediction endpoint to release the instance(s) associated with it

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