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- PyTorch
Distributed Training Scalable distributed training and performance optimization in research and production is enabled by the torch distributed backend
- Previous PyTorch Versions
OSX macOS is currently not supported in LTS Linux and Windows # CUDA 10 2 pip3 install torch==1 8 2 torchvision==0 9 2 torchaudio==0 8 2 --extra-index-url https: download pytorch org whl lts 1 8 cu102 # CUDA 11 1 pip3 install torch==1 8 2 torchvision==0 9 2 torchaudio==0 8 2 --extra-index-url https: download pytorch org whl lts 1 8 cu111
- Get Started - PyTorch
CUDA 13 0 ROCm 6 4 CPU pip3 install torch torchvision --index-url https: download pytorch org whl cu126
- Links for torch
torch-2 0 0+cpu cxx11 abi-cp310-cp310-linux_x86_64 whl torch-2 0 0+cpu cxx11 abi-cp311-cp311-linux_x86_64 whl torch-2 0 0+cpu cxx11 abi-cp38-cp38-linux_x86_64 whl
- Distributed communication package - torch. distributed — PyTorch 2. 9 . . .
This differs from the kinds of parallelism provided by Multiprocessing package - torch multiprocessing and torch nn DataParallel() in that it supports multiple network-connected machines and in that the user must explicitly launch a separate copy of the main training script for each process
- Probability distributions - torch. distributions
Parameters batch_shape (torch Size) – The shape over which parameters are batched event_shape (torch Size) – The shape of a single sample (without batching) validate_args (bool, optional) – Whether to validate arguments Default: None property arg_constraints: dict[str, torch distributions constraints Constraint] #
- torch — PyTorch 2. 9 documentation
The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors Additionally, it provides many utilities for efficient serialization of Tensors and arbitrary types, and other useful utilities
- CrossEntropyLoss — PyTorch 2. 9 documentation
class torch nn CrossEntropyLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean', label_smoothing=0 0) [source] # This criterion computes the cross entropy loss between input logits and target
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