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Description

Keepsake is a Python library that is open-source and specifically designed for managing version control in machine learning experiments and models. It allows users to automatically monitor various aspects such as code, hyperparameters, training datasets, model weights, performance metrics, and Python dependencies, ensuring comprehensive documentation and reproducibility of the entire machine learning process. By requiring only minimal code changes, Keepsake easily integrates into existing workflows, permitting users to maintain their usual training routines while it automatically archives code and model weights to storage solutions like Amazon S3 or Google Cloud Storage. This capability simplifies the process of retrieving code and weights from previous checkpoints, which is beneficial for re-training or deploying models. Furthermore, Keepsake is compatible with a range of machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, enabling efficient saving of files and dictionaries. In addition to these features, it provides tools for experiment comparison, allowing users to assess variations in parameters, metrics, and dependencies across different experiments, enhancing the overall analysis and optimization of machine learning projects. Overall, Keepsake streamlines the experimentation process, making it easier for practitioners to manage and evolve their machine learning workflows effectively.

Description

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

PyTorch Yes 
Python Yes 
TensorFlow Yes 
Amazon EC2 Trn2 Instances No 
Amazon EKS No 
Amazon SageMaker No 
Amazon Web Services (AWS) No 
Anyscale No 
Apache Airflow No 
Azure Kubernetes Service (AKS) No 
Dask No 
Databricks No 
Flyte No 
Google Cloud Platform No 
Google Kubernetes Engine (GKE) No 
JSON Yes 
Snowflake No 
Union Cloud No 
io.net No 
scikit-learn Yes 

Integrations

PyTorch Yes 
Python Yes 
TensorFlow Yes 
Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Anyscale Yes 
Apache Airflow Yes 
Azure Kubernetes Service (AKS) Yes 
Dask Yes 
Databricks Yes 
Flyte Yes 
Google Cloud Platform Yes 
Google Kubernetes Engine (GKE) Yes 
JSON No 
Snowflake Yes 
Union Cloud Yes 
io.net Yes 
scikit-learn No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

Free
Open source. Consumption-based.
Free Trial Yes 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Replicate

Country

United States

Website

keepsake.ai/

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Version Control

Branch Creation / Deletion No 
Centralized Version History No 
Code Review No 
Code Version Management No 
Collaboration Tools No 
Compare / Merge Branches No 
Digital Asset / Binary File Storage No 
Isolated Code Branches No 
Option to Revert to Previous No 
Pull Requests No 
Roles / Permissions No 

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

TensorBoard Reviews

TensorBoard

Tensorflow

Alternatives