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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_basics_incremental_pca.py>`
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.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_basics_incremental_pca.py:


Incremental PCA on GPU
======================

This example demonstrates how to use the `IncrementalPCA` class on GPU.
We compare the memory usage and time taken to fit the model with the regular
`PCA` class on GPU.

.. GENERATED FROM PYTHON SOURCE LINES 9-74

.. code-block:: Python


    import time
    import gc
    import torch

    from torchdr import IncrementalPCA, PCA

    # Choose the GPU device if available.
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    if device.type != "cuda":
        raise RuntimeError("This example requires a CUDA-capable GPU.")

    # Generate a large random dataset on CPU.
    n_samples = 100_000
    n_features = 500
    X = torch.randn(n_samples, n_features)  # Stored on CPU

    # --------------------------
    # Incremental PCA on GPU
    # --------------------------
    # IncrementalPCA is designed so that only each batch is moved to the GPU.
    # Set n_components=50 and choose an appropriate batch_size.
    ipca = IncrementalPCA(n_components=50, batch_size=1024, device=device)

    # Reset GPU memory stats before fitting.
    torch.cuda.reset_peak_memory_stats(device)

    start = time.time()
    ipca.fit(X, check_input=True)
    ipca_time = time.time() - start

    # Get the peak GPU memory allocated (in bytes).
    ipca_peak_mem = torch.cuda.max_memory_allocated(device)
    print(
        f"Incremental PCA on GPU:\n"
        f"  Time: {ipca_time:.2f} sec, "
        f"Peak GPU memory: {ipca_peak_mem / 1024**2:.2f} MB"
    )

    # Clean up and free GPU memory.
    del ipca
    torch.cuda.empty_cache()
    gc.collect()

    # --------------------------
    # Regular PCA on GPU
    # --------------------------
    # Regular PCA (from torchdr) will typically move the full dataset to GPU.
    pca = PCA(n_components=50, device=device)

    # Reset GPU memory stats before fitting.
    torch.cuda.reset_peak_memory_stats(device)

    start = time.time()
    # Here we assume that pca.fit will move the full dataset to the GPU.
    pca.fit(X)
    pca_time = time.time() - start

    # Get the peak GPU memory allocated (in bytes).
    pca_peak_mem = torch.cuda.max_memory_allocated(device)
    print(
        f"\nRegular PCA on GPU:\n"
        f"  Time: {pca_time:.2f} sec, "
        f"Peak GPU memory: {pca_peak_mem / 1024**2:.2f} MB"
    )


.. _sphx_glr_download_auto_examples_basics_incremental_pca.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: incremental_pca.ipynb <incremental_pca.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: incremental_pca.py <incremental_pca.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: incremental_pca.zip <incremental_pca.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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