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https://github.com/NVIDIA/nvbench.git
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Add Python stub file for cuda.nvbench API
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Oleksandr Pavlyk
183
python/cuda/nvbench/__init__.pyi
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183
python/cuda/nvbench/__init__.pyi
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from typing import Callable, Sequence, Tuple
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class CudaStream:
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"""Represents CUDA stream
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Note
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----
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The class is not directly constructible.
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"""
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def __cuda_stream__(self) -> Tuple[int]:
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"""
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Special method implement CUDA stream protocol
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from `cuda.core`. Returns a pair of integers:
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(protocol_version, integral_value_of_cudaStream_t pointer)
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"""
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...
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def addressof(self) -> int:
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"Integral value of address of driver's CUDA stream struct"
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...
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class Benchmark:
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"""Represents NVBench benchmark.
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Note
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----
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The class is not user-constructible.
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Use `~register` function to create Benchmark and register
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it with NVBench.
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"""
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def getName(self) -> str:
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"Get benchmark name"
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...
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def addInt64Axis(self, name: str, values: Sequence[int]) -> Benchmark:
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"Add integral type parameter axis with given name and values to sweep over"
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...
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def addFloat64Axis(self, name: str, values: Sequence[float]) -> Benchmark:
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"Add floating-point type parameter axis with given name and values to sweep over"
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...
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def addStringAxis(sef, name: str, values: Sequence[str]) -> Benchmark:
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"Add string type parameter axis with given name and values to sweep over"
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...
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class Launch:
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"""Configuration object for function launch.
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Note
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----
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The class is not user-constructible.
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"""
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def getStream(self) -> CudaStream:
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"Get CUDA stream of this configuration"
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...
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class State:
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"""Represent benchmark configuration state.
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Note
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----
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The class is not user-constructible.
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"""
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def hasDevice(self) -> bool:
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"True if configuration has a device"
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...
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def hasPrinters(self) -> bool:
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"True if configuration has a printer"
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...
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def getStream(self) -> CudaStream:
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"CudaStream object from this configuration"
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...
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def getInt64(self, name: str, default_value: int = None) -> int:
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"Get value for given Int64 axis from this configuration"
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...
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def getFloat64(self, name: str, default_value: float = None) -> float:
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"Get value for given Float64 axis from this configuration"
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...
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def getString(self, name: str, default_value: str = None) -> str:
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"Get value for given String axis from this configuration"
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...
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def addElementCount(self, count: int, column_name: str = None) -> None:
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"Add element count"
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...
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def setElementCount(self, count: int) -> None:
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"Set element count"
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...
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def getElementCount(self) -> int:
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"Get element count"
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...
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def skip(self, reason: str) -> None:
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"Skip this configuration"
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...
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def isSkipped(self) -> bool:
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"Has this configuration been skipped"
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...
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def getSkipReason(self) -> str:
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"Get reason provided for skipping this configuration"
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...
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def addGlobalMemoryReads(self, nbytes: int) -> None:
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"Inform NVBench that given amount of bytes is being read by the benchmark from global memory"
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...
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def addGlobalMemoryWrites(self, nbytes: int) -> None:
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"Inform NVBench that given amount of bytes is being written by the benchmark into global memory"
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...
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def getBenchmark(self) -> Benchmark:
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"Get Benchmark this configuration is a part of"
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...
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def getThrottleThreshold(self) -> float:
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"Get throttle threshold value"
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...
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def getMinSamples(self) -> int:
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"Get the number of benchmark timings NVBench performs before stopping criterion begins being used"
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...
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def setMinSamples(self, count: int) -> None:
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"Set the number of benchmark timings for NVBench to perform before stopping criterion begins being used"
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...
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def getDisableBlockingKernel(self) -> bool:
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"True if use of blocking kernel by NVBench is disabled, False otherwise"
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...
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def setDisableBlockingKernel(self, flag: bool) -> None:
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"Use flag = True to disable use of blocking kernel by NVBench"
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...
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def getRunOnce(self) -> bool:
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"Boolean flag whether configuration should only run once"
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...
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def setRunOnce(self, flag: bool) -> None:
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"Set run-once flag for this configuration"
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...
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def getTimeout(self) -> float:
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"Get time-out value for benchmark execution of this configuration"
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...
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def setTimeout(self, duration: float) -> None:
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"Set time-out value for benchmark execution of this configuration"
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...
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def getBlockingKernelTimeout(self) -> float:
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"Get time-out value for execution of blocking kernel"
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...
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def setBlockingKernelTimeout(self, duration: float) -> None:
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"Set time-out value for execution of blocking kernel"
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...
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def collectCUPTIMetrics(self) -> None:
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"Request NVBench to record CUPTI metrics while running benchmark for this configuration"
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...
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def isCUPTIRequired(self) -> bool:
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"True if (some) CUPTI metrics are being collected"
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...
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def exec(
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self, fn: Callable[[Launch], None], batched: bool = True, sync: bool = False
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):
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"""Execute callable running the benchmark.
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The callable may be executed multiple times.
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Parameters
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----------
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fn: Callable
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Python callable with signature fn(Launch) -> None that executes the benchmark.
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batched: bool, optional
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If `True`, no cache flushing is performed between callable invocations.
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Default: `True`.
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sync: bool, optional
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True value indicates that callable performs device synchronization.
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NVBench disables use of blocking kernel in this case.
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Default: `False`.
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"""
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...
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def register(fn: Callable[[State], None]) -> Benchmark:
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"""
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Register bencharking function with NVBench.
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"""
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...
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def run_all_benchmarks(argv: Sequence[str]) -> None:
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"""
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Run all benchmarks registered with NVBench.
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Parameters
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----------
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argv: List[str]
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Sequence of CLI arguments controlling NVBench. Usually, it is `sys.argv`.
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"""
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...
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