AWS and NVIDIA Expand Deep Learning Partnership at GTC 2017

by Joseph Spisak | on | | Comments

This year at NVIDIA’s GPU Technology Conference, AWS and NVIDIA partnered on multiple initiatives. The first is an exciting new Volta-based GPU instance that we think will completely change the face of the AI developer world through a 3x speedup on LSTM training. Second, we are announcing plans to train 100,000+ developers through the Deep Learning Institute (DLI) running on AWS. The third is the joint development of tools that enable large-scale deep learning for the broader developer community.

AWS is also delivering sessions at GTC including using Apache MXNet training at scale on Amazon EC2 P2 instances and at the edge through the support of NVIDIA’s Jetson TX2 platform. Here’s to a great partnership and some amazing innovation!

Volta—coming to an instance near you

The Tesla V100, based on the Volta architecture and equipped with 640 Tensor Cores, provides breakthrough performance of 120 teraflops of mixed precision deep learning performance. AWS is very excited to support V100 on Amazon EC2 instances. This support means that the growing deep learning community can take advantage of supercomputing class capabilities and train even deeper models, pushing the limits of AI. Also, in collaboration with NVIDIA, AWS engineers and researchers have pre-optimized neural machine translation (NMT) algorithms on Apache MXNet. This approach allows developers to train the fastest way now possible on Volta-based platforms. Overall, we expect the Volta-based instance to be very popular with developers!

Bringing deep learning to 100,000+ developers worldwide

We are excited to partner with NVIDIA to deliver coursework for their Deep Learning Institute on top of AWS. The DLI is broadening its curriculum to include the applied use of deep learning for self-driving cars, healthcare, web services, robotics, video analytics, and financial services. This curriculum includes instructor-led seminars, workshops, and classes to reach developers across Asia, Europe, and the Americas. With AWS’s global infrastructure spanning 42 Availability Zones (with 8 more planned) and 16 regions (with 3 more coming), AWS is the perfect infrastructure platform to reach a broad set of developers.

Bringing ease of use and scale to deep learners

In the past, meeting the demanding level of performance to train deep networks often required access to supercomputers at national labs. It also called for understanding distributed computing libraries such as message passing interface (MPI), and setting up multiple libraries and packages with several dependencies. With the focused goal of making scalable deep learning easy for developers, AWS has partnered with NVIDIA to create optimized developer tools. These tools are prebuilt using NVIDIA Deep Learning SDK libraries such as cuDNN, NCCL, TensorRT, and the CUDA toolkit. When developers use these tools, we’ve seen that they find it much easier to scale to large numbers of GPUs with very little friction at the scale of tens of millions of instance hours.

Collaborating to bring deep learning from the cloud to the edge

Deep learning at the edge on low-power devices is one of the biggest trends in deep learning today. From latency savings and data locality to network availability, there are many reasons to run models on devices at the edge. At the AWS session this week at GTC, we show how you can train a state of the art model using the P2 instance. We also show you how to deploy it easily on a variety of low-power devices, including the Jetson TX2 platform, to bring cutting-edge artificial intelligence capabilities to low power devices. You can then manage these devices through services such as AWS IoT and AWS Greengrass, providing an end-to-end AI workflow.

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