Python library for programatic interaction with Coretex experiment tracking and orchestration server.
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Updated
Jul 3, 2024 - Python
Python library for programatic interaction with Coretex experiment tracking and orchestration server.
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
A simple & elegant experiment tracking framework that integrates persistence logic & best practices directly into Python
🦁 An open-source NLP framework that offers high-level wrappers designed for effortless launch, enhanced reproducibility, superior control, and unmatched flexibility for your experiments.
Experiment tracking server focused on speed and scalability
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
SEML: Slurm Experiment Management Library
Visualise your Kedro data and machine-learning pipelines and track your experiments.
The repository for all your experiments
Model performance and tuning analysis conducted on the CIFAR10 and CIFAR100 datasets. Convolutional Neural Network (CNN), Gated Multilayer Perceptron (gMLP), and Vision Transformer (ViT) model architectures are utilized. The study is built using PyTorch, PyTorch Lightning for clean and concise code and Optuna for hyperparameter tuning.
A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀
Assignments and Projects of mlops zoomcamp
A web application that provides a Web GUI to the MLflow Authentication API
Automating machine learning experiment tracking with MLFlow on AWS and Dagshub.
This project applies machine learning models to predict BMI categories based on individual physical attributes, utilizing MLflow for experiment tracking and model management, with integration into DagsHub for collaborative data science workflows. It showcases the power of MLflow in enhancing model lifecycle management and reproducibility.
Experiment tracking tools example
Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.
Track model training experiments with MLflow and FiftyOne!
MLU is a modular ML toolkit resembling lodash, streamlining from data prep to deployment with chainable utility functions. It enhances ML workflows, seamlessly integrates with top frameworks, and supports efficient data handling and model evaluation. Open-source, MLU welcomes contributions to foster innovation and efficiency in the ML community.
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