PyTorch

What is PyTorch

An open source machine learning framework that accelerates the path from research prototyping to production deployment. It’s a Python-based scientific computing package targeted at two sets of audiences:

• A replacement for NumPy to use the power of GPUs

• a deep learning research platform that provides maximum flexibility and speed

PyTorch

How to Install

(2022.1 : Use Pytorch 1.10.x, CUDA=10.2)

Select your preferences and run the install command. Please ensure that you have met the prerequisites below (e.g., numpy)

https://pytorch.org/get-started/locally/

• Install CUDA or check the installed GPU CUDA version

nvcc --version

• Install Anaconda: To install Anaconda, you will use the 64-bit graphical installer for Python 3.x.

• Install Anaconda. After installation, run Anaconda Prompt

• You can use previous virtual environment e.g. py37 , where other necessary tools are installed.

  conda activate py37

• *(Optional) Make a new virtual environment e.g torch110.

  • For new virtual environment, In the (base) of Anaconda Prompt, create a new environment.

  • You need to install necessary tools again for this environment.

conda env list
conda create --name torch16
activate torch16

• Install Python, Numpy, Panda and other prerequisite. Also, install necessary IDE (Jupyter Notebook, Visual Studio Code etc.)

Check Python version. Need to have Python 3.x on Windows. python --version

Check the list of packages installed in the environmentconda list

• Select your preferences and run the install command in your environment. The command can be found in https://pytorch.org/get-started/locally/ **

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Verify installation

From the command line, type:

python

then enter the following code:

from __future__ import print_function
import torch
x = torch.rand(5, 3)
print(x)

The output should be something 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, to check if your GPU driver and CUDA is enabled and accessible by PyTorch, run the following commands to return whether or not the CUDA driver is enabled:

import torch
torch.cuda.is_available()