Exit hooks

docs/metaflow/composing-flows/custom-decorators.md

@@ -107,7 +107,9 @@ python waiterflow.py run --with myprofiler.my_profile
 
 Custom decorators don't require special treatment when [executing tasks in
 the cloud](/scaling/remote-tasks/introduction) or when [deploying flows to production](/production/introduction) -
-they will get [packaged automatically by Metaflow](/scaling/dependencies#unpacking-a-metaflow-project). Try it:
+they will get [packaged automatically by Metaflow](/scaling/dependencies#unpacking-a-metaflow-project). You don't
+even need to `import` the decorators if you add them with `--with`. Try it:
+
 ```
 python waiterflow.py run --with myprofiler.my_profile --with kubernetes
 ```

docs/metaflow/composing-flows/mutators.md

@@ -122,6 +122,8 @@ You can test the effect of the options with `@robust_flow` above. You can see th
 python failflow.py dump RUN_ID/start
 ```
 
+Note that the same options apply to adding flow-level decorators as well.
+
 ## Introspecting a flow and applying configs
 
 Let's walk through a more advanced mutator that shows how you can

docs/scaling/failures.md

@@ -383,6 +383,15 @@ if __name__ == "__main__":
 Note that when deployed on Argo Workflows, exit hook functions execute as separate
 containers (pods), so they will execute even if steps fail e.g. due to out of memory condition.
 
+### Custom dependencies in exit hooks
+
+Since exit hook functions are not steps, you can't use `@pypi` or `@conda` to manage
+their dependencies.
+Instead, you can provide a custom image in `options={'image': ...}` like here:
+```
+@exit_hook(on_error=[failure_print], options={"image": URL_TO_AN_IMAGE})
+```
+
 ## Summary
 
 Here is a quick summary of failure handling in Metaflow: