Making Your PyTorch Code Run Faster on GPUs

PyTorch, a popular open-source machine learning library, is widely used for deep learning applications. GPUs (Graphics Processing Units) can significantly speed up deep learning model training due to their capability of parallel computing. However, merely using a GPU does not always guarantee optimal performance. In this article, we’ll explore various techniques to make your PyTorch code run faster on GPUs.

1. Utilize CUDNN Benchmarking

PyTorch can automatically determine the best convolution algorithms for your hardware by using torch.backends.cudnn.benchmark. Enabling this can lead to performance gains:

import torch

torch.backends.cudnn.benchmark = True

Note that this may result in varying performance between iterations, which is not ideal for reproducibility but can enhance speed significantly.

2. Move Tensors to GPU

First and foremost, ensure that your tensors and model are moved to the GPU.

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = MyModel().to(device)
data = data.to(device)

This ensures that operations are conducted on the GPU, making full use of its computing power.

3. Use pin_memory for DataLoader

The DataLoader in PyTorch has an option to use pinned (page-locked) memory, which can speed up host-to-device data transfer.

train_loader = torch.utils.data.DataLoader(dataset,
                    batch_size=64,
                    shuffle=True,
                    pin_memory=True)

This is particularly useful when transferring large batches of data to the CUDA device.

4. Minimize Data Transfer Between CPU and GPU

Frequent data transfer between CPU and GPU can slow down computation. Strive to minimize such transfers by keeping computations on the GPU:

X, y = X.to(device), y.to(device)

output = model(X)
loss = criterion(output, y)
loss.backward()
optimizer.step()

Ensure all operations in the training loop occur on the GPU.

5. Use Mixed Precision Training

Mixed Precision Training leverages half-precision (float16) computations alongside single-precision (float32) during model training, which can accelerate training and reduce memory storage requirements:

from torch.cuda.amp import GradScaler, autocast

scaler = GradScaler()
# Training loop
for epoch in range(epochs):
    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        with autocast():
            output = model(data)
            loss = loss_fn(output, target)
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()

This can provide significant speedup without a noticeable impact on accuracy.

6. Profile Your GPU Utilization

Tools like NVIDIA’s nsight, cprofile, or PyTorch’s own torch.profiler can help you identify bottlenecks:

from torch.profiler import profile, record_function, ProfilerActivity

with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
                        record_shapes=True) as prof:
    with record_function("model_inference"):
        model(inputs)

print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))

This will help you understand where improvements can be further targeted.

7. Distribute and Parallelize Workloads

For users with multiple GPUs, Data Parallelism is an easy way to distribute workloads:

from torch.nn import DataParallel

model = MyModel()
model = DataParallel(model)
model.to(device)

This allows the model to be parallelized over several GPUs, enabling simultaneous processing of input batches.

Incorporating these techniques into your PyTorch workflows can drastically reduce training times and make the most out of your GPU hardware.