Whether your goal is to reduce the complexity of using a massive HPC cluster, or to simplify the development and deployment of an AI application, LiCO provides a simplified, intuitive interface that puts your cluster resources at your fingertips. From academic research to enterprise data centers, LiCO accelerates AI by eliminating complexity for non-technical users.
LiCO accelerates Artificial Intelligence training and traditional High Performance Computing deployment by providing an intuitive user interface to manage your software and hardware stack.
Supporting an extensive list of AI framework and open-source software, LiCO eliminates configuration complexity for users, reducing the time spent on setup and management of infrastructure. Optimized for the Lenovo ThinkSystem portfolio and supporting the latest Intel processor architecture and NVIDIA GPUs, LiCO puts powerful resources in the hands of researchers and data scientists.
The power of integration
By integrating open HPC cluster management software with popular AI frameworks & libraries, LiCO gives users command over system resources, and user jobs. With the LiCO platform, users can easily perform distributed AI training with optimal scaling & efficiency, generating results faster.
Built on an OpenHPC platform, LiCO consolidates powerful cluster administration tools for managing users, resources & workloads into a single comprehensive interface for both users & administrators, allowing for easy management of your HPC infrastructure.
LiCO’s pre-built workflows allow users to easily submit jobs on MPI, ANSYS and COMSOL applications and stores the job for future reuse.
Driving AI results
LiCO accelerates Deep Learning (DL) and Machine Learning (ML) with integrated support for the most popular AI libraries and frameworks, such as Tensorflow, MXNet and Caffe. Simple workflows, pre-trained models and job control allow Data Scientist and Researchers to quickly develop and iterate ML and DL models. LiCO automatically runs testing and validation once your training is complete, and even show you the performance of your model while it is in progress.
Efficiently manages workflows for Deep Learning and Machine Learning
Easy-to-use GUI & templates to simplify job management
Leverage pre-trained models, monitor training & manage job history for efficient training
Multi-user support & intuitive GUI
Flexibility with cluster infrastructure
Supports both NVIDIA GPUs and Intel processors to suit varying workloads
Simplifies open source deployment in enterprises
Validated stacks reduce time-to-development
Choice of multiple AI frameworks through containers
Optimally employs hardware resources for better TCO
Improve scaling efficiency for distributed model training
Manage cluster resources across multiple jobs and multiple users
LiCO is available in the Lenovo AI Innovation Labs around the world, allowing you to test your proof of concept before deploying. The Innovation labs feature Lenovo system and software resources, technical staff and AI experts, as well as experts from our partners, available to help you.
Already have a Lenovo Thinksystem HPC or AI cluster? Contact your Lenovo representative today to learn how you can implement LiCO into your data center.
NCSU: Solving world hunger with LiCO
With worldwide population expected to nearly double over the next 30 years, and global food supplies already constrained by unpredictable weather and natural disasters, NCSU is working on agricultural solutions using AI and LiCO.
LiCO puts the power of high-performance, scalable AI infrastructure at the fingertips of researchers & scientist who are using machine learning & AI to provide better insights for food production, guide policy and secure food supply for future generations. Non-technical researchers can submit and manage AI jobs in a saleable AI cluster without the complexity of command line interfaces in multiple applications. Watch the video to learn more about North Carolina State University research powered by LiCO.
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Software and accessories will be shipped separately and may have a different estimated ship date.