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In-memory large dataframe processing #4
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@romain-intel is the idea to support this locally or on an AWS instance? Wondering if the idea is just making it more integrated with Apache Spark (via pyspark), or finding a way like an IterableDataset in Pytorch, to split loading among workers and have them loaded at model time. I imagine the difficulty might be in the atomicity of a Would be curious to hear your thoughts on this. |
@tduffy000 We have an in-house implementation of |
Maybe the use of Metaflow could be somehow combined with Dask, which supports bigger-than-memory dataframes to solve this issue. I am not sure if/how it would be possible to serialize and restore Dasks big and lazy-evaluated dataframes between steps though. |
Maybe something like a dataflow transfered between steps like Bonobo. Also here is other example of software product that uses datapickle and Dask to run dataflows clusterized in cloud. |
I think the possibility to use Apache Spark within Metaflow would be extremely useful. When you have your feature engineering workflow written in pyspark it's kind of a pain to translate everything to pandas and also it's hard to predict how well this will work on large datasets. |
Would something like https://vaex.readthedocs.io/en/latest/index.html be a possible solution here? |
@savingoyal any update to release the dataframe implementation? Adding modin as a distributed drop-in for pandas dfs |
another mention Spark Pandas https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html |
Agree the Pandas on Spark reference by @talebzeghmi would be valuable, but you would still need a Spark context. I think being able to declare that your task run in AWS Glue would potentially allow for both Pandas on Spark or just vanilla pySpark as a step. |
Still interested! Would appreciate any update, especially if it's "yeah we're not going to do this in the forseeable future after all" |
Metaflow tries to make the life of data scientists easier; this sometimes means providing ways to optimize certain common but expensive operations. Processing large dataframes in memory can be difficult and Metaflow could provide ways to do this more efficiently.
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