"32", "48", "64", "80"] } ] MIND: [ { "type": "Categorical", "name": ”capsule_config.routing_logits_scale", “candidates”:[10, 20, 30] }, { "type": "Categorical", "name": "capsule_config

Functionality can be easily extended with common Python libraries such as NumPy, SciPy, and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level includes a collection of highly optimized modules for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. ‣ A preview of Torch-TensorRT commit 9130ab38 from July 31, 2019 as well as a cherry- picked TensorRT 5.1.5 Automatic Mixed Precision (AMP) Automatic Mixed Precision (AMP) for PyTorch is available in this container through the native

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cpu.FloatTensor • GPU - torch.cuda.FloatTensor *Assume 't' is a tensor Autograd • Autograd • Automatic Differentiation Package • Don’t need to worry about partial differentiation, chain rule etc.. •

framework in Python. ● Two main features: ○ N-dimensional Tensor computation (like NumPy) on GPUs ○ Automatic differentiation for training deep neural networks Training Neural Networks Training Define Neural

Text Generation Web UI Text Generation Web UI（简称 TGW，通常被称为“oobabooga”）是一款流行的文本生成 Web 界面工具，类似 于 AUTOMATIC1111/stable-diffusion-webui 。它拥有多个交互界面，并支持多种模型后端，包括 Transformers 、 llama.cpp（通过 llama-cpp-python

4节中所说，求导是几乎所有深度学习优化算法的关键步骤。虽然求导的计算很简单，只需要一些基 本的微积分。但对于复杂的模型，手工进行更新是一件很痛苦的事情（而且经常容易出错）。 深度学习框架通过自动计算导数，即自动微分（automatic differentiation）来加快求导。实际中，根据设计 好的模型，系统会构建一个计算图（computational graph），来跟踪计算是哪些数据通过哪些操作组合起来 产生输出 [Papineni et al., 2002] Papineni, K., Roukos, S., Ward, T., & Zhu, W.‐J. (2002). Bleu: a method for automatic evaluation of machine translation. Proceedings of the 40th annual meeting of the Association for