Train a Machine Learning Model With MNIST Dataset Using PyTorch

In this blog, we'll guide you through the installation of PyTorch on a linux based operating system, then followed by training Machine Learning Model with MNIST dataset using PyTorch.

Let's start with a step-by-step process, starting from creating virtual machines on the Alces Cloud platform, leading up to the installation of PyTorch and then train a machine learning model with MNIST dataset using PyTorch.

Launch the Instance

All the steps to launch and connection to instance is provided in link.

Setting Up PyTorch and Examples Repository

• Create a Python Virtual Environment and Install PyTorch:

  python3 -m venv myenv 
  source myenv/bin/activate
  pip install torch
  

• Clone the PyTorch Examples Repository:

  git clone https://github.com/pytorch/examples.git
  

• Navigate to the Example You Wish to Explore:

  cd examples/mnist
  

• Install Required Dependencies and then executemain.py, this script trains a CNN model to classify handwritten digits from the MNIST dataset.

  pip install -r requirements.txt
  python main.py
  

usage: main.py [-h] [--batch-size N] [--test-batch-size N] [--epochs N] [--lr LR] [--gamma M] [--no-cuda] [--no-mps] [--dry-run] [--seed S] [--log-interval N] [--save-model]

PyTorch MNIST Example

optional arguments:
  -h, --help           show this help message and exit
  --batch-size N       input batch size for training (default: 64)
  --test-batch-size N  input batch size for testing (default: 1000)
  --epochs N           number of epochs to train (default: 14)
  --lr LR              learning rate (default: 1.0)
  --gamma M            Learning rate step gamma (default: 0.7)
  --no-cuda            disables CUDA training
  --no-mps             disables macOS GPU training
  --dry-run            quickly check a single pass
  --seed S             random seed (default: 1)
  --log-interval N     how many batches to wait before logging training status
  --save-model         For Saving the current Model