Introduction to Modelstore
Modelstore is an immensely useful Python library designed to assist data scientists and machine learning engineers in effortlessly managing machine learning models. It provides functionalities to version, export, save, and download models, offering an efficient solution for handling complex model lifecycle operations.
Features of Modelstore
Open Source Model Registry
Modelstore acts as an open source model registry, meaning there is no requirement for a specialized server to track models. Users can store their models either locally or in cloud storage, as Modelstore supports numerous cloud service providers, including AWS, GCP, and Azure.
Model Versioning and Upload
One of the significant features of Modelstore is its model versioning capability. Every time a model is uploaded, Modelstore versions it, replacing the tedious boilerplate code usually required. This not only streamlines the process but also ensures all model versions are preserved and easily accessible.
Model Management
With Modelstore, managing models becomes efficient and organized. Users can categorize models by domains and states, listing models under a specific domain and handling their various states. This approach simplifies model management and retrieval.
Easy Model Download
Downloading models becomes hassle-free with Modelstore. Models can be downloaded directly by their ID, and they even offer the convenience of loading back into memory straight from the storage.
Command Line Tool Usage
For those who prefer working via the command line, Modelstore offers robust functionalities that allow downloading models directly through terminal commands, enhancing the overall user experience.
Getting Started with Modelstore
Installing Modelstore is straightforward. A simple pip install command is all that's needed:
pip install modelstore
Supported Storage Types
Modelstore supports various storage options to cater to different preferences and infrastructures, including:
- AWS S3 Bucket
- Azure Blob Storage
- Google Cloud Storage
- Any S3-compatible storage accessible via MinIO
- Local filesystem directories
Supported Machine Learning Libraries
Modelstore is compatible with a wide range of popular machine learning libraries, such as:
- Annoy
- Catboost
- CausalML
- Fast.AI
- Gensim
- Keras
- LightGBM
- Mxnet
- Onnx
- Prophet
- PyTorch & PyTorch Lightning
- Scikit Learn
- Skorch
- Shap
- Spark ML Lib
- Tensorflow
- Transformers
- XGBoost
If a certain library isn't directly supported, users have the flexibility to save and upload their models as raw files.
Conclusion
Modelstore is a powerful tool that simplifies the storage and versioning of machine learning models. With its support for various storage services, ease of use, and extensive compatibility with numerous machine learning libraries, Modelstore is an invaluable asset for any data science or machine learning project. For more detailed guidance and examples, users are encouraged to explore the documentation and engage with the community on Slack.