Installing TensorFlow 1.4.0 on macOS with CUDA support


Since version 1.2, Google dropped GPU support on macOS from TensorFlow. As of today, the last Mac that integrated an nVidia GPU was released in 2014. Only their latest operating system, macOS High Sierra, supports external GPUs via Thunderbolt 3.1 Who doesn't have the money to get one of the latest MacBook Pro, plus an external GPU enclosure, plus a GPU, has to purchase an old MacPro and fit a GPU in there. Any way you see it, it's quite a niche market. There's another community that Google forgot.

Where Major Chip Companies Are Investing In AI, AR/VR, And IoT


We dug into the private market bets made by major computer chip companies, including GPU makers. Our analysis encompasses the venture arms of NVIDIA, Intel, Samsung, AMD, and more. Recent developments in the semiconductor industry have been sending mixed signals. Stories about Moore's Law slowing have grown common, but analysts affirm that the latest crop of chips (specifically Intel's newest 10-nanometer technology) prove Moore's Law is still alive and well. Meanwhile, the vast application of graphics hardware in AI has propelled GPU (graphics processing unit) maker NVIDIA into tech juggernaut status: the company's shares were the best-performing stock over the past year.

Amazon And NVIDIA Simplify Machine Learning


While this announcement was completely expected, it is an important milestone along the road to simplifying and lowering the costs of Machine Learning development and deployment for AI projects. When NVIDIA announced the NVIDIA GPU Cloud last May at GTC, I explained in this blog that the purpose was to create a registry of compatible and optimized ML software containers which could then, in theory, run on the cloud of users' choice. That vision has now become a reality, at least for's I expect other Cloud Service Providers to follow soon, given the momentum in the marketplace for the 120 TFLOP Volta GPU's. Why do you need NVIDIA's GPU Cloud for ML?

Inference Emerges As Next AI Challenge


As developers flock to artificial intelligence frameworks in response to the explosion of intelligence machines, training deep learning models has emerged as a priority along with synching them to a growing list of neural and other network designs. All are being aligned to confront some of the next big AI challenges, including training deep learning models to make inferences from the fire hose of unstructured data. These and other AI developer challenges were highlighted during this week's Nvidia GPU technology conference in Washington. The GPU leader uses the events to bolster its contention that GPUs--some with up to 5,000 cores--are filling the computing gap created by the decline of Moore's Law. The other driving force behind the "era of AI" is the emergence of algorithm-driven deep learning that is forcing developers to move beyond mere coding to apply AI to a growing range of automated processes and predictive analytics.

Phones don't need a NPU to benefit from machine learning


Neural Networks and Machine Learning are some of this year's biggest buzzwords in the world of smartphone processors. Huawei's HiSilicon Kirin 970, Apple's A11 Bionic, and the image processing unit (IPU) inside the Google Pixel 2 all boast dedicated hardware support for this emerging technology. The trend so far has suggested that machine learning requires a dedicated piece of hardware, like a Neural Processing Unit (NPU), IPU, or "Neural Engine", as Apple would call it. However, the reality is these are all just fancy words for custom digital signal processors (DSP) -- that is, hardware specialized in performing complex mathematical functions quickly. Today's latest custom silicon has been specifically optimized around machine learning and neural network operations, the most common of which include dot product math and matrix multiply.

[session] End-to-End on the GPU @CloudExpo #AI #ML #MachineLearning


Companies are harnessing data in ways we once associated with science fiction. Analysts have access to a plethora of visualization and reporting tools, but considering the vast amount of data businesses collect and limitations of CPUs, end users are forced to design their structures and systems with limitations. As the cloud toolkit to analyze data has evolved, GPUs have stepped in to massively parallel SQL, visualization and machine learning.

TensorFlow (GPU) Setup for Developers – Michael Ramos – Medium


This probably isn't for the professional data scientists or anyone creating actual models -- I imagine their setups are a bit more verbose. This blog post will cover my manual implementation of setting up TensorFlow with GPU support. I've spent hours reading posts and going through walkthroughs… and learned a ton from them… so I pieced together this installation guide to which I've been routinely using since (should have a CloudFormation script soon). This installation guide is for simple/default configurations and settings. They were made specifically for what we want to do, which is to run intense computations on the GPU.

AWS says its new monster GPU array is 'most powerful' in the cloud


An introduction to cloud computing from IaaS and PaaS to hybrid, public and private cloud. Amazon Web Services (AWS) has launched new P3 instances on its EC2 cloud computing service which are powered by Nvidia's Tesla Volta architecture V100 GPUs and promise to dramatically speed up the training of machine learning models. The P3 instances are designed to handle compute-intensive machine learning, deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modelling, and genomics workloads. Amazon said the new services could reduce the training time for sophisticated deep learning models from days to hours. These are the first instances to include Nvidia Tesla V100 GPUs, and AWS said its P3 instances are "the most powerful GPU instances available in the cloud".

Will scikit-learn utilize GPU?


By default none of both are going to use GPU, especially if it is running inside Docker, unless you use nvidia-docker and an image capable of doing it. Scikit-learn is not intended to be used as a deep-learning framework, and seems that it doesn't support GPU computations. Why is there no support for deep or reinforcement learning / Will there be support for deep or reinforcement learning in scikit-learn? Deep learning and reinforcement learning both require a rich vocabulary to define an architecture, with deep learning additionally requiring GPUs for efficient computing. However, neither of these fit within the design constraints of scikit-learn; as a result, deep learning and reinforcement learning are currently out of scope for what scikit-learn seeks to achieve.

(BDT311) Deep Learning: Going Beyond Machine Learning


Chida Chidambaram Vishal Deshpande BDT311 Deep Learning Going Beyond Machine Learning October 2015 2. What to Expect from the Session Data analytics options on AWS Machine learning (ML) – high level Amazon ML from AWS ML sample use case Deep learning (DL) – high level DL sample use cases AWS GPU/HPCC server family Q&A 3. Data Analytics Options on AWS Amazon EMR AnalyzeStoreIngest Amazon Kinesis DynamoDB Amazon Redshift RDSS3 Amazon Kinesis Consumer Machine Learning Amazon Kinesis Producer Traditional Server Mobile Clients EC2 Machines 5. Machine Learning How can a machine identify Bruce Willis vs Jason Statham? Bruce Willis??? 6. Machine Learning Machine Learning Artificial Intelligence Optimization & Control Neuroscience and Neural Networks Statistical Modeling Information Theory 7. Machine Learning Bear Eagle People Sunset 8. Machine Learning • Using machines to discover trends and patterns and compute mathematical predictive models based on factual past data • ML models provide insights into likely outcomes based on the past – machine learning helps uncover the probability of an outcome in the future rather than merely state what has already happened in the past • Past data and statistical modeling is used to make predictions based on probability Where traditional business analytics aims at answering questions about past events, machine learning aims at answering questions about the possibilities of future events 9. Machine Learning Supervised learning Human intervention and validation required Photo classification and tagging Unsupervised learning No human intervention required Auto-classification of documents based on context 10. Machine Learning – Process How can a machine identify Bruce Willis vs Jason Statham? Image analysis – Input feature set for image 1 - bald, black suit Bruce Willis??? 14. Machine Learning – Process • Start with data for which the answer is already known • Identify the target – what you want to predict from the data • Pick the variables/features that can be used to identify the patterns to predict the target • Train the ML model with the dataset for which you already know the target answer • Use the trained model to predict the target on the data for which the answer is not known • Evaluate the model for accuracy • Improve the model accuracy as needed 15. Machine Learning – When to Use It You need ML if • Simple classification rules are inadequate • Scalability is an issue with large number of datasets You do not need ML if • You can predict the answers by using simple rules and computations • You can program predetermined steps without needing any data driven learning 16.