How to use GPU acceleration in PyTorch?

PyTorch is a well-liked deep learning framework that offers good GPU acceleration support, enabling users to take advantage of GPUs’ processing power for quicker neural network training. This post will discuss the advantages of GPU acceleration, how to determine whether a GPU is available, and how to set PyTorch to utilize GPUs effectively.

GPU Acceleration in PyTorch

GPU acceleration in PyTorch is a crucial feature that allows to leverage the computational power of Graphics Processing Units (GPUs) to accelerate the training and inference processes of deep learning models. PyTorch provides a seamless way to utilize GPUs through its torch.cuda module. Graphics processing units, or GPUs, are specialized hardware made to efficiently execute simultaneous computations.

When compared to using simply the Central Processing Unit (CPU), GPUs dramatically speed up training times in deep learning, which frequently entails heavy matrix operations. Especially for large-scale deep learning models and datasets, GPU acceleration is essential.

Setting Up PyTorch for GPU Acceleration

There are some hardware and software prerequisites in order to use GPU acceleration in PyTorch like software compatibility, CUDA Toolkit, etc.

Checking GPU compatibility

It’s important to make sure your computer has a compatible GPU and the necessary drivers installed before using GPU acceleration. To check for GPU availability in PyTorch, use the following code snippet:

Python

import torch
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)} is available.")
else:
    print("No GPU available. Training will run on CPU.")

Output:

 GPU: Tesla T4 is available.  

Note: You can alternatively utilize Google Colab with GPU support if your system isn’t GPU compatible. For reference click here.

Enabling GPU Acceleration

Steps for enabling GPU acceleration in PyTorch:

• Install CUDA Toolkit: From the NVIDIA website, download and install the NVIDIA CUDA Toolkit version that corresponds to your GPU. Make sure to add the CUDA binary directory to your system’s PATH.
• Install PyTorch with GPU support:Use the following command to install PyTorch with GPU support. Replace {CUDA_VERSION} with the version installed on your system

pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/cu{CUDA_VERSION}/torch_stable.html

• Configure device: Set the device configuration to GPU using the torch.device class in PyTorch:

Python

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

Output:

cuda

It will print ‘cpu’ if GPU or CUDA is not available in your system.

Moving Tensors to GPU

After configuring GPU in PyTorch, you can easily move your data and models to GPU using the to(‘cuda’) method.

Python3

import torch
# Create a tensor on the CPU
tensor = torch.randn((3, 3))
#Move the tensor to the GPU
tensor = tensor.to('cuda')

After moving a tensor to the GPU, the operations can be carried out just like they would with CPU tensors. PyTorch automatically utilizes the GPU for these operations, leading to quicker computation times.

Parallel Processing with PyTorch

Parallel processing with PyTorch for GPU acceleration involves distributing computation tasks across multiple GPUs or parallelizing computations within a single GPU. This means that instead of executing tasks sequentially, which is common in CPUs, GPUs can handle multiple tasks simultaneously.

PyTorch’s torch.nn.DataParallel module simplifies parallel processing across multiple GPUs.

This module replicates model on multiple GPUs, splits input data among the GPUs, computes forward and backward passes independently, and then averages the gradients across all GPUs. Wrap your model with DataParallel to distribute mini-batches across available GPUs. Below is the example of how DataParallel can be implemented:

Python3

import torch
import torch.nn as nn

model = nn.Conv2d(3, 64, 3, 1, 1) # Create a model

model = nn.DataParallel(model) # Wrap the model with DataParallel

model = model.to('cuda') # Move the model to the GPU
# Perform forward pass on the model
input_data = torch.randn(20, 3, 32, 32).to('cuda')
output = model(input_data)

Neural Network Training with GPU Acceleration

Here is a simple neural network code demonstrating the model and data transfer to GPU. In the provided example, GPU acceleration is leveraged to speed up the training and inference of the Generate model. Here’s an explanation of the steps involved:

1. Initialization of Model on GPU: The model.to('cuda') line moves the Generate model to the GPU (‘cuda’ device). This means that all subsequent computations involving the model will be performed on the GPU rather than the CPU.
2. Creation of Input Data on GPU: The input_data = torch.randn(16, 5, device=device) line creates a tensor of random input data directly on the GPU. This ensures that the input data is also located on the GPU memory, enabling seamless computation without the need for data transfer between CPU and GPU.
3. Forward Pass on GPU: The output = model(input_data) line performs a forward pass of the input data through the model. Since both the model and input data are located on the GPU, the computations involved in the forward pass are executed on the GPU, taking advantage of its parallel processing capabilities.
4. Output: The output variable contains the output of the model, computed on the GPU.

Python3

import torch
import torch.nn as nn

# Example model
class Generate(nn.Module):
    def __init__(self):
        super(Generate, self).__init__()
        self.gen = nn.Sequential(
            nn.Linear(5,1),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.gen(x)

model = Generate() # Initialize the model
model.to('cuda') # Move the model to the GPU

# Create input data inside GPU
input_data = torch.randn(16, 5, device=device)
output = model(input_data) # Forward pass on theGP
output

Output:

tensor([[0.6338],
        [0.5509],
        [0.3217],
        [0.4494],
        [0.4525],
        [0.3205],
        [0.3786],
        [0.4716],
        [0.3324],
        [0.5012],
        [0.4758],
        [0.4363],
        [0.5476],
        [0.6354],
        [0.4647],
        [0.3847]], device='cuda:0', grad_fn=<SigmoidBackward0>)

Advantages of GPU Acceleration

• Speed Boost: Training on a GPU is significantly faster than on a CPU, reducing the time required for model development and experimentation.
• Parallelism: GPUs handle parallel tasks efficiently, allowing for the simultaneous processing of multiple data points during training.
• Scalability: As the size of datasets and neural networks increases, GPU acceleration becomes increasingly valuable, ensuring scalability for demanding deep learning tasks.
• Complex Model Training: GPUs enable the training of complex models, such as deep convolutional neural networks, which might be impractical on CPUs alone.

GPU Memory Management for Deep Learning Tasks in PyTorch

Deep learning tasks often involve working with large datasets and complex neural network architectures, making efficient GPU memory management is crucial for smooth model training and inference. Optimizing GPU memory usage is crucial to prevent bottlenecks. Below techniques can be used:

1. Use Batch Processing: Training in smaller batches reduces the memory footprint, allowing for the processing of larger datasets.
2. Data Streaming: Load data onto the GPU in chunks, avoiding loading the entire dataset at once.
3. Gradient Accumulation: Gradient accumulation involves accumulating gradients over several batches before updating the model’s parameters. This allows to use larger effective batch sizes without increasing memory usage.
4. Memory Clearance: PyTorch supplies the torch.cuda.empty_cache() function, which aids in releasing GPU memory that is no longer in use. Employing this function strategically, such as after completing a task or encountering out-of-memory errors, proves beneficial for freeing up GPU memory.
5. Monitoring Memory Usage: PyTorch provides tools like torch.cuda.max_memory_allocated() and torch.cuda.max_memory_cached() to monitor the highest levels of memory allocation and caching on the GPU. Utilizing these functions allows for the tracking of memory usage throughout training, facilitating the identification of potential memory leaks or inefficiencies.

Conclusion

GPU acceleration is an essential tool for speeding up deep learning processes. PyTorch offers smooth GPU acceleration support, enabling users to fully utilize the processing power that GPUs have to offer.