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This is possible due to three portable SQL extensions that we’re building:\n- xarray-sql: a library that efficiently represents petabyte scale array datasets in the cloud as tables (relations).\n- ddx: a database extension that adds automatic differentiation in the style of JAX to standard SQL, assuming the above array data model.\n- einfold: a modular query planner for fast tensor contractions in vanilla SQL, again, assuming the above array data model.<p>My hope is that the relational model and algebra provide fresh perspectives on how we might do accelerator programming. Here, our thesis is that separating the logical layers in a tensor program from the physical layers is both more correct and more scalable.<p>If the Bitter Lesson is a central dogma of machine learning, then why not formalize it with relational algebra?<p>If this interests you and you’d like to tinker with us at the intersection of database theory and ML systems, join us on Discord at <a href=\"https:&#x2F;&#x2F;xql.systems\" rel=\"nofollow\">https:&#x2F;&#x2F;xql.systems</a>  (link at the bottom). 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