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Results from GH action on NVIDIA_RTX4090x1
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@arjunsuresh
arjunsuresh committed Jan 1, 2025
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16 changes: 8 additions & 8 deletions closed/MLCommons/systems/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config.json
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{
"accelerator_frequency": "2520000 MHz",
"accelerator_frequency": "2610000 MHz",
"accelerator_host_interconnect": "N/A",
"accelerator_interconnect": "N/A",
"accelerator_interconnect_topology": "",
"accelerator_memory_capacity": "23.64971923828125 GB",
"accelerator_memory_capacity": "23.54595947265625 GB",
"accelerator_memory_configuration": "N/A",
"accelerator_model_name": "NVIDIA GeForce RTX 4090",
"accelerator_on-chip_memories": "",
Expand All @@ -16,17 +16,17 @@
"host_network_card_count": "1",
"host_networking": "Gig Ethernet",
"host_networking_topology": "N/A",
"host_processor_caches": "L1d cache: 576 KiB, L1i cache: 384 KiB, L2 cache: 24 MiB, L3 cache: ",
"host_processor_core_count": "24",
"host_processor_frequency": "5800.0000",
"host_processor_caches": "L1d cache: 512 KiB, L1i cache: 512 KiB, L2 cache: 16 MiB, L3 cache: 64 MiB",
"host_processor_core_count": "16",
"host_processor_frequency": "5881.0000",
"host_processor_interconnect": "",
"host_processor_model_name": "13th Gen Intel(R) Core(TM) i9-13900K",
"host_processor_model_name": "AMD Ryzen 9 7950X 16-Core Processor",
"host_processors_per_node": "1",
"host_storage_capacity": "9.4T",
"host_storage_capacity": "6.8T",
"host_storage_type": "SSD",
"hw_notes": "",
"number_of_nodes": "1",
"operating_system": "Ubuntu 20.04 (linux-6.8.0-49-generic-glibc2.31)",
"operating_system": "Ubuntu 20.04 (linux-6.8.0-51-generic-glibc2.31)",
"other_software_stack": "Python: 3.8.10, GCC-9.4.0, Using Docker , CUDA 12.2",
"status": "available",
"submitter": "MLCommons",
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18 changes: 6 additions & 12 deletions ...ia_original-gpu-tensorrt-vdefault-default_config/resnet50/multistream/README.md
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This experiment is generated using the [MLCommons Collective Mind automation framework (CM)](https://github.com/mlcommons/cm4mlops).

*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.*

## Host platform
Expand All @@ -19,7 +17,7 @@ pip install -U cmind

cm rm cache -f

cm pull repo mlcommons@mlperf-automations --checkout=a90475d2de72bf0622cebe8d5ca8eb8c9d872fbd
cm pull repo mlcommons@mlperf-automations --checkout=48ea6b46a7606d1c5d74909e94d5599dbe7ff9e1

cm run script \
--tags=app,mlperf,inference,generic,_nvidia,_resnet50,_tensorrt,_cuda,_valid,_r4.1-dev_default,_multistream \
Expand All @@ -41,8 +39,8 @@ cm run script \
--env.CM_RUN_MLPERF_SUBMISSION_PREPROCESSOR=yes \
--env.CM_MLPERF_INFERENCE_PULL_CODE_CHANGES=yes \
--env.CM_MLPERF_INFERENCE_PULL_SRC_CHANGES=yes \
--env.OUTPUT_BASE_DIR=/home/arjun/gh_action_results \
--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/home/arjun/gh_action_submissions \
--env.OUTPUT_BASE_DIR=/cm-mount/home/arjun/gh_action_results \
--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/cm-mount/home/arjun/gh_action_submissions \
--env.CM_MLPERF_SUBMITTER=MLCommons \
--env.CM_USE_DATASET_FROM_HOST=yes \
--env.CM_USE_MODEL_FROM_HOST=yes \
Expand Down Expand Up @@ -71,7 +69,7 @@ cm run script \
--env.CM_DOCKER_REUSE_EXISTING_CONTAINER=yes \
--env.CM_DOCKER_DETACHED_MODE=yes \
--env.CM_MLPERF_INFERENCE_RESULTS_DIR_=/home/arjun/gh_action_results/valid_results \
--env.CM_DOCKER_CONTAINER_ID=2a6a3464ad42 \
--env.CM_DOCKER_CONTAINER_ID=bec71595fc42 \
--env.CM_MLPERF_LOADGEN_COMPLIANCE_TEST=TEST04 \
--add_deps_recursive.compiler.tags=gcc \
--add_deps_recursive.coco2014-original.tags=_full \
Expand Down Expand Up @@ -104,11 +102,7 @@ cm run script \
--v=False \
--print_env=False \
--print_deps=False \
--dump_version_info=True \
--env.CM_DATASET_IMAGENET_PATH=/home/cmuser/CM/repos/local/cache/14a94e79cca24141/imagenet-2012-val \
--env.OUTPUT_BASE_DIR=/cm-mount/home/arjun/gh_action_results \
--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/cm-mount/home/arjun/gh_action_submissions \
--env.MLPERF_SCRATCH_PATH=/home/cmuser/CM/repos/local/cache/a8c152aef5494496
--dump_version_info=True
```
*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts),
you should simply reload mlcommons@mlperf-automations without checkout and clean CM cache as follows:*
Expand All @@ -130,4 +124,4 @@ Model Precision: int8
`acc`: `76.064`, Required accuracy for closed division `>= 75.6954`

### Performance Results
`Samples per query`: `457694.0`
`Samples per query`: `798611.0`
20 changes: 10 additions & 10 deletions ...a_original-gpu-tensorrt-vdefault-default_config/resnet50/multistream/accuracy_console.out
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[2024-12-25 00:37:31,507 main.py:229 INFO] Detected system ID: KnownSystem.RTX4090x1
[2024-12-25 00:37:31,677 generate_conf_files.py:107 INFO] Generated measurements/ entries for RTX4090x1_TRT/resnet50/MultiStream
[2024-12-25 00:37:31,677 __init__.py:46 INFO] Running command: ./build/bin/harness_default --logfile_outdir="/cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/resnet50/multistream/accuracy" --logfile_prefix="mlperf_log_" --performance_sample_count=2048 --test_mode="AccuracyOnly" --gpu_copy_streams=1 --gpu_inference_streams=1 --use_deque_limit=true --gpu_batch_size=8 --map_path="data_maps/imagenet/val_map.txt" --mlperf_conf_path="/home/cmuser/CM/repos/local/cache/90c7069b92e34687/inference/mlperf.conf" --tensor_path="build/preprocessed_data/imagenet/ResNet50/int8_linear" --use_graphs=true --user_conf_path="/home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/f4e208664f514db1b83009a13b96a25a.conf" --gpu_engines="./build/engines/RTX4090x1/resnet50/MultiStream/resnet50-MultiStream-gpu-b8-int8.lwis_k_99_MaxP.plan" --max_dlas=0 --scenario MultiStream --model resnet50
[2024-12-25 00:37:31,677 __init__.py:53 INFO] Overriding Environment
[2025-01-01 09:51:03,848 main.py:229 INFO] Detected system ID: KnownSystem.RTX4090x1
[2025-01-01 09:51:04,021 generate_conf_files.py:107 INFO] Generated measurements/ entries for RTX4090x1_TRT/resnet50/MultiStream
[2025-01-01 09:51:04,021 __init__.py:46 INFO] Running command: ./build/bin/harness_default --logfile_outdir="/cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/resnet50/multistream/accuracy" --logfile_prefix="mlperf_log_" --performance_sample_count=2048 --test_mode="AccuracyOnly" --gpu_copy_streams=1 --gpu_inference_streams=1 --use_deque_limit=true --gpu_batch_size=8 --map_path="data_maps/imagenet/val_map.txt" --mlperf_conf_path="/home/cmuser/CM/repos/local/cache/90c7069b92e34687/inference/mlperf.conf" --tensor_path="build/preprocessed_data/imagenet/ResNet50/int8_linear" --use_graphs=true --user_conf_path="/home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/1fbc7af38b044fecb562b377ad90de53.conf" --gpu_engines="./build/engines/RTX4090x1/resnet50/MultiStream/resnet50-MultiStream-gpu-b8-int8.lwis_k_99_MaxP.plan" --max_dlas=0 --scenario MultiStream --model resnet50
[2025-01-01 09:51:04,021 __init__.py:53 INFO] Overriding Environment
benchmark : Benchmark.ResNet50
buffer_manager_thread_count : 0
data_dir : /home/cmuser/CM/repos/local/cache/a8c152aef5494496/data
Expand All @@ -11,7 +11,7 @@ gpu_copy_streams : 1
gpu_inference_streams : 1
input_dtype : int8
input_format : linear
log_dir : /home/cmuser/CM/repos/local/cache/ba8d5f2a6bc546f9/repo/closed/NVIDIA/build/logs/2024.12.25-00.37.30
log_dir : /home/cmuser/CM/repos/local/cache/ba8d5f2a6bc546f9/repo/closed/NVIDIA/build/logs/2025.01.01-09.51.02
map_path : data_maps/imagenet/val_map.txt
mlperf_conf_path : /home/cmuser/CM/repos/local/cache/90c7069b92e34687/inference/mlperf.conf
multi_stream_expected_latency_ns : 0
Expand All @@ -25,7 +25,7 @@ tensor_path : build/preprocessed_data/imagenet/ResNet50/int8_linear
test_mode : AccuracyOnly
use_deque_limit : True
use_graphs : True
user_conf_path : /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/f4e208664f514db1b83009a13b96a25a.conf
user_conf_path : /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/1fbc7af38b044fecb562b377ad90de53.conf
system_id : RTX4090x1
config_name : RTX4090x1_resnet50_MultiStream
workload_setting : WorkloadSetting(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
Expand All @@ -39,7 +39,7 @@ power_limit : None
cpu_freq : None
&&&& RUNNING Default_Harness # ./build/bin/harness_default
[I] mlperf.conf path: /home/cmuser/CM/repos/local/cache/90c7069b92e34687/inference/mlperf.conf
[I] user.conf path: /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/f4e208664f514db1b83009a13b96a25a.conf
[I] user.conf path: /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/1fbc7af38b044fecb562b377ad90de53.conf
Creating QSL.
Finished Creating QSL.
Setting up SUT.
Expand All @@ -58,7 +58,7 @@ Setting up SUT.
[I] Creating batcher thread: 0 EnableBatcherThreadPerDevice: false
Finished setting up SUT.
Starting warmup. Running for a minimum of 5 seconds.
Finished warmup. Ran for 5.02414s.
Finished warmup. Ran for 5.02403s.
Starting running actual test.

No warnings encountered during test.
Expand All @@ -71,8 +71,8 @@ Device Device:0.GPU processed:
PerSampleCudaMemcpy Calls: 0
BatchedCudaMemcpy Calls: 6250
&&&& PASSED Default_Harness # ./build/bin/harness_default
[2024-12-25 00:37:45,065 run_harness.py:166 INFO] Result: Accuracy run detected.
[2024-12-25 00:37:45,065 __init__.py:46 INFO] Running command: python3 /home/cmuser/CM/repos/local/cache/ba8d5f2a6bc546f9/repo/closed/NVIDIA/build/inference/vision/classification_and_detection/tools/accuracy-imagenet.py --mlperf-accuracy-file /cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/resnet50/multistream/accuracy/mlperf_log_accuracy.json --imagenet-val-file data_maps/imagenet/val_map.txt --dtype int32
[2025-01-01 09:51:46,180 run_harness.py:166 INFO] Result: Accuracy run detected.
[2025-01-01 09:51:46,180 __init__.py:46 INFO] Running command: python3 /home/cmuser/CM/repos/local/cache/ba8d5f2a6bc546f9/repo/closed/NVIDIA/build/inference/vision/classification_and_detection/tools/accuracy-imagenet.py --mlperf-accuracy-file /cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x1-nvidia_original-gpu-tensorrt-vdefault-default_config/resnet50/multistream/accuracy/mlperf_log_accuracy.json --imagenet-val-file data_maps/imagenet/val_map.txt --dtype int32
accuracy=76.064%, good=38032, total=50000

======================== Result summaries: ========================
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