A quick start guide to benchmarking AI models in Azure: MLPerf Training v2.0
Published Wednesday, August 3, 2022
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:
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}' '--' ${NSYSCMD} 'python' '-u')
7- Make the changes to run_with_docker.sh file to point to correct path in mounted run_and_time.sh (around line 170)
docker exec -it "${_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 DGXSOCKETCORES=48
export DGXNSOCKET=2
export DGXHT=1
Then, add the following variables
export UCX_TLS=tcp
export UCX_NET_DEVICES=eth0
export NCCL_SOCKET_IFNAME=eth0
export NCCL_DEBUG=INFO
export NCCL_TOPO_FILE=/opt/microsoft/ncv4/topo.xml
export NCCL_GRAPH_FILE=/opt/microsoft/ncv4/graph.xml
export NCCL_ALGO=Tree
export NCCL_SHM_USE_CUDA_MEMCPY=1
export CUDA_DEVICE_MAX_CONNECTIONS=32
export NCCL_CREATE_THREAD_CONTEXT=1
export NCCL_DEBUG_SUBSYS=ENV
export NCCL_IB_PCI_RELAXED_ORDERING=1
export CUDA_DEVICE_ORDER=PCI_BUS_ID
b. For NDm A100 v4-series, only the values below must be updated:
vi config_DGXA100_1x8x56x1.sh
export DGXNGPU=8
export DGXSOCKETCORES=48
export DGXNSOCKET=2
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
/opt/microsoft/ncv4/topo.xml:/opt/microsoft/ncv4/topo.xml
/opt/microsoft/ncv4/graph.xml:/opt/microsoft/ncv4/graph.xml
/usr/lib/x86_64-linux-gnu/libnccl.so:/usr/lib/x86_64-linux-gnu/libnccl.so
/usr/include/nccl.h:/usr/include/nccl.h
${PWD}/config_DGXA100_1x4x56x2.sh:/workspace/bert/config_DGXA100_1x4x56x2.sh
For the other benchmarks:
vi run_with_docker.sh
_cont_mounts+=”,/opt/microsoft/ncv4/topo.xml:/opt/microsoft/ncv4/topo.xml”
_cont_mounts+=”,/opt/microsoft/ncv4/graph.xml:/opt/microsoft/ncv4/graph.xml”
_cont_mounts+=”,/usr/lib/x86_64-linux-gnu/libnccl.so:/usr/lib/x86_64-linux-gnu/libnccl.so”
_cont_mounts+=”,/usr/include/nccl.h:/usr/include/nccl.h”
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
BERT
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.
RNNT
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.
ResNet50
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.
Minigo
CONT=<CONTAINER_NAME> DATADIR=/path/to/minigo_data/ ./run_with_docker.sh
DLRM
CONT=<CONTAINER_NAME> DATADIR=/path/to/dlrm_data / LOGDIR=./results ./run_with_docker.sh
SSD
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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