Anaconda Repository for IBM Cloud Pak for Data on Cloud Pak for Data

Version: 1.0.1    Premium   Partner

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Description

With the Anaconda Repository for IBM Cloud Pak for Data service, your enterprise can control the open source packages that data scientists can use in Jupyter notebooks and JupyterLab in Watson Studio analytics projects.

With the service, you can get Conda package updates in real time, as they are released, and you have access to:

  • More than 7500 Anaconda packages in Python and R
  • Numerous open source packages in Conda-Forge, CRAN, and PyPl

You can also use Anaconda Repository for IBM Cloud Pak for Data to store your own proprietary packages.

Anaconda Repository for IBM Cloud Pak for Data puts you in control:

  • Block, exclude, and include packages according to your enterprise standards
  • Specify which packages your team can download and who can access them
  • Keep vulnerabilities and unreliable software out of your data science and machine learning pipelines
  • Manage dependent packages and give users quicker access to open source software

Data scientists in analytics projects can create custom environment definitions that include the conda channels and packages from the repository and then use those environments to run Jupyter notebooks and scripts.

Quick links

Integrated services

Table 1. Prerequisite services. This service requires the following prerequisite services to be installed.
Service Capability
Watson™ Studio Prepare, analyze, and model data in a collaborative environment with tools for data scientists, developers, and domain experts.
Table 2. Related services. The following related services are often used with this service and provide complementary features, but they are not required.
Service Capability
Runtime 22.2 with Python 3.10 for GPU Access compute environments for Jupyter Notebooks that use GPU-accelerated Python 3.10 libraries.
Runtime 22.2 with R 4.2 Access compute environments to create Jupyter Notebooks that use R 4.2 libraries.
Watson Machine Learning Build, train, and deploy machine learning models with a full range of tools.