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GTC ON-DEMAND

Data Center & Cloud Infrastructure
Presentation
Media
Impact of Storage System Performance on TensorFlow Data Ingestion
Abstract:

As multi-GPU deep learning performance improves, the performance of the storage system hosting a dataset becomes critical in keeping these GPUs fully utilized. We survey the different methods for providing training data to a TensorFlow application on a GPU, and benchmark data throughput for a variety of popular neural network architectures. We look at performance and potential bottlenecks for local storage technologies (SCSI SSD and NVMe), high performance network-attached file systems, TensorFlow native connectors (HDFS and S3), and FUSE-connected object storage.

 
Topics:
Data Center & Cloud Infrastructure, AI Startup, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8544
Streaming:
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