Whether pre-installed or downloaded from the cloud, deep learning frameworks are key to executing AI workloads effectively – however many of the associated data science tasks required can be time consuming and impede progress. For example prior to deep learning, any data must be verified, resized and labelled – it may also be required to transfer the data between programs to do this.
You can learn more about data preparation for in our deep learning beginners guide.
Recognising this, Scan has teamed up with a number of partners to offer software packages that either aid provisioning of your GPUs for better utilisation, automate many of the data science processes, or deliver visual insight into what the data you have means. Adding software like these into your ecosystem not only reduces time to results, but minimises reliance on expensive data scientist resource.
NVIDIA Virtual Compute Server (vCS) software virtualises NVIDIA GPUs to accelerate large workloads, including more than 600 GPU accelerated applications for AI, deep learning, and HPC. With GPU sharing, multiple VMs can be powered by a single GPU, maximising utilisation and affordability, or a single VM can be powered by multiple virtual GPUs, making even the most intensive workloads possible. And with support for nearly all major hypervisor virtualisation platforms, datacentre admins can use the same management tools for their GPU-accelerated servers as they do for the rest of their datacentre.Learn more
Current powerful AI systems often feature multiple GPU acceleration cards and deliver enormous performance and workload throughput capabilities, but usually only for a single user per physical system. Virtualisation allows you to pool these resources in order to gain greater control and visibility. At each stage of the deep learning process, data scientists have specific needs for their compute resources.
The Run:AI software platform decouples data science workloads from the underlying hardware - regardless of what GPU hardware you have. By pooling resources and applying an advanced scheduling mechanism to data science workflows, Run:AI greatly increases the ability to fully utilise all available resources, essentially creating unlimited compute. Data scientists can increase the number of experiments they run, speed time to results, and ultimately meet the business goals of their AI initiatives.
The OmniSci platform is designed to overcome the scalability and performance limitations of legacy analytics tools faced with the scale, velocity and location attributes of today’s big datasets. Those tools are collapsing, becoming too slow and too hardware-intensive to be effective in big data analytics. OmniSci is a breakthrough technology, designed to leverage the massively parallel processing of GPUs alongside traditional CPU compute, for extraordinary performance at scale.Find out more
H2Oai’s Driverless AI platform, is now fully integrated on the NVIDIA DGX systems, and is available as part of your proof-of-concept with Scan. It allows business users, analysts and data scientists use an incredibly fast, intuitive, integrated computing platform. Customers can apply Automatic Feature Engineering and quickly develop hundreds of machine learning models to help your business mitigate risks and maximise revenue potential.
In addition to our software solutions, the Scan AI team can also provide consultative services around data science, infrastructure monitoring and system health checks.Learn more