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PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.
A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, [15] Uber's Pyro, [16] Hugging Face's Transformers, [17] PyTorch Lightning, [18] [19] and Catalyst. [20] [21] PyTorch provides two high-level features: [22] Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)
Format name Design goal Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration
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The core package of Torch is torch.It provides a flexible N-dimensional array or Tensor, which supports basic routines for indexing, slicing, transposing, type-casting, resizing, sharing storage and cloning.
Samy Bengio is a Canadian computer scientist, senior director of Artificial Intelligence and Machine Learning Research at Apple, [1] adjunct professor at École Polytechnique Fédérale de Lausanne, [2] and a former long-time scientist at Google [3] known for leading a large group of researchers working in machine learning including adversarial settings.
5. Pytorch tutorial Both encoder & decoder are needed to calculate attention. [42] Both encoder & decoder are needed to calculate attention. [48] Decoder is not used to calculate attention. With only 1 input into corr, W is an auto-correlation of dot products. w ij = x i x j. [49] Decoder is not used to calculate attention. [50]
It is compatible with the PyTorch, TensorFlow and JAX deep learning libraries and includes implementations of notable models like BERT and GPT-2. [17] The library was originally called "pytorch-pretrained-bert" [ 18 ] which was then renamed to "pytorch-transformers" and finally "transformers."