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The Python micro framework for building web applications.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
A toolkit for developing and comparing reinforcement learning algorithms.
Code for the paper "Language Models are Unsupervised Multitask Learners"
Universal LLM Deployment Engine with ML Compilation
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Jupyter metapackage for installation, docs and chat
Software to automate the management and configuration of any infrastructure or application at scale. Get access to the Salt software package repository here:
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Large Language Model Text Generation Inference
🚀 Build and manage real-life ML, AI, and data science projects with ease!
Ready-to-run Docker images containing Jupyter applications
An open source python library for automated feature engineering
Deis v1, the CoreOS and Docker PaaS: Your PaaS. Your Rules.
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
Container data volume manager for your Dockerized application
Hummingbird compiles trained ML models into tensor computation for faster inference.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Deep learning for recommender systems
Extended pickling support for Python objects
Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.