heed
A fully typed LMDB wrapper with minimum overhead 🐦 (by meilisearch)
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] (by burn-rs)
| heed | burn | |
|---|---|---|
| 17 | 34 | |
| 476 | 4,845 | |
| 11.6% | - | |
| 8.9 | 8.9 | |
| 4 days ago | 5 months ago | |
| Rust | Rust | |
| MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
heed
Posts with mentions or reviews of heed.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-06.
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What's everyone working on this week (10/2023)?
At Meilisearch we are currently trying to add a better error handling in heed v0.20, our LMDB key-value store wrapper. Unfortunately, when there are a lot of generics it can become harder to play with…
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We’re the Meilisearch team! To celebrate v1.0 of our open-source search engine, Ask us Anything!
There are issues and pull requests but I advise you to look at the milli folder in the Meilisearch repository, it’s where all the logic is done. We extensively use RoaringBitmaps, heed the LMDB wrapper and grenad when indexing.
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Release of an alpha version to perfect the heed library: the most maintained Rust LMDB wrapper
I’ll continue to introduce new features and new safety guards until v0.20.0. Can you tell me more about your project? Or is it private?
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Are there any embedded databases that have multiple-process support?
LMDB support multiple readers and one writer at the same time. It is ensured by the library. Note that LMDB is a key-value store. You can use the heed library which is the most maintained Rust wrapper.
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Key/Value Store Recommendations
Note that heed is ensuring that you are not trying to use transactions, databases and environments in the right way. I have added much more work in that regard in the important update that I am working on too!
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What's everyone working on this week (45/2022)?
I am currently working on exposing the new LMDB encryption feature from heed the safe LMDB wrapper with the help of the Cryptography community.
- Ask for advice from the cryptographic community about heed: the LMDB wrapper
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redb: high performance, embedded, key-value database in pure Rust
Have you considered heed or even sanakirja?
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[Requesting Help] LMDB Databases in Rust
rkv hasn't been updated for a while. I recommend using heed - https://docs.rs/heed
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I need a stable Key-Value database
For wrappers around LMBD, I'd recommend RKV or Heed https://github.com/mozilla/rkv https://github.com/Kerollmops/heed
burn
Posts with mentions or reviews of burn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-08.
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Burn 0.10.0 Released 🔥 (Deep Learning Framework)
Release Note: https://github.com/burn-rs/burn/releases/tag/v0.10.0
- Deep Learning Framework in Rust: Burn 0.10.0 Released
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Why Rust Is the Optimal Choice for Deep Learning, and How to Start Your Journey with the Burn Deep Learning Framework
The comprehensive, open-source deep learning framework in Rust, Burn, has recently undergone significant advancements in its latest release, highlighted by the addition of The Burn Book 🔥. There has never been a better moment to embark on your deep learning journey with Rust, as this book will guide you through your initial project, providing extensive explanations and links to relevant resources.
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Candle: Torch Replacement in Rust
Burn (deep learning framework in rust) has WGPU backend (WebGPU) already. Check it out https://github.com/burn-rs/burn. It was released recently.
- Burn – A Flexible and Comprehensive Deep Learning Framework in Rust
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Announcing Burn-Wgpu: New Deep Learning Cross-Platform GPU Backend
For more details about the latest release see the release notes: https://github.com/burn-rs/burn/releases/tag/v0.8.0.
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Are there any ML crates that would compile to WASM?
Tract is the most well known ML crate in Rust, which I believe can compile to WASM - https://github.com/sonos/tract/. Burn may also be useful - https://github.com/burn-rs/burn.
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Any working wgpu compute example that would run in a browser?
We, the burn team, are working on the wgpu backend (WebGPU) for Burn deep learning framework. You can check out the current state: https://github.com/burn-rs/burn/tree/main/burn-wgpu
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I’ve fallen in love with rust so now what?
Here is the project: https://github.com/burn-rs/burn
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Is anyone doing Machine Learning in Rust?
Disclaimer, I'm the main author of Burn https://burn-rs.github.io.
What are some alternatives?
When comparing heed and burn you can also consider the following projects:
sled - the champagne of beta embedded databases
candle - Minimalist ML framework for Rust
KeyDB - A Multithreaded Fork of Redis
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
lmdb-rs - Rust bindings for LMDB
tch-rs - Rust bindings for the C++ api of PyTorch.
rkv - A simple, humane, typed key-value storage solution.
Graphite - 2D raster & vector editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.
milli - Search engine library for Meilisearch ⚡️
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference [Moved to: https://github.com/sonos/tract]
nanodb-specification - Nano ledger database format specification and Python sample
L2 - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust