MLEM Prototype deployment tool
Project description
# mlem-prototype Project to share code ideas and concepts, track tasks and issues for upcoming MLEM tool
## Examples [DVC Pipeline with mlem](examples/dvc-pipeline/README.md)
## Current state Implemented mlem cli & api
### API #### mlem.api.save Saves object to fs in format of <name>.mlem file and <name> dir with artifacts. .mlem file contains all metadata needed to restore objects and some other fields, like requirements for models or columns and types for data frames #### mlem.api.load Loads object which was saved with mlem.api.save ### CLI #### mlem apply Usage: mlem apply -m <method name> <model> <output> <inputs> Loads model and input data, applies model.method to it and saves result to output path in mlem format.
#### mlem deploy ##### mlem deploy <model> heroku Deploys model to heroku. Needs HEROKU_API_KEY env (get it from heroku.com) and and also this ` REGISTRY_HEROKU_COM_PASSWORD=${HEROKU_API_KEY} REGISTRY_HEROKU_COM_USERNAME=_ ` Deployment metadata is written to .mlem model file (subject to change in future)
##### mlem deploy <model> sagemaker –method predict Deploys model to sagemaker. Need to set aws envs: ` export AWS_ACCESS_KEY_ID= export AWS_SECRET_ACCESS_KEY= export AWS_DEFAULT_REGION=us-east-1 `
##### mlem deploy <model> status Checks status of deployment. For now there is no conventions what it will return
##### mlem deploy <model> destroy Undeploy deployed model. Deployment meta is removed from .mlem file
#### mlem apply-remote Same as mlem apply, but actually sends data to deployed model
#### mlem pack <model> <path> Generate model package to <path>
### API2 #### mlem env create <name> <type> creates new target environment type is one of [sagemaker, heroku]
#### mlem deploy2 <model> <env_name> deploys model to chosen taget env deploy metadata is saved to <model>-<env_name>.deployed.yaml
#### mlem destory2 <deploy-name> destroy deploy described in some <model>-<env_name>.deployed.yaml file
#### mlem status2 <deploy-name> get deployment status
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