How To Free Gpu Memory Python

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When working with deep learning frameworks like TensorFlow or PyTorch, it is common to encounter memory issues with Graphics Processing Units (GPUs). In this post, we will discuss how to free GPU memory in Python to optimize performance and avoid out-of-memory errors.

Free GPU Memory for TensorFlow

When using TensorFlow, the memory is not released automatically after a session ends. This may cause issues when running multiple sessions or when there is not enough memory available. To free GPU memory in TensorFlow, you can run the following commands:

    from tensorflow.compat.v1 import ConfigProto
    from tensorflow.compat.v1 import InteractiveSession

    config = ConfigProto()
    config.gpu_options.allow_growth = True
    session = InteractiveSession(config=config)

The above code snippet will ensure that TensorFlow only allocates the necessary GPU memory and deallocates it once it is no longer needed. If you are using TensorFlow 2.0 or later, you can use a slightly different approach:

    import tensorflow as tf

    gpus = tf.config.experimental.list_physical_devices('GPU')
    if gpus:
        try:
            for gpu in gpus:
                tf.config.experimental.set_memory_growth(gpu, True)
        except RuntimeError as e:
            print(e)

Free GPU Memory for PyTorch

In PyTorch, you can free GPU memory by simply deleting the objects that are occupying it and then invoking the garbage collector. Here’s an example:

    import torch
    import gc

    # Your GPU-intensive code here

    # Free GPU memory
    del your_variable
    torch.cuda.empty_cache()
    gc.collect()

Make sure to replace your_variable with the variable(s) you want to delete. The garbage collector (gc.collect()) will then take care of any unused memory.

Use NVIDIA System Management Interface (nvidia-smi)

If you are using an NVIDIA GPU, you can also use the NVIDIA System Management Interface (nvidia-smi) command-line utility to monitor and manage the GPU memory. This tool allows you to check memory usage, processes running on the GPU, and even kill specific processes.

    # To check GPU memory usage
    nvidia-smi

    # To kill a specific process
    nvidia-smi --gpu-reset -i [GPU_ID]

Replace [GPU_ID] with the ID of the GPU that you wish to reset. This will forcefully kill any GPU processes and free up the memory.

Conclusion

Managing GPU memory is essential when working with deep learning frameworks like TensorFlow or PyTorch. By using the techniques mentioned in this article, you can optimize memory usage and avoid running into out-of-memory errors. Make sure to monitor your GPU memory regularly and apply the appropriate strategies to free up memory as needed.