Keepsake

Keepsake

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About

Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves code and weights to Amazon S3 or Google Cloud Storage. This facilitates the retrieval of code and weights from any checkpoint, aiding in re-training or model deployment. Keepsake supports various machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, by saving files and dictionaries in a straightforward manner. It also offers features such as experiment comparison, enabling users to analyze differences in parameters, metrics, and dependencies across experiments.

About

NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers in need of a tool to manage their code and enhance the efficiency of their workflows

Audience

Researchers, engineers, and developers who need an open source Python AI framework to build, train, fine-tune, and deploy physics-informed machine learning models for simulation, digital twins, and real-time prediction

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Replicate
United States
keepsake.ai/

Company Information

NVIDIA
Founded: 1993
United States
developer.nvidia.com/physicsnemo

Alternatives

Alternatives

TensorBoard

TensorBoard

Tensorflow
Alchemite

Alchemite

Intellegens
NeuralWing

NeuralWing

Emmi AI
NeuralMould

NeuralMould

Emmi AI

Categories

Categories

Integrations

PyTorch
Python
Amazon S3
Google Cloud Storage
JSON
TensorFlow
scikit-learn

Integrations

PyTorch
Python
Amazon S3
Google Cloud Storage
JSON
TensorFlow
scikit-learn
Claim Keepsake and update features and information
Claim Keepsake and update features and information
Claim NVIDIA PhysicsNeMo and update features and information
Claim NVIDIA PhysicsNeMo and update features and information