Comments (1)
Hi @sarahmish
Capturing debug information sounds good, but I would set this as an optional feature rather than the default behavior.
More precisely, when it comes to debugging I think that it would make sense to enable a debug mode (fit(..., debug=True)
and predict(..., debug=True)
for the pipeline which, if enabled, makes the pipeline return a dict
with information about what happened in each step, including the elapsed time but also input and output arguments. This would also allow us to later on add other profiling information, such as CPU time vs IO time information or memory consumption.
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Related Issues (20)
- Return specified intermediate outputs HOT 3
- Inconsistent names between json specification and code implementation HOT 3
- Support for static or class methods
- Ability to return intermediate context
- Get Pipeline Inputs
- Pipeline Diagrams
- Allow loading a primitive or pipeline directly from the JSON path
- Support sub-pipeline execution HOT 1
- Support undeclared parameters in MLBlock
- Expose required init_params
- Implement Save/Load using pickle HOT 1
- Tutorial 1 (Using and MLPipeline) .get_inputs() error
- Enabling passing of primitives/pipelines to hyperparameters
- Stop pipeline fitting after the last block with `fit` method
- Support dynamic inputs and outputs
- MLPipeline does not preserve metadata from JSON pipeline annotation
- How to compose TensorBoard into pipeline during training
- v0.5.0 Release Tag has wrong date in the description HOT 1
- Support python 3.9 and 3.10
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