[Data] Skip schema call in to_tf if tf.TypeSpec is provided
#42917
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c21
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c21
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tf.TypeSpec of columns in to_tfschema call in to_tf if tf.TypeSpec is provided
Signed-off-by: Cheng Su <scnju13@gmail.com>
Signed-off-by: Cheng Su <scnju13@gmail.com>
Signed-off-by: Cheng Su <scnju13@gmail.com>
bveeramani
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c21
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…y-project#42917) This PR is to skip `Dataset.schema()` call from `Dataset.to_tf()`. `Dataset.schema()` relies on `limit` to early stop execution, and sometimes the stop is not triggered timely so a lot of tasks get executed. This introduced problem to cause memory spilling. In addition, sometimes, it returns `None` (does not work with limit push down), and it breaks followed logic in `to_tf`, which all relies on `schema()` to work. In this PR: * Introduce two optional parameters in `to_tf`: `feature_type_spec` and `label_type_spec` (by default they are `None`). So user can set `tf.TypeSpec` explicitly and the `Dataset.schema()` call will be skipped. Signed-off-by: Cheng Su <scnju13@gmail.com>
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zhe-thoughts
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Feb 9, 2024
…2917) (#43073) This PR is to skip `Dataset.schema()` call from `Dataset.to_tf()`. `Dataset.schema()` relies on `limit` to early stop execution, and sometimes the stop is not triggered timely so a lot of tasks get executed. This introduced problem to cause memory spilling. In addition, sometimes, it returns `None` (does not work with limit push down), and it breaks followed logic in `to_tf`, which all relies on `schema()` to work. In this PR: * Introduce two optional parameters in `to_tf`: `feature_type_spec` and `label_type_spec` (by default they are `None`). So user can set `tf.TypeSpec` explicitly and the `Dataset.schema()` call will be skipped. Signed-off-by: Cheng Su <scnju13@gmail.com>
ratnopamc
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Feb 11, 2024
…y-project#42917) This PR is to skip `Dataset.schema()` call from `Dataset.to_tf()`. `Dataset.schema()` relies on `limit` to early stop execution, and sometimes the stop is not triggered timely so a lot of tasks get executed. This introduced problem to cause memory spilling. In addition, sometimes, it returns `None` (does not work with limit push down), and it breaks followed logic in `to_tf`, which all relies on `schema()` to work. In this PR: * Introduce two optional parameters in `to_tf`: `feature_type_spec` and `label_type_spec` (by default they are `None`). So user can set `tf.TypeSpec` explicitly and the `Dataset.schema()` call will be skipped. Signed-off-by: Cheng Su <scnju13@gmail.com> Signed-off-by: Ratnopam Chakrabarti <ratnopamc@yahoo.com>
tterrysun
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Feb 14, 2024
…y-project#42917) This PR is to skip `Dataset.schema()` call from `Dataset.to_tf()`. `Dataset.schema()` relies on `limit` to early stop execution, and sometimes the stop is not triggered timely so a lot of tasks get executed. This introduced problem to cause memory spilling. In addition, sometimes, it returns `None` (does not work with limit push down), and it breaks followed logic in `to_tf`, which all relies on `schema()` to work. In this PR: * Introduce two optional parameters in `to_tf`: `feature_type_spec` and `label_type_spec` (by default they are `None`). So user can set `tf.TypeSpec` explicitly and the `Dataset.schema()` call will be skipped. Signed-off-by: Cheng Su <scnju13@gmail.com> Signed-off-by: tterrysun <terry@anyscale.com>
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Why are these changes needed?
This PR is to skip
Dataset.schema()call fromDataset.to_tf().Dataset.schema()relies onlimitto early stop execution, and sometimes the stop is not triggered timely so a lot of tasks get executed. This introduced problem to cause memory spilling. In addition, sometimes, it returnsNone(does not work with limit push down), and it breaks followed logic into_tf, which all relies onschema()to work.In this PR:
to_tf:feature_type_specandlabel_type_spec(by default they areNone). So user can settf.TypeSpecexplicitly and theDataset.schema()call will be skipped.Related issue number
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.