mindspore.runtime

Runtime encapsulates interfaces for executor, memory, stream and event. MindSpore abstracts the corresponding modules from different backends, allowing users to schedule hardware resources at the Python layer.

Executor

`mindspore.runtime.dispatch_threads_num`	Set the threads number of runtime used.
`mindspore.runtime.launch_blocking`	Indicates that synchronizing the execution of the startup device reduces the execution performance of the program.
`mindspore.runtime.set_cpu_affinity`	Enable thread-level core binding to allocate specific CPU cores for key MindSpore modules (main thread, pynative, runtime, and minddata), preventing performance instability caused by CPU core contention among MindSpore threads.
`mindspore.runtime.set_kernel_launch_capture`	In O0/O1 mode, the incremental inference scenario supports graph capture.
`mindspore.runtime.set_kernel_launch_group`	O0 mode supports operator batch parallel delivery interface, supports enabling parallel delivery, and configures parallel number.

Memory

`mindspore.runtime.MemPool`	A MemPool warp a `mindspore.runtime.PluggableAllocator`, and pass it to `mindspore.runtime.use_mem_pool()`.
`mindspore.runtime.PluggableAllocator`	Receive a .so file via ctypes, and dynamically load the alloc and free functions within it.
`mindspore.runtime.empty_cache`	Empty cache in the memory pool.
`mindspore.runtime.max_memory_allocated`	Returns the peak memory size of the memory pool actually occupied by Tensor since the process was started.
`mindspore.runtime.max_memory_reserved`	Returns the peak value of the total memory managed by the memory pool since the process was started.
`mindspore.runtime.memory_allocated`	Returns the actual memory size currently occupied by Tensor.
`mindspore.runtime.memory_replay`	Replay the memory operation based on the application and release order of memory_block.csv.
`mindspore.runtime.memory_reserved`	Returns the total amount of memory currently managed by the memory pool.
`mindspore.runtime.memory_stats`	Returns status information queried from the memory pool.
`mindspore.runtime.memory_summary`	Returns readable memory pool status information.
`mindspore.runtime.reset_max_memory_reserved`	Reset the peak memory size managed by the memory pool.
`mindspore.runtime.reset_max_memory_allocated`	Reset the peak memory size of the memory pool actually occupied by Tensor.
`mindspore.runtime.reset_peak_memory_stats`	Reset the "peak" stats tracked by memory manager.
`mindspore.runtime.set_memory`	Set the memory parameters of runtime device memory management that is implemented using a memory pool.
`mindspore.runtime.use_mem_pool`	A context manager that routes allocations and deallocations to a given pool.

Stream

`mindspore.runtime.communication_stream`	Return communication stream on this device.
`mindspore.runtime.current_stream`	Return current stream used on this device.
`mindspore.runtime.default_stream`	Return default stream on this device.
`mindspore.runtime.set_cur_stream`	Sets the current stream.This is a wrapper API to set the stream.
`mindspore.runtime.synchronize`	Synchronize all streams on current device.(Each MindSpore process only occupies one device)
`mindspore.runtime.Stream`	Wrapper around a device stream.
`mindspore.runtime.StreamCtx`	Context-manager that selects a given stream.

Event

mindspore.runtime.Event

Wrapper around a device event.