LevelDB
tinygrad
Our great sponsors
LevelDB | tinygrad | |
---|---|---|
27 | 17 | |
35,046 | 23,864 | |
1.2% | 5.8% | |
0.0 | 10.0 | |
7 days ago | 3 days ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
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.
LevelDB
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Codebases to read
I'm partial to how cleanly written https://github.com/google/leveldb is. It is a reasonable size to fully read & grok in not too long.
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Unpacking LSM-Trees: The Powerhouse Behind Modern Databases
[4] leveldb/doc/impl.md at main · google/leveldb. GitHub. Retrieved October 21, 2023 from https://github.com/google/leveldb/blob/main/doc/impl.md
- Bloom filter support to leveldb by Sanjay Ghemawat
- SQLite performance tuning: concurrent reads, multiple GBs and 100k SELECTs/s
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The Witty Guide to Installing LevelDB on Ubuntu: HostRooster® Edition
git clone https://github.com/google/leveldb.git
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Is there a lightweight, stable and embedded database library?
leveldb?
- Ask HN: What's the best source code you've read?
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LevelDB VS ZoneTree - a user suggested alternative
2 projects | 22 Aug 2022
- Is Mongo as popular in the job world as it is with tutorial makers?
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Open Source Databases in Go
goleveldb - Implementation of the LevelDB key/value database in Go.
tinygrad
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AMD Unveils Ryzen 8000G Series Processors: Zen 4 APUs for Desktop with Ryzen AI
Not sure if I completely understand what "Ryzen AI" does, but Tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of performance though, as you can read in the comments of that file.
There's also a small tutorial by AMD on how to use the WMMA intrinsic[1] using AMD's hipcc[2] compiler. Documentation is sparse kinda sparse, but the instruction set is not huge. The RDNA3 ISA guide[3] might also be helpful (and only a fraction of the pages are relevant.)
0. https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/...
1. https://gpuopen.com/learn/wmma_on_rdna3/
2. https://github.com/ROCm/HIPCC
3. https://www.amd.com/content/dam/amd/en/documents/radeon-tech...
- Tinygrad 0.8.0 Release
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Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
what do you think about tinygrad? I think its a good example of growing and well written, (partially) well documented library with many close to reference implementations
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AMD MI300 Performance – Faster Than H100, but How Much?
The idea of model architecture making fast hardware design easier is what makes https://github.com/tinygrad/tinygrad so interesting.
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💻 7 Open-Source DevTools That Save Time You Didn't Know to Exist ⌛🚀
🌟 Support on GitHub Website: https://tinygrad.org/
- Tinygrad
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How to train an Iris dataset classifier with Tinygrad
Before we begin, make sure you have TinyGrad and the required dependencies installed. You can find the installation instructions here.
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Decomposing Language Models into Understandable Components
Try to get something like tinygrad[1] running locally, that way you can tweak things a bit run it again and see how it performs. While doing this you'll pick up most of the concepts and get a feeling of how things work. Also, take a look at projects like llama.cpp[2], you don't have to fully understand what's going on here, tho.
You may need some intermediate knowledge of linear algebra and this thing called "data science" nowadays, which is pretty much knowing how to mangle data and visualize it.
Try creating a small model on your own, it doesn't have to be super fancy just make sure it does something you want it to do. And then ... you'll probably could go on your own then.
1: https://github.com/tinygrad/tinygrad
2: https://github.com/ggerganov/llama.cpp
- Tinygrad 0.7.0
What are some alternatives?
RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
MongoDB - The MongoDB Database
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Redis - Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.
llama.cpp - LLM inference in C/C++
SQLite - Unofficial git mirror of SQLite sources (see link for build instructions)
llama - Inference code for Llama models
LMDB - Read-only mirror of official repo on openldap.org. Issues and pull requests here are ignored. Use OpenLDAP ITS for issues.
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
CouchDB - Seamless multi-master syncing database with an intuitive HTTP/JSON API, designed for reliability
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.