A quick start guide to benchmarking AI models in Azure: MLPerf Inferencing v2.0

This blog was authored by Aimee Garcia, Program Manager - AI Benchmarking.  Additional contributions by Program Manager Daramfon Akpan, Program Manager Gaurav Uppal, Program Manager Hugo Affaticati.

 

Microsoft Azure’s publicly available AI inferencing capabilities are led by the NDm A100 v4, ND A100 v4 and NC A100 v4 virtual machines (VMs) powered by the latest NVIDIA A100 Tensor Core GPUs. These results showcase Azure’s commitment to making AI inferencing available to all researchers and users in the most accessible way while raising the bar in AI inferencing in Azure.  To see the announcement on Azure.com please click here.

 

Highlights from the results

ND96amsr A100 v4 powered by NVIDIA A100 80G SXM Tensor Core GPU

Benchmark	Samples/second	Queries/second	Scenarios
bert-99	27.5K+	~22.5K	Offline and server
resnet	300K+	~200K+	Offline and server
3d-unet	24.87		Offline

 

NC96ads A100 v4 powered by NVIDIA A100 80G PCIe Tensor Core GPU

Benchmark	Samples/second	Queries/second	Scenarios
bert-99.9	~6.3K	~5.3K	Offline and server
resnet	144K	~119.6K	Offline and server
3d-unet	11.7		Offline

 

The results were generated by deploying the environment using the VM offerings and Azure’s Ubuntu 18.04-HPC marketplace image.

 

Steps to reproduce the results in Azure

Set up and connect to a VM via SSH - decide which VM you want to benchmark

• Image: Ubuntu 18.04-HPC marketplace image
• Availability: Depending on client need (ex. No redundancy)
• Region: Depending on client need (ex. South Central US)

Set up the dependencies

1. Verify the nvidia-docker version:

cd /mnt

nvidia-smi

 

2. If the driver version is less than 510, install the following: CUDA Toolkit 11.6 Downloads | NVIDIA Developer

sudo wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin

sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600

sudo wget https://developer.download.nvidia.com/compute/cuda/11.6.1/local_installers/cuda-repo-ubuntu1804-11-6-local_11.6.1-510.47.03-1_amd64.deb

sudo dpkg -i cuda-repo-ubuntu1804-11-6-local_11.6.1-510.47.03-1_amd64.deb

sudo apt-key add /var/cuda-repo-ubuntu1804-11-6-local/7fa2af80.pub

sudo apt-get update

sudo apt-get -y install cuda

 

3. Update docker to the latest version:

sudo dpkg -P moby-cli

curl https://get.docker.com | sh && sudo systemctl --now enable docker​

sudo chmod 777 /var/run/docker.sock

 

4. To check the docker version run:

docker info (add check docker version by running this)

sudo reboot

 

You should have version 20.10.12 or newer

 

5. To verify the version again:

nvidia-smi

 

6. Create and run the script to mount the nvme disk using the following:

cd /mnt

sudo touch nvme.sh

sudo vi nvme.sh

 

7. RAID the nvme disks and mount onto the machine by copying and inserting the following into your file:

#!/bin/bash 

 

NVME_DISKS_NAME=`ls /dev/nvme*n1`

NVME_DISKS=`ls -latr /dev/nvme*n1 | wc -l`

 

echo "Number of NVMe Disks: $NVME_DISKS"

 

if [ "$NVME_DISKS" == "0" ]

then

    exit 0

else

    mkdir -p /mnt/resource_nvme

    # Needed incase something did not unmount as expected. This will delete any data that may be left behind

    mdadm  --stop /dev/md*

    mdadm --create /dev/md128 -f --run --level 0 --raid-devices $NVME_DISKS $NVME_DISKS_NAME

    mkfs.xfs -f /dev/md128

    mount /dev/md128 /mnt/resource_nvme

fi

chmod 1777 /mnt/resource_nvme

 

8. Run the script:

sudo sh nvme.sh

 

9. Update Docker root directory in the docker daemon config file:

sudo vi /etc/docker/daemon.json

Add this line after the first curly bracket:

"data-root": "/mnt/resource_nvme/data",

 

10. Run the following:

sudo systemctl restart docker

cd resource_nvme

 

11. Now that your environment is set up, get the repository from the MLCommons github and run the benchmarks:
    • When setting up the scratch path, the path should be /mnt/resource_nvme/scratch

export MLPERF_SCRATCH_PATH=/mnt/resource_nvme/scratch

 

12. Run benchmarks by following the steps in the README.md file in the working directory. To open the file:

vi README.md

 

Below are graphs showing the achieved results for the NDm A100 v4, NC A100 v4 and ND A100 v4 VMs. The units are in throughput/second (samples and queries).

 

 

 

 

 

More about MLPerf

To learn more about MLCommons benchmarks, visit the MLCommons website.