DNN Training Appliance

GIGABYTE's DNN Training Appliance combines powerful computing performance together with a user-friendly GUI, providing DNN developers an easy to use environment to conduct dataset management, training jobs management, real time system environment monitoring and model analysis.
Introduction
GIGABYTE's DNN Training Appliance is a fully integrated turnkey appliance, combining a cost efficient off the shelf hardware stack with a full software stack that includes Linux OS, Deep Learning libraries such as DIGITS, NCCL, cDNN and CUDA, Deep Learning frameworks such as Caffe & Tensorflow, together with a web-browser based GUI for DNN training job management and management. The appliance also includes powerful hardware and software optimization features that help to reduce DNN training job time and improve accuracy.
Use Case Scenarios
Pattern Recognition
A hospital can generate hundreds if not thousands of X-ray images a day, which currently need to be read by a radiologist. The DNN Training Appliance can be used to train and implement pattern recognition that could be used in the medical field to automatically detect and flag abnormalities from X-ray images, reducing the strain on staff resources and shortening reaction times.
Image Recognition
The DNN Training Appliance can be used to train algorithms for image recognition - such as for people, cars or other objects, which can be used for an intelligent video analytics platform.
Object Detection
The DNN Training Appliance can be used to train an algorithm in object detection, which can be used in applications that implement autonomous vehicle or movement technology - such as self driving cars, robots or drones.
Providing Developers and Data Scientists the Following Benefits
suitable to Time saving
Saves Time
All in one package eliminates the painful process of setting up your own hardware & software environment
suitable to Reduce Expenses, Money Saving, Reduces Cost
Saves Money
Achieves maximum utilization of your hardware investment with powerful optimization features
suitable to User Friendly, Ease of Use& Lower maintenance requirement
Ease of Use
Shortens the learning curve for developers unfamiliar with setting up a DNN training environment; spend less time and resources on employee training
suitable to Flexibility, Scalability, production capacity
Flexible Choice of Standard or Customized Solutions
The standard version is enabled for image classification and object detection, or talk to us about a customized solution for model / application type
Reduces the Complexity & Cost of DNN Training Environment Setup
To generate a production grade DNN model, a developer will need to go through many difficult and time consuming steps, including dataset collection, dataset cleansing, dataset labeling, dataset augmentation, dataset format conversion, DNN model selection, model design, hyperparameters tuning, model training, model evaluation, and model format conversion. Each step requires different tools and configurations that require time and effort for preparation.

GIGABYTE's DNN Training Appliance aims to reduce this complexity by providing a complete training and management platform, incorporating all these processes into a single appliance enabled with a web-browser based GUI. Users can import, convert and manage their dataset; design, train and evaluate different DNN models; and test inferencing of their trained models. Built on GIGABYTE's G481-HA1 server, the Appliance is fully optimized to use the bare metal resources available to deliver maximum training performance on cost efficient hardware.
DNN Training Appliance Hardware and Software Stack
Reduces the Time and Improves the Accuracy for Each DNN Training Job
DNN models need to be trained on a large dataset to achieve an acceptable level of accuracy. Depending on the dataset size, this training could take days or even weeks. And in order to adapt to the latest business circumstances or situations (such as new products, new regulations, etc.), the DNN model needs to be periodically retrained through the latest datasets. If running a DNN training job takes too long, it will have a serious impact on an organization's operations, resource management and competiveness.

GIGABYTE's DNN Training Appliance helps to reduce training time by incorporating many different optimization features, such as GPU memory optimization to allow larger size of training input or fit larger DNN models into GPU memory, automatic hyperparameters tuning during a training job to achieve higher accuracy, and dataset cleaning features to reduce the training time by removing by mislabeled or duplicate training data.
DNN Training Appliance Main Dashboard View
EFFORTLESS TRAINING MANAGEMENT AND MONITORING
Project-Based Training Management
GIGABYTE's DNN Training Appliance manages the training process by project grouping. Create a new project to import datasets and run training jobs via the web portal GUI. Within each project, quickly and easily keep track of your model training history, including hyperparameter modification, each training job result and the trained model from each job.
Project View Dashboard
Training Wizard & Automatic Optimization
GIGABYTE's DNN Training Appliance features a step by step wizard, guiding the user on how to train different types of models (for image classification, object detection etc.). This wizard provides different datasets, DNN models and network and hyperparameter settings according to the DNN application type. The Appliance also includes many powerful optimization features (such as memory optimization, automatic hyperparameters tuning and mixed precision training) that are "one-click" enabled.
Create and run a new DNN training job with the training wizard
Real-Time Monitoring & Quick Result Verification
Once training starts, keep track of training progress in real-time via the training monitoring interface.
Training Result
After each training job is completed, quickly verify your DNN model with the inferencing validation feature.
Inference Testing
Intuitive Editor & Setting Interface
The user can easily create a new training job based on the result output of the previous job, by editing hyperparameters, editing the DNN model architecture, changing the dataset or adjusting the number of GPUs utilized for the training job.
DNN Training Model (Network) Editor
EFFECTIVE DATASET MANAGEMENT TOOLS
Dataset Augmentation
A common problem for DNN training is the lack of good quality datasets or an uneven balance of dataset classifications. The Dataset Augmentation function provides a way to overcome these problems by enhancing your existing datasets, supporting standard or custom augmentations like randomly flipping images left or right, or randomly distorting the color of images to generate more variations of a certain image.
Dataset Augmentation
Dataset Import and Conversion
The platform supports multiple dataset formats (such as Cifar10, KITTI, COCO, ChestXray, etc.), and automatically converts the raw data into the dataset format of the deep learning framework that will be used.
Dataset Error Detection & Cleaning
By using model analysis features like the confusion matrix, the user can find suspect data in their dataset. The GUI can then be used for data re-labelling, deletion or duplication accordingly. The easy to use interface saves users time when cleaning up their data, and allows datasets to be managed quickly and easily.
POWERFUL OPTIMIZATION FEATURES
GPU Memory Optimization
The GPU memory optimization feature enhances GPU memory utilization performance during DNN training. This optimization allows for larger image batch sizes to be used during image classification training, reducing the total training time required based on a certain size dataset. This feature can be of particular benefit when larger DNN model sizes or GPUs with smaller memory capacities are used, and reduces the occurrence of GPU OOM (Out of Memory) errors.
GPU Memory Optimization feature demonstration
Automatic Hyperparameters Tuning
It takes a lot of time and effort to test and find a proper set of hyperparameters which can optimize the training accuracy of a DNN model. GIGABYTE's DNN Training Appliance includes a tool that can automatically discover optimal hyperparameters settings (such as batch size, learning rate, learning rate gamma, learning rate step) during a training job to achieve the most efficient time / accuracy ratio.
INTELLIGENT ENVIRONMENT MONITORING
GPU Thermal Aware Management
GIGABYTE's DNN Training Appliance features a real-time GPU monitoring feature (for GPU utilization, memory usage and temperature), including a protection mechanism that will automatically adjust a training job in process when the temperature of a GPU rises over a certain threshold.
OPTIMIZED HARDWARE PLATFORM
Single-Root GPU Server
GIGABYTE's DNN Training Appliance is built with G481-HA1, a server optimized for a single cluster DNN training appliance by employing a single root GPU system architecture. Since DNN training requires frequent communication between each GPU in the system, utilizing a single-root architecture (all GPUs can communicate via the same CPU root) helps reduce GPU to GPU latency and decrease DNN training job time.
Build your AI innovations ever faster and simpler
GIGABYTE Servers as the Hardware Base of DNN
1/2
DNN Training Appliance 1
G481-S80 (rev. 100/200)
NVLink 8 x SXM2 V100 GPGPU Server
2/2
DNN Training Appliance 2
G481-HA1 (rev. 100)
Single Root 8 / 10 x PCIe GPU Server
Related Technologies
Machine Learning
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence.
Inference Engine
In the field of Artificial Intelligence, inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The inference engine applies logical rules to the knowledge base and deduced new knowledge typically represented as IF-THEN rules.
Artificial Intelligence
Artificial Intelligence (AI) is a broad branch of computer science. The goal of AI is to create machines that can function intelligently and independently, and that can work and react the same way as humans. To build these abilities, machines and the software & applications that enable them need to derive their intelligence in the same way that humans do – by retaining information and becoming smarter over time. AI is not a new concept – the idea has been in discussion since the 1950s – but it has only become technically feasible to develop and deploy into the real world relatively recently due to advances in technology – such as our ability to now collect and store huge amounts of data that are required for machine learning, and also the rapid increases in processing speeds and computing capabilities which make it possible to process the data collected to train a machine / application and make it "smarter".
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