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Graph Neural Network using R-GAT

MLPerf Reference Implementation in Python

Tip

  • MLCommons reference implementations are only meant to provide a rules compliant reference implementation for the submitters and in most cases are not best performing. If you want to benchmark any system, it is advisable to use the vendor MLPerf implementation for that system like Nvidia, Intel etc.

RGAT

Edge category

In the edge category, rgat has Offline, SingleStream scenarios and all the scenarios are mandatory for a closed division submission.

Pytorch framework

CPU device

Please click here to see the minimum system requirements for running the benchmark

  • Disk Space: 50GB
Docker Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

# Docker Container Build and Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=test \
   --device=cpu  \
   --docker --quiet \
   --test_query_count=10
The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.

Please click here to see more options for the docker launch

  • --docker_cm_repo=<Custom CM GitHub repo URL in username@repo format>: to use a custom fork of cm4mlops repository inside the docker image

  • --docker_cm_repo_branch=<Custom CM GitHub repo Branch>: to checkout a custom branch of the cloned cm4mlops repository inside the docker image

  • --docker_cache=no: to not use docker cache during the image build

  • --docker_os=ubuntu: ubuntu and rhel are supported.
  • --docker_os_version=20.04: [20.04, 22.04] are supported for Ubuntu and [8, 9] for RHEL
Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cpu \
   --quiet
SingleStream
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=SingleStream \
   --execution_mode=valid \
   --device=cpu \
   --quiet
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge  \
   --execution_mode=valid \
   --device=cpu \
   --quiet

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

Native Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

# Setup a virtual environment for Python
cm run script --tags=install,python-venv --name=mlperf
export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"
# Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=test \
   --device=cpu  \
   --quiet \
   --test_query_count=10
The above command should do a test run of Offline scenario and record the estimated offline_target_qps.

Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cpu \
   --quiet
SingleStream
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=SingleStream \
   --execution_mode=valid \
   --device=cpu \
   --quiet
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge  \
   --execution_mode=valid \
   --device=cpu \
   --quiet

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

CUDA device

Please click here to see the minimum system requirements for running the benchmark

  • Device Memory: 8GB

  • Disk Space: 50GB

Docker Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

# Docker Container Build and Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=test \
   --device=cuda  \
   --docker --quiet \
   --test_query_count=50
The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.

Please click here to see more options for the docker launch

  • --docker_cm_repo=<Custom CM GitHub repo URL in username@repo format>: to use a custom fork of cm4mlops repository inside the docker image

  • --docker_cm_repo_branch=<Custom CM GitHub repo Branch>: to checkout a custom branch of the cloned cm4mlops repository inside the docker image

  • --docker_cache=no: to not use docker cache during the image build

Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cuda \
   --quiet
SingleStream
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=SingleStream \
   --execution_mode=valid \
   --device=cuda \
   --quiet
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge  \
   --execution_mode=valid \
   --device=cuda \
   --quiet

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

Native Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

Tip

  • It is advisable to use the commands in the Docker tab for CUDA. Run the below native command only if you are already on a CUDA setup with cuDNN and TensorRT installed.
# Setup a virtual environment for Python
cm run script --tags=install,python-venv --name=mlperf
export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"
# Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=test \
   --device=cuda  \
   --quiet \
   --test_query_count=50
The above command should do a test run of Offline scenario and record the estimated offline_target_qps.

Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cuda \
   --quiet
SingleStream
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge \
   --scenario=SingleStream \
   --execution_mode=valid \
   --device=cuda \
   --quiet
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=edge  \
   --execution_mode=valid \
   --device=cuda \
   --quiet

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

Datacenter category

In the datacenter category, rgat has Offline, Server scenarios and all the scenarios are mandatory for a closed division submission.

Pytorch framework

CPU device

Please click here to see the minimum system requirements for running the benchmark

  • Disk Space: 50GB
Docker Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

# Docker Container Build and Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=test \
   --device=cpu  \
   --docker --quiet \
   --test_query_count=10
The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.

Please click here to see more options for the docker launch

  • --docker_cm_repo=<Custom CM GitHub repo URL in username@repo format>: to use a custom fork of cm4mlops repository inside the docker image

  • --docker_cm_repo_branch=<Custom CM GitHub repo Branch>: to checkout a custom branch of the cloned cm4mlops repository inside the docker image

  • --docker_cache=no: to not use docker cache during the image build

  • --docker_os=ubuntu: ubuntu and rhel are supported.
  • --docker_os_version=20.04: [20.04, 22.04] are supported for Ubuntu and [8, 9] for RHEL
Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cpu \
   --quiet
Server
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Server\
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cpu \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cpu \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

Native Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

# Setup a virtual environment for Python
cm run script --tags=install,python-venv --name=mlperf
export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"
# Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=test \
   --device=cpu  \
   --quiet \
   --test_query_count=10
The above command should do a test run of Offline scenario and record the estimated offline_target_qps.

Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cpu \
   --quiet
Server
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Server\
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cpu \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cpu \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

CUDA device

Please click here to see the minimum system requirements for running the benchmark

  • Device Memory: 8GB

  • Disk Space: 50GB

Docker Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

# Docker Container Build and Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=test \
   --device=cuda  \
   --docker --quiet \
   --test_query_count=50
The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.

Please click here to see more options for the docker launch

  • --docker_cm_repo=<Custom CM GitHub repo URL in username@repo format>: to use a custom fork of cm4mlops repository inside the docker image

  • --docker_cm_repo_branch=<Custom CM GitHub repo Branch>: to checkout a custom branch of the cloned cm4mlops repository inside the docker image

  • --docker_cache=no: to not use docker cache during the image build

Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cuda \
   --quiet
Server
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Server\
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cuda \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cuda \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

Native Environment

Please refer to the installation page to install CM for running the automated benchmark commands.

Tip

  • It is advisable to use the commands in the Docker tab for CUDA. Run the below native command only if you are already on a CUDA setup with cuDNN and TensorRT installed.
# Setup a virtual environment for Python
cm run script --tags=install,python-venv --name=mlperf
export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"
# Performance Estimation for Offline Scenario

Tip

  • Number of threads could be adjusted using --threads=#, where # is the desired number of threads. This option works only if the implementation in use supports threading.

  • Batch size could be adjusted using --batch_size=#, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.

  • Add --env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset> if you have already downloaded the dataset. The path will be automatically mounted when using docker run.

  • Add --env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint> if you have already downloaded the model. The path will be automatically mounted when using docker run.

  • Add --adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the official MLPerf Inference implementation in a custom fork.

  • Add --adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK> if you are modifying the model config accuracy script in the submission checker within a custom fork.

  • Add --adr.inference-src.version=custom if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=test \
   --device=cuda  \
   --quiet \
   --test_query_count=50
The above command should do a test run of Offline scenario and record the estimated offline_target_qps.

Offline
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Offline \
   --execution_mode=valid \
   --device=cuda \
   --quiet
Server
cm run script --tags=run-mlperf,inference,_r4.1-dev \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --scenario=Server\
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cuda \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.
All Scenarios
cm run script --tags=run-mlperf,inference,_r4.1-dev,_all-scenarios \
   --model=rgat \
   --implementation=reference \
   --framework=pytorch \
   --category=datacenter \
   --server_target_qps=<SERVER_TARGET_QPS> \
   --execution_mode=valid \
   --device=cuda \
   --quiet

Tip

  • <SERVER_TARGET_QPS> must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.

Please click here to see more options for the RUN command

  • Use --division=closed to do a closed division submission which includes compliance runs

  • Use --rerun to do a rerun even when a valid run exists

  • If you want to download the official MLPerf model and dataset for rgat you can follow this README.