facebookresearch / facebookresearch/dlrm
how to inference ./dlrm_s_criteo_kaggle.sh
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Description
Hello
I recently read your DLRM paper and decided to try running the code myself. However, I'm a silly teenager who finds this field quite challenging.
I've tried training the model using this command ./bench/dlrm_s_criteo_kaggle.sh --test-freq=1024 and tweaked the values of the Criteo Kaggle dataset to train in various ways. What I'm really curious about is inference.
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Does using
./bench/dlrm_s_criteo_kaggle.sh --inference-onlyexecute the inference process simply? (I don't think so, based on the git issues you've responded to in the past. It seems like the model needs to be saved first. Is that correct?) -
In the case where
--data-generation = datasetwhat does the input data set structure look like during inference? (Does the original Criteo Kaggle dataset and a dataset where I've manually modified some values have the same input data?)
Thank you.
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Research direction
Start with bench/dlrm_s_criteo_kaggle.sh and inspect how --inference-only and --data-generation=dataset are handled. Document whether a saved model is required and what input structure inference expects, including how modified Criteo data relates to the original dataset.
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Assessment
- Tech stack
- python, shell
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100