torch.mtia¶

The MTIA backend is implemented out of the tree, only interfaces are be defined here.

This package enables an interface for accessing MTIA backend in python

`StreamContext`	Context-manager that selects a given stream.
`current_device`	Return the index of a currently selected device.
`current_stream`	Return the currently selected `Stream` for a given device.
`default_stream`	Return the default `Stream` for a given device.
`device_count`	Return the number of MTIA devices available.
`init`
`is_available`	Return true if MTIA device is available
`is_initialized`	Return whether PyTorch's MTIA state has been initialized.
`memory_stats`	Return a dictionary of MTIA memory allocator statistics for a given device.
`set_device`	Set the current device.
`set_stream`	Set the current stream.This is a wrapper API to set the stream.
`stream`	Wrap around the Context-manager StreamContext that selects a given stream.
`synchronize`	Waits for all jobs in all streams on a MTIA device to complete.
`device`	Context-manager that changes the selected device.
`set_rng_state`	Sets the random number generator state.
`get_rng_state`	Returns the random number generator state as a ByteTensor.
`DeferredMtiaCallError`

Streams and events¶

Query and record Stream status to identify or control dependencies across Stream and measure timing.

An in-order queue of executing the respective tasks asynchronously in first in first out (FIFO) order.