Inside Linux Foundation’s Vision for Open-Source Models
The Linux Foundation is at the forefront of redefining open-source models, especially in the domains of AI and cloud-native technologies. The foundation uses initiatives such as the Open Model Initiative (OMI) and the Model Openness Framework (MOF) to tackle concerns related to proprietary AI models by encouraging innovation and collaboration. In fact, this is particularly significant for developers who are transitioning into a fast-paced technological landscape. The transition of projects like Kubernetes from proprietary control to community-driven governance illustrates the Linux Foundation’s efforts. During the KubeCon + CloudNativeCon India 2024 in Delhi, Chris Aniszczyk, CTO of Cloud Native Computing Foundation (CNCF), said, “It takes time for a project to move from single-vendor control to a truly multi-community model.” Taking PyTorch as an example, Aniszczyk noted that while Meta was once the sole contributor, external contributions now account for 33% of its development. Notably, over 530 organisations are involved. This change reflects a bigger trend in open source, where projects, initially controlled by single vendors, gradually open up to diverse contributors. “Kubernetes recently transitioned to a fully multi-cloud infrastructure, moving away from being tied exclusively to GCP. This shift required significant effort but ultimately made the project more resilient and inclusive,” Aniszczyk added. Addressing Data and Model Openness One critical challenge in AI is ensuring openness while navigating proprietary constraints. Arpit Joshipura, SVP/GM at the Linux Foundation, stressed in his keynote, “Every disruption starts proprietary but scales with open source.” He pointed out that the Linux Foundation is addressing two key issues: opening up large language models (LLMs) and datasets. To simplify data sharing, the foundation promotes licenses like the Community Data License Agreement (CDLA). “CDLA ensures that developers can use shared datasets without legal concerns. It’s about creating a governance framework that fosters collaboration,” Joshipura further said. The Model Openness Framework (MOF), developed by LF AI & Data Foundation, adopts a spectrum-based approach to openness. It evaluates models on levels of openness along dimensions of data accessibility and licensing flexibility. While highlighting the growing trend of domain-specific AI models, Joshipura said, “For telecommunications or finance, you don’t need large LLMs; small fine-tuned models are sufficient.” These smaller models are easier to train and deploy while being cost-effective. This approach is similar to the evolution of cloud-native systems from centralised architectures to edge computing. Lessons from Cloud Native for AI The Linux Foundation is using lessons from cloud-native infrastructures to make AI systems more scalable and reliable. “Cloud-native architectures started with centralised data centres but have since evolved to edge computing and domain-specific applications. AI is following a similar trajectory,” Joshipura added. He noted that domain-specific AI models are becoming increasingly popular because they require less computational power than general-purpose LLMs. The Unified Acceleration Foundation (UXL) was extensively discussed at the KubeCon. This initiative aims to create a multi-vendor ecosystem for running AI workloads on Kubernetes without vendor lock-in. It reflects the broader push toward interoperability and flexibility in AI deployment. Cloud-native principles are proving invaluable for deploying AI workloads. Kubernetes, for example, allows developers to package AI models into containers for seamless deployment across environments from on-premises data centres to public clouds. According to the CNCF, cloud-native technologies enable modular and resilient environments ideal for scaling AI workloads. The Role of DevRel in an AI-Driven World As AI transforms software development workflows, DevRel (developer relations) strategies are becoming crucial. “AI tools can improve developer productivity by up to 30%, but the winners will be those who learn how to use these tools effectively,” Joshipura said. He stressed that developers must adapt by acquiring skills like prompt engineering to remain competitive. However, concerns about restrictive licensing in open-source projects have sparked debates within developer communities. Joshipura assured attendees at KubeCon that CNCF remains committed to maintaining truly open projects. “When restrictive licenses emerge, we see community-driven forks thrive under open licenses – examples include OpenTofu and OpenBao.” India’s developer community is already among the largest contributors to CNCF projects like Kubernetes. “India is the fourth-largest contributor to CNCF projects globally. With LF India, we anticipate even more innovation coming out of this region,” Aniszczyk pointed out. LF India aims to support local developers through training programs and events while focusing on domain-specific AI technologies across verticals like telecommunications and finance.
The Linux Foundation is using lessons from cloud-native infrastructures to make AI systems more scalable and reliable.
The Linux Foundation is using lessons from cloud-native infrastructures to make AI systems more scalable and reliable.
["Linux Foundation", "Open Source", "Open Source LLM"]
["GCP", "Open Source", "AI", "PyTorch", "ML", "RAG", "Open Source LLM", "Aim", "prompt engineering", "edge computing", "Linux Foundation", "R", "kubernetes"]
["AI", "ML", "Aim", "PyTorch", "RAG", "prompt engineering", "GCP", "kubernetes", "edge computing", "R"]
A software engineer who loves to experiment with new-gen AI. He also happens to love testing hardware and sometimes they crash. While reviving his crashed system, you can find him reading literature, manga, or watering plants.