Comments (1)
jeancochrane
commented on July 26, 2024
I did a bunch of investigation into taichi in #236. TL;DR:
- Taichi makes the code more complicated than numba and does not appear to be significantly faster, even with GPU support
- We could speed up the comps pipeline 2x by simply using a bigger instance type (c5.24xlarge)
- I am not going to update the pipeline to use a new instance type right now, for a couple of reasons:
- Performance improvement work is still ongoing, and we may end up improving the algorithm and changing the instance type shortly anyway
- We may not want to use the same instance type for comps and model training, since their resource requirements are different; we will sort out this design, including the specific instance types for our compute environments, when we work on ccao-data/actions#16 later this year
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Related Issues (20)
- Diagnostics: "Challenge groups" HOT 1
- Create aggregate maps using comps output HOT 2
- Refactor comps to calculate tree weights on a per-card basis HOT 1
- Add sales val run_id to res model outputs
- Add sale ratio column to desk review sheets HOT 1
- Flesh out characteristics in desk review workbooks
- Refactor `export` stage to use a config dict representing the workbook structure
- Comps check HOT 1
- Update `ingest` stage to use `noctua` `unload = TRUE` option HOT 3
- Improve modeling multi-cards
- Spike model rebuild in Python/polars
- Create explainer/diagrams for multi-PIN and multi-card aggregations
- Investigate merging the residential and condo model codebases
- Adjust `model_fetch_run()` to optionally fetch input data
- Update comps algorithm to save `instruno` in addition to `parid` HOT 1
- Generate historical model API workbooks
- Edit `dvc.yaml` to include files invoked as deps
- Change `/reports/performance/_outliers.qmd` to accomodate new sales val spec
- Create a feature selection/evaluation template HOT 1
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