RuntimeError: Attempting to deserialize object on a CUDA device
This error occurs in PyTorch when you try to load a serialized object (e.g., a model, tensor, or other data) directly onto a CUDA device without first transferring it to the CPU. PyTorch’s serialization/deserialization mechanisms are designed to operate on the CPU, and attempting to do it on the GPU results in this error.
Understanding the Error
Background
- CUDA: A parallel computing platform and application programming interface (API) developed by NVIDIA for general-purpose computation on graphics processing units (GPUs).
- PyTorch: A popular deep learning framework that allows you to leverage GPUs for faster training and inference.
- Serialization: The process of converting an object into a stream of bytes (e.g., a file) for storage or transmission.
- Deserialization: The reverse process of converting a stream of bytes back into an object.
The Issue
PyTorch’s serialization and deserialization operations are typically performed on the CPU. When you try to deserialize an object directly onto a CUDA device, PyTorch encounters this incompatibility, leading to the “RuntimeError: Attempting to deserialize object on a CUDA device” error.
Resolving the Error
The key to fixing this error is to ensure that the serialized object is first deserialized on the CPU and then transferred to the CUDA device. Follow these steps:
1. Deserialize on CPU
When loading a serialized object, use the cpu() method to ensure it’s deserialized on the CPU:
import torch
# Load the serialized object
model = torch.load('model.pth')
# Move the object to CPU
model = model.cpu()
2. Transfer to CUDA Device
Once the object is on the CPU, you can move it to the CUDA device using the cuda() method:
# Assuming you have a CUDA device available
device = torch.device('cuda')
# Move the model to the CUDA device
model = model.to(device)
Example: Loading a Model
import torch
# Load the serialized model
model = torch.load('model.pth').cpu()
# Check if a CUDA device is available
if torch.cuda.is_available():
device = torch.device('cuda')
model = model.to(device)
# Use the model for inference or further processing
Important Notes
- Always deserialize on the CPU and then transfer to the CUDA device.
- Ensure that the CUDA device is available before attempting to move objects to it.
- Consider using the
torch.no_grad()context manager for inference to avoid unnecessary computation on the GPU.
Summary
The “RuntimeError: Attempting to deserialize object on a CUDA device” error is a common issue encountered when working with PyTorch and CUDA. By understanding the underlying concepts and following the recommended steps, you can effectively resolve this error and ensure your code runs smoothly.