Using RAPIDS with Pytorch

Deep Learning Software – PyTorch | Exxact – End to End Deep Learning with PyTorch. PyTorch is a widely used, open source deep learning platform used for easily writing neural network layers in Python enabling a seamless workflow from research to production. Based on Torch, PyTorch has become a powerful machine learning framework favored by esteemed researchers around the world.

RAPIDS Release Selector. RAPIDS is available as conda packages, docker images, and from source builds. Use the tool below to select your preferred method, packages, and environment to install RAPIDS. Certain combinations may not be possible and are dimmed automatically. Be sure you’ve met the required prerequisites above and see the details blow.

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Azure Machine Learning service now supports NVIDIA's RAPIDS. – Azure Machine Learning service users will be able to use RAPIDS in the same way they currently use other machine learning frameworks, and they will be able to use RAPIDS in conjunction with Pandas, Scikit-learn, PyTorch, TensorFlow, etc. We strongly encourage the community to try it out and look forward to your feedback!

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ValueError: axes don't match array Issue #47 milesial. – Running into an issue when training on my own data set. I cloned the repo, added folders data/train and data/train_masks, only part of the code I changed was in load.py

A place to discuss PyTorch code, issues, install, research. IndexError: When performing advanced indexing the indexing objects must be LongTensors or convertible to LongTensors

Using RAPIDS with Pytorch – RAPIDS AI – Medium – In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we.

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OPEN SOURCE AT NVIDIA | NVIDIA Developer – RAPIDS. Open GPU data science. Website> GitHub> TensorRT. High-performance platform for deep learning inference. Website> GitHub> NCCL.. A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch. Website> GitHub> DIGITS.