PyTocrch

PyTorch is a Tensors and Dynamic neural networks in Python with strong GPU acceleration.

PyTorch installation

PyTorch is a very fast library, using all possibilities of computer CPU and CUDA on GPU. Therefore, it is very important to install proper configuration of this library. It is possible to install PyTorch with Conda or PIP.

PyTorch installation for Linux

Conda installation


# CUDA version 10.2
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
# CUDA version 11.3
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
# CPU only version
conda install pytorch torchvision torchaudio cpuonly -c pytorch

Or if you prefer to use PIP, you can do the same with PIP


# CUDA version 10.2
pip3 install torch torchvision torchaudio
# CUDA version 11.3
pip3 install torch torchvision torchaudio 
--extra-index-url https://download.pytorch.org/whl/cu113
# CPU only version
pip3 install torch torchvision torchaudio 
--extra-index-url https://download.pytorch.org/whl/cpu

PyTorch installation for Windows

For Windows it is a bit more complicated. CUDA-10.2 PyTorch builds are no longer available for Windows, please use CUDA-11.3


# CUDA version 11.3
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
# or with pip
pip3 install torch torchvision torchaudio 
--extra-index-url https://download.pytorch.org/whl/cu113

# CPU only 
conda install pytorch torchvision torchaudio cpuonly -c pytorch
# or with pip
pip3 install torch torchvision torchaudio

Access to GPU

GPU is much faster than CPU for tensor calculations, but it is not always available. Therefore, it is better to write program, which can work with GPU and CPU. This can be done by assigning calculations on different devices.


import torch
torch.cuda.is_available() # True is CUDA available, or False
# selection device for calculations
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# transfer torch object (x) to a new device
x = x.to(device)

Published: 2022-05-04 19:41:45
Updated: 2022-05-04 19:42:39

Last 10 artitles

2023-12-25 01:48:38 - In-place and out-place Numpy functions
2023-12-25 00:58:08 - First order ordinary differential equations
2023-12-25 00:22:15 - How to increase swap space in Ubuntu
2023-12-24 04:03:12 - How to accelerate math calculations with Numba and Cython
2023-12-19 00:38:20 - 3 ways of function caching in Python
2023-12-19 00:16:55 - Accelerating Python
2023-12-16 12:26:55 - BioPython
2023-12-02 11:05:34 - Reading FITS files with astropy
2023-12-02 05:23:42 - Astropy
2023-12-01 00:33:16 - Circle length calculation with the Monte-Carlo method

9 popular artitles

Python Panda (3443)
Python NumPy (2979)
Python DICTionary HowTo (1592)
Python LIST HowTo (1471)
Python (1422)
Python Generators (1281)
Python: How to make absolute links in BeautifulSoup (1084)
Programming language (967)
NumPy: What the difference between mgrid and meshgrid (947)



PyTocrch » PyTorch - Calculate one step of gradient PyTorch - Calculate few steps of gradient PyTorch - Neural network for simple regression analysis	My Coding > Programming language > Python > PyTocrch PyTocrch PyTorch is a Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch installation PyTorch is a very fast library, using all possibilities of computer CPU and CUDA on GPU. Therefore, it is very important to install proper configuration of this library. It is possible to install PyTorch with Conda or PIP. PyTorch installation for Linux Conda installation `# CUDA version 10.2 conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch # CUDA version 11.3 conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch # CPU only version conda install pytorch torchvision torchaudio cpuonly -c pytorch` Or if you prefer to use PIP, you can do the same with PIP `# CUDA version 10.2 pip3 install torch torchvision torchaudio # CUDA version 11.3 pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 # CPU only version pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu` PyTorch installation for Windows For Windows it is a bit more complicated. CUDA-10.2 PyTorch builds are no longer available for Windows, please use CUDA-11.3 `# CUDA version 11.3 conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch # or with pip pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 # CPU only conda install pytorch torchvision torchaudio cpuonly -c pytorch # or with pip pip3 install torch torchvision torchaudio` Access to GPU GPU is much faster than CPU for tensor calculations, but it is not always available. Therefore, it is better to write program, which can work with GPU and CPU. This can be done by assigning calculations on different devices. `import torch torch.cuda.is_available() # True is CUDA available, or False # selection device for calculations device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') # transfer torch object (x) to a new device x = x.to(device)` Published: 2022-05-04 19:41:45 Updated: 2022-05-04 19:42:39	Last 10 artitles 2023-12-25 01:48:38 - In-place and out-place Numpy functions 2023-12-25 00:58:08 - First order ordinary differential equations 2023-12-25 00:22:15 - How to increase swap space in Ubuntu 2023-12-24 04:03:12 - How to accelerate math calculations with Numba and Cython 2023-12-19 00:38:20 - 3 ways of function caching in Python 2023-12-19 00:16:55 - Accelerating Python 2023-12-16 12:26:55 - BioPython 2023-12-02 11:05:34 - Reading FITS files with astropy 2023-12-02 05:23:42 - Astropy 2023-12-01 00:33:16 - Circle length calculation with the Monte-Carlo method 9 popular artitles Python Panda (3443) Python NumPy (2979) Python DICTionary HowTo (1592) Python LIST HowTo (1471) Python (1422) Python Generators (1281) Python: How to make absolute links in BeautifulSoup (1084) Programming language (967) NumPy: What the difference between mgrid and meshgrid (947)
© 2020 MyCoding.uk -My blog about coding and further learning. This blog was writen with pure Perl and front-end output was performed with TemplateToolkit.