Command Line Tool
We provide a simple command line tool called furiosa-models to allow users to
evaluate or run quickly one of models with FuriosaAI NPU.
Installing
To install furiosa-models command, please refer to Installing.
Then, furiosa-models command will be available.
Synopsis
furiosa-models command has three subcommands: list, desc, and bench.
Subcommand: list
list subcommand prints out the list of models with attributes.
You will be able to figure out what models are available.
Example
$ furiosa-models list
+-----------------+------------------------------+----------------------+-------------------------+
| Model name | Model description | Task type | Available postprocesses |
+-----------------+------------------------------+----------------------+-------------------------+
| ResNet50 | MLCommons ResNet50 model | Image Classification | Python |
| SSDMobileNet | MLCommons MobileNet v1 model | Object Detection | Python, Rust |
| SSDResNet34 | MLCommons SSD ResNet34 model | Object Detection | Python, Rust |
| YOLOv5l | YOLOv5 Large model | Object Detection | Python |
| YOLOv5m | YOLOv5 Medium model | Object Detection | Python |
| EfficientNetB0 | EfficientNet B0 model | Image Classification | Python |
| EfficientNetV2s | EfficientNetV2-s model | Image Classification | Python |
+-----------------+------------------------------+----------------------+-------------------------+
Subcommand: bench
bench subcommand runs a specific model with a given path where the input sample data are located.
It will print out the performance benchmark results like QPS.
Example
$ furiosa-models bench ResNet50 .
libfuriosa_hal.so --- v0.11.0, built @ 43c901f
Running 4 input samples ...
----------------------------------------------------------------------
WARN: the benchmark results may depend on the number of input samples,
sizes of the images, and a machine where this benchmark is running.
----------------------------------------------------------------------
----------------------------------------------------------------------
Preprocess -> Inference -> Postprocess
----------------------------------------------------------------------
Total elapsed time: 618.86050 ms
QPS: 6.46349
Avg. elapsed time / sample: 154.71513 ms
----------------------------------------------------------------------
Inference -> Postprocess
----------------------------------------------------------------------
Total elapsed time: 5.40490 ms
QPS: 740.06865
Avg. elapsed time / sample: 1.35123 ms
----------------------------------------------------------------------
Inference
----------------------------------------------------------------------
Total elapsed time: 5.05762 ms
QPS: 790.88645
Avg. elapsed time / sample: 1.26440 ms
Subcommand: desc
desc subcommand shows the details of a specific model.
Example
$ furiosa-models desc ResNet50
family: ResNet
format: onnx
metadata:
description: ResNet50 v1.5 int8 ImageNet-1K
publication:
authors: null
date: null
publisher: null
title: null
url: https://arxiv.org/abs/1512.03385.pdf
name: ResNet50
version: v1.5
task type: Image Classification
available postprocess versions: Python