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+date = "2024-11-11T08:00:00+00:00"
+author = "Athan Reines"
+title = "CZI EOSS 6 Award to Advance Array Interoperability within the PyData Ecosystem"
+tags = ["APIs", "standard", "consortium", "arrays", "community", "funding", "czi", "eoss6"]
+categories = ["Consortium", "Standardization"]
+description = "The Chan Zuckerberg Initiative (CZI) awarded an EOSS Cycle 6 grant to the Data APIs Consortium to advance array interoperability within the PyData ecosystem."
+draft = false
+weight = 30
++++
+
+We are thrilled to announce that the Chan Zuckerberg Initiative (CZI) recently
+awarded the Consortium for Python Data API Standards an Essential Open Source
+Software for Science(EOSS) Cycle 6 grant to support ongoing work within the
+Consortium and to accelerate the adoption of the Array API Standard across the
+PyData ecosystem. With this award, we'll drive forward our vision of
+standardizing a universal API for array operations, enhancing library
+interoperability, and increasing accessibility to high-performance
+computational resources across scientific domains.
+
+## The Importance of the EOSS Program
+
+The EOSS program by CZI was launched to support open source software that is
+foundational for scientific research, especially within biology and medicine.
+As software tools underpin modern scientific investigation, ensuring these
+tools receive adequate funding is crucial for sustainable growth and long-term
+impact. Through EOSS, CZI has committed to funding development, usability
+improvements, community engagement, and maintenance efforts for critical open
+source tools. This support enables open source software to be more accessible,
+reliable, and adaptable to researchers' evolving needs.
+
+With the EOSS Cycle 6 award, Quansight, in cooperation with collaborators within
+the Consortium and the broader ecosystem, will focus on advancing
+interoperability, improving ease of Array API adoption, and reducing array
+library fragmentation within the PyData ecosystem.
+
+## Addressing Fragmentation in the PyData Ecosystem
+
+As Python's popularity has grown, so has the number of frameworks and libraries
+for numerical computing, data science, and machine learning. Researchers and
+data science practitioners now have access to a vast suite of tools and
+libraries for computation, but this diversity comes with the challenge of
+fragmented APIs for fundamental data structures such as multidimensional
+arrays. While array libraries largely follow similar paradigms, their API
+differences present a real challenge for users who need to switch between or
+integrate multiple libraries in their workflows.
+
+The Consortium for Python Data API Standards, founded in 2020, addresses this
+issue directly. By standardizing a universal array API, the Consortium seeks to
+simplify the process for users moving between libraries and foster an ecosystem
+where array operations are seamless across libraries such as NumPy, CuPy,
+PyTorch, and JAX. To date, the Array API Standard has seen adoption by major
+libraries, laying the groundwork for an interoperable PyData ecosystem that
+emphasizes compatibility and ease of use.
+
+If you're curious to learn more about the Consortium, its origins, and the
+benefits of standardization, be sure to read our 2023 SciPy Proceedings paper
+["Python Array API Standard: Toward Array Interoperability in the Scientific
+Python Ecosystem"](https://proceedings.scipy.org/articles/gerudo-f2bc6f59-001).
+
+## Scope of Work for the EOSS 6 Award
+
+The EOSS 6 award will help the Consortium focus on key initiatives to expand
+adoption and improve compatibility across the ecosystem. The proposed work
+includes:
+
+### Array API Adoption in Downstream Libraries
+
+One of our primary goals is to further adoption of the Array API Standard in
+downstream libraries, such as SciPy, scikit-learn, and scikit-image.
+Historically, many downstream libraries have been dependent on NumPy, thus
+limiting their execution model to CPU-bound computation and thus their ability
+to leverage the performance advantages of GPU- or TPU-based computation. By
+adopting the Standard, downstream libraries will be able to support array
+libraries such as CuPy and PyTorch, empowering researchers to take advantage of
+the hardware acceleration options suitable to their needs.
+
+### Infrastructure for Adoption and Compliance Tracking
+
+We're also committed to building infrastructure to monitor compliance and
+adoption of the Array API across the ecosystem. While we have already developed
+a [test suite](https://github.com/data-apis/array-api-tests) to measure
+compliance for array libraries, this tool has been largely developer-facing,
+leaving end users with limited visibility into compatibility across different
+libraries. To address this gap, we will create public mechanisms, such as
+compatibility tables, for tracking which libraries are adopting the Standard
+and helping users make informed decisions about which libraries to use.
+
+Additionally, we plan to develop mechanisms for automating compliance tracking
+within array library continuous integration (CI) workflows, allowing real-time
+monitoring of adoption and compatibility regressions. This infrastructure will
+hopefully instill greater confidence among end users in array library
+compatibility and help array library developers maintain interoperability.
+
+### Comprehensive Documentation and Migration Guides
+
+As adoption grows, we recognize the need for high-quality documentation and
+migration guides to help users and developers transition seamlessly to using
+the Array API Standard. Through our collaborations with library maintainers,
+we've gathered insights into best practices for building array library-agnostic
+applications. With EOSS 6 funding, we'll transform these insights into
+tutorials, case studies, and migration guides to facilitate adoption among
+downstream libraries. By offering clear and accessible resources, we aim to
+reduce the learning curve for new users and provide developers with the tools
+they need to confidently build array library-agnostic applications.
+
+## Value to the Scientific Community and End Users
+
+The work funded by this award will provide significant benefits to users within
+the scientific research community. Our hope is that this work will yield three
+primary outcomes:
+
+1. **Interoperability Across Libraries**: Fragmentation within the ecosystem has
+often led to duplication of effort, limited access to hardware acceleration,
+and the need for repeated re-implementation of foundational array structures.
+By fostering interoperability across libraries, we aim to simplify the process
+of moving between technical stacks and unlock new performance gains for array
+library consumers.
+
+2. **Standardization and Reduced Switching Costs**: Users will benefit from
+shorter learning curves and lower costs associated with switching libraries.
+With standardized APIs and robust compliance infrastructure, users will have
+greater confidence that their workflows will be portable across array
+libraries, regardless of the underlying computational backend.
+
+3. **Enhanced Performance for Array-Consuming Libraries**: Array API adoption
+has [already shown](https://proceedings.scipy.org/articles/gerudo-f2bc6f59-001)
+promising performance improvements across several libraries in the ecosystem.
+For example, performance gains of up to 50x in SciPy and 10-40x in scikit-learn
+were observed upon integrating support for alternative array libraries such as
+CuPy and PyTorch. We hope to observe similar acceleration in other downstream
+libraries, which could dramatically reduce analysis time for computationally
+intensive research tasks, ultimately improving efficiency and access for users
+working with high-dimensional data.
+
+## Looking Forward
+
+As we embark on this phase of our work, we're excited to continue pushing
+forward the Array API Standard as a unifying foundation for the PyData
+ecosystem. Support from CZI's EOSS program is instrumental in making this
+vision a reality, and we're committed to expanding the impact of the Array API
+Standard through real-world applications and community engagement.
+
+With this award, we're not only addressing technical fragmentation but also
+advancing a more inclusive, accessible, and robust future for scientific
+computing. We look forward to collaborating with the community to make array
+interoperability a reality across the ecosystem and to empower researchers with
+tools that help them achieve scientific breakthroughs more efficiently and
+effectively.
+
+Stay tuned for updates as we implement these initiatives and continue to
+strengthen the foundations of the PyData ecosystem!
+
+---
+
+## Funding Acknowledgment
+
+This project has been made possible in part by grant number EOSS6-0000000621
+from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley
+Community Foundation. Athan Reines is the grant's principal investigator and
+Quansight Labs is the entity receiving and executing on the grant.
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