Setting up the system and running the Automated installer

• Prerequisites
• Install the operating system
• Open necessary ports
• Ensure user access of client machines to cluster hosts
• Set the appropriate heap size
• Log in with root permission
• Mount a shared file system
• Complete further setup steps and start installing the product

Prerequisites

Install the operating system

The Deep Learning packages require one of the following operating systems:

Red Hat Enterprise Linux (RHEL) 7.6 little endian for IBM® POWER8® and IBM POWER9™

• PowerAI can be installed and run directly on a bare-metal RHEL 7.6 system
• The RHEL installation image and license must be acquired from Red Hat

For more information about installing operating systems on IBM Power Systems servers, see Quick start guides for Linux on IBM® Power System servers.

Open necessary ports

If a firewall is enabled, the following default ports must be granted access on all management hosts for IBM Spectrum Conductor Deep Learning Impact: 9243, 9280, 5000, 5001, 27017, and 6379. If you change these ports after installation, make sure to update firewall rules accordingly.

Review this topic to determine which ports need to be opened for IBM Spectrum Conductor™: Summary of ports used by IBM Spectrum Conductor.

Review this topic to determine which ports need to be opened for IBM Spectrum Conductor Deep Learning Impact: Summary of ports used by IBM Spectrum Conductor Deep Learning Impact.

Ensure user access of client machines to cluster hosts

Spark workload runs on non-management hosts in your cluster. Therefore, the Apache Spark UI and RESTful APIs that are available from Spark applications and the Spark history server must be accessible to your end users. This access is also required for any notebooks that you configure for use with IBM Spectrum Conductor.

If the hosts and the ports used are not accessible from your client machines, you can encounter errors when you access notebooks and IBM Spectrum Conductor user interfaces. The management hosts also must be able to access these hosts and the ports used.

Set the appropriate heap size

The default Elasticsearch installation uses a 2-4 GB heap for the Elasticsearch services. Elasticsearch recommends that you assign 50 percent of available memory to the Elasticsearch client service, but not exceed 30.5 GB. Based on these recommendations, configure the Elasticsearch client and data services heap in IBM Spectrum Conductor to use 6~8 GB. Further, the default garbage collector for Elasticsearch is Concurrent-Mark and Sweep (CMS). To prevent long stop-the-world pauses, do not configure the heap size to be higher than what the CMS garbage collector was designed for (approximately 6-8 GB).

For instructions to change the heap size, see How do I change the heap size for Elasticsearch?.

Log in with root permission

The following tasks all require that you log in as a user that has root or sudo to root permission.

Mount a shared file system

If you are using multiple nodes, you must mount a shared file system. The shared file system is used for user data, such as datasets, tuning data, validation results, training models and more. In this step, the default cluster administrator account (egoadmin) is used and the mount point is /dli_shared_fs. The shared file system must meet these requirements:

• The shared file system must be mounted to a clean directory. If you are reinstalling IBM Spectrum Conductor Deep Learning Impact, make sure that the directory specified is empty.
• The shared file system must have a minimum of 2 GB of free disk space.
• The cluster administrator account (the account that was specified by the CLUSTERADMIN variable during IBM Spectrum Conductor installation) must have read and write permissions to the shared file system.
• setfacl must be supported on the hosts mounting the file system. This does not apply if IBM Watson Studio Local is installed.
• When using an IBM Spectrum Scale shared file system, ensure that the file system allows traditional (Posix) ACLs, not just NFSv4 ACLs.

To verify that you mounted the shared file system correctly, assuming that cluster administrator account is egoadmin and the mount point is /dli_shared_fs, follow these steps:

1. Export the environment variables:
   Note: The directory specified as the shared file system must exist. Before exporting the shared file system environment variable, make sure that the directory specified exists, if not, manually create it.

   export CLUSTERADMIN=egoadmin
   export ADMINGROUP=egoadmin
   export DLI_SHARED_FS=/dli_shared_fs

2. Change the ownership of DLI_SHARED_FS to CLUSTERADMIN:

   chown -Rh $CLUSTERADMIN:$ADMINGROUP $DLI_SHARED_FS

3. Make sure DLI_SHARED_FS is owned by CLUSTERADMIN and remove all other access from DLI_SHARED_FS:

   chmod -R 755 $DLI_SHARED_FS

4. Set the correct ownership for DLI_RESULT_FS, which is the mount point for shared result data storage:

   chown $CLUSTERADMIN:$ADMINGROUP $DLI_RESULT_FS
   chmod 733 $DLI_RESULT_FS
   chmod o+t $DLI_RESULT_FS

Complete further setup steps and start installing the product

Follow the instructions in Running the Watson Machine Learning (WML) Accelerator Software Install Module to complete the system setup and start installing IBM Watson Machine Learning Accelerator.