dask-cuda | Utilities for Dask and CUDA interactions | GPU library

driver.get("https://www.newegg.com/p/pl?d=RTX+3080")
print([my_elem.get_attribute("innerHTML") for my_elem in driver.find_elements(By.CSS_SELECTOR, "a[title = 'View Details']")])

driver.get("https://www.newegg.com/

import cuml
from sklearn import datasets
​
from sklearn.preprocessing import MinMaxScaler
​
from imblearn.over_sampling import SMOTE
​
from imblearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split, GridSearchC

import cudf
import cupy
​
s = cudf.Series([0,1,2])
cupy.asarray(s)
array([0, 1, 2])

import cudf
import cupy
​
s = cudf.Series(["a","b","c"])
cupy.asarray(s)
---------------------------------------------------------

import cudf
import dask_cudf
​
df = cudf.DataFrame({
    "a": ["dog"]*10
})
ddf = dask_cudf.from_cudf(df, 3)
​
ddf["temp"] = 1
ddf["monotonic_id"] = ddf["temp"].cumsum()
del ddf["temp"]
​
print(ddf.partitions[2].compute())
     a  monotoni

from cuml.preprocessing.model_selection import train_test_split
import cudf
import cupy as cp

df = cudf.DataFrame({
    "a":range(5),
    "b":range(5)
})
y = cudf.Series(range(5))

# train_test_split(df, y.values, test_size=0.20, random_s