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Results from GH action on NVIDIA_RTX4090x2
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@arjunsuresh
arjunsuresh committed Dec 25, 2024
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8 changes: 4 additions & 4 deletions ...a_original-gpu-tensorrt-vdefault-default_config/retinanet/multistream/README.md
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Expand Up @@ -19,7 +19,7 @@ pip install -U cmind

cm rm cache -f

cm pull repo mlcommons@mlperf-automations --checkout=225220c7d9bb7e66e5b9a1e1ebfc3e0180fbd094
cm pull repo mlcommons@mlperf-automations --checkout=a90475d2de72bf0622cebe8d5ca8eb8c9d872fbd

cm run script \
--tags=app,mlperf,inference,generic,_nvidia,_retinanet,_tensorrt,_cuda,_valid,_r4.1-dev_default,_multistream \
Expand Down Expand Up @@ -71,7 +71,7 @@ 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=e891b1c381b6 \
--env.CM_DOCKER_CONTAINER_ID=f77641321894 \
--env.CM_MLPERF_LOADGEN_COMPLIANCE_TEST=TEST01 \
--add_deps_recursive.compiler.tags=gcc \
--add_deps_recursive.coco2014-original.tags=_full \
Expand Down Expand Up @@ -129,7 +129,7 @@ Platform: RTX4090x2-nvidia_original-gpu-tensorrt-vdefault-default_config
Model Precision: int8

### Accuracy Results
`mAP`: `37.356`, Required accuracy for closed division `>= 37.1745`
`mAP`: `37.327`, Required accuracy for closed division `>= 37.1745`

### Performance Results
`Samples per query`: `5654438.0`
`Samples per query`: `5623507.0`
70 changes: 35 additions & 35 deletions ..._original-gpu-tensorrt-vdefault-default_config/retinanet/multistream/accuracy_console.out
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@@ -1,8 +1,8 @@
[2024-12-23 02:36:38,287 main.py:229 INFO] Detected system ID: KnownSystem.RTX4090x2
[2024-12-23 02:36:38,369 harness.py:249 INFO] The harness will load 2 plugins: ['build/plugins/NMSOptPlugin/libnmsoptplugin.so', 'build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so']
[2024-12-23 02:36:38,369 generate_conf_files.py:107 INFO] Generated measurements/ entries for RTX4090x2_TRT/retinanet/MultiStream
[2024-12-23 02:36:38,369 __init__.py:46 INFO] Running command: ./build/bin/harness_default --plugins="build/plugins/NMSOptPlugin/libnmsoptplugin.so,build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so" --logfile_outdir="/cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x2-nvidia_original-gpu-tensorrt-vdefault-default_config/retinanet/multistream/accuracy" --logfile_prefix="mlperf_log_" --performance_sample_count=64 --test_mode="AccuracyOnly" --gpu_copy_streams=1 --gpu_inference_streams=1 --use_deque_limit=true --gpu_batch_size=2 --map_path="data_maps/open-images-v6-mlperf/val_map.txt" --mlperf_conf_path="/home/cmuser/CM/repos/local/cache/5860c00d55d14786/inference/mlperf.conf" --tensor_path="build/preprocessed_data/open-images-v6-mlperf/validation/Retinanet/int8_linear" --use_graphs=true --user_conf_path="/home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/c44de86dadc4420199ad25d6fa563275.conf" --gpu_engines="./build/engines/RTX4090x2/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan" --max_dlas=0 --scenario MultiStream --model retinanet --response_postprocess openimageeffnms
[2024-12-23 02:36:38,369 __init__.py:53 INFO] Overriding Environment
[2024-12-25 01:40:51,607 main.py:229 INFO] Detected system ID: KnownSystem.RTX4090x2
[2024-12-25 01:40:51,689 harness.py:249 INFO] The harness will load 2 plugins: ['build/plugins/NMSOptPlugin/libnmsoptplugin.so', 'build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so']
[2024-12-25 01:40:51,689 generate_conf_files.py:107 INFO] Generated measurements/ entries for RTX4090x2_TRT/retinanet/MultiStream
[2024-12-25 01:40:51,689 __init__.py:46 INFO] Running command: ./build/bin/harness_default --plugins="build/plugins/NMSOptPlugin/libnmsoptplugin.so,build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so" --logfile_outdir="/cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x2-nvidia_original-gpu-tensorrt-vdefault-default_config/retinanet/multistream/accuracy" --logfile_prefix="mlperf_log_" --performance_sample_count=64 --test_mode="AccuracyOnly" --gpu_copy_streams=1 --gpu_inference_streams=1 --use_deque_limit=true --gpu_batch_size=2 --map_path="data_maps/open-images-v6-mlperf/val_map.txt" --mlperf_conf_path="/home/cmuser/CM/repos/local/cache/5860c00d55d14786/inference/mlperf.conf" --tensor_path="build/preprocessed_data/open-images-v6-mlperf/validation/Retinanet/int8_linear" --use_graphs=true --user_conf_path="/home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/00cdca2a511c4f33bad6658d0557db67.conf" --gpu_engines="./build/engines/RTX4090x2/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan" --max_dlas=0 --scenario MultiStream --model retinanet --response_postprocess openimageeffnms
[2024-12-25 01:40:51,689 __init__.py:53 INFO] Overriding Environment
benchmark : Benchmark.Retinanet
buffer_manager_thread_count : 0
data_dir : /home/cmuser/CM/repos/local/cache/4db00c74da1e44c8/data
Expand All @@ -12,7 +12,7 @@ gpu_copy_streams : 1
gpu_inference_streams : 1
input_dtype : int8
input_format : linear
log_dir : /home/cmuser/CM/repos/local/cache/94a57f78972843c6/repo/closed/NVIDIA/build/logs/2024.12.23-02.36.37
log_dir : /home/cmuser/CM/repos/local/cache/94a57f78972843c6/repo/closed/NVIDIA/build/logs/2024.12.25-01.40.50
map_path : data_maps/open-images-v6-mlperf/val_map.txt
mlperf_conf_path : /home/cmuser/CM/repos/local/cache/5860c00d55d14786/inference/mlperf.conf
multi_stream_expected_latency_ns : 0
Expand All @@ -26,7 +26,7 @@ tensor_path : build/preprocessed_data/open-images-v6-mlperf/validation/Retinanet
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/c44de86dadc4420199ad25d6fa563275.conf
user_conf_path : /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/00cdca2a511c4f33bad6658d0557db67.conf
system_id : RTX4090x2
config_name : RTX4090x2_retinanet_MultiStream
workload_setting : WorkloadSetting(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
Expand All @@ -40,81 +40,81 @@ power_limit : None
cpu_freq : None
&&&& RUNNING Default_Harness # ./build/bin/harness_default
[I] mlperf.conf path: /home/cmuser/CM/repos/local/cache/5860c00d55d14786/inference/mlperf.conf
[I] user.conf path: /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/c44de86dadc4420199ad25d6fa563275.conf
[I] user.conf path: /home/cmuser/CM/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/00cdca2a511c4f33bad6658d0557db67.conf
Creating QSL.
Finished Creating QSL.
Setting up SUT.
[I] [TRT] Loaded engine size: 72 MiB
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +6, GPU +10, now: CPU 125, GPU 881 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +2, GPU +10, now: CPU 127, GPU 891 (MiB)
[I] [TRT] Loaded engine size: 73 MiB
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +6, GPU +10, now: CPU 126, GPU 881 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +2, GPU +10, now: CPU 128, GPU 891 (MiB)
[I] [TRT] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +68, now: CPU 0, GPU 68 (MiB)
[I] Device:0.GPU: [0] ./build/engines/RTX4090x2/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan has been successfully loaded.
[I] [TRT] Loaded engine size: 72 MiB
[I] [TRT] Loaded engine size: 73 MiB
[W] [TRT] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +6, GPU +10, now: CPU 159, GPU 624 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 160, GPU 634 (MiB)
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +6, GPU +10, now: CPU 161, GPU 624 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 162, GPU 634 (MiB)
[I] [TRT] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +69, now: CPU 0, GPU 137 (MiB)
[I] Device:1.GPU: [0] ./build/engines/RTX4090x2/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan has been successfully loaded.
[E] [TRT] 3: [runtime.cpp::~Runtime::401] Error Code 3: API Usage Error (Parameter check failed at: runtime/rt/runtime.cpp::~Runtime::401, condition: mEngineCounter.use_count() == 1 Destroying a runtime before destroying deserialized engines created by the runtime leads to undefined behavior.)
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 88, GPU 893 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 88, GPU 901 (MiB)
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 89, GPU 893 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +8, now: CPU 90, GPU 901 (MiB)
[I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +1, GPU +1528, now: CPU 1, GPU 1665 (MiB)
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 89, GPU 636 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 89, GPU 644 (MiB)
[I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +1528, now: CPU 1, GPU 3193 (MiB)
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 90, GPU 636 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 90, GPU 644 (MiB)
[I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +1527, now: CPU 1, GPU 3192 (MiB)
[I] Start creating CUDA graphs
[I] Capture 2 CUDA graphs
[I] Capture 2 CUDA graphs
[I] Finish creating CUDA graphs
[I] Creating batcher thread: 0 EnableBatcherThreadPerDevice: false
Finished setting up SUT.
Starting warmup. Running for a minimum of 5 seconds.
Finished warmup. Ran for 5.14292s.
Finished warmup. Ran for 5.14343s.
Starting running actual test.

No warnings encountered during test.

No errors encountered during test.
Finished running actual test.
Device Device:0.GPU processed:
6195 batches of size 2
6196 batches of size 2
Memcpy Calls: 0
PerSampleCudaMemcpy Calls: 0
BatchedCudaMemcpy Calls: 6195
BatchedCudaMemcpy Calls: 6196
Device Device:1.GPU processed:
6197 batches of size 2
6196 batches of size 2
Memcpy Calls: 0
PerSampleCudaMemcpy Calls: 0
BatchedCudaMemcpy Calls: 6197
BatchedCudaMemcpy Calls: 6196
&&&& PASSED Default_Harness # ./build/bin/harness_default
[2024-12-23 02:37:18,118 run_harness.py:166 INFO] Result: Accuracy run detected.
[2024-12-23 02:37:18,118 __init__.py:46 INFO] Running command: python3 /home/cmuser/CM/repos/local/cache/94a57f78972843c6/repo/closed/NVIDIA/build/inference/vision/classification_and_detection/tools/accuracy-openimages.py --mlperf-accuracy-file /cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x2-nvidia_original-gpu-tensorrt-vdefault-default_config/retinanet/multistream/accuracy/mlperf_log_accuracy.json --openimages-dir /home/cmuser/CM/repos/local/cache/4db00c74da1e44c8/preprocessed_data/open-images-v6-mlperf --output-file build/retinanet-results.json
[2024-12-25 01:41:30,299 run_harness.py:166 INFO] Result: Accuracy run detected.
[2024-12-25 01:41:30,299 __init__.py:46 INFO] Running command: python3 /home/cmuser/CM/repos/local/cache/94a57f78972843c6/repo/closed/NVIDIA/build/inference/vision/classification_and_detection/tools/accuracy-openimages.py --mlperf-accuracy-file /cm-mount/home/arjun/gh_action_results/valid_results/RTX4090x2-nvidia_original-gpu-tensorrt-vdefault-default_config/retinanet/multistream/accuracy/mlperf_log_accuracy.json --openimages-dir /home/cmuser/CM/repos/local/cache/4db00c74da1e44c8/preprocessed_data/open-images-v6-mlperf --output-file build/retinanet-results.json
loading annotations into memory...
Done (t=0.52s)
Done (t=0.53s)
creating index...
index created!
Loading and preparing results...
DONE (t=17.77s)
DONE (t=17.92s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=133.59s).
DONE (t=133.92s).
Accumulating evaluation results...
DONE (t=31.87s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374
DONE (t=32.42s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.373
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.522
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.404
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.403
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.022
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.125
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.413
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.419
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.599
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.628
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.082
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.345
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678
mAP=37.356%
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.344
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677
mAP=37.327%

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

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