Anaconda for Cluster Management
NOTE: Anaconda for cluster management has been replaced by Anaconda Scale. This documentation is made available for existing installations only.
To use Anaconda with a cluster, please use Anaconda Scale and refer to the Anaconda Scale documentation.
Anaconda for cluster management provides resource management tools to easily deploy Anaconda across a cluster. It helps you manage multiple conda environments and packages (including Python and R) on bare-metal or cloud-based clusters. Supported platforms include Amazon EC2, bare-metal clusters, or even a collection of virtual machines.
Anaconda for cluster management can be used with distributed computation frameworks such as Spark or Dask and works alongside enterprise Hadoop distributions such as Cloudera CDH or Hortonworks HDP.
Anaconda for cluster management is freely available via Anaconda Cloud for unlicensed, unsupported use with up to 4 cluster nodes. Anaconda Workgroup and Enterprise include licenses for 8 and 16 nodes, respectively. If you would like to use Anaconda for cluster management with additional nodes on a bare-metal, on-premises, or cloud-based cluster, please contact firstname.lastname@example.org.
Features of Anaconda for cluster management:
- Easily install Python and R packages across multiple cluster nodes
- Manage multiple conda environments across a cluster
- Push local conda environments to all cluster nodes
- Manage both cloud-based and bare-metal clusters
- Remotely SSH and upload/download files to and from cluster nodes
Typical configuration of Anaconda for cluster management:
Table of contents:
- Creating a cluster
- Cluster management
- Conda management
- Python with Spark How-tos
- Cluster cheat sheet
- Using Anaconda with Cloudera CDH
- FAQ / Known issues
- Release notes