tensorplay.accelerator

TensorPlay can run on CPUs and on accelerators such as GPUs. The tensorplay.accelerator module exposes a small, device-agnostic surface for asking which accelerator is currently active, so code that needs to know where tensors live does not have to special-case each backend.

import tensorplay as tp

dev = tp.accelerator.current_accelerator()
print(dev)                      # e.g. 'cuda:0', or None on a CPU-only machine

Functions

`tensorplay.accelerator.current_accelerator`	The accelerator device selected at build time, if any.
`tensorplay.accelerator.device_count`	Number of devices for the current accelerator, or zero without one.
`tensorplay.accelerator.is_available`	Whether an accelerator was built and at least one device is visible.
`tensorplay.accelerator.current_device_index`	Index of the currently selected accelerator device.
`tensorplay.accelerator.set_device_index`	Select the accelerator device by index; negative indices are no-ops.
`tensorplay.accelerator.get_device_capability`	Capability map for an accelerator device.
`tensorplay.accelerator.synchronize`	Wait for all work on an accelerator device to complete.
`tensorplay.accelerator.Graph`	Capture/replay graph on the current accelerator device.

Submodules

`tensorplay.accelerator.memory`	Device-agnostic memory queries for the current accelerator.
`tensorplay.accelerator.random`	Device-agnostic random-number helpers for the current accelerator.
`tensorplay.accelerator.graphs`	Device-agnostic capture/replay graphs for the current accelerator.

Where to go next

CUDA semantics — the CUDA backend the accelerator is most often backed by, and its device-management functions.
Device concepts — how device objects are spelled and compared.

tensorplay.accelerator

Functions

Submodules

Where to go next

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