GPU Appliance for Artificial Intelligence
#whenperformancematters
Artificial Intelligence is no longer just a science fiction topic, it now pervades our daily lives
Cutting edge companies already use Machine Learning. The integration of human capabilities with new intelligent systems is making work processes more agile and accurate: thanks to new Artificial Intelligence applications, your business can now evolve rapidly and anticipate changes in the market
Big Data is the basis of this revolution: GAIA is a concentration of computing power to process Big Data using complex mathematical models
The technologies for the management and processing of this data require extensive computing resources and often turn out to be complex and hard to access and configure
Why GAIA?
“In Greek mythology, Gaia (/ ˈɡaɪə, ˈɡeɪə / GHY-ə, GAY-ə; from the mythical Greek Γαῖα, a poetic form of Γῆ Gē,” earth “),
is the personification of the Earth and one of the primordial Greek divinities.
Gaia is the ancestral mother of all life: the primitive goddess of Mother Earth”
Wikipedia – Gaia
GAIA is the platform which enables you to implement Artificial Intelligence solutions in your company and reduce time to market. The challenge is that AI requires enormous processing capacity near the storage areas for the data to be analyzed and a work environment designed to increase the productivity of the Data Scientist to the utmost. The delicate work of cleaning and transformation of raw data will be up to him, as well as the definition and refining of the Data Models, the information to make useful to those not directly involved. GAIA is the computing power required to “democratize” access to modern Artificial Intelligence technologies!
MULTI GPU IN A BOX
GAIA is the ideal solution for training of the most complex Data Models. It can integrate up to 8 GPUs (each equipped with at least 16 GB of dedicated RAM), which can be used simultaneously to execute the individual workload, thanks to the NVlink interconnection
CONTAINERIZED DEVELOPMENT ENVIRONMENT
The development environments are isolated in containers. It is possible to assign computing resources to each of the omputing resource fractions to share them in an agile manner within a work group. GAIA is designed to maintain more than one version of the same development environment online, thus overcoming all the management difficulties related to the rapid progress of the ML & DL frameworks
EASY
GAIA’s computing power is accessible from a multi-user web interface based on Notebooks. This permits the integration of the data analysis code with descriptive text, graphical visualization and multimedia content
FLEXIBLE
If you don’t love to work with Notebooks, the various development environments in GAIA are also accessible from Microsoft Visual Studio Code, an open source IDE which offers the working mode typical of Microsoft development tools
The Artificial Intelligence appliance engineered by E4 exploits the power of parallel computing performed on GPUs to ensure extremely reduced execution times and flexibility in the execution of the workload performed on containerized tools.
Our engineers have designed a platform that integrates a multicomponent software stack in a powerful multi-GPU server equipped with large all-flash storage. The stack is equipped with the best open source framewoks for data analysis on a large scale, Machine Learning and Deep Learning, configured to use all the hardware resources of the server at the highest efficiency possible.
The result is a solution, an Appliance which is ready for use for development, training and testing of complex Data Models. These turn out to be indispensable to introduce intelligence in very many areas: from Natural Language Processing to Smart Medical Imaging, from forecasting of future demand of a portfolio of products to the recognition of objects in real time in a video sequence, to name just a few. The E4 AI Appliance is available in 3 versions with various resource sizes: Silver, Gold and Platinum
Discover the advantages
What is Artificial Intelligence
Artificial Intelligence is no longer just a science fiction topic, it now pervades our daily lives: software that advises us on purchases interpreting our needs, production lines capable of detecting the earliest signs of a malfunction and directing maintenance programs, conversation systems which emulate human dialogue and respond to our questions. The companies in the forefront already use Machine Learning to accelerate innovation and give an unprecedented boost to growth: the integration of human abilities with new intelligent systems is making work processes more agile and accurate: thanks to AI, business can now evolve rapidly to anticipate market changes.
How GAIA works with complex data models
GAIA is a platform designed for high performance, scalable Machine Learning and Deep Learning workloads. Thanks to a careful selection of Hardware components and the integration of a Hardware Stack configured to best use the underlying hardware resources, GAIA is the ready to use resource to implement burdensome and complex Data Models, the only ones capable of extracting value from the enormous volume of data available today in a wide variety of use cases.
GAIA runs the Ubuntu Linux Server 18.04 operating system and includes an interactive web-based (JupyterLab Notebook) and multiuser (thanks to JupyterHub) development environment to support a vast variety of workflows in the areas of Data Science, Machine Learning and Deep Learning.
After the authentication phase, the user can select the development environment to instantiate as a function of the programming language (Python, R, Julia) and/or of the Data Analytics/Machine Learning frameworks to be used (nVidia Rapids, Scikit-Learn, Tensorflow, Keras, pyTorch, Caffe2, etc.). The instantiated development environment always resides in a container to ensure both the highest performance in the use of computing resources and access to different versions of the development environment itself on the same system. The images of the Containers used by GAIA are developed and maintained by the E4 team of Data Scientists with the goal of providing a robust development environment to the customer which is able to use the CPU and GPU present in the system in the best manner possible.
In addition to through the Jupyter Notebook interface, the containers which implement the various high performance development environments can be used within Microsoft Visual Studio Code and in command line type interactive sessions.
