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Benchmarks

NVIDIA Quadro RTX 8000 BERT Large Fine Tuning Benchmarks in TensorFlow

December 18, 2019
13 min read
RTX-8000-BERT-Large.jpg

Fine Tune BERT Large in Less Than 20 Minutes

For this post, we measured fine-tuning performance (training and inference) for the BERT (Bidirectional Encoder Representations from Transformers) implementation in TensorFlow using NVIDIA Quadro RTX 8000 GPUs. For testing, we used an Exxact Valence Workstation fitted with 4x Quadro RTX 8000’s with NVLink, giving us 192 GB of GPU memory for our system. These tests measure performance for a popular use case for BERT and NLP in general and are meant to show typical GPU performance for such a task.

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 metrics.

The script runs multiple tests on the SQuAD v1.1 dataset using batch sizes 1, 2, 4, 8, 16, 32, and 64 for training, and 1, 2, 4, and 8 for inference. We conducted tests using 1, 2, and 4 GPU configurations on BERT Large (We used 1 GPU for inference benchmark). In addition, we ran all benchmarks using TensorFlow's XLA across all runs. Furthermore, other training settings can be viewed at the end of this blog in the Appendix/Additional Information section.

Key Points and Observations

  • In terms of total training time, the 2x GPU configuration outperformed the 4x until the batch size increased to the 16 range, when the 4x configuration began to pull away and outperform.
  • Measuring 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 8000 system may be a great choice to start with, giving the opportunity to add additional cards as budget/scaling needs increase. Even a Deep Learning Workstation with a single RTX 8000 can fine-tune BERT Large in about 30 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 8000 BERT Fine tuning Benchmark Snapshot

Slide1-1024x576.png

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

dl_clusters-1024x127.jpg

4x Quadro RTX 8000 BERT LARGE Fine-tune Benchmark

Slide2-1024x576.png

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

[supsystic-tables id=56]

Run these benchmarks

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

scripts/finetune_train_benchmark.sh large true 4 squad

2x Quadro RTX 8000 BERT LARGE Fine-tune Benchmark

Slide3-1024x576.png

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

[supsystic-tables id=57]

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 8000 BERT LARGE Fine-tune Benchmark

Slide4-1024x576.png

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

[supsystic-tables id=58]

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 8000 BERT LARGE Inference Benchmark

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

Training SettingsTotal Inference TimeNumber of sentencesLatency Confidence Level 50msLatency Confidence Level 90 ms
Latency Confidence Level 95 ms
Latency Confidence Level 99 ms
Latency Confidence Level 100 ms
Latency Avg
Throughput (sentences/sec)
FP16 Seq 384, BS1
18.4
1042
17.9
18.33
18.59
19.25
21.78
17.66
56.64
FP16 Seq 384, BS2
24.66
2068
23.54
24.16
24.37
25.28
637.7
23.85
83.87
FP16 Seq 128, BS2
24.73
3402
15.07
16.17
16.47
17.07
22.4
14.54
137.57
FP16 Seq 128, BS1
26.23
1800
15.42
16.56
16.76
17.6
20.43
14.57
68.36
FP16 Seq 128, BS4
29.39
7128
16.13
16.84
17.2
17.89
687.32
16.49
242.54
FP32 Seq 128, BS1
34.34
1800
20.93
22.66
23.16
24.63
28.59
19.08
52.41
FP32 Seq 128, BS2
39.02
3402
23.1
23.92
24.15
24.71
29.27
22.94
87.18
FP16 Seq 384, BS4
40.64
4128
39.09
39.71
39.93
41.64
624.06
39.38
101.58
FP32 Seq 384, BS1
42.74
1042
42.77
44.19
44.3
44.54
46.82
41.02
24.38
FP16 Seq 128, BS8
45.61
16040
22.68
23.4
23.71
24.43
531.04
22.75
351.67
FP32 Seq 384, BS2
74.92
2068
73.38
74.53
74.68
74.95
90.78
72.46
27.6
FP16 Seq 128, BS8
76.95
8432
72.57
74.13
74.46
75.62
624.44
73.01
109.58
FP32 Seq 128, BS4
82.92
7128
47.23
48.57
48.71
49.23
208.24
46.53
85.96
FP32 Seq 384, BS4
159.02
4128
156.41
159.25
159.95
160.72
175.58
154.09
25.96
FP32 Seq 128, BS8
172.07
16040
86.33
87.54
87.71
88.23
98.14
85.82
93.33
FP32 Seq 128, BS8
301.05
8432
287.52
289.06
289.48
291.17
499.43
285.62
28.01

Run these benchmarks

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 8000
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 (usingNVIDIA 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] showprefixforinfo: 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_pretrained_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_pretrained_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_prec_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] predict_file: None
I1212 17:24:48.137849 139750589261632 run_squad.py:950] init_checkpoint: data/download/google_pretrained_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_predict: 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] predict_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] **************************

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RTX-8000-BERT-Large.jpg
Benchmarks

NVIDIA Quadro RTX 8000 BERT Large Fine Tuning Benchmarks in TensorFlow

December 18, 2019 13 min read

Fine Tune BERT Large in Less Than 20 Minutes

For this post, we measured fine-tuning performance (training and inference) for the BERT (Bidirectional Encoder Representations from Transformers) implementation in TensorFlow using NVIDIA Quadro RTX 8000 GPUs. For testing, we used an Exxact Valence Workstation fitted with 4x Quadro RTX 8000’s with NVLink, giving us 192 GB of GPU memory for our system. These tests measure performance for a popular use case for BERT and NLP in general and are meant to show typical GPU performance for such a task.

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 metrics.

The script runs multiple tests on the SQuAD v1.1 dataset using batch sizes 1, 2, 4, 8, 16, 32, and 64 for training, and 1, 2, 4, and 8 for inference. We conducted tests using 1, 2, and 4 GPU configurations on BERT Large (We used 1 GPU for inference benchmark). In addition, we ran all benchmarks using TensorFlow's XLA across all runs. Furthermore, other training settings can be viewed at the end of this blog in the Appendix/Additional Information section.

Key Points and Observations

  • In terms of total training time, the 2x GPU configuration outperformed the 4x until the batch size increased to the 16 range, when the 4x configuration began to pull away and outperform.
  • Measuring 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 8000 system may be a great choice to start with, giving the opportunity to add additional cards as budget/scaling needs increase. Even a Deep Learning Workstation with a single RTX 8000 can fine-tune BERT Large in about 30 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 8000 BERT Fine tuning Benchmark Snapshot

Slide1-1024x576.png

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

dl_clusters-1024x127.jpg

4x Quadro RTX 8000 BERT LARGE Fine-tune Benchmark

Slide2-1024x576.png

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

[supsystic-tables id=56]

Run these benchmarks

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

scripts/finetune_train_benchmark.sh large true 4 squad

2x Quadro RTX 8000 BERT LARGE Fine-tune Benchmark

Slide3-1024x576.png

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

[supsystic-tables id=57]

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 8000 BERT LARGE Fine-tune Benchmark

Slide4-1024x576.png

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

[supsystic-tables id=58]

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 8000 BERT LARGE Inference Benchmark

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

Training SettingsTotal Inference TimeNumber of sentencesLatency Confidence Level 50msLatency Confidence Level 90 ms
Latency Confidence Level 95 ms
Latency Confidence Level 99 ms
Latency Confidence Level 100 ms
Latency Avg
Throughput (sentences/sec)
FP16 Seq 384, BS1
18.4
1042
17.9
18.33
18.59
19.25
21.78
17.66
56.64
FP16 Seq 384, BS2
24.66
2068
23.54
24.16
24.37
25.28
637.7
23.85
83.87
FP16 Seq 128, BS2
24.73
3402
15.07
16.17
16.47
17.07
22.4
14.54
137.57
FP16 Seq 128, BS1
26.23
1800
15.42
16.56
16.76
17.6
20.43
14.57
68.36
FP16 Seq 128, BS4
29.39
7128
16.13
16.84
17.2
17.89
687.32
16.49
242.54
FP32 Seq 128, BS1
34.34
1800
20.93
22.66
23.16
24.63
28.59
19.08
52.41
FP32 Seq 128, BS2
39.02
3402
23.1
23.92
24.15
24.71
29.27
22.94
87.18
FP16 Seq 384, BS4
40.64
4128
39.09
39.71
39.93
41.64
624.06
39.38
101.58
FP32 Seq 384, BS1
42.74
1042
42.77
44.19
44.3
44.54
46.82
41.02
24.38
FP16 Seq 128, BS8
45.61
16040
22.68
23.4
23.71
24.43
531.04
22.75
351.67
FP32 Seq 384, BS2
74.92
2068
73.38
74.53
74.68
74.95
90.78
72.46
27.6
FP16 Seq 128, BS8
76.95
8432
72.57
74.13
74.46
75.62
624.44
73.01
109.58
FP32 Seq 128, BS4
82.92
7128
47.23
48.57
48.71
49.23
208.24
46.53
85.96
FP32 Seq 384, BS4
159.02
4128
156.41
159.25
159.95
160.72
175.58
154.09
25.96
FP32 Seq 128, BS8
172.07
16040
86.33
87.54
87.71
88.23
98.14
85.82
93.33
FP32 Seq 128, BS8
301.05
8432
287.52
289.06
289.48
291.17
499.43
285.62
28.01

Run these benchmarks

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 8000
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 (usingNVIDIA 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] showprefixforinfo: 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_pretrained_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_pretrained_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_prec_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] predict_file: None
I1212 17:24:48.137849 139750589261632 run_squad.py:950] init_checkpoint: data/download/google_pretrained_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_predict: 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] predict_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] **************************

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