pytorch cuda alloc conf windows

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PyTorch is a popular deep learning framework that provides support for GPU acceleration using CUDA. When working with large models or datasets, efficient memory management becomes crucial. This tutorial will guide you through configuring PyTorch CUDA memory allocation on Windows, specifically focusing on the CUDA_LAUNCH_BLOCKING and CUDA_VISIBLE_DEVICES environment variables.
Before you begin, ensure that you have the following installed on your system:
PyTorch: Install the latest version of PyTorch using pip install torch.
NVIDIA CUDA Toolkit: Download and install the CUDA Toolkit compatible with your GPU from the NVIDIA website (https://developer.nvidia.com/cuda-downloads).
cuDNN: Download and install the cuDNN library compatible with your CUDA version from the NVIDIA website (https://developer.nvidia.com/cudnn).
CUDA_LAUNCH_BLOCKING is an environment variable that can be set to control the behavior of CUDA launches. When set to 1, it makes CUDA calls synchronous, allowing you to trace and debug CUDA operations more effectively.
Open a command prompt or terminal.
Set the CUDA_LAUNCH_BLOCKING variable:
This command sets the variable for the current session.
Run your Python script or launch a Python interpreter within the same session.
With CUDA_LAUNCH_BLOCKING set to 1, CUDA operations will be synchronous, making it easier to identify and debug issues.
CUDA_VISIBLE_DEVICES is an environment variable that allows you to specify which GPU devices should be visible to your CUDA-enabled applications. This can be useful in cases where you want to control which GPU your PyTorch code uses.
Open a command prompt or terminal.
Set the CUDA_VISIBLE_DEVICES variable:
Replace 0 with the index of the GPU you want to use. You can also specify multiple GPUs separated by commas (e.g., set CUDA_VISIBLE_DEVICES=0,1).
Run your Python script or launch a Python interpreter within the same session.
With CUDA_VISIBLE_DEVICES set, PyTorch will only use the specified GPU(s).
Here's a simple PyTorch code example that demonstrates the impact of these environment variables:
Experiment with setting CUDA_LAUNCH_BLOCKING and CUDA_VISIBLE_DEVICES to observe the changes in behavior.
Configuring PyTorch CUDA memory allocation on Windows can significantly impact the performance and debugging capabilities of your deep learning applications. Experiment with the provided environment variables to optimize GPU usage and streamline your development process.
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