Our early roadmap in 2024 has been largely focused on improving orchestration and compute infrastructure management capabilities. Last month we released a Resource Allocation Policy Management Control Center with a new, streamlined UI to help teams visualize their compute infrastructure and understand which users have access to what resources. We also enabled fractional GPU capabilities for all NVIDIA GPUs (old and new) in our open source version of ClearML available on GitHub, so now all self-hosted ClearML users can take advantage of GPU slicing and maximize the utilization of their hardware ..read more

It is a fairly recent trend for companies to amass GPU firepower to build their own AI computing infrastructure and support the growing number of compute requests. Many recent AI tools now enable data scientists to work on data, run experiments, and train models seamlessly with the ability to submit their jobs and monitor their progress. However, for many organizations with mature supercomputing capabilities, Slurm has been the scheduling tool of choice for managing computing clusters. In this blog, we will cover how ClearML works with Slurm and the benefits that ClearML delivers for organizat ..read more

The Challenge with LLMs With the explosion of generative AI tools available for providing information, making recommendations, or creating images, LLMs have captured the public imagination. Although we cannot expect an LLM to have all the information we want, or sometimes even include inaccurate information, consumer enthusiasm for using generative AI tools continues to build. When applied to a business scenario, however, the tolerance for models that provide incorrect or missing answers rapidly approaches 0%. We are quickly learning that broad, generic LLMs are not suitable for domain-specifi ..read more

If you’ve been following our news, you know we just announced free fractional GPU capabilities for open source users, enabling multi-tenancy for NVIDIA GPUs and allowing users to optimize their GPU utilization to support multiple AI workloads as part of our open source and free tier offering. With this latest open source release, you can now optimize your organization’s compute utilization by partitioning GPUs, run more efficient HPC and AI workloads, and get better ROI from your current AI infrastructure and GPU investments. Global enterprise AI projects are being slowed and stalled by legacy ..read more

Unveiling Future Landscapes, Key Insights, and Business Benchmarks In our latest research, conducted this year with AIIA and FuriosaAI, we wanted to know more about global AI Infrastructure plans, including respondents’: 1) Compute infrastructure growth plans 2) Current scheduling and compute solutions experience, and 3) Model and AI framework use and plans for 2024.  Read on to dive into key findings! Download the survey report now → Key Findings 1. 96% of companies plan to expand their AI compute capacity and investment with availability, cost, and infrastructure challenges weighin ..read more

Now you can create and manage your control plane on-prem or on-cloud, regardless of where your data and compute are. We recently announced extensive new orchestration,scheduling, and compute management capabilities for optimizing control of enterprise AI & ML. Machine learning and DevOps practitioners can now fully utilize GPUs for maximal usage with minimal costs. Now, with GPU fractioning/MIG offering, multiple workloads can share the compute power of a single GPU based on priorities, time slicing ratios, or upper hard limits. Global ML teams using these capabilities report they can bett ..read more

Adopting and deploying Generative AI within your organization is pivotal to driving innovation and outsmarting the competition while at the same time, creating efficiency, productivity, and sustainable growth. Acknowledging that AI adoption is not a one-size-fits-all process, each organization will have its unique set of use cases, challenges, objectives, and resources. The framework below tries to recognize this diversity and provides foundational pillars and key considerations to take into account when planning for effective adoption and deployment of Generative AI within your organization ..read more

This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit.  To view our code example, visit our GitHub page.   It’s easy to use ClearML handlers with the MONAI Toolkit to accelerate AI development for medical workflows.  For technical information on the integration, check out our documentat ..read more

In one of our recent blog posts, about six key predictions for Enterprise AI in 2024, we noted that while businesses will know which use cases they want to test, they likely won’t know  which ones will deliver ROI against their AI and ML investments. That’s problematic, because in our first survey this year, we found that 57% of respondents’ boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year, while 37% expect a single-digit increase.  That’s one of the reasons why critical prioritization is key to selecting the right use cases for implem ..read more

Large Language Models (LLMs) have now evolved to include capabilities that simplify and/or augment a wide range of jobs.  As enterprises consider wide-scale adoption of LLMs for use cases across their workforce or within applications, it’s important to note that while foundation models provide logic and the ability to understand commands, they lack the core knowledge of the business. That’s where fine-tuning becomes a critical step.  This blog explains how to attain the highest-performing LLM for your use case through the process of fine-tuning an available open source model with you ..read more