Keepsake vs. Ray Comparison


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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 Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.
Platforms Supported Windows Not Supported Mac Not Supported Linux Not Supported Cloud Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Supported On-Premises Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Developers in need of a tool to manage their code and enhance the efficiency of their workflows	Audience ML and AI Engineers, Software Developers
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing Free Free Version Supported Free Trial Not Supported	Pricing Free Open source. Consumption-based. Free Version Supported Free Trial Supported
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 Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Supported Live Online Supported In Person Supported
Company Information Replicate United States keepsake.ai/	Company Information Anyscale Founded: 2019 United States ray.io
Alternatives Azure Machine Learning Microsoft	Alternatives Anyscale
TensorBoard Tensorflow	Horovod
Flower	Determined AI
Weights & Biases	Neuralhub
Google Cloud Deep Learning VM Image Google View All	Automaton AI View All
Categories Machine Learning Supported ML Experiment Tracking Supported Version Control Supported	Categories Deep Learning Supported Machine Learning Supported ML Model Deployment Supported

Integrations PyTorch Supported Python Supported TensorFlow Supported Amazon EC2 Trn2 Instances Not Supported Amazon EKS Not Supported Amazon S3 Supported Amazon SageMaker Not Supported Amazon Web Services (AWS) Not Supported Anyscale Not Supported Azure Kubernetes Service (AKS) Not Supported Dask Not Supported Feast Not Supported Flyte Not Supported Google Cloud Platform Not Supported Google Kubernetes Engine (GKE) Not Supported JSON Supported Kubernetes Not Supported MLflow Not Supported Snowflake Not Supported Union Cloud Not Supported Show More Integrations View All 7 Integrations	Integrations PyTorch Supported Python Supported TensorFlow Supported Amazon EC2 Trn2 Instances Supported Amazon EKS Supported Amazon S3 Not Supported Amazon SageMaker Supported Amazon Web Services (AWS) Supported Anyscale Supported Azure Kubernetes Service (AKS) Supported Dask Supported Feast Supported Flyte Supported Google Cloud Platform Supported Google Kubernetes Engine (GKE) Supported JSON Not Supported Kubernetes Supported MLflow Supported Snowflake Supported Union Cloud Supported Show More Integrations View All 22 Integrations
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