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What is deep learning?
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.
Why use deep learning?
Deep learning is the fastest growing subcategory of machine learning and uses software neural networks as a way to help develop patterns of analysis within the system to generate predictive capability. Deep learning is a platform that is capable of effectively learning how to learn, and it is immensely powerful for helping clients get the most out of their data.
Deep learning thrives when other traditional techniques for solving your problems fail: when you to want derive insightful or complex relationships from vast amounts of data, custom programming is impossible, or on visual or auditory data.
Deep learning does require a larger data set in order to produce results commensurate with machine learning techniques. However, the amount of interpretive work done by data scientists is minimized in deep learning scenarios. Enter IBM PowerAI, Watson Machine Learning Accelerator (formerly know as PowerAI Enterprise) and PowerAI Vision.
PowerAI is an enterprise software distribution that combines popular open source deep learning frameworks, efficient AI development tools, and accelerated IBMÂ® Power Systemsâ„˘ servers to take your deep learning projects to the next level.
PowerAI includes industry leading open source frameworks and can support up to four nodes.
WML Accelerator includes the open frameworks, libraries, and tools built into PowerAI plus additional components such as IBM Spectrum Conductorâ„˘ Deep Learning Impact and IBM Spectrum Conductor. WML Accelerator can scale from a single node to 100’s of nodes.
PowerAI Vision includes tools and interfaces for anyone with limited skills in deep learning technologies. You can use PowerAI Vision to easily label images and videos that can be used to train and validate a model. The model can then be validated and deployed in customized solutions that demand image classification and object detection.
Intro to PowerAI, Watson ML Accelerator, and PowerAI Vision
The PowerAI product line includes three different options:
Open source software well suited to developers and data scientists just getting started with their development efforts and prototypes.
Watson ML Accelerator
Fully supported suite of deep learning frameworks intended for Enterprises looking to rapidly scale their AI applications.
Fully supported enterprise-grade suite of tools for labeling raw datasets for training, creating, and deploying deep learning-based vision models.
Continue reading for information about each of the different offerings.
Develop with PowerAI
PowerAI is an open source software distribution of popular open source deep learning frameworks, such as, Tensorflow and Caffe.
- Deploy in hours, not months, through a binary download of the key open source frameworks
- Available paid support
Performance for faster training times
- Faster training times and incredible cluster scaling efficiency (up to 56X and 95%, demonstrated). Learn more by reading this blog, Scaling TensorFlow and Caffe to 256 GPUs
- Large model support, enabling you to use higher resolution data
- Check out all of the Machine Learning and Deep Learning performance proof-points on IBM Power Systems
Tools for ease of development
- Reduce data preparation time by an order of magnitude, with upcoming tools
- Automated hyper-parameter tuning and optimization to make your models faster and more accurate
PowerAI includes all necessary dependencies and removes the time, effort, and difficulty associated with getting a deep learning environment operational and performing optimally.
Deploy with Watson ML Accelerator (formerly PowerAI Enterprise)
For enterprises looking to rapidly scale their deep-learning applications, Watson ML Accelerator combines the PowerAI features above with additional functionality to optimize and speed up the completion of your training, testing, and validation. Watson ML Accelerator truly shines when you are looking to expand into distributed deep learning with more than four nodes.
Analyze images and video with PowerAI Vision
PowerAI Vision provides tools and interfaces for business analysts, subject matter experts, and developers without any skills in deep learning technologies to begin using deep learning. This enterprise-grade software provides a complete ecosystem to label raw data sets for training, creating, and deploying deep learning-based models. It can help train highly accurate models to classify images and detect objects in images and videos.
The tools assist users to focus on rapidly identifying datasets and labeling them. They can then train and validate a model in a GUI interface to build customized solutions for image classification and object detection.
PowerAI helps you get started faster with your deep learning development. Here are some tips for using PowerAI to add deep learning and AI to your application.
Ready your tools
Follow these simple steps to get your PowerAI-based application development started.
PowerAI deploys on a system far more rapidly than manual installation of frameworks. Start with:
- Red Hat Enterprise Linux (RHEL) 7.6 or Ubuntu 18.04
- NVIDIA CUDA SDK
- NVIDIA GPU Driver for Linux
The PowerAI binaries are available as Conda packages and run on IBM Power Systems S822LC and AC922. See the PowerAI release notes for more information.
Test your frameworks
Once youâ€™ve deployed PowerAI, you can test each of the deep learning training frameworks. Each framework included in PowerAI is unique and selecting a preferred framework for your application is important.
The integrated installer for PowerAI means you have everything installed and performant, so you can rapidly try examples in each and select suited to your preferences.
Devise your approach and start training
Collecting great input data to train on is critical to your modelâ€™s success. Donâ€™t neglect existing data inside your organization or consider training on external datasets. Your data can be visual, audio, text, or beyond.
Packages like TensorFlow in PowerAI incorporate tools to help make your training network design even easier.
Ideas to get you started
Here are some recommendations for how to add deep learning to your applications.
- Layer Deep Learning atop your existing data-store
Tease out value from your existing data by applying deep learning as a technique for advanced analysis.
- Reshape or augment an existing business process
Augment human insight or manual labor with machine intelligence. Use deep learning to train a visual or audio recognition system that helps guide decisions.
- Apply deep learning before HPC simulation
Improve the quality of your HPC simulation runs by using deep learning to identify which kinds of simulations to run or run first. Then run those high-likelihood simulations with greater precision.
- Apply deep learning after HPC simulation
Drowning in data after your HPC simulations run? Sift through existing unstructured data or vast outputs of a simulation with Deep Learning and gain new insights rapidly.
If you’re already working on other hardware…
- Use PowerAI to get started faster, with all of the performance benefits of IBM Power
- Use PowerAI to compare frameworks
PowerAI and deep learning blogs
Read what the experts are saying about deep learning with IBM PowerAI and IBM PowerAI vision.
Introduction In PowerAI 1.6, the TensorFlow Large Model Support (TFLMS) module has a new implementation and has graduated from tech preview status. This new implementation can achieve much higher levels of swapping which in turn, can provide training and inferencing with higher resolution data, deeper models, and larger batch sizes. In this article, we investigated...
In PowerAI 1.6, the TensorFlow Large Model Support (TFLMS) module has a new implementation and has graduated from tech preview status. This new implementation can achieve much higher levels of swapping which in turn can provide training and inferencing with higher resolution data, deeper models, and larger batch sizes. For a review of TFLMS and...
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