migperf 0.0.1

Multi-Instance-GPU profiling tool

MIG Profiler

MIGProfiler is a toolkit for benchmark study on NVIDIA MIG techniques. It provides profiling on multiple deep learning training and inference tasks on MIG GPUs.

MIGProfiler is featured for:

• 🎨 Support a lot of deep learning tasks and open-sourced models on a various of benchmark type
• 📈 Present comprehensive benchmark results
• 🐣 Easy to use with a configuration file (WIP)

The project is under rapid development! Please check our benchmark website and join us!

• Benchmark Website
• Install
• Quick Start
• Cite Us
• Contributors
• Acknowledgement
• License

Benchmark Website 📈

Coming soon!

Install 📦️

Manual install

Requirements:

• PyTorch with CUDA
• OpenCV
• Sanic
• Transformers
• Tqdm
• Prometheus client

# create virtual environment
conda create -n mig-perf python=3.8
conda activate mig-perf

# install required packages
conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
conda install -c conda-forge opencv
pip install transformers
pip install sanic tqdm prometheus_client

PyPI install

WIP

Use Docker

WIP

Quick Start 🚚

You can easily to profile on MIG GPU. Below are some common deep learning tasks to play with.

1. MIG training benchmark

We first create a 1g.10gb MIG device

# enable MIG
sudo nvidia-smi -i 0 -mig 1
# create MIG instance
sudo nvidia-smi mig -cgi 1g.10gb -C

Start DCGM metric exporter

docker run -d --rm --gpus all --net mig_perf -p 9400:9400  \
    -v "${PWD}/mig_perf/profiler/client/dcp-metrics-included.csv:/etc/dcgm-exporter/customized.csv" \
    --name dcgm_exporter --cap-add SYS_ADMIN   nvcr.io/nvidia/k8s/dcgm-exporter:2.4.7-2.6.11-ubuntu20.04 \
    -c 500 -f /etc/dcgm-exporter/customized.csv -d f

Start to profile

cd mig_perf/profiler
export PYTHONPATH=$PWD
python train/train_cv.py --bs=32 --model=resnet50 --num_batches=500 --mig-device-id=0

Remeber to disable MIG after finish benchmark

sudo nvidia-smi -i 0 -dci
sudo nvidia-smi -i 0 -dgi
sudo nvidia-smi -i 0 -mig 0

2. MIG inference benchmark

Start DCGM metric exporter

docker run -d --rm --gpus all --net mig_perf -p 9400:9400  \
    -v "${PWD}/mig_perf/profiler/client/dcp-metrics-included.csv:/etc/dcgm-exporter/customized.csv" \
    --name dcgm_exporter --cap-add SYS_ADMIN   nvcr.io/nvidia/k8s/dcgm-exporter:2.4.7-2.6.11-ubuntu20.04 \
    -c 500 -f /etc/dcgm-exporter/customized.csv -d f

Start to profile

cd mig_perf/profiler
export PYTHONPATH=$PWD
python client/block_infernece_cv.py --bs=32 --model=resnet50 --num_batches=500 --mig-device-id=0

See more benchmark experiments in ./exp.

3. Visualize

• in notebook
• in Prometheus (under improvement)

Cite Us 🌱

@article{zhang2022migperf,
  title={MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs},
  author={Zhang, Huaizheng and Li, Yuanming and Xiao, Wencong and Huang, Yizheng and Di, Xing and Yin, Jianxiong and See, Simon and Luo, Yong and Lau, Chiew Tong and You, Yang},
  journal={arXiv preprint arXiv:2301.00407},
  year={2023}
}

Contributors 👥

• Yuanming Li
• Huaizheng Zhang
• Yizheng Huang
• Xing Di

Ackowledgement

Special thanks to Aliyun and NVIDIA AI Tech Center to provide MIG GPU server for benchmarking.

License

This repository is open-sourced under MIT License.