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Get started with Python
Step 0: Orient yourself to DominoStep 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Get started with R
Step 0: Orient yourself to Domino (R Tutorial)Step 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Get Started with MATLAB
Step 1: Orient yourself to DominoStep 2: Create a Domino ProjectStep 3: Configure Your Domino ProjectStep 4: Start a MATLAB WorkspaceStep 5: Fetch and Save Your DataStep 6: Develop Your ModelStep 7: Clean Up Your Workspace
Step 8: Deploy Your Model
Scheduled JobsLaunchers
Step 9: Working with Domino Datasets
Domino Reference
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Publish a Model API
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Set up your Project to Ignore Files

Set up your Project to Ignore Files

Domino lets you ignore certain files from your project, excluding them from the domino sync operation. This includes syncing from your local machine (using the CLI or R package, as well as syncing at the end of a run or during a workspace session.

To ignore a file, add it to the file named .dominoignore that is automatically created at the root of every new project. Adding a folder will ignore it along with its contents. The * symbol can be used as a wildcard to match files of a similar pattern. All paths must be relative to the project root.

Note

Using isolated results branches

You can configure your project to save results to an isolated branch. You can access these results from the UI as well as CLI, but they will not be downloaded by domino sync, nor will they be used as input to future runs unless you explicitly specify so. More information is available in Results.

Using project importing

In certain situations, neither .dominoignore nor results branches are sufficient approaches. For example, suppose you are developing a recurring data cleaning task that produces large files used by other projects. You want to sync these results back to the mainline project at the end of the run, but don’t want to sync them to your local machine during development.

This can be accomplished by putting your code in one project and importing it into a different project in which the large-output runs will occur. This way you can sync with the "code project" and avoid downloading the results stored in the "data output project". You can even configure the code project to be imported as a package, and it will be automatically installed and accessible at runtime.

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