cuml | cuML - RAPIDS Machine Learning Library | Machine Learning library

I have been reading about perfroming Hyperparameters Tuning for KNN Algorthim, and understood that the best practice of implementing it is to make sure that for each fold, my dataset should be normalized and oversamplmed using a pipeline (To avoid data leakage and overfitting). What I'm trying to do is that I'm trying to identify the best number of neighbors (n_neighbors) possible that gives me the best accuracy in training. In the code I have set the number of neighbors to be a list range (1,50), and the number of iterations cv=10.

My code below:

I am pulling data with fetch api. but I could not retrieve the data in the todosApi section of the last data I pulled. how can i pull data?

Getting this error while converting array to cuPy array: TypeError: Implicit conversion to a host NumPy array via array is not allowed, To explicitly construct a GPU array, consider using cupy.asarray(...) To explicitly construct a host array, consider using .to_array()

I have to convert a code written using cuml (RAPIDS) into sklearn.

I found out that in cuml.truncatedSVD the parameter n_components which is the output dimensions (number of singular values) can equal to the number of inputs/features in cuml, but not in sklearn.decomposition.truncatedSVD which requires a value strictly inferior to the input dimensions.

The cuml code I'm converting takes two features as inputs and computes two singular values, which is impossible with sklearn.

Is there a way workaround or a way to make it work with sklearn?

I'm trying to find right parameters for ARIMA but not able to use parameters higher than 4. Here is the code.

I've been trying to run RAPIDS on Google Colab pro, and have successfully installed the cuml and cudf packages, however I am unable to run even the example scripts.

Anytime I try to run the fit function for cuml on Google Colab I get the following error. I get this when using the demo examples both for installation and then for cuml. This happens for a range of cuml examples (I first hit this trying to run UMAP).

I'm following the guide here: https://cloudprovider.dask.org/en/latest/packer.html#ec2cluster-with-rapids

In particular I set up my instance with packer, and am now trying to run the final piece of code:

i am trying to split data into training and validation data, for this i am using train_test_split from cuml.preprocessing.model_selection module.

but got an error:

I have the following data frame: