truefoundry-workflows

You build ML training pipelines by defining Python tasks with @task decorators and composing them into directed acyclic graphs using @workflow. This approach allows you to orchestrate end-to-end model training and data processing natively in Python.

Scheduling cron workflows for data pipelines involves configuring your Python-defined directed acyclic graph for time-based execution. You can schedule ETL pipelines and data processing runs across environments after verifying prerequisites and credentials.

You deploy Flyte workflows to TrueFoundry using the CLI command tfy deploy workflow. An API-based fallback is also available if you need to deploy your Python task definitions programmatically.

Yes, Flyte workflow orchestration on TrueFoundry verifies prerequisites and checks credentials as part of the deployment process. This ensures your environment is correctly configured before tasks and directed acyclic graphs run.

You can monitor ETL pipeline runs across environments after deploying them. The orchestration system tracks workflow execution, allowing you to observe data processing and ML training pipelines from deployment through completion.

You should use the API fallback instead of the CLI for workflow deployment when you need to programmatically deploy Python task definitions. The CLI command tfy deploy workflow remains the primary method for standard deployments.