dfdx
drizzle-orm
dfdx | drizzle-orm | |
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22 | 48 | |
1,611 | 19,921 | |
- | 5.2% | |
8.7 | 9.7 | |
2 months ago | 5 days ago | |
Rust | TypeScript | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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dfdx
- Shape Typing in Python
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Candle: Torch Replacement in Rust
I keep checking the progress on dfdx for this reason. It does what I (and, I assume from context, you) want: Provides static checking of tensor shapes. Which is fantastic. Not quite as much inference as I'd like but I love getting compile-time errors that I forgot to transpose before a matmul.
It depends on the generic_const_exprs feature which is still, to quote, "highly experimental":
https://github.com/rust-lang/rust/issues/76560
Definitely not for production use, but it gives a flavor for where things can head in the medium term, and it's .. it's nice. You could imagine future type support allowing even more inference for some intermediate shapes, of course, but even what it has now is really nice. Like this cute little convnet example:
https://github.com/coreylowman/dfdx/blob/main/examples/night...
- Dfdx: Shape Checked Deep Learning in Rust
- Are there some machine or deep learning crates on Rust?
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[Discussion] What crates would you like to see?
And for transformers, it's really early days for dfdx, but it's a library that aims to sit basically at the Pytorch level of abstraction, that the difference is it's not just coded in Rust, but it follows the Rust-y/functional-y philosophy of "if it compiles it runs".
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rapl: Rank Polymorphic array library for Rust.
Wow that is super interesting. I actually tried to use GATs at first to be generic over shapes, but I couldn't do it, I'm sure it would be possible in the future though. There is this library dfdx that does something similar to what you mentioned, but it feels a little clumsy to me.
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Announcing cudarc and fully GPU accelerated dfdx: ergonomic deep learning ENTIRELY in rust, now with CUDA support and tensors with mixed compile and runtime dimensions!
Awesome, I added an issue here https://github.com/coreylowman/dfdx/issues/597. We can discuss more there! The first step will just be adding the device and implementing tensor creation methods for it.
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In which circumstances is C++ better than Rust?
The next release of dfdx includes a CUDA device and implements many ops. The same dev created a new crate, cudarc, for a wrapper around CUDA toolkit.
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
A question I have is: what are the philosophical/design differences with dfdx? As someone who's played around with dfdx and only skimmed the README of burn, it seems like dfdx leans into Rust's type system/type inference for compile time checking of as much as is possible to check at compile time. I wonder if you've gotten a chance to look at dfdx and would like to outline what you think the differences are. Thanks!
drizzle-orm
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A Software Engineer's Tips and Tricks #1: Drizzle
Enter Drizzle, a lightweight typesafe ORM for TypeScript that comes with one promise: If you know SQL — you know Drizzle.
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Get started with Drizzle ORM and Xata's Postgres service
Drizzle ORM is a very popular TypeScript ORM that provides type safe access to your database, automated migrations, and a custom data model definition.
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Shape Typing in Python
> being able to have a completely typesafe ORM such as Drizzle (https://orm.drizzle.team/) feels like a Rubicon moment, and touching anything else feels like a significant step backwards.
Alright, but there's nothing stopping you from having a completely typesafe ORM in python, is there?
Sure, there's isn't really one that everyone uses yet, but the python community tends to be a bit more cautious and slower to adopt big changes like that.
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Don't use your ORM entities for everything – embrace the SQL
Drizzle [1] comes pretty close the last time I checked.
[1]: https://orm.drizzle.team
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I Deployed My Own Cute Lil’ Private Internet (a.k.a. VPC)
Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).
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Exploring Astro DB
It's just SQL so you can take it out at any moment and move to any other DB provider. The package for working with Astro DB, @astrojs/db, includes Drizzle ORM so migration to a different provider should be relatively painless
- ORMs are nice but they are the wrong abstraction
- Drizzle TypeScript ORM
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Basic analytics with Vercel Postgres, Drizzle & Astro
Since Vercel's analytics pricing is a bit too expensive for my use case (where I hit the limit of 2,500 requests per month), and I didn't like using Google Analytics (not a big fan of Google), I decided to build my own analytics dashboard. Databases was something I didn't work with much before directly, so I decided to use an ORM, Drizzle, which is quite lightweight and easy to use.
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Edge Functions: Node and native NPM compatibility
do yourself a favor and ditch Prisma. It's a bloody mess of a project and codebase. I recommend https://github.com/drizzle-team/drizzle-orm to anyone that'll listen.
What are some alternatives?
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn]
kysely - A type-safe typescript SQL query builder
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
Prisma - Next-generation ORM for Node.js & TypeScript | PostgreSQL, MySQL, MariaDB, SQL Server, SQLite, MongoDB and CockroachDB
DiffSharp - DiffSharp: Differentiable Functional Programming
MikroORM - TypeScript ORM for Node.js based on Data Mapper, Unit of Work and Identity Map patterns. Supports MongoDB, MySQL, MariaDB, MS SQL Server, PostgreSQL and SQLite/libSQL databases.
executorch - On-device AI across mobile, embedded and edge for PyTorch
knex-tree - Query hierarchical data structures in sql with knex
rust - Empowering everyone to build reliable and efficient software.
MongoDB - The MongoDB Database
triton - Development repository for the Triton language and compiler
hono - Web Framework built on Web Standards