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High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5
By Gaurav Menghani, Software Engineer at Google AI on July 16, 2021 in Deep Learning, Efficiency, Google, Hardware, Machine Learning, NVIDIA, PyTorch, Scalability, TensorFlowTraining efficient deep learning models with any software tool is nothing without an infrastructure of robust and performant compute power. Here, current software and hardware ecosystems are reviewed that you might consider in your development when the highest performance possible is needed.
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High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4
With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.
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High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3
Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.
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High-Performance Deep Learning: How to train smaller, faster, and better models – Part 2
As your organization begins to consider building advanced deep learning models with efficiency in mind to improve the power delivered through your solutions, the software and hardware tools required for these implementations are foundational to achieving high-performance.
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High Performance Deep Learning, Part 1
Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have profound impacts in many ways, so developing future models with this intension in mind will only help to further expand the reach, applicability, and value of what deep learning has to offer.
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Gaurav Menghani (@GauravML on Twitter, Gaurav Menghani on LinkedIn) is a Staff Software Engineer at Google Research where he leads research projects geared towards optimizing large machine learning models for efficient training and inference on devices ranging from tiny microcontrollers to Tensor Processing Unit (TPU)-based servers. His work has positively impacted over 1 Billion of active users across YouTube, Cloud, Ads, Chrome, etc. He is also an author of an upcoming book with Manning Publication on Efficient Machine Learning. Before Google, Gaurav worked at Facebook for 4.5 years and has contributed significantly to Facebook’s Search system and large-scale distributed databases. He has an M.S. in Computer Science from Stony Brook University.
