Blog

Benchmarks

NVIDIA Quadro RTX 6000 BERT Large Fine-tune Benchmarks with SQuAD Dataset

January 7, 2020
56 min read
IMG_4198.jpg

Fine Tuning BERT Large on a GPU Workstation

For this post, we measured fine-tuning performance (training and inference) for the BERT implementation of TensorFlow on NVIDIA Quadro RTX 6000 GPUs. For testing we used an Exxact Valence Workstation was fitted with 4x Quadro RTX 6000 GPUs with NVLink, giving us 96 GB of GPU memory for our system.

Benchmark scripts we used for evaluation was the finetune_train_benchmark.sh and finetune_inference_benchmark.sh from NVIDIA NGC Repository BERT for TensorFlow. We made slight modifications to the training benchmark script to get the larger batch size numbers.

The script runs multiple tests on the SQuAD v1.1 dataset using batch sizes 1, 2, 4, 8, 16, and 32, for training, and 1, 2, 4, and 8 for inference and conducted tests using 1, 2, and 4 GPU configurations on BERT Large (We used 1 GPU for inference benchmark). In addition ran all benchmarks using TensorFlow's XLA on across the board. Other training settings can be viewed at the end of this blog in the Appendix section.

Key Points and Observations

  • Performance wise, the RTX 6000 Performs well, and in some cases better than the RTX 8000, however resources max out when batch size 32 is reached at sequence size 128.
  • In terms of Throughput, the 2x and 4x configs really started to shine when the batch size reached 8 and above. The 4x configuration began to break away around batch size 16.
  • For those interested in training BERT Large, a 2x Quadro RTX 6000 system is a great choice, with the opportunity to add additional cards later as budget/scaling needs increase. Even a Deep Learning Workstation with a single RTX 6000 can fine tune BERT Large in about 40 minutes!
  • NOTE: In order to run these benchmarks, or be able to fine-tune BERT Large with 4x GPUs you'll need a system with at least 64GB RAM.

Quadro RTX 6000 BERT Large Fine tuning GPU Benchmark Snapshot

Slide1-1-1024x576.png

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch Size

4x Quadro RTX 6000 BERT Large GPU Fine-tune Benchmark

<="" a><="" h3>

fp="Floating" point="" codecision,="" seq="Sequence" length,="" bs="Batch" size<="" strong><="" h3>=""

[supsystic-tables="" id="60]

"

run="" these="" benchmarks<="" p>="" <="" <="" strong><="" p>=""

assuming="" you're="" using="" the="" ngc="" bert="" for="" tensorflow="" container,="" run="" following="" command.<="" scripts/finetune_train_benchmark.sh large true 4 squad

Slide3-1-1024x576.png

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch Size

[supsystic-tables id=61]

Run these benchmarks

Assuming you're using the NGC BERT for TensorFlow container, run the following command.

scripts/finetune_train_benchmark.sh large true 2 squad


eBook-DL-1024x202.jpg

1x Quadro RTX 6000 BERT LARGE GPU Fine-tune Benchmark

Slide4-1-1024x576.png

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch Size

[supsystic-tables id=62]

Run these benchmarks

Assuming you're using the NGC BERT for TensorFlow container, run the following command.

scripts/finetune_train_benchmark.sh large true 1 squad


1x Quadro RTX 6000 BERT LARGE GPU Inference Benchmark

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch SizeRun these benchmarks

SettingsTotal Inference Timenum of sentencesLatency Confidence Level 50msLatency Confidence Level 90 msLatency Confidence Level 95 msLatency Confidence Level 99 ms
Latency Confidence Level 100 ms
Latency Avg
Throughput (sentences/sec)
FP16 Seq 384, BS1
17.43
1042
16.92
17.35
17.57
18.25
20.41
16.73
59.78
FP16 Seq 384, BS2
24.23
2068
23.13
23.67
23.81
24.51
529.93
23.43
85.36
FP16 Seq 128, BS2
24.69
3402
15.09
16.21
16.35
17.29
25.46
14.52
137.76
FP16 Seq 128, BS1
26.4
1800
15.55
16.66
16.86
17.59
25.35
14.67
68.19
FP16 Seq 128, BS4
29.18
7128
16.12
16.68
17.02
17.7
556.13
16.37
244.31
FP32 Seq 128, BS1
34.04
1800
20.79
22.08
22.47
22.97
26.48
18.91
52.88
FP32 Seq 128, BS2
38.72
3402
22.92
23.79
23.98
24.58
27.02
22.76
87.87
FP16 Seq 384, BS4
40.08
4128
38.55
39.09
39.32
40.93
530.08
38.84
102.99
FP32 Seq 384, BS1
41.99
1042
42.3
43.79
43.93
44.19
45.8
40.3
24.81
FP16 Seq 128, BS8
66.94
16040
23.06
23.75
24.01
24.61
543.94
23.16
345.5
FP32 Seq 384, BS2
74.73
2068
73.25
74.51
74.71
75.09
89.2
72.27
27.67
FP16 Seq 128, BS8
76.66
8432
72.3
74.22
74.63
75.63
628.88
72.73
109.99
FP32 Seq 128, BS4
83.22
7128
47.45
48.84
48.99
49.25
207.56
46.7
86.66
FP32 Seq 384, BS4
160.17
4128
158.24
160.38
160.38
160.82
161.59
176.45
25.77
FP32 Seq 128, BS8
171.83
16040
86.26
87.56
87.72
88.32
97.13
85.7
93.35
FP32 Seq 128, BS8
300.44
8432
287.05
288.46
288.78
290.09
493.14
285.05
28.07

Assuming you're using the NGC BERT for TensorFlow container, run the following command.

scripts/finetune_inference_benchmark.sh large squad

System Specifications:

System
Exxact Valence Workstation
GPU
4 x NVIDIA Quadro RTX 6000
CPU
Intel CORE I7-7820X 3.6GHZ
RAM
64GB DDR4
SSD
480 GB SSD
HDD (data)
10 TB HDD
OS
Ubuntu 18.04
NVIDIA DRIVER
435.21
CUDA Version
10.1
Python
2.7/3.6
TensorFlow
1.14
Container (using NVIDIA Docker)
TensorFlow 19.08-py3+ NGC container

Additional GPU Benchmarks


have-any-questions-1024x202.jpg


Appendix/Additional settings

NOTE: these will change with each run depending on batch size, sequence length, etc.

***** Configuaration *****
I1212 17:24:48.136919 139750589261632 run_squad.py:950] logtostderr: False
I1212 17:24:48.136960 139750589261632 run_squad.py:950] alsologtostderr: False
I1212 17:24:48.137000 139750589261632 run_squad.py:950] log_dir:
I1212 17:24:48.137040 139750589261632 run_squad.py:950] v: 0
I1212 17:24:48.137079 139750589261632 run_squad.py:950] verbosity: 0
I1212 17:24:48.137117 139750589261632 run_squad.py:950] stderrthreshold: fatal
I1212 17:24:48.137156 139750589261632 run_squad.py:950] showcodefixforinfo: True
I1212 17:24:48.137195 139750589261632 run_squad.py:950] run_with_pdb: False
I1212 17:24:48.137233 139750589261632 run_squad.py:950] pdb_post_mortem: False
I1212 17:24:48.137271 139750589261632 run_squad.py:950] run_with_profiling: False
I1212 17:24:48.137310 139750589261632 run_squad.py:950] profile_file: None
I1212 17:24:48.137349 139750589261632 run_squad.py:950] use_cprofile_for_profiling: True
I1212 17:24:48.137388 139750589261632 run_squad.py:950] only_check_args: False
I1212 17:24:48.137426 139750589261632 run_squad.py:950] op_conversion_fallback_to_while_loop: False
I1212 17:24:48.137465 139750589261632 run_squad.py:950] test_random_seed: 301
I1212 17:24:48.137504 139750589261632 run_squad.py:950] test_srcdir:
I1212 17:24:48.137542 139750589261632 run_squad.py:950] test_tmpdir: /tmp/absl_testing
I1212 17:24:48.137581 139750589261632 run_squad.py:950] test_randomize_ordering_seed: None
I1212 17:24:48.137620 139750589261632 run_squad.py:950] xml_output_file:
I1212 17:24:48.137658 139750589261632 run_squad.py:950] bert_config_file: data/download/google_codetrained_weights/uncased_L-24_H-1024_A-16/bert_config.json
I1212 17:24:48.137696 139750589261632 run_squad.py:950] vocab_file: data/download/google_codetrained_weights/uncased_L-24_H-1024_A-16/vocab.txt
I1212 17:24:48.137734 139750589261632 run_squad.py:950] output_dir: /results/bert_large_gpu_1_sl_128_codec_fp16_bs_1
I1212 17:24:48.137772 139750589261632 run_squad.py:950] train_file: data/download/squad/v1.1/train-v1.1.json
I1212 17:24:48.137810 139750589261632 run_squad.py:950] codedict_file: None
I1212 17:24:48.137849 139750589261632 run_squad.py:950] init_checkpoint: data/download/google_codetrained_weights/uncased_L-24_H-1024_A-16/bert_model.ckpt
I1212 17:24:48.137887 139750589261632 run_squad.py:950] do_lower_case: True
I1212 17:24:48.137926 139750589261632 run_squad.py:950] max_seq_length: 128
I1212 17:24:48.137964 139750589261632 run_squad.py:950] doc_stride: 64
I1212 17:24:48.138002 139750589261632 run_squad.py:950] max_query_length: 64
I1212 17:24:48.138040 139750589261632 run_squad.py:950] do_train: True
I1212 17:24:48.138079 139750589261632 run_squad.py:950] do_codedict: False
I1212 17:24:48.138117 139750589261632 run_squad.py:950] train_batch_size: 1
I1212 17:24:48.138156 139750589261632 run_squad.py:950] codedict_batch_size: 8
I1212 17:24:48.138199 139750589261632 run_squad.py:950] learning_rate: 5e-06
I1212 17:24:48.138237 139750589261632 run_squad.py:950] use_trt: False
I1212 17:24:48.138276 139750589261632 run_squad.py:950] horovod: False
I1212 17:24:48.138315 139750589261632 run_squad.py:950] num_train_epochs: 2.0
I1212 17:24:48.138354 139750589261632 run_squad.py:950] warmup_proportion: 0.1
I1212 17:24:48.138392 139750589261632 run_squad.py:950] save_checkpoints_steps: 1000
I1212 17:24:48.138430 139750589261632 run_squad.py:950] iterations_per_loop: 1000
I1212 17:24:48.138469 139750589261632 run_squad.py:950] num_accumulation_steps: 1
I1212 17:24:48.138506 139750589261632 run_squad.py:950] n_best_size: 20
I1212 17:24:48.138545 139750589261632 run_squad.py:950] max_answer_length: 30
I1212 17:24:48.138583 139750589261632 run_squad.py:950] verbose_logging: False
I1212 17:24:48.138622 139750589261632 run_squad.py:950] version_2_with_negative: False
I1212 17:24:48.138660 139750589261632 run_squad.py:950] null_score_diff_threshold: 0.0
I1212 17:24:48.138699 139750589261632 run_squad.py:950] use_fp16: True
I1212 17:24:48.138737 139750589261632 run_squad.py:950] use_xla: True
I1212 17:24:48.138775 139750589261632 run_squad.py:950] num_eval_iterations: None
I1212 17:24:48.138813 139750589261632 run_squad.py:950] export_trtis: False
I1212 17:24:48.138851 139750589261632 run_squad.py:950] trtis_model_name: bert
I1212 17:24:48.138890 139750589261632 run_squad.py:950] trtis_model_version: 1
I1212 17:24:48.138928 139750589261632 run_squad.py:950] trtis_server_url: localhost:8001
I1212 17:24:48.138966 139750589261632 run_squad.py:950] trtis_model_overwrite: False
I1212 17:24:48.139004 139750589261632 run_squad.py:950] trtis_max_batch_size: 8
I1212 17:24:48.139043 139750589261632 run_squad.py:950] trtis_dyn_batching_delay: 0.0
I1212 17:24:48.139081 139750589261632 run_squad.py:950] trtis_engine_count: 1
I1212 17:24:48.139120 139750589261632 run_squad.py:950] ?: False
I1212 17:24:48.139158 139750589261632 run_squad.py:950] help: False
I1212 17:24:48.139196 139750589261632 run_squad.py:950] helpshort: False
I1212 17:24:48.139235 139750589261632 run_squad.py:950] helpfull: False
I1212 17:24:48.139273 139750589261632 run_squad.py:950] helpxml: False
I1212 17:24:48.139307 139750589261632 run_squad.py:951] **************************

dl_clusters-1024x127.jpg


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Benchmarks

NVIDIA Quadro RTX 6000 BERT Large Fine-tune Benchmarks with SQuAD Dataset

January 7, 2020 56 min read

Fine Tuning BERT Large on a GPU Workstation

For this post, we measured fine-tuning performance (training and inference) for the BERT implementation of TensorFlow on NVIDIA Quadro RTX 6000 GPUs. For testing we used an Exxact Valence Workstation was fitted with 4x Quadro RTX 6000 GPUs with NVLink, giving us 96 GB of GPU memory for our system.

Benchmark scripts we used for evaluation was the finetune_train_benchmark.sh and finetune_inference_benchmark.sh from NVIDIA NGC Repository BERT for TensorFlow. We made slight modifications to the training benchmark script to get the larger batch size numbers.

The script runs multiple tests on the SQuAD v1.1 dataset using batch sizes 1, 2, 4, 8, 16, and 32, for training, and 1, 2, 4, and 8 for inference and conducted tests using 1, 2, and 4 GPU configurations on BERT Large (We used 1 GPU for inference benchmark). In addition ran all benchmarks using TensorFlow's XLA on across the board. Other training settings can be viewed at the end of this blog in the Appendix section.

Key Points and Observations

  • Performance wise, the RTX 6000 Performs well, and in some cases better than the RTX 8000, however resources max out when batch size 32 is reached at sequence size 128.
  • In terms of Throughput, the 2x and 4x configs really started to shine when the batch size reached 8 and above. The 4x configuration began to break away around batch size 16.
  • For those interested in training BERT Large, a 2x Quadro RTX 6000 system is a great choice, with the opportunity to add additional cards later as budget/scaling needs increase. Even a Deep Learning Workstation with a single RTX 6000 can fine tune BERT Large in about 40 minutes!
  • NOTE: In order to run these benchmarks, or be able to fine-tune BERT Large with 4x GPUs you'll need a system with at least 64GB RAM.

Quadro RTX 6000 BERT Large Fine tuning GPU Benchmark Snapshot

Slide1-1-1024x576.png

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch Size

4x Quadro RTX 6000 BERT Large GPU Fine-tune Benchmark

<="" a><="" h3>

fp="Floating" point="" codecision,="" seq="Sequence" length,="" bs="Batch" size<="" strong><="" h3>=""

[supsystic-tables="" id="60]

"

run="" these="" benchmarks<="" p>="" <="" <="" strong><="" p>=""

assuming="" you're="" using="" the="" ngc="" bert="" for="" tensorflow="" container,="" run="" following="" command.<="" scripts/finetune_train_benchmark.sh large true 4 squad

Slide3-1-1024x576.png

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch Size

[supsystic-tables id=61]

Run these benchmarks

Assuming you're using the NGC BERT for TensorFlow container, run the following command.

scripts/finetune_train_benchmark.sh large true 2 squad


eBook-DL-1024x202.jpg

1x Quadro RTX 6000 BERT LARGE GPU Fine-tune Benchmark

Slide4-1-1024x576.png

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch Size

[supsystic-tables id=62]

Run these benchmarks

Assuming you're using the NGC BERT for TensorFlow container, run the following command.

scripts/finetune_train_benchmark.sh large true 1 squad


1x Quadro RTX 6000 BERT LARGE GPU Inference Benchmark

FP = Floating Point Codecision, Seq = Sequence Length, BS = Batch SizeRun these benchmarks

SettingsTotal Inference Timenum of sentencesLatency Confidence Level 50msLatency Confidence Level 90 msLatency Confidence Level 95 msLatency Confidence Level 99 ms
Latency Confidence Level 100 ms
Latency Avg
Throughput (sentences/sec)
FP16 Seq 384, BS1
17.43
1042
16.92
17.35
17.57
18.25
20.41
16.73
59.78
FP16 Seq 384, BS2
24.23
2068
23.13
23.67
23.81
24.51
529.93
23.43
85.36
FP16 Seq 128, BS2
24.69
3402
15.09
16.21
16.35
17.29
25.46
14.52
137.76
FP16 Seq 128, BS1
26.4
1800
15.55
16.66
16.86
17.59
25.35
14.67
68.19
FP16 Seq 128, BS4
29.18
7128
16.12
16.68
17.02
17.7
556.13
16.37
244.31
FP32 Seq 128, BS1
34.04
1800
20.79
22.08
22.47
22.97
26.48
18.91
52.88
FP32 Seq 128, BS2
38.72
3402
22.92
23.79
23.98
24.58
27.02
22.76
87.87
FP16 Seq 384, BS4
40.08
4128
38.55
39.09
39.32
40.93
530.08
38.84
102.99
FP32 Seq 384, BS1
41.99
1042
42.3
43.79
43.93
44.19
45.8
40.3
24.81
FP16 Seq 128, BS8
66.94
16040
23.06
23.75
24.01
24.61
543.94
23.16
345.5
FP32 Seq 384, BS2
74.73
2068
73.25
74.51
74.71
75.09
89.2
72.27
27.67
FP16 Seq 128, BS8
76.66
8432
72.3
74.22
74.63
75.63
628.88
72.73
109.99
FP32 Seq 128, BS4
83.22
7128
47.45
48.84
48.99
49.25
207.56
46.7
86.66
FP32 Seq 384, BS4
160.17
4128
158.24
160.38
160.38
160.82
161.59
176.45
25.77
FP32 Seq 128, BS8
171.83
16040
86.26
87.56
87.72
88.32
97.13
85.7
93.35
FP32 Seq 128, BS8
300.44
8432
287.05
288.46
288.78
290.09
493.14
285.05
28.07

Assuming you're using the NGC BERT for TensorFlow container, run the following command.

scripts/finetune_inference_benchmark.sh large squad

System Specifications:

System
Exxact Valence Workstation
GPU
4 x NVIDIA Quadro RTX 6000
CPU
Intel CORE I7-7820X 3.6GHZ
RAM
64GB DDR4
SSD
480 GB SSD
HDD (data)
10 TB HDD
OS
Ubuntu 18.04
NVIDIA DRIVER
435.21
CUDA Version
10.1
Python
2.7/3.6
TensorFlow
1.14
Container (using NVIDIA Docker)
TensorFlow 19.08-py3+ NGC container

Additional GPU Benchmarks


have-any-questions-1024x202.jpg


Appendix/Additional settings

NOTE: these will change with each run depending on batch size, sequence length, etc.

***** Configuaration *****
I1212 17:24:48.136919 139750589261632 run_squad.py:950] logtostderr: False
I1212 17:24:48.136960 139750589261632 run_squad.py:950] alsologtostderr: False
I1212 17:24:48.137000 139750589261632 run_squad.py:950] log_dir:
I1212 17:24:48.137040 139750589261632 run_squad.py:950] v: 0
I1212 17:24:48.137079 139750589261632 run_squad.py:950] verbosity: 0
I1212 17:24:48.137117 139750589261632 run_squad.py:950] stderrthreshold: fatal
I1212 17:24:48.137156 139750589261632 run_squad.py:950] showcodefixforinfo: True
I1212 17:24:48.137195 139750589261632 run_squad.py:950] run_with_pdb: False
I1212 17:24:48.137233 139750589261632 run_squad.py:950] pdb_post_mortem: False
I1212 17:24:48.137271 139750589261632 run_squad.py:950] run_with_profiling: False
I1212 17:24:48.137310 139750589261632 run_squad.py:950] profile_file: None
I1212 17:24:48.137349 139750589261632 run_squad.py:950] use_cprofile_for_profiling: True
I1212 17:24:48.137388 139750589261632 run_squad.py:950] only_check_args: False
I1212 17:24:48.137426 139750589261632 run_squad.py:950] op_conversion_fallback_to_while_loop: False
I1212 17:24:48.137465 139750589261632 run_squad.py:950] test_random_seed: 301
I1212 17:24:48.137504 139750589261632 run_squad.py:950] test_srcdir:
I1212 17:24:48.137542 139750589261632 run_squad.py:950] test_tmpdir: /tmp/absl_testing
I1212 17:24:48.137581 139750589261632 run_squad.py:950] test_randomize_ordering_seed: None
I1212 17:24:48.137620 139750589261632 run_squad.py:950] xml_output_file:
I1212 17:24:48.137658 139750589261632 run_squad.py:950] bert_config_file: data/download/google_codetrained_weights/uncased_L-24_H-1024_A-16/bert_config.json
I1212 17:24:48.137696 139750589261632 run_squad.py:950] vocab_file: data/download/google_codetrained_weights/uncased_L-24_H-1024_A-16/vocab.txt
I1212 17:24:48.137734 139750589261632 run_squad.py:950] output_dir: /results/bert_large_gpu_1_sl_128_codec_fp16_bs_1
I1212 17:24:48.137772 139750589261632 run_squad.py:950] train_file: data/download/squad/v1.1/train-v1.1.json
I1212 17:24:48.137810 139750589261632 run_squad.py:950] codedict_file: None
I1212 17:24:48.137849 139750589261632 run_squad.py:950] init_checkpoint: data/download/google_codetrained_weights/uncased_L-24_H-1024_A-16/bert_model.ckpt
I1212 17:24:48.137887 139750589261632 run_squad.py:950] do_lower_case: True
I1212 17:24:48.137926 139750589261632 run_squad.py:950] max_seq_length: 128
I1212 17:24:48.137964 139750589261632 run_squad.py:950] doc_stride: 64
I1212 17:24:48.138002 139750589261632 run_squad.py:950] max_query_length: 64
I1212 17:24:48.138040 139750589261632 run_squad.py:950] do_train: True
I1212 17:24:48.138079 139750589261632 run_squad.py:950] do_codedict: False
I1212 17:24:48.138117 139750589261632 run_squad.py:950] train_batch_size: 1
I1212 17:24:48.138156 139750589261632 run_squad.py:950] codedict_batch_size: 8
I1212 17:24:48.138199 139750589261632 run_squad.py:950] learning_rate: 5e-06
I1212 17:24:48.138237 139750589261632 run_squad.py:950] use_trt: False
I1212 17:24:48.138276 139750589261632 run_squad.py:950] horovod: False
I1212 17:24:48.138315 139750589261632 run_squad.py:950] num_train_epochs: 2.0
I1212 17:24:48.138354 139750589261632 run_squad.py:950] warmup_proportion: 0.1
I1212 17:24:48.138392 139750589261632 run_squad.py:950] save_checkpoints_steps: 1000
I1212 17:24:48.138430 139750589261632 run_squad.py:950] iterations_per_loop: 1000
I1212 17:24:48.138469 139750589261632 run_squad.py:950] num_accumulation_steps: 1
I1212 17:24:48.138506 139750589261632 run_squad.py:950] n_best_size: 20
I1212 17:24:48.138545 139750589261632 run_squad.py:950] max_answer_length: 30
I1212 17:24:48.138583 139750589261632 run_squad.py:950] verbose_logging: False
I1212 17:24:48.138622 139750589261632 run_squad.py:950] version_2_with_negative: False
I1212 17:24:48.138660 139750589261632 run_squad.py:950] null_score_diff_threshold: 0.0
I1212 17:24:48.138699 139750589261632 run_squad.py:950] use_fp16: True
I1212 17:24:48.138737 139750589261632 run_squad.py:950] use_xla: True
I1212 17:24:48.138775 139750589261632 run_squad.py:950] num_eval_iterations: None
I1212 17:24:48.138813 139750589261632 run_squad.py:950] export_trtis: False
I1212 17:24:48.138851 139750589261632 run_squad.py:950] trtis_model_name: bert
I1212 17:24:48.138890 139750589261632 run_squad.py:950] trtis_model_version: 1
I1212 17:24:48.138928 139750589261632 run_squad.py:950] trtis_server_url: localhost:8001
I1212 17:24:48.138966 139750589261632 run_squad.py:950] trtis_model_overwrite: False
I1212 17:24:48.139004 139750589261632 run_squad.py:950] trtis_max_batch_size: 8
I1212 17:24:48.139043 139750589261632 run_squad.py:950] trtis_dyn_batching_delay: 0.0
I1212 17:24:48.139081 139750589261632 run_squad.py:950] trtis_engine_count: 1
I1212 17:24:48.139120 139750589261632 run_squad.py:950] ?: False
I1212 17:24:48.139158 139750589261632 run_squad.py:950] help: False
I1212 17:24:48.139196 139750589261632 run_squad.py:950] helpshort: False
I1212 17:24:48.139235 139750589261632 run_squad.py:950] helpfull: False
I1212 17:24:48.139273 139750589261632 run_squad.py:950] helpxml: False
I1212 17:24:48.139307 139750589261632 run_squad.py:951] **************************

dl_clusters-1024x127.jpg


xtagstartz/h3xtagstartz/h3

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