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Natural Language Processing Library for Apache Spark – free to use

@machinelearnbot

Apache Spark is a general-purpose cluster computing framework, with native support for distributed SQL, streaming, graph processing, and machine learning. Now, the Spark ecosystem also has an Spark Natural Language Processing library. Get it on GitHub or begin with the quickstart tutorial. The John Snow Labs NLP Library is under the Apache 2.0 license, written in Scala with no dependencies on other NLP or ML libraries. It natively extends the Spark ML Pipeline API.


Top 10 deep learning Framesworks everyone should know

#artificialintelligence

This is the age of artificial intelligence. Machine Learning and predictive analytics are now established and integral to just about every modern businesses, but artificial intelligence expands the scale of what's possible within those fields. It's what makes deep learning possible. Systems with greater ostensible autonomy and complexity can solve similarly complex problems. If Deep Learning is able to solve more complex problems and perform tasks of greater sophistication, building them is naturally a bigger challenge for data scientists and engineers.


Getting started with TensorFlow

@machinelearnbot

In the context of machine learning, tensor refers to the multidimensional array used in the mathematical models that describe neural networks. In other words, a tensor is usually a higher-dimension generalization of a matrix or a vector. Through a simple notation that uses a rank to show the number of dimensions, tensors allow the representation of complex n-dimensional vectors and hyper-shapes as n-dimensional arrays. Tensors have two properties: a datatype and a shape. TensorFlow is an open source deep learning framework that was released in late 2015 under the Apache 2.0 license.


AWS Announces Availability of P3 Instances for Amazon EC2

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The first instances to include NVIDIA Tesla V100 GPUs, P3 instances are the most powerful GPU instances available in the cloud. P3 instances allow customers to build and deploy advanced applications with up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances, and reduce training of machine learning applications from days to hours. With up to eight NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance, as well as a 300 GB/s second-generation NVIDIA NVLink interconnect that enables high-speed, low-latency GPU-to-GPU communication. P3 instances also feature up to 64 vCPUs based on custom Intel Xeon E5 (Broadwell) processors, 488 GB of DRAM, and 25 Gbps of dedicated aggregate network bandwidth using the Elastic Network Adapter (ENA). "When we launched our P2 instances last year, we couldn't believe how quickly people adopted them," said Matt Garman, Vice President of Amazon EC2.


Introducing the Natural Language Processing Library for Apache Spark - The Databricks Blog

@machinelearnbot

This is a community blog and effort from the engineering team at John Snow Labs, explaining their contribution to an open-source Apache Spark Natural Language Processing (NLP) library. Apache Spark is a general-purpose cluster computing framework, with native support for distributed SQL, streaming, graph processing, and machine learning. Now, the Spark ecosystem also has an Spark Natural Language Processing library. Get it on GitHub or begin with the quickstart tutorial. The John Snow Labs NLP Library is under the Apache 2.0 license, written in Scala with no dependencies on other NLP or ML libraries.


TensorFlow* Optimizations on Modern Intel Architecture

@machinelearnbot

TensorFlow* is a leading deep learning and machine learning framework, which makes it important for Intel and Google to ensure that it is able to extract maximum performance from Intel's hardware offering. This paper introduces the Artificial Intelligence (AI) community to TensorFlow optimizations on Intel Xeon and Intel Xeon Phi processor-based platforms. These optimizations are the fruit of a close collaboration between Intel and Google engineers announced last year by Intel's Diane Bryant and Google's Diane Green at the first Intel AI Day. We describe the various performance challenges that we encountered during this optimization exercise and the solutions adopted. We also report out performance improvements on a sample of common neural networks models.


New Optimizations Improve Deep Learning Frameworks For CPUs

#artificialintelligence

Since most of us need more than a "machine learning only" server, I'll focus on the reality of how Intel Xeon SP Platinum processors remain the best choice for servers, including servers needing to do machine learning as part of their workload. Here is a partial run down of key software for accelerating deep learning on Intel Xeon Platinum processor versions enough that the best performance advantage of GPUs is closer to 2X than to 100X. There is also a good article in Parallel Universe Magazine, Issue 28, starting on page 26, titled Solving Real-World Machine Learning Problems with Intel Data Analytics Acceleration Library. High-core count CPUs (the Intel Xeon Phi processors – in particular the upcoming "Knights Mill" version), and FPGAs (Intel Xeon processors coupled with Intel/Altera FPGAs), offer highly flexible options excellent price/performance and power efficiencies.


New Optimizations Improve Deep Learning Frameworks For CPUs

#artificialintelligence

Since most of us need more than a "machine learning only" server, I'll focus on the reality of how Intel Xeon SP Platinum processors remain the best choice for servers, including servers needing to do machine learning as part of their workload. Here is a partial run down of key software for accelerating deep learning on Intel Xeon Platinum processor versions enough that the best performance advantage of GPUs is closer to 2X than to 100X. There is also a good article in Parallel Universe Magazine, Issue 28, starting on page 26, titled Solving Real-World Machine Learning Problems with Intel Data Analytics Acceleration Library. High-core count CPUs (the Intel Xeon Phi processors – in particular the upcoming "Knights Mill" version), and FPGAs (Intel Xeon processors coupled with Intel/Altera FPGAs), offer highly flexible options excellent price/performance and power efficiencies.


AWS AI Blog

#artificialintelligence

Second, framework developers need to maintain multiple backends to guarantee performance on hardware ranging from smartphone chips to data center GPUs. Diverse AI frameworks and hardware bring huge benefits to users, but it is very challenging to AI developers to deliver consistent results to end users. Motivated by the compiler technology, a group of researchers including Tianqi Chen, Thierry Moreau, Haichen Shen, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy from Paul G. Allen School of Computer Science & Engineering, University of Washington, together with Ziheng Jiang from the AWS AI team, introduced the TVM stack to simplify this problem. Today, AWS is excited to announce, together with the research team from UW, an end-to-end compiler based on the TVM stack that compiles workloads directly from various deep learning frontends into optimized machine codes.


A load balancer that learns, WebTorch – UnifyID – Medium

#artificialintelligence

In my previous blog post "How I stopped worrying and embraced docker microservices" I talked about why Microservices are the bees knees for scaling Machine Learning in production. If only there was a tool that made this decision easy and allowed you to even go to the extreme case of writing a monolith, without sacrificing either HTTP performance (and pretty HTTP server semantics) or ML performance and relevance in the rapid growing Deep Learning market. WebTorch is the freak child of the fastest, most stable HTTP server, nginx and the fastest, most relevant Deep Learning framework Torch. Now of course that doesn't mean WebTorch is either the best performance HTTP server and/or the best performing Deep Learning framework, but it's at least worth a look right?