C++ Parallel Computing and Asynchronous Networking Framework
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📚 Parameterize, execute, and analyze notebooks
- jupyter
- notebooks
- notebook-generator
- nteract
Self-hostable workflow orchestrator for teams whose main work isn't orchestration. Declarative YAML over your scripts, SSH commands, containers, etc; keep workflows separate from business logic. One binary, no database, runs on limited H/W resources. Alternative to Airflow / Cron / Job Scheduler.
- cron
- workflow-engine
- workflow-scheduler
- workflow-orchestration
Kubernetes-native platform to run massively parallel data/streaming jobs
- kubernetes
- stream-processing
- data-processing
- k8s
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
One advanced and mature open-source MPP (Massively Parallel Processing) database. Open source alternative to Greenplum Database.
- database
- data-analysis
- data-warehouse
- olap
visual data prep powered by python
- data
- data-pipelines
- etl
- structured-data
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
- hpc
- workflow
- workflow-automation
- workflow-management
Build platforms that flexibly mix SQL, batch, and stream processing paradigms
- golang
- event-sourcing
- stream-processing
- brokers
🏎️ Streams & Reactive Programming paradigm for Go: declarative and composable API for event-driven applications
- event-driven
- generics
- go
- golang
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
- pipeline
- pipeline-framework
- machine-learning
- deep-learning
A Go port of the Java Streams API. Type-safe and functional Go Streams processing
- golang
- go
- stream-processing
- generics
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning
Parallelize _anything_ //
- ocaml-library
- parallel-computing
- stream-processing
- multi-processing
Ordered-concurrently a library for concurrent processing with ordered output in Go. Process work concurrently and returns output in a channel in the order of input. It is useful in concurrently processing items in a queue, and get output in the order provided by the queue.
- concurrent
- golang
- golang-library
- data-science
🎲 Dotflow turns an idea into flow! — Lightweight Python library for execution pipelines
- etl
- etl-framework
- etl-pipeline
- python
Pipeline de ingestão e busca semântica de documentos para RAG — com rate limiting, circuit breaker e fila resiliente.