PyTorch
Last updated
Last updated
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
(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)
Install CUDA or check the installed GPU CUDA 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.
*(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.
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 --versionCheck the list of packages installed in the environment
conda 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
From the command line, type:
then enter the following code:
The output should be something similar to:
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: