• Managing Deep Learning Development Complexity

    August 1, 2017

    For developers, deep learning systems are becoming more interactive and complex. From the building of more malleable datasets that can be iteratively augmented, to more dynamic models, to more continuous learning being built into neural networks, there is a greater need to manage the process from start to finish with lightweight tools.

    “New training samples, human insights, and operation experiences can consistently emerge even after deployment. The ability of updating a model and tracking its changes thus becomes necessary,” says a team from Imperial College London that has developed a library to manage the iterations deep learning developers make across …

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  • Under The Hood Of Google’s TPU2 Machine Learning Clusters

    May 22, 2017 Paul Teich

    As we previously reported, Google unveiled its second-generation TensorFlow Processing Unit (TPU2) at Google I/O last week. Google calls this new generation “Google Cloud TPUs”, but provided very little information about the TPU2 chip and the systems that use it other than to provide a few colorful photos. Pictures do say more than words, so in this article we will dig into the photos and provide our thoughts based the pictures and on the few bits of detail Google did provide.

    To start with, it is unlikely that Google will sell TPU-based chips, boards, or servers – TPU2 …

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  • Crunching Machine Learning And Databases Together On GPUs

    May 8, 2017

    While it is always best to have the right tool for the job, it is better still if a tool can be used by multiple jobs and therefore have its utilization be higher than it might otherwise be. This is one of the reasons why general purpose, X86-based computing took over the datacenter. Economies of scale trumped the efficiency that can come from limited scope or just leaving legacy applications alone in place on alternate platforms.

    The idea of offloading computational tasks from CPUs to GPU accelerators took off in academia a little more than a decade ago, and …

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  • Cluster Management for Distributed Machine Learning at Scale

    May 1, 2017

    Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. However, large-scale clusters are being asked to operate in different ways, namely by chewing on large-scale deep learning workloads—and this requires a specialized approach to get high utilization, efficiency, and performance.

    Nearly all of the cluster management tools from the high performance computing community are being bent in the machine learning direction, but for production deep learning shops, there appears to be a DIY tendency. This is not as complicated as it might sound, given the range of container-based open source tools, …

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  • Taking the Heavy Lifting Out of TensorFlow at Extreme Scale

    April 25, 2017

    There is no real middle ground when it comes to TensorFlow use cases. Most implementations take place either in a single node or at the drastic Google-scale, with few scalability stories in between.

    This is starting to change, however, as more users find an increasing array of open source tools based on MPI and other approaches to hop to multi-GPU scalability for training, but it still not simple to scale Google’s own framework across larger machines. Code modifications get hairy beyond single node and for the MPI uninitiated, there is a steep curve to scalable deep learning.

    Although high performance …

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  • Does Google’s TPU Investment Make Sense Going Forward?

    April 12, 2017

    Google created quite a stir when it released architectural details and performance metrics for its homegrown Tensor Processing Unit (TPU) accelerator for machine learning algorithms last week. But as we (and many of you reading) pointed out, comparing the TPU to earlier “Kepler” generation GPUs from Nvidia was not exactly a fair comparison. Nvidia has done much in the “Maxwell” and “Pascal” GPU generations specifically to boost machine learning performance.

    To set the record straight, Nvidia took some time and ran some benchmarks of its own to put the performance of its latest Pascal accelerators, particularly the ones it aims …

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  • Baidu Targets Deep Learning Scalability Challenges

    February 22, 2017

    When it comes to solving deep learning cluster and software stack problems at scale, few companies are riding the bleeding edge like Chinese search giant, Baidu. As we have detailed in the past, the company’s Silicon Valley AI Lab (SVAIL) has some unique hardware and framework implementations that put AI to the test at scale. As it turns out, scalability of the models they specialize in (beginning with speech recognition) is turning out to be one of the great challenges ahead on all fronts—hardware, compiler/runtime, and framework alike.

    As we have described across multiple use cases, at Baidu and elsewhere …

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  • Facebook Flow Is An AI Factory Of The Future

    May 10, 2016

    We have been convinced for many years that machine learning, the kind of artificial intelligence that actually works in practice, not in theory, would be a key element of the next platform. In fact, it might be the most important part of the stack. And therefore, those who control how we deploy machine learning will, to a large extent, control the nature of future applications and the systems that run them.

    Machine learning is the killer app for the hyperscalers, just like modeling and simulation were for supercomputing centers decades ago, and we believe we are only seeing the tip …

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