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Neural Coder as Python API

Neural Coder can be used as Python APIs. We currently provide 3 main user-facing APIs for Neural Coder: enable, bench and superbench.

Enable

Users can use enable() to enable specific features into DL scripts:

from neural_coder import enable
enable(
    code="neural_coder/examples/vision/resnet50.py",
    features=[
        "pytorch_jit_script",
        "pytorch_channels_last",
    ],
)

To run benchmark directly on the optimization together with the enabling:

from neural_coder import enable
enable(
    code="neural_coder/examples/vision/resnet50.py",
    features=[
        "pytorch_jit_script",
        "pytorch_channels_last"
    ],
    run_bench=True,
)

Bench

To run benchmark on your code with an existing patch:

from neural_coder import bench
bench(
    code="neural_coder/examples/vision/resnet50.py",
    patch_path="${your_patch_path}",
)

SuperBench

To sweep on optimization sets with a fixed benchmark configuration:

from neural_coder import superbench
superbench(code="neural_coder/examples/vision/resnet50.py")

To sweep on benchmark configurations for a fixed optimization set:

from neural_coder import superbench
superbench(
    code="neural_coder/examples/vision/resnet50.py",
    sweep_objective="bench_config",
    bench_feature=[
        "pytorch_jit_script",
        "pytorch_channels_last",
    ],
)