ML Executor
🐳 Declarative interface for building images and running commands in containers using Docker.
TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.
Collection of simple functions reusable across ML projects.
Research workflows made easy, locally and in the Cloud.
Dora is an experiment management framework. It expresses grid searches as pure python files as part of your repo. It identifies experiments with a unique hash signature. Scale up to hundreds of exp…
The TensorFlow Cloud repository provides APIs that will allow to easily go from debugging and training your Keras and TensorFlow code in a local environment to distributed training in the cloud.
Super simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.
UnionML: the easiest way to build and deploy machine learning microservices
dstack is an open-source alternative to Kubernetes and Slurm, designed to simplify GPU allocation and AI workload orchestration for ML teams across top clouds, on-prem clusters, and accelerators.
Open Source Continuous File Synchronization
Python library to easily log experiments and parallelize hyperparameter search for neural networks
The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.
xpk (Accelerated Processing Kit, pronounced x-p-k,) is a software tool to help Cloud developers to orchestrate training jobs on accelerators such as TPUs and GPUs on GKE.
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.