aws / aws/sagemaker-python-sdk
ModelStep should have a 'adds_depends_on' class method
- Dominant language
- Python
- Stars
- 2.3k
- Forks
- 1.3k
- Avg merge
- 1d 22h
- Merged PRs (30d)
- 35
Description
**Describe the feature you'd like**
When constructing SageMaker pipelines, I want to construct steps that sequence Training jobs (TrainingStep) and Inference jobs (ModelStep and TransformStep) together. As part of this, I would use `add_depends_on` from the TrainingStep to allow me to string together tasks. However, we cannot do the same for ModelStep. It has to be passed via the `depends_on` parameter during initialization.
**How would this feature be used? Please describe.**
```
def setup(…):
model_step = ModelStep(…)
transform_step = TransformStep(…, model_step…)
model_step.add_depends_on(other_steps)
```
**Describe alternatives you've considered**
I am forced to pass in dependencies throughout my abstractions, rather than construct the model_step first, and then add dependencies later.
**Additional context**
happy to add more details if needed.
Contributor guide
Research direction
Start by locating the ModelStep, TrainingStep, and TransformStep entry points named in the issue, then compare how TrainingStep handles add_depends_on with ModelStep's depends_on initialization parameter. Confirm the requested usage by constructing the shown pipeline sequence and checking that the resulting dependency is preserved.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100