[SPARK-51551] [ML] [PYTHON] [CONNECT] For tuning algorithm, allow using save / load to replace cache #50324
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Signed-off-by: Weichen Xu <[email protected]>
python/pyspark/ml/tuning.py
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| _SPARKML_TUNING_TEMP_DFS_PATH = "SPARKML_TUNING_TEMP_DFS_PATH" | |||
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what about a new parameter instead of this env variable?
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Spark config is not available in some circumstances. so I suggest to use environmental variable to config.
Signed-off-by: Weichen Xu <[email protected]>
Signed-off-by: Weichen Xu <[email protected]>
Signed-off-by: Weichen Xu <[email protected]>
Signed-off-by: Weichen Xu <[email protected]>
Signed-off-by: Weichen Xu <[email protected]>
zhengruifeng
approved these changes
Mar 21, 2025
| validation = datasets[i][1] | ||
| train = datasets[i][0] | ||
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| if tmp_dfs_path: |
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let's define a helper function:
def _cache(df):
if ...:
df.cache()
else:
spark = df._session
df.save
spark.read...
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then we need to handler the uncache step together in the helper function, i.e., it should be a context manager. is this you want ?
Signed-off-by: Weichen Xu <[email protected]>
Signed-off-by: Weichen Xu <[email protected]>
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What changes were proposed in this pull request?
For tuning algorithm, allow using save / load to replace cache.
Why are the changes needed?
Dataframe persisting is not well supported in certain cases, so we need a replacement.
Does this PR introduce any user-facing change?
No.
How was this patch tested?
Manually.
Was this patch authored or co-authored using generative AI tooling?
No.