Workflows¶
In order to run the example workflows, make sure that you have datmo properly installed with the latest stable or development version. You can install the latest stable version with the following command:
$ pip install datmo
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.
Setting up your environment¶
| Environment Setup Examples | ||
|---|---|---|
| Feature | Scenario | Link |
| Project Environment Setup | For fresh repository | Docs |
| For existing datmo project (pre-configured env) | GitHub | |
| For existing datmo project (bring your own env) | GitHub | |
| Opening a Workspace | Opening a Jupyter Notebook | GitHub |
| Opening RStudio | Github | |
| Opening JupyterLab | Github | |
| Opening a Terminal | Github | |
Running an experiment¶
| Experiment Run Examples | ||
|---|---|---|
| Method | Example | Link |
| CLI | Run a single experiment | Coming Soon |
| CLI + Python | Run a single experiment | Coming Soon |
| Run two tasks and compare results | Coming Soon | |
Creating a Snapshot¶
Note: All of the following flows involve using the CLI to some extent, even in conjunction with the python SDK. The standalone CLI version, while the most manual method, is compatible with any language and files, even those not listed here.
| Snapshot Create Examples | ||
|---|---|---|
| Method | Example | Link |
| CLI | Iris dataset sk-learn classifier | GitHub |
| CLI + Python | Iris dataset sk-learn classifier | GitHub |
| CLI + Jupyter Notebook | Iris dataset sk-learn classifier | GitHub |
| CLI + R | Iris dataset caret decision tree | GitHub |
You can view the latest examples on the master branch on Github