Use RAPIDS on a GPU cluster

Scale to larger data sizes with multiple GPUs

We perform the same machine learning exercise as the previous notebook, except on a cluster of multiple GPUs with Dask. This exercise uses the following RAPIDS packages:

  • dask-cudf: distributed cudf dataframes using Dask
  • cuml.dask: distributed cuml algorithms using Dask

Connect to Dask Cluster

This project has a Dask cluster defined for it, which you can start or connect to in the below cell. For more information about Dask clusters in Saturn Cloud, check out the docs.

from dask.distributed import Client, wait
from dask_saturn import SaturnCluster

n_workers = 3
cluster = SaturnCluster(n_workers=n_workers)
client = Client(cluster)
client.wait_for_workers(n_workers)

Load data

The code below loads the data into a dask-cudf data frame. This is similar to a pandas or cudf dataframe, but it is distributed across GPUs in the cluster.

import dask_cudf

taxi = dask_cudf.read_csv(
    "s3://nyc-tlc/trip data/yellow_tripdata_2019-01.csv",
    parse_dates=["tpep_pickup_datetime", "tpep_dropoff_datetime"],
    storage_options={"anon": True},
    assume_missing=True,
)

Many dataframe operations that you would execute on a pandas dataframe also work for a dask-cudf dataframe:

len(taxi)
taxi.head()

When we say that a dask-cudf dataframe is a distributed data frame, that means that it comprises multiple smaller cudf data frames. Run the following to see how many of these pieces (called “partitions”) there are.

taxi

Train model

Now that the data have been prepped, it’s time to build a model!

For this task, we’ll use the RandomForestClassifier from cuml.dask (notice the .dask!). If you’ve never used a random forest or need a refresher, consult “Forests of randomized trees” in the scikit-learn documentation. We cast to 32-bit types for compatibility with older versions of cuml.Cast to 32-bit types for compatibility with older versions of cuml

X = taxi[["PULocationID", "DOLocationID", "passenger_count"]].astype("float32").fillna(-1)
y = (taxi["tip_amount"] > 1).astype("int32")

Dask performs computations in a lazy manner, so we persist the dataframe to perform data loading and feature processing and load into GPU memory.

X, y = client.persist([X, y])
_ = wait([X, y])
from cuml.dask.ensemble import RandomForestClassifier

rfc = RandomForestClassifier(n_estimators=100, ignore_empty_partitions=True)
_ = rfc.fit(X, y)

Calculate metrics

We’ll use another month of taxi data for the test set and calculate the AUC score

taxi_test = dask_cudf.read_csv(
    "s3://nyc-tlc/trip data/yellow_tripdata_2019-02.csv",
    parse_dates=["tpep_pickup_datetime", "tpep_dropoff_datetime"],
    storage_options={"anon": True},
    assume_missing=True,
)

X_test = taxi_test[["PULocationID", "DOLocationID", "passenger_count"]].astype("float32").fillna(-1)
y_test = (taxi_test["tip_amount"] > 1).astype("int32")
from cuml.metrics import roc_auc_score

preds = rfc.predict_proba(X_test)[1]
roc_auc_score(y_test.compute(), preds.compute())



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