How to Use

NetsPresso Benchmarker can be used through two methods: PyNetsPresso and LaunchX.


PyNetsPresso

For first-time use or to obtain detailed information about PyNetsPresso, please visit https://github.com/Nota-NetsPresso/PyNetsPresso.

from netspresso.enums import DeviceName, SoftwareVersion # 1. Declare benchmarker benchmarker = netspresso.benchmarker_v2() # 2. Run benchmark benchmark_result = benchmarker.benchmark_model( input_model_path="./outputs/converted/TENSORRT_JETSON_AGX_ORIN_JETPACK_5_0_1/TENSORRT_JETSON_AGX_ORIN_JETPACK_5_0_1.trt", target_device_name=DeviceName.JETSON_AGX_ORIN, target_software_version=SoftwareVersion.JETPACK_5_0_1, ) print(f"model inference latency: {benchmark_result.benchmark_result.latency} ms") print(f"model gpu memory footprint: {benchmark_result.benchmark_result.memory_footprint_gpu} MB") print(f"model cpu memory footprint: {benchmark_result.benchmark_result.memory_footprint_cpu} MB")

LaunchX

To use LaunchX, please visit https://launchx.netspresso.ai/main.​

  1. Click on Benchmark in the top menu bar.











  1. Upload your AI model.







  1. Select the desired device and click the Benchmark button.

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