In order to run the examples, make sure that you have datmo properly installed with the latest stable or development version. You can install it with the following command:
$ pip install datmo
The datmo CLI can work entirely as a standalone tool. It can also be used in conjunction with our language-specific SDKs to enhance the experience of the user and enable more granular control over model management during runtime.
See CLI flow examples for instructions.
We offer a fully supported Python SDK, that works in conjunction with the CLI.
See CLI + Python flow examples for instructions
For Python kernels, the Python SDK is compatible with Jupyter notebooks. For running a containerized notebook environment, the user can do this with the CLI.
See CLI + Jupyter Notebook flow examples for instructions
Users can call the CLI natively using the system2()
command in R, allowing for granular control over snapshot creation.
In the future, these system calls will be replaced with a more intuitive SDK.
See R flow examples for instructions
Listed below are actions you might want to take with Datmo. For each we have listed if there are any example for each type of flow. You can navigate to the specific flow folder to find the exact instructions for each example.
- CLI flow
- snapshot_create_iris_sklearn
- CLI + Python flow
- snapshot_create_iris_sklearn
- CLI + Jupyter Notebook flow
- snapshot_create_iris_sklearn
- CLI + R flow
- snapshot_create_iris_caret
- CLI + Python flow
- task_run_iris_sklearn_basic
- task_run_iris_sklearn_compare
Rerun a single task (command/script or workspace) CLI * task_rerun