Modules§
- Dataset iterators.
- Indexing operations
- JIT interface to run model trained/saved using PyTorch Python API.
- The different kind of elements supported in Torch.
- A small neural-network library based on Torch.
- The
vision
module groups functions and models related to computer vision.
Structs§
- A jit PyTorch module.
- A RAII guard that prevents gradient tracking until deallocated.
- A single scalar value.
- A tensor object.
- The trainable version of a jit PyTorch module.
Enums§
- Cuda related helper functions.
- A torch device.
- Argument and output values for JIT models. These represent arbitrary values, e.g. tensors, atomic values, pairs of values, etc.
- The different kind of elements that a Tensor can hold.
- A tensor layout.
- Quantization engines
- Main library error type.
Traits§
Functions§
- Runs a closure in mixed precision.
- Get the number of threads used by torch for inter-op parallelism.
- Get the number of threads used by torch in parallel regions.
- Sets the random seed used by torch.
- Runs a closure without keeping track of gradients.
- Disables gradient tracking, this will be enabled back when the returned value gets deallocated. Note that it is important to bind this to a name like
_guard
and not to_
as the latter would immediately drop the guard. See https://internals.rust-lang.org/t/pre-rfc-must-bind/12658/46 for more details. - Set the number of threads used by torch for inter-op parallelism.
- Set the number of threads used by torch in parallel regions.
- Runs a closure explicitly keeping track of gradients, this could be run within a no_grad closure for example.