Deep Learning consists of algorithms that permit software to train itself— by exposing multilayered neural networks to vast amounts of data. It is most frequently used to perform tasks like speech and image recognition.
The intelligence in the process sits within the deep learning software frameworks themselves— which develop that neural model of understanding by building weights and connections between many, many data points— often millions in a training data set.
Deep learning thrives when other traditional techniques to solving your problem fail: where you want derive insightful or complex relationships from vast data, custom programming is impossible, or on visual or auditory data.
Enterprise-ready software distribution built on open source
- Deploy in hours, not months through a binary download of the key open-source frameworks
- Available paid support
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Tools for ease of development
- Reduce data preparation time by an order of magnitude, with upcoming tools
- Automated hyper-parameter tuning & optimization to make your models faster and more accurate
- Learn more: PowerAI Technology Previews
Performance with faster training times
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- Run your own code or try one of the pre-packaged popular frameworks such as Caffe or TensorFlow. Find information about all of the components in PowerAI here.
- Frameworks are available built, installed, and configured ready to use immediately with GPUs or without.
- Don’t have an application but simply want to experience compelling industry use cases? The trial includes several cognitive journey demos to try out as Jupyter notebooks.
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The computer vision space is experiencing a dramatic technology shift, with a growing number of engineers using deep learning and convolutional neural networks to develop the next generation of computer vison applications. And when it comes to designing, training, evaluating and deploying neural networks, TensorFlow is stealing the show. Since its introduction two years ago,...
Continue reading Deep Learning for Computer Vision with TensorFlow
This blog provides steps and instructions that are required to bring up Data Science Experience Local on IBM Power Systems. DSX (Data Science Experience) is an interactive environment, which is useful for data scientists to collaborate on machine learning projects and solve the toughest data challenges with the best tools available in DSX like Rstudio,...
Continue reading Installation and bring up of Data Science Experience on IBM Power Systems
IBM Data Science Experience (DSX) Local and PowerAI are enterprise software offerings from IBM for data scientists, built with open source components and transparently accelerated with specialized hardware components such as GPUs in IBM Power Systems S822 LC HPC with GPU acceleration. In an earlier blog, I outlined some of the motivations and getting started steps...
Continue reading Deploy IBM Data Science Experience Local with PowerAI on IBM Cloud Private
As we start a new year in 2018, I feel very excited to see many innovative enterprises make agile forays into adopting Cognitive, AI, Machine Learning and Deep Learning techniques. Their agile adoption is enabled by open source tools such as Tensorflow, Caffe, Spark, Docker, Kubernetes etc. But I also see two technical inhibitors: Trusted...
Continue reading Accelerate open source with IBM Data Science Experience and PowerAI
IBM PowerAI 4.0 has been released with Large Model Support (LMS) in IBM Caffe. LMS uses system memory in conjunction with GPU memory to overcome GPU memory limitations in Deep Learning Training. LMS enables processing of high definition images, large models, and higher batch sizes that doesn’t fit in GPU memory today (Maximum GPU memory...
Continue reading Realizing the value of Large Model Support (LMS) with PowerAI IBM Caffe
From days to minutes. This is the time difference a data scientist can expect to save using IBM PowerAI Vision. One day, I was looking through one of our internal user groups and happened upon a group called Deep Learning. Within that group is a forum for sharing information and experiences on deep learning. I...
Continue reading IBM PowerAI Vision speeds transfer learning with greater accuracy: A real world example
IBM recently announced a technical preview of Distributed Deep Learning (DDL) for TensorFlow and Caffe in the IBM PowerAI 4.0.0 distribution. IBM Research has demonstrated close to ideal scaling with DDL software achieving record communication overhead and 95% scaling efficiency on the Caffe deep learning framework over 256 NVIDIA GPUs in 64 IBM Power systems....
Continue reading PowerAI Distributed Deep Learning with Tensorflow in NIMBIX
I’m excited to share with you that IBM has just released PowerAI release 4 which includes a technology preview of the record breaking Distributed Deep Learning technology we announced earlier this week. Drawing on IBM’s deep expertise in AI, in high-performance computing and system design, we have announced breakthrough results in both accuracy and performance...
Continue reading PowerAI Revolutionizes Deep Learning (Again!) with Release 4
The Search for Extra Terrestrial Intelligence (SETI) is a scientific endeavor with hopes of finding intelligent life forms in the cosmos. The SETI Institute is a non-profit research institute that was founded in 1984 in Mountain View, CA to specifically accomplish this mission. IBM and the SETI institute has partnered together for the past two...
Continue reading PowerAI accelerates the search for extra terrestrial intelligence
PowerAI makes deep learning, machine learning, and AI more accessible and more performant. By combining this software platform for deep learning with IBM Power Systems, enterprises can rapidly deploy a fully optimized and supported platform for machine learning with blazing performance. The PowerAI platform includes the most popular machine learning frameworks and their dependencies, and...
Continue reading IBM PowerAI: Machine Learning and Deep Learning frameworks on Power Systems
Even more blogs about PowerAI and deep learning on POWER
Containerize PowerAI with nvidia-docker
Published on August 31, 2017 / Updated on February 2, 2018
Learn how to build and run Dockerized deep learning analytics using PowerAI libraries on an IBM Power System S822 for High Performance Computing (“Minsky”) system with GPUs.
Deep Learning with High Resolution Images & Large Models
Published on December 12, 2017
Deep learning has had a profound impact on our ability to build highly accurate AI models. In the field of computer vision, we have gone from a 26% error rate of machine learning based models in 2011, to around 3% error rates using deep learning based computer vision. As a result, it is possible to see as well as humans on many vision tasks now.
IBM Research achieves record deep learning performance with new software technology
Published on August 8, 2017
IBM Research publishes in arXiv close to ideal scaling with new distributed deep learning software which achieved record communication overhead and 95% scaling efficiency on the Caffe deep learning framework over 256 NVIDIA GPUs in 64 IBM Power systems.
New PowerAI Developer Tools Make Deep Learning Easier to Use
Published on May 11, 2017
PowerAI started off as package of software distributions of many of the major deep learning software frameworks for model training like TensorFlow, Caffe, Torch, Theano, and the associated libraries like cuDNN. The PowerAI software has always been optimized for performance using the NVLink-based Power server, the IBM Power 822LC for HPC (“Minsky”).
New HPC and Deep Learning POWER8-Pascal GPU Cloud Available Now
Published on November 7, 2016
Nimbix, a high-performance computing (HPC) cloud provider, deployed a new cloud capability this week that features the extremely high-performance IBM servers that include the new POWER8 with NVIDIA NVLink processor and the new NVIDIA Tesla P100 Pascal GPU accelerator.
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