rapids

Describe the bug
Clipping a DataFrame or Series using ints causes a cudf Failure because it won't handle the different dtypes (int and float)

Steps/Code to reproduce bug

data = cudf.Series([-0.43, 0.1234, 1.5, -1.31])
data.clip(0, 1)

...
  File "cudf/_lib/replace.pyx", line 216, in cudf._lib.replace.clip
  File "cudf/_lib/replace.pyx", line 198, in cudf._lib.replace.clamp

We no longer need to control the number of concurrent kernels, since now we control the number of concurrent tasks

Most classes are either under the com.nvidia.spark.rapids, org.apache.spark.sql.rapids, or some other Spark package prefix that ends in .rapids, but there are a few classes in the plugin that appear directly within an Apache Spark package:

• org.apache.spark.sql.catalyst.CudfUnsafeRow
• org.apache.spark.shuffle.RapidsShuffleExceptions.scala

Ideally these classes should be moved to an existi

It would be nice to be able to set the initial_pool_size with a string like "500mb" or "2gb" as opposed to integer sizes like 500000000. We could vendor the code Dask uses to accomplish this:

https://github.com/dask/dask/blob/31af7f7040643c447a72c87a8f12457094ec15ff/dask/utils.py#L1171

Let's show some examples of integration with kgextension
https://kgextension.readthedocs.io/en/latest/

Could be another notebook added to the tutorial.

Where it fits, we might also integrate as a dependency?

In trying to write tests for #189, I'm finding very difficult to add columns to existing tests, as in some cases like the all_types table, the table is defined in a separate file than the tests and multiple tests try to write to the same table.

Additionally, our test suite doesn't prove that the data that are uploaded are the same as the data downloaded for all types.

We should consider m

As seen with gumdropsteve/turbo-telegram@3e2f3b3, the data for the first half of 2016 can be downloaded & preprocessed just like that of 2015. Is there any other data in the effective range? I.e. is pre-2015 data recorded the same?

If so, let's add it.