fxmarty/20220911-h13m58s53_squad_qa_distilbert_dynamic

By fxmarty · ↓ 0 · ♥ 0 · Updated 2022-09-11

What's in this repo

This page lists the files in fxmarty/20220911-h13m58s53_squad_qa_distilbert_dynamic, including model.onnx, quantized_model.onnx and more.

33 ONNX models (.onnx files)
89 files, including 35 .json, 33 .onnx, 1 .md, 1 .1, 1 .2, 1 .3
Last updated 2022-09-11

ONNX files

File	Size	Download
20220911-h14m23s56_0/model.onnx	—	Download
20220911-h14m23s56_0/quantized_model.onnx	—	Download
20220911-h14m53s50_1/model.onnx	—	Download
20220911-h14m53s50_1/quantized_model.onnx	—	Download
20220911-h15m19s07_2/model.onnx	—	Download
20220911-h15m19s07_2/quantized_model.onnx	—	Download
20220911-h15m49s21_3/model.onnx	—	Download
20220911-h15m49s21_3/quantized_model.onnx	—	Download
20220911-h16m14s23_4/model.onnx	—	Download
20220911-h16m14s23_4/quantized_model.onnx	—	Download

Showing 10 of 33 .onnx files — see the full file list on the right.

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

task: question-answering
Backend: sagemaker-training
Backend args: {'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}
Number of evaluation samples: All dataset

Fixed parameters:

dataset: [{'path': 'squad', 'eval_split': 'validation', 'data_keys': {'question': 'question', 'context': 'context'}, 'ref_keys': ['answers'], 'name': None, 'calibration_split': None}]
name_or_path: distilbert-base-uncased-distilled-squad
from_transformers: True
quantization_approach: dynamic

Benchmarked parameters:

framework: onnxruntime, pytorch
operators_to_quantize: ['Add', 'MatMul'], ['Add']
node_exclusion: [], ['layernorm', 'gelu', 'residual', 'gather', 'softmax']
per_channel: False, True
framework_args: {'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}, {}
reduce_range: True, False
apply_quantization: True, False

Evaluation

Non-time metrics

framework	operators_to_quantize	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		exact_match		f1
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	78.884	\|	86.690
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	76.764	\|	85.053
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	69.622	\|	79.914
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.435	\|	5.887
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.165	\|	85.973
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	76.764	\|	85.053
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	69.622	\|	79.914
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	0.435	\|	5.887
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.165	\|	85.973
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	78.884	\|	86.690
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	78.884	\|	86.690
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	78.884	\|	86.690

Time metrics

Time benchmarks were run for 15 seconds per config.

Below, time metrics for batch size = 1, input length = 32.

framework	operators_to_quantize	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		latency_mean (ms)		throughput (/s)
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	14.26	\|	70.13
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.08	\|	99.20
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.60	\|	94.33
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.88	\|	91.93
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.84	\|	92.27
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.34	\|	96.73
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.41	\|	96.07
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	10.96	\|	91.27
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	10.69	\|	93.53
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.43	\|	69.33
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.52	\|	68.87
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.35	\|	69.73
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.50	\|	69.00
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.20	\|	70.47
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.24	\|	70.27
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	14.58	\|	68.67
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	14.73	\|	67.87
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	31.49	\|	31.80

Below, time metrics for batch size = 1, input length = 64.

framework	operators_to_quantize	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		latency_mean (ms)		throughput (/s)
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	24.83	\|	40.33
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	18.49	\|	54.13
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.87	\|	53.00
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	19.17	\|	52.20
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.92	\|	52.87
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	19.13	\|	52.33
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	18.95	\|	52.80
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	19.08	\|	52.47
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	19.14	\|	52.27
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.83	\|	40.33
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.84	\|	40.27
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.66	\|	40.60
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.76	\|	40.40
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	25.07	\|	39.93
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	25.27	\|	39.60
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	24.76	\|	40.40
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	24.70	\|	40.53
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	41.26	\|	24.27

Below, time metrics for batch size = 1, input length = 128.

framework	operators_to_quantize	node_exclusion	per_channel	framework_args	reduce_range	apply_quantization		latency_mean (ms)		throughput (/s)
`onnxruntime`	`None`	`None`	`None`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`None`	`False`	\|	46.89	\|	21.33
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	34.84	\|	28.73
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.88	\|	27.93
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	36.92	\|	27.13
`onnxruntime`	`['Add', 'MatMul']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	36.25	\|	27.60
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	36.17	\|	27.67
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.59	\|	28.13
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	37.36	\|	26.80
`onnxruntime`	`['Add', 'MatMul']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	35.97	\|	27.87
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	46.94	\|	21.33
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	47.19	\|	21.20
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	47.05	\|	21.27
`onnxruntime`	`['Add']`	`['layernorm', 'gelu', 'residual', 'gather', 'softmax']`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	46.79	\|	21.40
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	46.87	\|	21.40
`onnxruntime`	`['Add']`	`[]`	`False`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	47.04	\|	21.27
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`False`	`True`	\|	47.08	\|	21.27
`onnxruntime`	`['Add']`	`[]`	`True`	`{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4}`	`True`	`True`	\|	47.05	\|	21.27
`pytorch`	`None`	`None`	`None`	`{}`	`None`	`None`	\|	54.61	\|	18.33

Model info

question-answering

tensorboard onnx distilbert question-answering dataset:squad region:us

↓ 0 ♥ 0

Created: 2022-09-11

Updated: 2022-09-11

View on Hugging Face

Files (89)

.gitattributes

20220911-h14m23s56_0/model.onnx ONNX

20220911-h14m23s56_0/ort_config.json

20220911-h14m23s56_0/quantized_model.onnx ONNX

20220911-h14m23s56_0/results.json

20220911-h14m53s50_1/model.onnx ONNX

20220911-h14m53s50_1/ort_config.json

20220911-h14m53s50_1/quantized_model.onnx ONNX

20220911-h14m53s50_1/results.json

20220911-h15m19s07_2/model.onnx ONNX

20220911-h15m19s07_2/ort_config.json

20220911-h15m19s07_2/quantized_model.onnx ONNX

20220911-h15m19s07_2/results.json

20220911-h15m49s21_3/model.onnx ONNX

20220911-h15m49s21_3/ort_config.json

20220911-h15m49s21_3/quantized_model.onnx ONNX

20220911-h15m49s21_3/results.json

20220911-h16m14s23_4/model.onnx ONNX

20220911-h16m14s23_4/ort_config.json

20220911-h16m14s23_4/quantized_model.onnx ONNX

20220911-h16m14s23_4/results.json

20220911-h16m39s55_5/model.onnx ONNX

20220911-h16m39s55_5/ort_config.json

20220911-h16m39s55_5/quantized_model.onnx ONNX

20220911-h16m39s55_5/results.json

20220911-h17m10s11_6/model.onnx ONNX

20220911-h17m10s11_6/ort_config.json

20220911-h17m10s11_6/quantized_model.onnx ONNX

20220911-h17m10s11_6/results.json

20220911-h17m40s23_7/model.onnx ONNX

20220911-h17m40s23_7/ort_config.json

20220911-h17m40s23_7/quantized_model.onnx ONNX

20220911-h17m40s23_7/results.json

20220911-h18m05s42_8/model.onnx ONNX

20220911-h18m05s42_8/ort_config.json

20220911-h18m05s42_8/quantized_model.onnx ONNX

20220911-h18m05s42_8/results.json

20220911-h18m35s59_9/model.onnx ONNX

20220911-h18m35s59_9/ort_config.json

20220911-h18m35s59_9/quantized_model.onnx ONNX

20220911-h18m35s59_9/results.json

20220911-h19m06s15_10/model.onnx ONNX

20220911-h19m06s15_10/ort_config.json

20220911-h19m06s15_10/quantized_model.onnx ONNX

20220911-h19m06s15_10/results.json

20220911-h19m36s15_11/model.onnx ONNX

20220911-h19m36s15_11/ort_config.json

20220911-h19m36s15_11/quantized_model.onnx ONNX

20220911-h19m36s15_11/results.json

20220911-h20m06s26_12/model.onnx ONNX

20220911-h20m06s26_12/ort_config.json

20220911-h20m06s26_12/quantized_model.onnx ONNX

20220911-h20m06s26_12/results.json

20220911-h20m32s12_13/model.onnx ONNX

20220911-h20m32s12_13/ort_config.json

20220911-h20m32s12_13/quantized_model.onnx ONNX

20220911-h20m32s12_13/results.json

20220911-h20m57s50_14/model.onnx ONNX

20220911-h20m57s50_14/ort_config.json

20220911-h20m57s50_14/quantized_model.onnx ONNX

20220911-h20m57s50_14/results.json

20220911-h21m23s01_15/model.onnx ONNX

20220911-h21m23s01_15/ort_config.json

20220911-h21m23s01_15/quantized_model.onnx ONNX

20220911-h21m23s01_15/results.json

20220911-h21m53s17_16/model.onnx ONNX

20220911-h21m53s17_16/results.json

20220911-h22m20s47_17/results.json

README.md

runs.json

tensorboard/1662934853.040312/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.1

tensorboard/1662934853.0417204/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.2

tensorboard/1662934853.042942/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.3

tensorboard/1662934853.0442307/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.4

tensorboard/1662934853.0453482/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.5

tensorboard/1662934853.04647/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.6

tensorboard/1662934853.0478818/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.7

tensorboard/1662934853.0491567/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.8

tensorboard/1662934853.0507572/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.9

tensorboard/1662934853.0518975/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.10

tensorboard/1662934853.0530026/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.11

tensorboard/1662934853.0541465/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.12

tensorboard/1662934853.0553083/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.13

tensorboard/1662934853.056435/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.14

tensorboard/1662934853.0575647/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.15

tensorboard/1662934853.058674/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.16

tensorboard/1662934853.0610743/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.17

tensorboard/1662934853.062237/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.18

tensorboard/events.out.tfevents.1662934853.ip-10-0-142-213.ec2.internal.1.0