Table of Contents
What is PyTorch
PyTorch is an open source machine learning framework that uses Python 3.7 or greater. You can start locally with the instructions to get started on their website.
Pre-requisites include:
- Python 3.7 or greater
- A package manager (Anaconda or pip) - Anaconda is the recommended package manager
To install Anaconda, right-click the 64-bit installer link, select copy link location and use the following commands:
# The version of Anaconda may be different depending on when you are installing`
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
sh Miniconda3-latest-Linux-x86_64.sh
# and follow the prompts. The defaults are generally good.`
To ensure that PyTorch was installed correctly, run the sample PyTorch code:
import torch
x = torch.rand(5, 3)
print(x)
The output should be similar to:
tensor([[0.3380, 0.3845, 0.3217],
[0.8337, 0.9050, 0.2650],
[0.2979, 0.7141, 0.9069],
[0.1449, 0.1132, 0.1375],
[0.4675, 0.3947, 0.1426]])
Additionally, you can check if your GPU driver and CUDA is enabled and accessible by PyTorch by running the following commands:
import torch
torch.cuda.is_available()
The PyTorch EcoSystem
There are many tools and libraries available for you including the following:
A library for quantum ML, automatic differentation, and optimization of hybrid quantum-classical computations.
A framework for PyTorch to improve the performance and robustness of DL models.
A Keras-like ML library for PyTorch
A library for deep learning on irregular input data such as graphs, point clouds, and manifolds.
Basic utilities for PyTorch natural language processing
A resource-adaptive deep learning training and scheduling framework
And there are so many more. Check them out right here
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