Comparison of Benchmark Test Performance on NVIDIA® Jetson™ AGX Xavier™, Jetson™ Xavier™ NX and Jetson™ Nano™
WHAT YOU WILL LEARN?
1- Cloning the test repository
2- Setting up the benchmark test
3- Testing the performance in each device
ENVIRONMENT
Hardware 1: Jetson AGX Xavier Developer Kit
Hardware 2: DSBOX-NX2
Hardware 3: DSBOX-N2
OS 1: JetPack 4.4 (L4T-32.4.3)
OS 2: JetPack 4.5 (L4T-32.5)
OS 3: JetPack 4.5 (L4T-32.5)
Cloning The Test Repository
First, let's set our test environment from github:
git clone https://github.com/NVIDIA-AI-IOT/jetson_benchmarks.git
cd jetson_benchmarks
mkdir models
sudo sh install_requirements.sh
Benchmark Test Setup
Now let's download all model files:
For AGX Xavier:
python3 utils/download_models.py --all --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --save_dir ${PWD}/models
For Xavier NX:
python3 utils/download_models.py --all --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --save_dir ${PWD}/models
For Nano:
python3 utils/download_models.py --all --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --save_dir ${PWD}/models
Performance Test in Each Device
Jetson AGX Xavier Test Results
Let's test all models one by one:
sudo python3 benchmark.py --model_name inception_v4 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0
sudo python3 benchmark.py --model_name vgg19 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0
sudo python3 benchmark.py --model_name super_resolution --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0
sudo python3 benchmark.py --model_name unet --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0
sudo python3 benchmark.py --model_name pose_estimation --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0
sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0
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sudo python3 benchmark.py --model_name ssd-mobilenet-v1 --csv_file_path ${PWD}/benchmark_csv/xavier-benchmarks.csv --model_dir ${PWD}/models --jetson_devkit xavier --gpu_freq 1377000000 --dla_freq 1395200000 --power_mode 0
Jetson Xavier NX Test Results
Let's test all models one by one:
sudo python3 benchmark.py --model_name inception_v4 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode
sudo python3 benchmark.py --model_name vgg19 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2
sudo python3 benchmark.py --model_name super_resolution --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2
sudo python3 benchmark.py --model_name unet --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2
sudo python3 benchmark.py --model_name pose_estimation --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2
sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2
sudo python3 benchmark.py --model_name resnet --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2
sudo python3 benchmark.py --model_name ssd-mobilenet-v1 --csv_file_path ${PWD}/benchmark_csv/nx-benchmarks.csv --model_dir ${PWD}/models/ --power_mode 2
Jetson Nano Test Results
Let's test all models one by one (with fp16 model):
sudo python3 benchmark.py --model_name inception_v4 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
sudo python3 benchmark.py --model_name vgg19 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
sudo python3 benchmark.py --model_name super_resolution --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
sudo python3 benchmark.py --model_name unet --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
sudo python3 benchmark.py --model_name pose_estimation --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
sudo python3 benchmark.py --model_name tiny-yolov3 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
sudo python3 benchmark.py --model_name ssd-mobilenet-v1 --csv_file_path ${PWD}/benchmark_csv/tx2-nano-benchmarks.csv --model_dir ${PWD}/models/ --jetson_devkit nano --gpu_freq 921600000 --power_mode 0 --precision fp16
Comparison of Results
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