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Results from GH action on NVIDIA_RTX4090x2
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@arjunsuresh
arjunsuresh committed Feb 3, 2025
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4 changes: 2 additions & 2 deletions ...ts/RTX4090x2-nvidia-gpu-TensorRT-default_config/retinanet/multistream/README.md
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Expand Up @@ -17,7 +17,7 @@ pip install -U mlcflow

mlc rm cache -f

mlc pull repo mlcommons@mlperf-automations --checkout=03d9201c1c9305c7c3eaa0262984af76c7f2287f
mlc pull repo mlcommons@mlperf-automations --checkout=6a917925e946fcf6a1511578ba101067d4a88532


```
Expand All @@ -40,4 +40,4 @@ Model Precision: int8
### Accuracy Results

### Performance Results
`Samples per query`: `5646056.0`
`Samples per query`: `5645863.0`
68 changes: 34 additions & 34 deletions ...s/RTX4090x2-nvidia-gpu-TensorRT-default_config/retinanet/multistream/accuracy_console.out
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@@ -1,8 +1,8 @@
[2025-02-02 02:07:19,844 main.py:229 INFO] Detected system ID: KnownSystem.dd805e2fec5f
[2025-02-02 02:07:19,926 harness.py:249 INFO] The harness will load 2 plugins: ['build/plugins/NMSOptPlugin/libnmsoptplugin.so', 'build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so']
[2025-02-02 02:07:19,927 generate_conf_files.py:107 INFO] Generated measurements/ entries for dd805e2fec5f_TRT/retinanet/MultiStream
[2025-02-02 02:07:19,927 __init__.py:46 INFO] Running command: ./build/bin/harness_default --plugins="build/plugins/NMSOptPlugin/libnmsoptplugin.so,build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so" --logfile_outdir="/mlc-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/mlcuser/MLC/repos/local/cache/get-git-repo_14157262/inference/mlperf.conf" --tensor_path="build/preprocessed_data/open-images-v6-mlperf/validation/Retinanet/int8_linear" --use_graphs=true --user_conf_path="/home/mlcuser/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/2daada4f809841509c848f618114c672.conf" --gpu_engines="./build/engines/dd805e2fec5f/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan" --max_dlas=0 --scenario MultiStream --model retinanet --response_postprocess openimageeffnms
[2025-02-02 02:07:19,927 __init__.py:53 INFO] Overriding Environment
[2025-02-03 01:47:25,289 main.py:229 INFO] Detected system ID: KnownSystem.Nvidia_9babf6fca247
[2025-02-03 01:47:25,368 harness.py:249 INFO] The harness will load 2 plugins: ['build/plugins/NMSOptPlugin/libnmsoptplugin.so', 'build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so']
[2025-02-03 01:47:25,368 generate_conf_files.py:107 INFO] Generated measurements/ entries for Nvidia_9babf6fca247_TRT/retinanet/MultiStream
[2025-02-03 01:47:25,368 __init__.py:46 INFO] Running command: ./build/bin/harness_default --plugins="build/plugins/NMSOptPlugin/libnmsoptplugin.so,build/plugins/retinanetConcatPlugin/libretinanetconcatplugin.so" --logfile_outdir="/mlc-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/mlcuser/MLC/repos/local/cache/get-git-repo_14157262/inference/mlperf.conf" --tensor_path="build/preprocessed_data/open-images-v6-mlperf/validation/Retinanet/int8_linear" --use_graphs=true --user_conf_path="/home/mlcuser/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/9a9fd2e8ec244924a49c0072a84549ac.conf" --gpu_engines="./build/engines/Nvidia_9babf6fca247/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan" --max_dlas=0 --scenario MultiStream --model retinanet --response_postprocess openimageeffnms
[2025-02-03 01:47:25,368 __init__.py:53 INFO] Overriding Environment
benchmark : Benchmark.Retinanet
buffer_manager_thread_count : 0
data_dir : /home/mlcuser/MLC/repos/local/cache/get-mlperf-inference-nvidia-scratch-space_5aab030f/data
Expand All @@ -12,7 +12,7 @@ gpu_copy_streams : 1
gpu_inference_streams : 1
input_dtype : int8
input_format : linear
log_dir : /home/mlcuser/MLC/repos/local/cache/get-git-repo_0ab377fc/repo/closed/NVIDIA/build/logs/2025.02.02-02.07.18
log_dir : /home/mlcuser/MLC/repos/local/cache/get-git-repo_0ab377fc/repo/closed/NVIDIA/build/logs/2025.02.03-01.47.23
map_path : data_maps/open-images-v6-mlperf/val_map.txt
mlperf_conf_path : /home/mlcuser/MLC/repos/local/cache/get-git-repo_14157262/inference/mlperf.conf
multi_stream_expected_latency_ns : 0
Expand All @@ -21,14 +21,14 @@ multi_stream_target_latency_percentile : 99
precision : int8
preprocessed_data_dir : /home/mlcuser/MLC/repos/local/cache/get-mlperf-inference-nvidia-scratch-space_5aab030f/preprocessed_data
scenario : Scenario.MultiStream
system : SystemConfiguration(host_cpu_conf=CPUConfiguration(layout={CPU(name='Intel(R) Xeon(R) w7-2495X', architecture=<CPUArchitecture.x86_64: AliasedName(name='x86_64', aliases=(), patterns=())>, core_count=24, threads_per_core=2): 1}), host_mem_conf=MemoryConfiguration(host_memory_capacity=Memory(quantity=197.33452799999998, byte_suffix=<ByteSuffix.GB: (1000, 3)>, _num_bytes=197334528000), comparison_tolerance=0.05), accelerator_conf=AcceleratorConfiguration(layout=defaultdict(<class 'int'>, {GPU(name='NVIDIA GeForce RTX 4090', accelerator_type=<AcceleratorType.Discrete: AliasedName(name='Discrete', aliases=(), patterns=())>, vram=Memory(quantity=23.98828125, byte_suffix=<ByteSuffix.GiB: (1024, 3)>, _num_bytes=25757220864), max_power_limit=450.0, pci_id='0x268410DE', compute_sm=89): 1, GPU(name='NVIDIA GeForce RTX 4090', accelerator_type=<AcceleratorType.Discrete: AliasedName(name='Discrete', aliases=(), patterns=())>, vram=Memory(quantity=23.98828125, byte_suffix=<ByteSuffix.GiB: (1024, 3)>, _num_bytes=25757220864), max_power_limit=500.0, pci_id='0x268410DE', compute_sm=89): 1})), numa_conf=NUMAConfiguration(numa_nodes={}, num_numa_nodes=1), system_id='dd805e2fec5f')
system : SystemConfiguration(host_cpu_conf=CPUConfiguration(layout={CPU(name='Intel(R) Xeon(R) w7-2495X', architecture=<CPUArchitecture.x86_64: AliasedName(name='x86_64', aliases=(), patterns=())>, core_count=24, threads_per_core=2): 1}), host_mem_conf=MemoryConfiguration(host_memory_capacity=Memory(quantity=197.33452799999998, byte_suffix=<ByteSuffix.GB: (1000, 3)>, _num_bytes=197334528000), comparison_tolerance=0.05), accelerator_conf=AcceleratorConfiguration(layout=defaultdict(<class 'int'>, {GPU(name='NVIDIA GeForce RTX 4090', accelerator_type=<AcceleratorType.Discrete: AliasedName(name='Discrete', aliases=(), patterns=())>, vram=Memory(quantity=23.98828125, byte_suffix=<ByteSuffix.GiB: (1024, 3)>, _num_bytes=25757220864), max_power_limit=450.0, pci_id='0x268410DE', compute_sm=89): 1, GPU(name='NVIDIA GeForce RTX 4090', accelerator_type=<AcceleratorType.Discrete: AliasedName(name='Discrete', aliases=(), patterns=())>, vram=Memory(quantity=23.98828125, byte_suffix=<ByteSuffix.GiB: (1024, 3)>, _num_bytes=25757220864), max_power_limit=500.0, pci_id='0x268410DE', compute_sm=89): 1})), numa_conf=NUMAConfiguration(numa_nodes={}, num_numa_nodes=1), system_id='Nvidia_9babf6fca247')
tensor_path : build/preprocessed_data/open-images-v6-mlperf/validation/Retinanet/int8_linear
test_mode : AccuracyOnly
use_deque_limit : True
use_graphs : True
user_conf_path : /home/mlcuser/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/2daada4f809841509c848f618114c672.conf
system_id : dd805e2fec5f
config_name : dd805e2fec5f_retinanet_MultiStream
user_conf_path : /home/mlcuser/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/9a9fd2e8ec244924a49c0072a84549ac.conf
system_id : Nvidia_9babf6fca247
config_name : Nvidia_9babf6fca247_retinanet_MultiStream
workload_setting : WorkloadSetting(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
optimization_level : plugin-enabled
num_profiles : 1
Expand All @@ -40,81 +40,81 @@ power_limit : None
cpu_freq : None
&&&& RUNNING Default_Harness # ./build/bin/harness_default
[I] mlperf.conf path: /home/mlcuser/MLC/repos/local/cache/get-git-repo_14157262/inference/mlperf.conf
[I] user.conf path: /home/mlcuser/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/2daada4f809841509c848f618114c672.conf
[I] user.conf path: /home/mlcuser/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/9a9fd2e8ec244924a49c0072a84549ac.conf
Creating QSL.
Finished Creating QSL.
Setting up SUT.
[I] [TRT] Loaded engine size: 73 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] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +68, now: CPU 0, GPU 68 (MiB)
[I] Device:0.GPU: [0] ./build/engines/dd805e2fec5f/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan has been successfully loaded.
[I] Device:0.GPU: [0] ./build/engines/Nvidia_9babf6fca247/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan has been successfully loaded.
[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 160, GPU 624 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 161, GPU 634 (MiB)
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +6, GPU +10, now: CPU 160, GPU 626 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 161, GPU 636 (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/dd805e2fec5f/retinanet/MultiStream/retinanet-MultiStream-gpu-b2-int8.lwis_k_99_MaxP.plan has been successfully loaded.
[I] Device:1.GPU: [0] ./build/engines/Nvidia_9babf6fca247/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 +1, GPU +8, now: CPU 89, GPU 893 (MiB)
[I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 89, GPU 893 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 89, 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 +1, 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] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +1, GPU +8, now: CPU 90, GPU 638 (MiB)
[I] [TRT] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 90, GPU 646 (MiB)
[I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +1528, now: CPU 1, GPU 3193 (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 5.14387s.
Finished warmup. Ran for 5.14302s.
Starting running actual test.

No warnings encountered during test.

No errors encountered during test.
Finished running actual test.
Device Device:0.GPU processed:
6204 batches of size 2
6196 batches of size 2
Memcpy Calls: 0
PerSampleCudaMemcpy Calls: 0
BatchedCudaMemcpy Calls: 6204
BatchedCudaMemcpy Calls: 6196
Device Device:1.GPU processed:
6188 batches of size 2
6196 batches of size 2
Memcpy Calls: 0
PerSampleCudaMemcpy Calls: 0
BatchedCudaMemcpy Calls: 6188
BatchedCudaMemcpy Calls: 6196
&&&& PASSED Default_Harness # ./build/bin/harness_default
[2025-02-02 02:07:58,440 run_harness.py:166 INFO] Result: Accuracy run detected.
[2025-02-02 02:07:58,440 __init__.py:46 INFO] Running command: python3 /home/mlcuser/MLC/repos/local/cache/get-git-repo_0ab377fc/repo/closed/NVIDIA/build/inference/vision/classification_and_detection/tools/accuracy-openimages.py --mlperf-accuracy-file /mlc-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/mlcuser/MLC/repos/local/cache/get-mlperf-inference-nvidia-scratch-space_5aab030f/preprocessed_data/open-images-v6-mlperf --output-file build/retinanet-results.json
[2025-02-03 01:48:01,778 run_harness.py:166 INFO] Result: Accuracy run detected.
[2025-02-03 01:48:01,778 __init__.py:46 INFO] Running command: python3 /home/mlcuser/MLC/repos/local/cache/get-git-repo_0ab377fc/repo/closed/NVIDIA/build/inference/vision/classification_and_detection/tools/accuracy-openimages.py --mlperf-accuracy-file /mlc-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/mlcuser/MLC/repos/local/cache/get-mlperf-inference-nvidia-scratch-space_5aab030f/preprocessed_data/open-images-v6-mlperf --output-file build/retinanet-results.json
loading annotations into memory...
Done (t=0.44s)
creating index...
index created!
Loading and preparing results...
DONE (t=17.83s)
DONE (t=17.74s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=132.09s).
DONE (t=131.70s).
Accumulating evaluation results...
DONE (t=31.97s).
DONE (t=32.28s).
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=0.50 | area= all | maxDets=100 ] = 0.523
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.403
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023
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= 10 ] = 0.598
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 ] = 0.083
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 ] = 0.344
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678
mAP=37.330%
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677
mAP=37.323%

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

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