Metadata-Version: 2.1
Name: aimd
Version: 0.1.1
Summary: AI Deployment tool
Home-page: https://github.com/SGevorg/aim-deploy
Author: Gevorg Soghomonyan
Author-email: sgevorg@aimhub.io
License: MIT
Description: 
        # Aim
        
        Aim is an AI deployment and version control system.
        It can handle both small and large projects through their whole life cycle with efficiency and speed.
        It is built to seamlessly blend in with existing ML stack and become an integral part of the development lifecycle.
        
        ## Aim CLI
        Aim CLI is a command line tool for building end-to-end AI.
        Aim is built to be:
        compatible with the existing ecosystem of tools
        be familiar
        just work
        make building AI productive
        
        Aim has three main features: tracking of training, export and deploy.
        
        ### Tracking - ML Training
        Command: `aim train`
        Aim train runs training for the given aim repository. Aim train tracks the gradients and updates in the model with given interval and saves them for visualization and analysis.
        Aim Train is paired with UI that visualizes the artifacts tracked.
        Aim Tracking is used to debug and have a detailed understanding of the process of training.
        
        ### Export - ML Model
        Command: `aim export`
        Aim export creates the saved model checkpoint file and exports .aim model which could be committed and pushed to the Aimhub and/or deployed to different platforms.
        Exported .aim model could also be converted to .onnx, .tf and other checkpoints for other frameworks.
        Aim CLI Export is based on aim Intermediate Representation that allows for automatic deployment of the model.
        Aim Export can also export pre-processing steps similarly to the model and could be included in the model deployment process.
        
        ### Deploy - Aim Model
        Command: `aim deploy`
        Aim Deploy produces a deployable artifact from .aim (model and preprocessing) files. The produced artifacts can run in cloud, on different hardware and as a hybrid.
        Deployments are also reflected on Aimhub to track and version the deployed artifacts.
        
        
        ### Other Commands
        
        ```shell
        aim fork
        aim branch off
        aim pause, continue
        aim convert
        ```
        
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
