Connect to Workbench kernel from local VSCode

Create a Data Science Project ai-bootcamp

Create a workbench with following parameters: Name workbench-1 image Standard Data Science

Open VS Code in local. Create a new jupyter notebook.

Principal Component Analysis Plots: This example uses Iris Data, performs a PCA and plots the data against the first two principal components in a scatter plot. It then prints the eigenvalues and eigenvectors of the covariance matrix and finally prints the precentage of total variance explained by each component.

In a new cell, add the below contents.

from __future__ import division
import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.datasets
import sklearn.metrics as metrics
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

# load the iris dataset
dataset = sklearn.datasets.load_iris()
# define feature vectors (X) and target (y)
X = dataset.data
y = dataset.target
labels = dataset.target_names

# <help:scikit_pca>
# define the number of components to compute, recommend n_components < y_features
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

# plot the first two principal components
fig, ax = plt.subplots()
plt.scatter(X_pca[:,0], X_pca[:,1])
plt.grid()
plt.title('PCA of the dataset')
ax.set_xlabel('Component #1')
ax.set_ylabel('Component #2')
plt.show()

# <help:scikit_pca>
# eigendecomposition on the covariance matrix
cov_mat = np.cov(X_pca.T)
eig_vals, eig_vecs = np.linalg.eig(cov_mat)
print('Eigenvectors \n%s' %eig_vecs)
print('\nEigenvalues \n%s' %eig_vals)

# <help:scikit_pca>
# prints the percentage of overall variance explained by each component
print(pca.explained_variance_ratio_)

Login to OpenShift from terminal on your laptop.

Switch to project and view pods

Start port-forwarding to the workbench pod You need to forward to the port the pod is listening on. It is usually 8888 for RHOAI workbench. You can find this port from the service in your project with name same as your workbench.

Open the Jupyter Notebook in your VSCode

Click on Select Kernel in the top right corner of the notebook.

From the options, select Existing Jupyter Server and then enter the url as follows: localhost [:port] /context-path

Select Yes for the prompt: Connecting over HTTP without a token may be an insecure connection. Do you want to connect to a possibly insecure server?

Choose a new name or click 'Enter' to accept the suggested server name.

Choose Python 3.9 from the list of available kernels.

We should be able to see the selected kernel in top-right corner. Execute the cells in the notebook. This will execute using the remote kernel on the RHOAI workbench.