Habana Model Performance Data - 1.3.0

See the latest TensorFlow and PyTorch model performance data. Visit the Habana catalog for information on models and containers that are currently integrated with Habana’s Synapse AI software suite. For more information on future model support, please refer to our SynapseAI roadmap page.

TensorFlow Reference Models Performance

Framework	Model	#HPU	Precision	Time to Train	Accuracy	Throughput	Batch Size
2.8.0	ResNet50 Keras LARS	32	bf16	22.33 min	75.68	49441.8 img/sec	256
2.8.0	ResNet50 Keras LARS	16	bf16	40.68 min	75.48	24779.8 img/sec	256
2.8.0	ResNet50 Keras LARS	8	bf16	69 min	75.99	13018.31 img/sec	256
2.8.0	ResNet50 Keras LARS	1	bf16	516 min	75.87	1636.7 img/sec	256
2.8.0	BERT-Large Pre Training combined	32	bf16	1591 min		5485.87 sent/sec	64
2.8.0	BERT-Large Pre Training combined	8	bf16			1378.49 sent/sec	64
2.8.0	BERT-Large Pre Training combined	1	bf16			173 sent/sec	64
2.8.0	BERT-Large Pre Training phase 1	32	bf16			6621.8 sent/sec	64
2.8.0	BERT-Large Pre Training phase 1	8	bf16			1666 sent/sec	64
2.8.0	BERT-Large Pre Training phase 1	1	bf16			210.3 sent/sec	64
2.8.0	BERT-Large Pre Training phase 2	32	bf16			2134.9 sent/sec	8
2.8.0	BERT-Large Pre Training phase 2	8	bf16			534.5 sent/sec	8
2.8.0	BERT-Large Pre Training phase 2	1	bf16			67.6 sent/sec	8
2.8.0	BERT-Large Fine Tuning (SQUAD)	8	bf16	17.41 min	93.52	398.91 sent/sec	24
2.8.0	BERT-Large Fine Tuning (SQUAD)	1	bf16	69 min	92.84	53.45 sent/sec	24
2.8.0	SSD	8	bf16	41.06 min	23.26	3860.46 img/sec	128
2.8.0	SSD	1	bf16	35.13 min	23.66	515.57 img/sec	128
2.8.0	Resnext-101	8	bf16	382 min	79	4451.32 img/sec	128
2.8.0	Resnext-101	1	bf16	2718 min	79.08	701.2 img/sec	128
2.8.0	UNet2D	8	bf16	4.05 min	88.16	391.52 img/sec	8
2.8.0	UNet2D	1	bf16	17.67 min	88.71	51.8 img/sec	8
2.8.0	UNet3D	8	bf16	14.25 min	88.63	42.98 img/sec	2
2.8.0	UNet3D	1	bf16	81 min	87.59	6.7 img/sec	2
2.8.0	Transformer	8	bf16	1054 min	26.8	157051.57 sent/sec	4.096
2.8.0	Transformer	1	bf16	1012 min	21.3	22327.68 sent/sec	4.096
2.8.0	MaskRCNN	8	bf16	215 min	33.87	120.82 img/sec	4
2.8.0	MaskRCNN	1	bf16	1388 min	33.89	17.84 img/sec	4
2.8.0	Vision Transformer	8	bf16	396 min	84.54	530.32 img/sec	32
2.8.0	RetinaNet	8	bf16	461 min	37.43	82.03 img/sec	64
2.8.0	Densenet 121 tf.distribute	8	bf16	381 min	74.72	5300.07 img/sec	1.024
2.8.0	T5 Base	1	bf16	17.48 min	93.67	100.38 img/sec	16
2.8.0	VGG SegNet	1	bf16	9 min	89.4	108.05 img/sec	16
2.8.0	EfficientDet	8	fp32	87.25 min	33.56	192.5 img/sec	8
2.8.0	CycleGAN	1	bf16	226 min		15.62 img/sec	2
2.8.0	Albert-Large Fine Tuning (SQUAD)	1	bf16	75 min	90.9	55.08 sent/sec	32
2.8.0	Albert-Large Fine Tuning (SQUAD)	8	bf16	22.8 min	91.01	438.79 sent/sec	32
2.8.0	ResNet50 Keras LARS tf.distribute	8	bf16	73 min	76.07	12838.65 img/sec	256
2.8.0	ResNet50 Keras LARS Host NIC (HVD and Libfabric)	16	bf16			23084.74 img/sec	256
2.8.0	ResNet50 Keras LARS Host NIC (tf.distribute and Libfabric)	16	bf16			21865.04 img/sec	256
2.8.0	WideAndDeep	1	bf16	33 min	65.42	722899.3 smpl/sec	131.072
2.8.0	Electra Fine Tuning	1	bf16	76 min	92.13	118.84 img/sec	16
2.8.0	DistilBERT	8	bf16	2.6 min	85.55	2403 sent/sec	32
2.8.0	Unet Industrial	8	bf16	2.01 min	96.67	613.3 img/sec	2

PyTorch Reference Models Performance

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System Configuration:
HPU: Habana Gaudi® HL-205 Mezzanine cards
System: HLS-1 with eight HL-205 HPU and two Intel® Xeon® Platinum 8280 CPU @ 2.70GHz, and 756GB of System Memory
Software: Ubuntu20.04, SynapseAI Software version 1.3.0-499
Tensorflow: Models run with Tensorflow v2.8.0 use this Docker image; ones with v2.7.1 use this Docker image
PyTorch: Models run with PyTorch v1.10.1 use this Docker image
Environment: These workloads are run using the Docker images running directly on the Host OS

Performance varies by use, configuration and other factors. All information provided here is subject to change without notice. Habana Labs may make changes to its test conditions and internal reliability goals at any time. Contact your Habana Labs representative to obtain the latest Habana Labs product specifications and roadmaps. Your costs and results may vary.