e-bartsch

langchain

🦜🔗 Build context-aware reasoning applications

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

rustlings

:crab: Small exercises to get you used to reading and writing Rust code!

DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

kubectx

Faster way to switch between clusters and namespaces in kubectl

pyo3

Rust bindings for the Python interpreter

dagster

An orchestration platform for the development, production, and observation of data assets.

modin

Modin: Scale your Pandas workflows by changing a single line of code

hydra

Hydra is a framework for elegantly configuring complex applications

pytorch-metric-learning

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

snorkel

A system for quickly generating training data with weak supervision

silero-vad

Silero VAD: pre-trained enterprise-grade Voice Activity Detector

lance

Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..

nogil

Multithreaded Python without the GIL

mlops-course

Learn how to design, develop, deploy and iterate on production-grade ML applications.

s4

Structured state space sequence models

greykite

A flexible, intuitive and fast forecasting library

denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

torchtyping

Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.

rustworkx

A high performance Python graph library implemented in Rust.

xmanager

A platform for managing machine learning experiments

circuit_training

torcharrow

High performance model preprocessing library on PyTorch

cleora

Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.

deepconsensus

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

annlite

⚡ A fast embedded library for approximate nearest neighbor search

recsim_ng

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

feast-workshop

A workshop with several modules to help learn Feast, an open-source feature store