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Signed-off-by: Joaquin Anton <janton@nvidia.com>
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JanuszL
reviewed
Feb 24, 2021
JanuszL
reviewed
Feb 24, 2021
README.rst
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| The NVIDIA Data Loading Library (DALI) is a library for data loading and | ||
| pre-processing to accelerate deep learning applications. It provides a | ||
| collection of highly optimized building blocks for loading and processing | ||
| image, video and audio data, and it can be used as a portable drop-in replacement |
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| image, video and audio data, and it can be used as a portable drop-in replacement | |
| image, video and audio data. It can be used as a portable drop-in replacement |
JanuszL
reviewed
Feb 24, 2021
README.rst
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| pre-processing to accelerate deep learning applications. It provides a | ||
| collection of highly optimized building blocks for loading and processing | ||
| image, video and audio data, and it can be used as a portable drop-in replacement | ||
| for built in data loaders and data iterations in popular deep learning frameworks. |
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| for built in data loaders and data iterations in popular deep learning frameworks. | |
| for built in data loaders and data iterators in popular deep learning frameworks. |
JanuszL
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Feb 24, 2021
README.rst
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| raw formats, LMDB, RecordIO, TFRecord. | ||
| - Extensible for user-specific needs through open source license. | ||
| - Easy-to-use functional style Python API. | ||
| - Multiple data formats support - LMDB, RecordIO, TFRecord, COCO, JPEG, JPEG 2000, WAV, FLAC, OGG, H.264 and HEVC. |
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| - Multiple data formats support - LMDB, RecordIO, TFRecord, COCO, JPEG, JPEG 2000, WAV, FLAC, OGG, H.264 and HEVC. | |
| - Multiple data formats support - LMDB, RecordIO, TFRecord, COCO, JPEG, JPEG 2000, WAV, FLAC, OGG, H.264, VP9 and HEVC. |
JanuszL
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Feb 24, 2021
README.rst
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| -------------------- | ||
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| - GPU Technology Conference 2018; Fast data pipeline for deep learning training, T. Gale, S. Layton and P. Trędak: |slides1|_, |recording1|_. | ||
| - GPU Technology Conference 2019; Fast AI data pre-preprocessing with DALI; Janusz Lisiecki, Michał Zientkiewicz: |slides2|_, |recording2|_. | ||
| - GPU Technology Conference 2019; Integration of DALI with TensorRT on Xavier; Josh Park and Anurag Dixit: |slides3|_, |recording3|_. | ||
| - GPU Technology Conference 2020; Fast Data Pre-Processing with NVIDIA Data Loading Library (DALI); Albert Wolant, Joaquin Anton Guirao |recording4|_. | ||
| - `Developer page <https://developer.nvidia.com/DALI>`_. | ||
| - `Blog post <https://devblogs.nvidia.com/fast-ai-data-preprocessing-with-nvidia-dali/>`_. | ||
| - `Blog post: Fast AI Data Preprocessing with NVIDIA DALI <https://devblogs.nvidia.com/fast-ai-data-preprocessing-with-nvidia-dali/>`_. |
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Maybe we can refer all blog posts related to DALI - https://developer.nvidia.com/blog/tag/dali/ ?
Signed-off-by: Joaquin Anton <janton@nvidia.com>
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banasraf
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Feb 25, 2021
README.rst
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| - Portable accross popular deep learning frameworks: TensorFlow, PyTorch, MXNet, PaddlePaddle. | ||
| - Supports CPU and GPU execution. | ||
| - Scalable across multiple GPUs. | ||
| - Flexible graphs lets developers create custom pipelines. |
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graphs let or graph lets
Also it's not clear for me what does it mean. What makes our graphs flexible?
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I copied it from the developer page. It means that users can write their own processing graphs.
JanuszL
approved these changes
Feb 25, 2021
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Signed-off-by: Joaquin Anton janton@nvidia.com
Why we need this PR?
Pick one, remove the rest
What happened in this PR?
Fill relevant points, put NA otherwise. Replace anything inside []
Added diagram
Listed supported formats
Mention GDS and Triton integration
Added info from developer page
Added quick installation command
Readme
NA
NA
NA
JIRA TASK: [DALI-1870]