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.. autoclass:: GDSStore | ||
:members: | ||
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Defaults | ||
-------- | ||
.. currentmodule:: kvikio.defaults | ||
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Welcome to KvikIO's documentation! | ||
================================== | ||
Welcome to KvikIO's Python documentation! | ||
========================================= | ||
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KvikIO is a Python library providing bindings to `cuFile <https://docs.nvidia.com/gpudirect-storage/api-reference-guide/index.html>`_, which enables `GPUDirectStorage <https://developer.nvidia.com/blog/gpudirect-storage/>`_ (GDS). | ||
KvikIO is a Python and C++ library for high performance file IO. It provides C++ and Python | ||
bindings to `cuFile <https://docs.nvidia.com/gpudirect-storage/api-reference-guide/index.html>`_, | ||
which enables `GPUDirect Storage <https://developer.nvidia.com/blog/gpudirect-storage/>`_ (GDS). | ||
KvikIO also works efficiently when GDS isn't available and can read/write both host and device data seamlessly. | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
:caption: Contents: | ||
KvikIO is a part of the `RAPIDS <https://rapids.ai/>`_ suite of open-source software libraries for GPU-accelerated data science. | ||
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api | ||
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.. note:: | ||
This is the documentation for the Python library. For the C++ documentation of KvikIO, see under **libkvikio**. | ||
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Indices and tables | ||
================== | ||
Contents | ||
-------- | ||
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* :ref:`genindex` | ||
* :ref:`search` | ||
.. toctree:: | ||
:maxdepth: 1 | ||
:caption: Getting Started | ||
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install | ||
quickstart | ||
api | ||
genindex |
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Installation | ||
============ | ||
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KvikIO can be installed using Conda/Mamba or from source. | ||
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Conda/Mamba | ||
----------- | ||
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We strongly recommend to use `mamba <https://github.com/mamba-org/mamba>`_ inplace of conda, which we will do throughout the documents. | ||
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Install the **stable release** from the ``rapidsai`` channel like: | ||
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.. code-block:: | ||
# Install in existing environment | ||
mamba install -c rapidsai -c conda-forge kvikio | ||
# Create new environment (CUDA 11.8) | ||
mamba create -n kvikio-env -c rapidsai -c conda-forge python=3.10 cuda-version=11.8 kvikio | ||
# Create new environment (CUDA 12.0) | ||
mamba create -n kvikio-env -c rapidsai -c conda-forge python=3.10 cuda-version=12.0 kvikio | ||
Install the **nightly release** from the ``rapidsai-nightly`` channel like: | ||
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.. code-block:: | ||
# Install in existing environment | ||
mamba install -c rapidsai-nightly -c conda-forge kvikio | ||
# Create new environment (CUDA 11.8) | ||
mamba create -n kvikio-env -c rapidsai-nightly -c conda-forge python=3.10 cuda-version=11.8 kvikio | ||
# Create new environment (CUDA 12.0) | ||
mamba create -n kvikio-env -c rapidsai-nightly -c conda-forge python=3.10 cuda-version=12.0 kvikio | ||
.. note:: | ||
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If the nightly install doesn't work, set ``channel_priority: flexible`` in your ``.condarc``. | ||
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Build from source | ||
----------------- | ||
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In order to setup a development environment run: | ||
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.. code-block:: | ||
# CUDA 11.8 | ||
mamba env create --name kvikio-dev --file conda/environments/all_cuda-118_arch-x86_64.yaml | ||
# CUDA 12.0 | ||
mamba env create --name kvikio-dev --file conda/environments/all_cuda-120_arch-x86_64.yaml | ||
To build and install the extension run: | ||
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.. code-block:: | ||
./build.sh kvikio | ||
One might have to define ``CUDA_HOME`` to the path to the CUDA installation. | ||
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In order to test the installation, run the following: | ||
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.. code-block:: | ||
pytest tests/ | ||
And to test performance, run the following: | ||
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.. code-block:: | ||
python benchmarks/single-node-io.py |
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