曦云系列通用GPU mcPyTorch用户指南
1. 概述
2. 快速安装
2.1. 基于pip安装
2.1.1. 环境准备
2.1.2. 开始安装
2.1.3. 验证安装
2.1.4. 如何卸载
2.2. 使用Docker运行
3. 功能支持
3.1. torch
3.2. torch.nn
3.2.1. Convolution Layers
3.2.2. Recurrent Layers
3.2.3. Dropout Layers
3.3. torch.nn.functional
3.4. torch.Tensor
3.5. Tensor Attributes
3.6. Tensor Views
3.7. torch.amp
3.8. torch.autograd
3.9. torch.library
3.10. torch.cuda
3.10.1. cuda
3.10.2. Random Number Generator
3.10.3. Communication collectives
3.10.4. Streams and events
3.10.5. Memory management
3.10.6. Tools Extension
3.10.7. Jiterator (beta)
3.11. torch.backends
3.11.1. torch.backends.cuda
3.11.2. torch.backends.cudnn
3.11.3. torch.backends.mps
3.11.4. torch.backends.mkl
3.11.5. torch.backends.mkldnn
3.11.6. torch.backends.openmp
3.11.7. torch.backends.opt_einsum
3.12. torch.distributed
3.13. torch.distributions
3.14. torch._dynamo
3.15. torch.fft
3.16. torch.fx
3.17. torch.jit
3.18. torch.linalg
3.19. torch.package
3.20. torch.profiler
3.21. torch.nn.init
3.22. torch.onnx
3.23. torch.optim
3.24. Complex Numbers
3.25. DDP Communication Hooks
3.26. torch.random
3.27. torch.masked
3.28. torch.nested
3.29. torch.sparse
3.30. torch.Storage
3.31. torch.testing
3.32. torch.utils.benchmark
3.33. torch.utils.bottleneck
3.34. torch.utils.checkpoint
3.35. torch.utils.cpp_extension
3.36. torch.utils.data
3.37. torch.utils.jit
3.38. torch.utils.model_zoo
3.39. torch.utils.tensorboard
3.40. Type Info
3.41. torch.__config__
4. 常用环境变量
4.1. PYTORCH_DEFAULT_NCHW
4.2. TORCH_ALLOW_TF32_CUBLAS_OVERRIDE
曦云系列通用GPU mcPyTorch用户指南
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