How to Make Pytorch Run on the GPU By Default?

14 minutes read

By default, PyTorch runs on the CPU. However, you can make PyTorch run on the GPU by default by following these steps:

  1. Check for GPU availability: Before running the code, ensure that you have a GPU available on your machine. PyTorch uses CUDA, so you need to have an NVIDIA GPU with CUDA support. You can check if a GPU is available using the torch.cuda.is_available() method, which returns True if a GPU is available, or False if not.
  2. Set the device: If a GPU is available, set the device to the GPU using torch.cuda.set_device() method. This will ensure that all tensors and operations are performed on the GPU. If a GPU is not available, PyTorch will automatically use the CPU.


Here is an example code snippet to make PyTorch run on the GPU by default:

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import torch

# Check for GPU availability
if torch.cuda.is_available():
    # Set device to GPU
    torch.cuda.set_device(0)
    device = torch.device("cuda")
    print("Using GPU:", torch.cuda.get_device_name(0))
else:
    # Set device to CPU
    device = torch.device("cpu")
    print("Using CPU.")

# Use the device for tensor operations
a = torch.tensor([1, 2, 3]).to(device)
b = torch.tensor([4, 5, 6]).to(device)
c = a + b

print("Result:", c)


In this code, the if statement checks for GPU availability. If a GPU is available, it sets the device to the GPU (in this case, GPU 0). If a GPU is not available, it sets the device to the CPU. The tensors a and b are then created on the selected device, and the addition operation c = a + b is performed on the selected device.


Remember that running code on a GPU may require additional setup, such as installing CUDA and appropriate GPU drivers.

Best PyTorch Books to Read in 2024

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Rating is 5 out of 5

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

  • Use scikit-learn to track an example ML project end to end
  • Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
  • Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
  • Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
  • Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
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Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play

Rating is 4.9 out of 5

Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Rating is 4.8 out of 5

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)

Rating is 4.7 out of 5

Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)

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Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Rating is 4.6 out of 5

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Tiny Python Projects: 21 small fun projects for Python beginners designed to build programming skill, teach new algorithms and techniques, and introduce software testing

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Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition

Rating is 4.3 out of 5

Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition


What is the default GPU device in PyTorch?

The default GPU device in PyTorch is the one specified by the environment variable CUDA_VISIBLE_DEVICES, or if not set, it is the device with index 0.


What is the default device in PyTorch?

The default device in PyTorch is the CPU.


What are the best practices for GPU memory management in PyTorch?

Here are some best practices for GPU memory management in PyTorch:

  1. Use torch.cuda.empty_cache(): PyTorch keeps a cache of GPU memory, and sometimes memory is not immediately released after tensors go out of scope. Calling torch.cuda.empty_cache() can release unused memory from the cache.
  2. Use the proper data types: PyTorch has different data types like torch.float32, torch.float16, etc. Use the smallest data type that still maintains the required precision to reduce memory usage.
  3. Limit the number of parallel workers: If you are using DataLoader for loading data in parallel, reduce the number of parallel workers to limit memory consumption. Higher numbers of workers can lead to higher memory usage.
  4. Use smaller batch sizes: Larger batch sizes consume more memory. If memory usage is a concern, consider using smaller batch sizes during training.
  5. Use gradient accumulation instead of larger batch sizes: If you need larger effective batch sizes for better convergence, consider using gradient accumulation. This involves accumulating gradients over multiple small batches before performing weight updates.
  6. Use the pin_memory flag: When using DataLoader, set the pin_memory flag to True. This can improve data transfer from CPU to GPU by enabling pinned memory, which avoids memory copies.
  7. Use to(device) for moving tensors: Instead of manually moving tensors using .cuda() or .cpu(), use the to(device) method to move tensors between CPU and GPU. This provides flexibility in case you want to use different devices in the future.
  8. Use torch.no_grad() when evaluating: When evaluating a model, wrap the evaluation code with torch.no_grad() to disable gradient tracking. This avoids unnecessary memory usage for gradient computations.
  9. Use gradient checkpointing: If your model has a large memory footprint due to intermediate activations, consider using gradient checkpointing. This technique trades off some computation time for reduced memory usage by recomputing some intermediate activations during backpropagation.
  10. Reduce unnecessary memory allocations: Minimize unnecessary tensor allocations by reusing existing tensors whenever possible. Creating new tensors unnecessarily can lead to memory fragmentation.


Remember that the best practices for GPU memory management may depend on the specific task, model, and available hardware resources.


How to debug errors related to PyTorch GPU usage?

To debug errors related to PyTorch GPU usage, you can follow these steps:

  1. Check if your system supports GPU: Confirm that your system has a compatible GPU and the necessary drivers are installed. You can check this using the nvidia-smi command in the terminal.
  2. Verify PyTorch installation: Ensure that PyTorch is installed with GPU support. You can check this by running the following code:
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import torch
print(torch.cuda.is_available())


If it returns True, PyTorch is installed with GPU support.

  1. Verify GPU device availability: You can check the available GPU devices using the following code:
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import torch
print(torch.cuda.device_count())


This will print the number of available GPUs.

  1. Place tensors on the GPU: When working with PyTorch, make sure to move tensors and models to the GPU explicitly. You can do this using the to() method. For example:
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import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
x = torch.tensor([1, 2, 3]).to(device)


This will ensure that the tensor is placed on the GPU if available, or on the CPU if not.

  1. Check for GPU memory issues: In some cases, you may encounter CUDA out-of-memory errors. This can happen if the GPU memory is not sufficient for the operation. You can monitor GPU memory usage using the nvidia-smi command, or in the PyTorch code using:
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import torch

print(torch.cuda.memory_allocated())
print(torch.cuda.memory_cached())


These commands will print the currently allocated and cached GPU memory.

  1. Enable CUDA error tracking: PyTorch provides an option to enable CUDA error tracking, which can help in identifying specific errors. You can enable it by setting the CUDA_LAUNCH_BLOCKING environment variable before running your script:
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export CUDA_LAUNCH_BLOCKING=1
python your_script.py


This will raise an error whenever there is an issue with the GPU operations, providing more specific information about the error.

  1. Check error messages and stack traces: When encountering errors, carefully read the error messages and stack traces. These often provide valuable information about the source of the error, such as specific functions or lines in your code that caused the problem.


By following these steps, you should be able to identify and debug errors related to PyTorch GPU usage effectively.


What is the difference between GPU and CPU memory in PyTorch?

In PyTorch, GPU (Graphics Processing Unit) and CPU (Central Processing Unit) memory perform different roles in the training and inference processes:

  1. GPU Memory: GPU memory refers to the memory resources available on the graphics card. It is used for storing the intermediate computations and data during the training process. GPUs excel at performing massively parallel computations, making them efficient at handling matrix multiplications and other operations involved in deep learning. PyTorch allows you to move tensors from CPU memory to GPU memory using functions like .to('cuda').
  2. CPU Memory: CPU memory, also known as system memory or RAM (Random Access Memory), refers to the memory resources available to the central processing unit. It is used for general-purpose computing tasks and for storing data that doesn't fit in the GPU memory. PyTorch tensors residing in CPU memory can be transferred to GPU memory for computations by calling .to('cuda') or .cuda().


In summary, GPU memory is specifically allocated on the GPU and is used for efficient parallel computations, while CPU memory is the system's general-purpose memory used to store data and perform various computations.


How to profile PyTorch GPU usage?

To profile PyTorch GPU usage, you can follow these steps:

  1. PyTorch provides a tool called torch.cuda.profiler that allows you to profile GPU usage. First, import this module along with other required modules:
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import torch
from torch.cuda import profiler


  1. Start and stop the profiler around the code snippet you want to profile. You can wrap your code snippet using the torch.cuda.profiler.profile() context manager:
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with profiler.profile(record_shapes=True, profile_memory=True, use_cuda=True) as prof:
    # code snippet to profile
    ...


  • record_shapes=True records shape information for each operation (Optional).
  • profile_memory=True profiles GPU memory usage (Optional).
  • use_cuda=True uses CUDA to collect events (Required, as we are profiling GPU usage).
  1. Make sure to place your PyTorch tensors and models on the GPU for profiling:
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device = torch.device("cuda")
model.to(device)
inputs = inputs.to(device)


  1. After running your code, you can print the stats to analyze the GPU usage:
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print(prof.key_averages().table(sort_by="cuda_time_total"))


  • key_averages() will return the profiling results.
  • table() sorts the results by "cuda_time_total" for detailed information.
  1. Run the script and observe the profiling output that shows the execution time of each operation on the GPU.


Here is a complete example:

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import torch
from torch.cuda import profiler

device = torch.device("cuda")

# Put your PyTorch tensors and models on the GPU
model = Model()
model.to(device)
inputs = torch.randn(10, 3, 224, 224).to(device)

# Start profiling
with profiler.profile(record_shapes=True, profile_memory=True, use_cuda=True) as prof:
    # Code snippet to profile
    output = model(inputs)

# Print the profiling stats
print(prof.key_averages().table(sort_by="cuda_time_total"))


By analyzing the profiling results, you can identify bottlenecks and optimize your PyTorch code for better GPU utilization.

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