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GTC ON-DEMAND

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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.

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.

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

In the era of cloud computing, new approaches to the underlying technologies will be required to fully realize the benefits of AI and deep learning. The combination of IBM Bluemix with NVIDIA GPUs provides the infrastructure foundation for customers to scale their deep learning and AI workloads. IBM Bluemix and cloud hosting partner Rescale will discuss NVIDIAs new P100 GPUs, including benchmark results and how they can enable next-gen AI and deep learning applications.

In the era of cloud computing, new approaches to the underlying technologies will be required to fully realize the benefits of AI and deep learning. The combination of IBM Bluemix with NVIDIA GPUs provides the infrastructure foundation for customers to scale their deep learning and AI workloads. IBM Bluemix and cloud hosting partner Rescale will discuss NVIDIAs new P100 GPUs, including benchmark results and how they can enable next-gen AI and deep learning applications.

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Topics:
Artificial Intelligence and Deep Learning, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7843
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Abstract:
Training deep neural networks can be time consuming when searching a large hyper-parameter space. Using the Rescale optimization platform, we present a simple interface for doing parallelized hyper-parameter grid search for deep learning models from a number of different machine learning packages of a user's choice. Offered packages are pre-configured to take advantage of NVIDIA cuDNN accelerated training, allowing the user to tradeoff cost vs. training time vs. model performance. We will demo the Rescale DNN optimization system and give performance results when trained on NVIDIA GPU hardware available on Rescale. Benchmarking will be done against the MNIST and CIFAR datasets.
Training deep neural networks can be time consuming when searching a large hyper-parameter space. Using the Rescale optimization platform, we present a simple interface for doing parallelized hyper-parameter grid search for deep learning models from a number of different machine learning packages of a user's choice. Offered packages are pre-configured to take advantage of NVIDIA cuDNN accelerated training, allowing the user to tradeoff cost vs. training time vs. model performance. We will demo the Rescale DNN optimization system and give performance results when trained on NVIDIA GPU hardware available on Rescale. Benchmarking will be done against the MNIST and CIFAR datasets.  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6634
Streaming:
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