Richard (Rick) Zamora rjzamora

## rearrange_cudf_test.ipynb

      
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                / rearrange_cudf_test.ipynb
            
            
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              July 30, 2019 21:01
            
              
                Comparing behavior of cudf and pandas rearrange_by_divisions behavior in Dask
              
          
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## Hang_Reproducer.ipynb

      
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              September 6, 2019 23:03
            
          
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## Simple_percentile.ipynb

      
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                / Simple_percentile.ipynb
            
            
              Created
              September 5, 2019 23:16
            
          
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## Categorical_Debugging.ipynb

      
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                / Categorical_Debugging.ipynb
            
            
              Created
              September 25, 2019 14:15
            
              
                Attempt to reproduce behavior described in cudf#2850 (instead reproduces behavior described in cudf#2862)
              
          
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## dask_cudf_merge_benchmark.ipynb

      
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                rjzamora
                / dask_cudf_merge_benchmark.ipynb
            
            
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              December 9, 2019 22:29
            
              
                dask_cudf + UCX merge benchmark
              
          
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## tcp_set_index_prof_256tasks_4gpus.html


<!DOCTYPE html>
<html lang="en">

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## tcp_merge_prof_4gpus.html


<!DOCTYPE html>
<html lang="en">

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## CPU_caching_experiment.ipynb

      
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                rjzamora
                / CPU_caching_experiment.ipynb
            
            
              Created
              April 25, 2020 16:46
            
              
                Experiments for caching cudf DataFrame objects that will be re-read many times
              
          
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## nvtabular-dask-criteo-tutorial.ipynb

      
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                / nvtabular-dask-criteo-tutorial.ipynb
            
            
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              October 11, 2020 17:16
            
              
                NVTabular-0.2-Dask-Blog-Tutorial
              
          
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## dask_pq_bench.py
from dask.distributed import Client, LocalCluster, wait
import dask.dataframe as dd
from dask.datasets import timeseries
import glob
import time
import argparse
import numpy as np


def run(args):
	from dask.distributed import Client, LocalCluster, wait
	import dask.dataframe as dd
	from dask.datasets import timeseries
	import glob
	import time
	import argparse
	import numpy as np


	def run(args):