python-pytorchHow can I configure Python PyTorch CUDA allocations?

To configure Python PyTorch CUDA allocations, you first need to check if your system has a CUDA-capable GPU and if it is supported by PyTorch. This can be done by running the following code:

import torch
print(torch.cuda.is_available())

If the output is True, your system is CUDA-capable and supports PyTorch.

To configure the CUDA allocations, you need to set the CUDA_VISIBLE_DEVICES environment variable. This can be done by running the following code:

import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"

This will set the environment variable to allow access to the 0th GPU device.

You can also specify multiple GPU devices by setting the environment variable to a comma-separated list of device numbers, for example:

os.environ["CUDA_VISIBLE_DEVICES"]="0,1"

This will set the environment variable to allow access to the 0th and 1st GPU devices.

You can also use the torch.cuda.device_count() function to get the number of available GPU devices and then use a loop to set the environment variable accordingly.

device_count = torch.cuda.device_count()
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in range(device_count)])

This will set the environment variable to allow access to all available GPU devices.

Code explanation

1. import torch: imports the PyTorch library
2. print(torch.cuda.is_available()): prints a boolean value indicating if the system is CUDA-capable and supports PyTorch
3. import os: imports the os library
4. os.environ["CUDA_VISIBLE_DEVICES"]="0": sets the environment variable to allow access to the 0th GPU device
5. torch.cuda.device_count(): returns the number of available GPU devices
6. os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in range(device_count)]): sets the environment variable to allow access to all available GPU devices

Helpful links

1. PyTorch Documentation - CUDA Support
2. PyTorch Documentation - torch.cuda.device_count

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