• There is No Such Thing as Easy AI — But We’re Getting Closer

  March 22, 2018 James Cuff

  

  The dark and mysterious art of artificial intelligence and machine learning is neither straightforward, or easy. AI systems have been termed “black boxes” for this reason for decades now. We desperately continue to present ever larger, more unwieldy datasets to increasingly sophisticated “mystery algorithms” in our attempts to rapidly infer and garner new knowledge.  
  
  How can we try to make all of this just a little easier?
  
  Hyperscalers with multi-million dollar analytics teams have access to vast, effectively unlimited compute and storage of all shapes and sizes. Huge teams of analysts, systems managers, resilience and reliability experts are standing up …

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• IBM Unwinds Tangled Data for Enterprise AI

  March 16, 2018 Jeffrey Burt

  

  These days, organizations are creating and storing massive amounts of data, and in theory this data can be used to drive business decisions through application development, particularly with new techniques such as machine learning. Data is arguably the most important asset, and it is also probably the most difficult thing to manage. Well, excepting people.

  Data is tangled mess. It can be structured or unstructured, and it is increasingly scattered in different locations – in on-premises infrastructure, in a public cloud, on a mobile device. It is a challenge to move, thanks to the costs in everything from bandwidth to …

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• Google Boots Up Tensor Processors On Its Cloud

  February 12, 2018 Jeffrey Burt

  

  Google laid down its path forward in the machine learning and cloud computing arenas when it first unveiled plans for its tensor processing unit (TPU), an accelerator designed by the hyperscaler to speeding up machine learning workloads that are programmed using its TensorFlow framework.

  Almost a year ago, at its Google I/O event, the company rolled out the architectural details of its second-generation TPUs – also called the Cloud TPU – for both neural network training and inference, with the custom ASICs providing up to 180 teraflops of floating point performance and 64 GB of High Bandwidth Memory. …

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• Managing Deep Learning Development Complexity

  August 1, 2017 Nicole Hemsoth

  

  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 Timothy Prickett Morgan

  

  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 Nicole Hemsoth

  

  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 Nicole Hemsoth

  

  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 Timothy Prickett Morgan

  

  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 Nicole Hemsoth

  

  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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