<h2>Overview</h2>
<p>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.</p>
<p>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.</p>
<h2>Features</h2>
<ul>
<li><strong>Graph-Based Representation</strong>: Offers a clear visualization of AI/ML workloads, highlighting compute, memory, and communication dependencies.</li>
<li><strong>Execution Traces</strong>: Captures key operations and resource constraints critical for analyzing AI performance.</li>
<li><strong>Interoperability</strong>: Seamlessly integrates with a variety of simulators, emulators, and replay tools to facilitate widespread use.</li>
<li><strong>MLCommons Collaboration</strong>: As part of an active research initiative, Chakra benefits from ongoing development and community contributions.</li>
<li><strong>Open Source</strong>: Released under the MIT license, allowing users to freely use and modify the codebase.</li>
<li><strong>Active Community</strong>: Encourages collaboration through open pull requests and contributions to enhance the toolset and features.</li>
<li><strong>Comprehensive Documentation</strong>: Provides detailed user guides and resources to aid in installation, usage, and contribute effectively.</li>
</ul>


Mlcommons

Chakra