Oracle Machine Learning Notebooks provides a Conda interpreter to enable administrators to create conda environments with custom third-party Python and R libraries. Once created, you can download and activate Conda environments inside a notebook session also using the Conda interpreter.

1. Type %conda at the beginning of the paragraph to connect to the Conda interpreter, and press Enter.

   %conda

2. Next, download and activate the Conda environment. Type:

   download sbenv
   activate sbenv

   In this example, the Conda environment is downloaded and activated. The name of the Conda environment in this example is sbenv.
3. You can view all the packages that are present in the Conda environment. To view the list of packages, type list.
   
4. Here's an example that demonstrates how to use the seaborn library package for visualization. Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. This example
   • Imports Pandas and seaborn
   • Loads the Iris dataset
   • Plots the datapoints, that is, the three different species of the Iris flower - Setosa, Virginica, and Versicolor based on its dimensions. It creates a scatter plot
   Type:

   %python
   
   def sb_plot():
       import pandas as pd
       import seaborn as sb
       from matplotlib import pyplot as plt
       df = sb.load_dataset('iris')
       sb.set_style("ticks")
       sb.pairplot(df,hue = 'species',diag_kind = "kde",kind = "scatter",palette = "husl")
       plt.show()

   
5. Run the function in a Python paragraph.
   Type:

   %python 
   sb_plot()

   

• About the Conda Environment and Conda Interpreter
  Conda is an open-source package and environment management system that allows the use of environments containing third-party Python and R libraries. Oracle Machine Learning User Interface (UI) provide the conda interpreter to install third-party Python and R libraries inside a notebook session.
• Conda Interpreter Commands
  This table lists the commands for the Conda interpreter.
• Create a Conda Environment for Python and Install Python Packages
  This topic demonstates how to create a Conda environment for Python packages, with Python 3.12.3 that is compatible with OML4Py. Here, you will also install the Python packages—tensorflow and seaborn from the conda-forge channel.
• Create a Conda Environment for R and Install R Packages
  This topic shows how to create a Conda environment for R packages, with R-4.0.5 for OML4R compatibility, and install the forecast and ggplot2 packages.
• Upload the Conda environments to an Object Storage bucket associated with the Autonomous Database
  There is one Object Storage bucket for each data center region. The Conda environment is saved to a folder in Object Storage corresponding to the tenancy and database. The folder is managed by Autonomous Database and is only available to users through OML Notebooks. There is an 8G maximum size for a single Conda environment, and no size limit on Object Storage.