Usage

Jupyter Notebooks and IPython

Load the cudf.pandas extension at the beginning of your notebook. After that, just import pandas and operations will use the GPU:

%load_ext cudf.pandas

import pandas as pd

URL = "https://github.com/plotly/datasets/raw/master/tips.csv"
df = pd.read_csv(URL)                 # uses the GPU
df["size"].value_counts()             # uses the GPU
df.groupby("size").total_bill.mean()  # uses the GPU
df.apply(list, axis=1)                # uses the CPU (fallback)

Command-line usage

From the command line, run your Python scripts with -m cudf.pandas:

python -m cudf.pandas script.py

Understanding performance - the cudf.pandas profiler

cudf.pandas will attempt to use the GPU whenever possible and fall back to CPU for certain operations. Running your code with the cudf.pandas.profile magic generates a report showing which operations used the GPU and which used the CPU. This can help you identify parts of your code that could be rewritten to be more GPU-friendly:

%load_ext cudf.pandas
import pandas as pd

%%cudf.pandas.profile
df = pd.DataFrame({'a': [0, 1, 2], 'b': [3,4,3]})

df.min(axis=1)
out = df.groupby('a').filter(
    lambda group: len(group) > 1
)

When an operation falls back to using the CPU, it’s typically because that operation isn’t implemented by cuDF. The profiler generates a handy link to report the missing functionality to the cuDF team.

To profile a script being run from the command-line, pass the --profile argument:

python -m cudf.pandas --profile script.py