Chakra

Overview

Chakra is an innovative framework designed to streamline the representation and execution of AI and ML workloads. By providing a graph-based structure, Chakra enhances the co-design of software and hardware, allowing for more efficient and effective development processes. Its focus on interoperability means that it can be integrated with various tools and environments, making it a versatile choice for researchers and practitioners working in AI.

As an active project under MLCommons®, Chakra is continually evolving to meet the needs of its users. It offers a wealth of features, including detailed execution traces that capture critical operational data, which can be leveraged by a range of simulators and emulators. Whether you are a developer looking to optimize your AI applications or a researcher exploring new methodologies, Chakra provides valuable capabilities to support your efforts.

Features

• Graph-Based Representation: Offers a clear visualization of AI/ML workloads, highlighting compute, memory, and communication dependencies.
• Execution Traces: Captures key operations and resource constraints critical for analyzing AI performance.
• Interoperability: Seamlessly integrates with a variety of simulators, emulators, and replay tools to facilitate widespread use.
• MLCommons Collaboration: As part of an active research initiative, Chakra benefits from ongoing development and community contributions.
• Open Source: Released under the MIT license, allowing users to freely use and modify the codebase.
• Active Community: Encourages collaboration through open pull requests and contributions to enhance the toolset and features.
• Comprehensive Documentation: Provides detailed user guides and resources to aid in installation, usage, and contribute effectively.