A collection of learning resources for curious software engineers

Machine Learning Engineering Open Book

An open collection of implementation tips, tricks and resources for training large language models

An open collection of methodologies to help with successful training of large language models.

Python Actor concurrency library

Chaos and resiliency testing tool for Kubernetes with a focus on improving performance under failure conditions. A CNCF sandbox project.

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).

Python Actor concurrency library

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

Hazelcast Python Client

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Representation learning-based graph alignment based on implicit matrix factorization and structural embeddings

Guardian of Kubernetes clusters. Tool to monitor clusters health and signal/alert on failures.

[ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)

Tool for using the battle-tested `bin/celery` worker to consume vanilla AMQP messages (i.e. not Celery tasks)

PyTorch code for "Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention" (WACV 2023)

Content-aware Scalable Deep Compressed Sensing (TIP 2022) [PyTorch]

[ICLR 2023] Link Prediction with Non-Contrastive Learning

Source code for Centrifugo grand tutorial – Building WebSocket chat/messenger application from scratch. See the tutorial here - https://centrifugal.dev/docs/tutorial/intro