PyTorch RuntimeError: CUDA error: invalid device ordinal

What is PyTorch RuntimeError: CUDA error: invalid device ordinal

Understanding PyTorch and Its Purpose

PyTorch is a popular open-source machine learning library developed by Facebook's AI Research lab. It is widely used for applications such as computer vision and natural language processing. PyTorch provides a flexible and efficient platform for building deep learning models, offering dynamic computation graphs and seamless integration with Python.

Identifying the Symptom: RuntimeError

When working with PyTorch, you might encounter the error: RuntimeError: CUDA error: invalid device ordinal. This error typically occurs when you attempt to use a GPU device that is not available or does not exist on your system.

Exploring the Issue: Invalid Device Ordinal

The error message indicates that PyTorch is trying to access a GPU device using an index that is out of range. This can happen if you specify a device index that exceeds the number of available GPUs on your machine. For instance, if you have only one GPU, trying to access cuda:1 will result in this error.

Common Causes

Incorrect device index specified in your code. Misconfiguration of environment variables related to CUDA devices. Dynamic changes in available GPU resources, especially in shared environments.

Steps to Fix the Issue

To resolve the invalid device ordinal error, follow these steps:

Step 1: Check Available GPU Devices

First, verify the number of available GPU devices on your system using the following PyTorch command:

import torchprint(torch.cuda.device_count())

This command will output the number of GPUs that PyTorch can access. Ensure that your code does not attempt to access a device index greater than or equal to this number.

Step 2: Correct the Device Index

Review your code to ensure that the device index specified is within the valid range. For example, if you have one GPU, use cuda:0 instead of cuda:1 or higher.

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

Step 3: Environment Configuration

Check if any environment variables related to CUDA devices are set incorrectly. For instance, the CUDA_VISIBLE_DEVICES environment variable can limit the GPUs visible to PyTorch. Ensure it is set correctly or unset it to allow PyTorch to see all available devices.

import osos.environ['CUDA_VISIBLE_DEVICES'] = '0'

Additional Resources

For more information on managing CUDA devices in PyTorch, refer to the official PyTorch CUDA Semantics documentation. Additionally, the PyTorch Forums are a great place to seek help and share experiences with the community.