PyTorch RuntimeError: CUDA error: invalid device ordinal
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 torch
print(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 os
os.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.
Master
PyTorch
debugging in Minutes
— Grab the Ultimate Cheatsheet
(Perfect for DevOps & SREs)
PyTorch
Cheatsheet
(Perfect for DevOps & SREs)
MORE ISSUES
Backed by
