Comments (3)
what input and output should we want? And for predicting, does it should be the model serving part?
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I'm not sure whom raydp's target user is. Looks like the evaluate function is enough.
If I'm a kaggler, I need the prediction result of test dataset which is without label and submit the result to the competition. For now, I need to get the model and predict by myself.
The purpose of predicting is different from serving. We just want to obtain the prediction in a python script
Input should support both RayDp dataset and spark dataframe. For output, should it be consistent with input or just numpy array/pandas dataframe?
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close as stale
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Related Issues (20)
- Add shim layer for Spark 3.4.x
- Support/Validate Ray 2.4.0 Release
- Performance benchmark on RayDP v.s. Spark HOT 1
- Data ownership transfer tests fail in Ray 2.4.0
- Wanted to configure spark to use mysql as metastore instead of derby hive metastore which is default HOT 5
- enable_hive not working with raydp HOT 24
- Threads hang on getting from object store when calling `to_spark`
- raydp.init_spark fails HOT 3
- Issues with running on Ray Client HOT 3
- raydp start occur null error HOT 1
- Executor lost triggered by JVM OOM can't recover and cause subsequent steps stuck HOT 12
- Support ray 2.3.1 HOT 3
- When submit a script another process start locally in the head node HOT 1
- Add the ability to set the owner of objects when calling _save_spark_df_to_object_store() HOT 1
- Runtime env_vars cannot be passed down to executors HOT 1
- Getting Java gateway process exited before sending its port number during init_spark() HOT 9
- Issue with Running Multiple Spark Jobs Concurrently on Ray Cluster HOT 1
- RayDP Docker Image with Python 3.10 Failing to Build HOT 2
- [stream] does raydp support spark stream data? HOT 1
- Is it possible for raydp to utilize a Spark session created by Apache Livy? HOT 1
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