《Designing Data-Intensive Application》DDIA中文翻译
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Updated
Mar 27, 2024 - Python
《Designing Data-Intensive Application》DDIA中文翻译
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
Python Stream Processing
High-Performance Symbolic Regression in Python and Julia
FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.
A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
Problem statements on System Design and Software Architecture as part of Arpit's System Design Masterclass
Python Stream Processing. A Faust fork
A library for event sourcing in Python.
GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.
RayLLM - LLMs on Ray
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Scalable Python DS & ML, in an API compatible & lightning fast way.
Docker-based utility for testing network failures and partitions in distributed applications
Bagua Speeds up PyTorch
Kubernetes-native Deep Learning Framework
A library for replicating your python class between multiple servers, based on raft protocol
A collection of Docker containers for running Blender headless or distributed ✨
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