A quick start guide to benchmarking AI models in Azure: MLPerf Training v2.0
Published Aug 03 2022 06:45 AM 1,053 Views

By: Sonal Doomra, Program Manager 2, Hugo Affaticati, Program Manager, and Daramfon Akpan, Program Manager


Useful resources

Information on the NC A100 v4-series

Information on the NDm A100 v4-series



MLCommons® provides a distributed AI training benchmark suite: MLPerfTM Training. Here is how to run MLPerfTM training v2.0 benchmarks on NC A100 v4 and NDm A100 v4 virtual machines.


1- Select and set up the virtual machine: NC96ads A100 v4 or ND96amsr A100 v4 using the information given in this document.


2- Git clone the MLcommons® repo:

cd /mnt/resource_nvme
git clone https://github.com/mlcommons/training_results_v2.0.git


3- Set permissions

sudo chown -R $USER:$USER training_results_v2.0/

4- Navigate into the benchmark directory. BENCHMARK_NAME should be replaced by the benchmark you want to run (e.g. bert, rnnt, dlrm...).

cd training_results_v2.0/Azure/benchmarks/<BENCHMARK_NAME>/implementations/ND96amsr_A100_v4/

5- Make changes for NUMA bindings in azure.sh

vi azure.sh 

a. For NC A100 v4-series, paste the following lines in the file.

bind_cpu_cores=([0]="0-23" [1]="24-47" [2]="48-71" [3]="72-95")
bind_mem=([0]="0" [1]="1" [2]="2" [3]="3")

b. For NDm A100 v4-series, paste the following lines in the file.

bind_cpu_cores=([0]="24-47" [1]="24-47" [2]="0-23" [3]="0-23" [4]="72-95" [5]="72-95" [6]="48-71" [7]="48-71")
bind_mem=([0]="1" [1]="1" [2]="0" [3]="0" [4]= "3" [5]="3" [6]="2" [7]="2")


6- Make changes to run_and_time.sh to reflect the right path to azure.sh (around line 125)

vi run_and_time.sh 

Replace the line with the following.


CMD=( '/bm_utils/bind.sh' '--cpu=/bm_utils/azure.sh' '--mem=/bm_utils/azure.sh' '--ib=single' '--cluster=${cluster}' '--' ${NSYSCMD} 'python' '-u')  

7- Make the changes to run_with_docker.sh file to point to correct path in mounted run_and_time.sh (around the middle of the file).

docker exec -it "${_config_env[@]}" "${CONT_NAME}" \
${TORCH_RUN} --nproc_per_node=${DGXNGPU} /bm_utils/run_and_time.sh
) |& tee "${LOG_FILE_BASE}_${_experiment_index}.log"


8- Make changes to config file to account for hyperthreads and number of GPUs

a. For NC A100 v4-series, paste the following lines in the file.

vi config_DGXA100_4gpu_common.sh

First, replace with the following values.

export DGXNGPU=4
export DGXHT=1

Then, add the following variables

export UCX_TLS=tcp
export UCX_NET_DEVICES=eth0
export NCCL_TOPO_FILE=/opt/microsoft/ncv4/topo.xml
export NCCL_GRAPH_FILE=/opt/microsoft/ncv4/graph.xml
export NCCL_ALGO=Tree

b. For NDm A100 v4-series, only the values below must be updated:

vi config_DGXA100_1x8x56x1.sh
export DGXNGPU=8
export DGXHT=1

9- We need to edit mounts.txt (BERT) and run_with_docker.sh (other benchmarks) as well to mount these changes inside container

a. For NC A100 v4-series

For BERT benchmark:

vi mounts.txt

 For the other benchmarks:

vi run_with_docker.sh

 b. For NDm A100 v4-series

No change is needed for this step.


10- Run the command to source the config file:

a. For NC A100 v4-series

source ./config_DGXA100_1x4x56x2.sh

 b. For NDm A100 v4-series

source ./config_DGXA100_1x8x56x1.sh

The next steps are different for each benchmark.


11- Follow Readme.txt for the benchmark to download and prepare the data.

Note: While downloading the data, make sure you have enough space. Tip: Use the /mnt/resource_nvme directory to store the data.


12- Run the following command to get the docker image name and tag.

docker images

Note the image name and tag associated with the benchmark you are running. <CONTAINER_NAME> in the next step is <REPOSITORY>:<TAG>


13- The command below runs the benchmark. Note that each benchmark has its own environment variables to set before we run. Please read the explanation of the variables to understand what value to give to each variable.


Run the command below to set the number of experiments to run

export NEXP=10 



CONT=<CONTAINER_NAME> DATADIR=<path/to/4320_shards_varlength/dir> DATADIR_PHASE2=<path/to/4320_shards_varlength/dir> EVALDIR=<path/to/eval_varlength/dir> CHECKPOINTDIR=<path/to/result/checkpointdir> CHECKPOINTDIR_PHASE1=<path/to/pytorch/ckpt/dir> ./run_with_docker.sh

The variables in the above command refer to the directory structure created by the Data download and preprocessing steps.

DATADIR: Point this to the 4320_shards_varlength folder downloaded with the training dataset.
DATADIR_PHASE2: Point this to the 4320_shards_varlength folder downloaded with the training dataset.
EVALDIR: Point this to the eval_varlength folder downloaded with the validation dataset.
CHECKPOINTDIR: Point this to a new results folder under bert data directory.
CHECKPOINTDIR_PHASE1: Point this to the phase1 folder within the bert data directory.



CONT=<CONTAINER_NAME> DATADIR= </path/to/rnnt/datasets/dir> METADATA_DIR=</path/to/tokenized/folder/under/data/dir> SENTENCEPIECES_DIR=</path/to/sentencepieces/folder/under/data/dir> LOGDIR=./results ./run_with_docker.sh

DATADIR: Point this to the directory where RNNT data is downloaded.
METADATA_DIR: Point this to the folder called ‘tokenized’ within the downloaded RNNT data.
SENTENCEPIECES_DIR: Point this to the folder called “sentencepieces” within the downloaded RNNT data.



CONT=<CONTAINER_NAME> DATADIR=/path/to/resnet_data/prep_data/ LOGDIR=./results ./run_with_docker.sh

DATADIR: Point this to the folder called “prep_data” inside the downloaded Resnet data.



CONT=<CONTAINER_NAME> DATADIR=/path/to/minigo_data/ ./run_with_docker.sh


CONT=<CONTAINER_NAME> DATADIR=/path/to/dlrm_data / LOGDIR=./results ./run_with_docker.sh


CONT=<CONTAINER_NAME> DATADIR=/path/to/ssd_data TORCH_HOME=/torch-home LOGDIR=./results ./run_with_docker.sh

TORCH_HOME: Create a new folder. Mkdir /torch-home.
Point this variable to the newly created /torch-home directory.


Mask R-CNN

CONT=<CONTAINER_NAME> DATADIR=/path/to/maskrcnn_data/ LOGDIR=./results ./run_with_docker.sh


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Last update:
‎Sep 27 2022 07:24 AM
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