@@ -103,12 +103,18 @@ You can deploy agents on your varying compute resources as long as it has access
- deploy agent and let it listen to a queue (for example: test). The following code also creates that queue and service mode makes sure that the agent can run multiple different jobs (needed for pipelines)
- clearml-agent daemon --services-mode --queue test --create-queue
## example workflow
- Run stage_one.py
- You are able to change arguments inside the script
- Run stage_two.py
- You are able to change arguments inside the script
- This will create drafts of the scripts in the Clearml workspace
- Now run controller.py to create and run a pipeline that uses the drafts
- You can change the pipe.start command at the end to use remote agents
## example pipeline
- Run stage_one.py
- You are able to change arguments inside the script
- Run stage_two.py
- You are able to change arguments inside the script
- This will create drafts of the scripts in the Clearml workspace
- Now run controller.py to create and run a pipeline that uses the drafts
- You can change the pipe.start command at the end to use remote agents
## Add pipeline trigger
There is a example for a dataset trigger in trigger.py (more triggers uses can be found at https://clear.ml/docs/latest/docs/references/sdk/trigger). This triggers when a mutation happens to the registered dataset.
- Change the trigger.py file to your specific pipeline and dataset
