pytorch-tutorial
mixture-of-experts
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| pytorch-tutorial | mixture-of-experts | |
|---|---|---|
| 3 | 2 | |
| 29,128 | 827 | |
| - | - | |
| 0.0 | 5.3 | |
| 9 months ago | 10 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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pytorch-tutorial
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PyTorch - What does contiguous() do?
I was going through this example of a LSTM language model on github (link).What it does in general is pretty clear to me. But I'm still struggling to understand what calling contiguous() does, which occurs several times in the code.
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How to 'practice' pytorch after finishing its basic tutorial?
I tried to move straight to practicing implementing papers and trying to understand other people's codes but failed miserably. I feel like there was too much of a gap between the basic tutorial and being able to implement ideas into code....hence the question: Is there any resource/way to practice pytorch in general? I did find this and this, but I just wanted to hear what others have gone through to become better at PyTorch up to the point they can build stuff from their own ideas
- [P] Probabilistic Machine Learning: An Introduction, Kevin Murphy's 2021 e-textbook is out
mixture-of-experts
- [Rumor] Potential GPT-4 architecture description
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Local and Global loss
I have a requirement of training pipeline similar to Mixture of Experts (https://github.com/davidmrau/mixture-of-experts/blob/master/moe.py) but I want to train the Experts on a local loss for 1 epoch before predicting outputs from them (which would then be concatenated for the global loss of MoE). Can anyone suggest what’s the best way to set up this training pipeline?
What are some alternatives?
InceptionTime - InceptionTime: Finding AlexNet for Time Series Classification
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Conv-TasNet - A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
mmdetection - OpenMMLab Detection Toolbox and Benchmark
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
bonito - A PyTorch Basecaller for Oxford Nanopore Reads
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
OpenNMT-py - Open Source Neural Machine Translation and (Large) Language Models in PyTorch
tutel - Tutel MoE: An Optimized Mixture-of-Experts Implementation