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Source channel @githubtrending · Post #14834 · Jun 17

#cuda DeepEP is a special communication library for Mixture-of-Experts (MoE) models. It helps these models work faster and more efficiently by improving how data is shared between different parts of the system. DeepEP supports low-precision operations and can handle data transfer between different types of connections, like NVLink and RDMA. This makes it very useful for both training and using AI models, especially when speed is important. Users benefit from faster processing times and better performance overall. https://github.com/deepseek-ai/DeepEP

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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