There are several ways to optimize GPU memory usage in PyTorch:
- Reduce the batch size: One of the most straightforward ways to reduce GPU memory usage is to reduce the batch size. This can be done by setting the batch_size parameter to a lower value when creating the DataLoader object.
- Use mixed-precision training: PyTorch offers support for mixed-precision training, which can significantly reduce GPU memory usage without sacrificing accuracy. This technique involves using lower precision data types for certain computations, such as using half-precision (float16) instead of single-precision (float32) for some tensor operations. This can be done by using the apex.amp library.
- Free up memory: PyTorch keeps intermediate values in memory to enable backward propagation for automatic differentiation. However, this can quickly lead to memory exhaustion. To free up memory, you can use the torch.cuda.empty_cache() function to clear the GPU memory.
- Use gradient checkpointing: Gradient checkpointing is a technique that allows you to trade-off computation time for memory usage. It involves recomputing some intermediate values during the backward pass instead of storing them in memory. This can be done using the torch.utils.checkpoint library.
- Use the right data type: The choice of data type can have a significant impact on memory usage. For example, using int8 instead of int32 for certain operations can reduce memory usage by a factor of four. However, this comes at the cost of reduced precision.
- Use data augmentation on the CPU: Performing data augmentation on the CPU instead of the GPU can help reduce GPU memory usage. This can be achieved by using PyTorch’s transforms.Compose function to apply the desired data augmentations to the input data.
By implementing the above techniques, you can optimize GPU memory usage in PyTorch and train models that are too large to fit in GPU memory.
