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* use dask.dataframe's aca * support on_host * fix split-out (band-aid) * second attempt to refactor categorify * use temporary write * save state - still need to figure out how to 'always' write cats to a directory * using dask to write/read categories * use official dispatch * support multi-partition cats * avoid manual bcast merge for single partition * clean up _to_parquet_dask a bit * deprecate tree_width * fix split_out default * update join_groupby and target_encoding options * improve split_out=1 performance * uuse np.arange(... like) * improve test coverage * update docs * use schema=None too avoid need for head() * arbitrary change * improve options and test coverage * use dask to calculate divisions for now * cast cats_n type to be consistent with cats_1 * catch expected warnings * formatting * formatting * formatting (one more time) * Update bench/examples/dask-nvtabular-criteo-benchmark.py * Update nvtabular/ops/categorify.py * enable multi-columnn vocabs and simplify dask utilities * minor change to trigger CI * formatting * deprecate start_index * deprecate start_index and na_sentinel * pushing on categorify redesign * get tests passing with new categorify standard * update size stats when split_out == 1 * fix test_categorify_freq_limit * create _save_embeddings function * start writing meta.*.parquet file for non-unique statistics * use meta.*.parquet * fix embedding size info * revert stale test changes * update test_drop_low_cardinality * fix test_cpu_workflow.py * fix test_workflow.py * catch TypeError in dask sort * fix single_table * update documentation * cpp experiment * missing parentheses --------- Co-authored-by: Karl Higley <[email protected]>
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