Installation, requirements and versions
Lists system requirements, installation instructions, Python and Modeling Assistant versions and Community Edition limitations.
IBM® Decision Optimization for Watson Studio was previously known as IBM Decision Optimization for Data Science Experience.
To find details of the hardware and software configurations required for installation of IBM Watson Studio refer to the system requirements page.
To install Watson Studio follow the detailed step-by-step installation instructions provided for Watson Studio.
Select the appropriate version of the Decision Optimization add-on, according to your installed version of Watson Studio, as shown in the following table.
|Watson Studio (DSX Local) Version||Decision Optimization for Watson Studio (Data Science Experience) Installer|
|Watson Studio V1.2.2 and later||Decision Optimization for Watson Studio V1.2.2 and later|
|DSX Local V1.2.1||Decision Optimization for Data Science Experience V1.1|
|DSX Local V1.2 Fix Pack 3||Decision Optimization for Data Science Experience V18.104.22.168 from Fix Central|
|DSX Local V1.2||Decision Optimization for Data Science Experience V1.0|
- Login as root on the master node of Watson Studio installation
- Download the installation package TAR file into any directory
tar xzvf <add-on-installer>.tar.gz ./install.sh <namespace where Watson Studio is installed, the default is 'dsx'>
dods_addon_install.login the current directory.
To uninstall the add-on, run
./uninstall.sh from the same directory.
For Watson Studio on IBM Cloud Private you can either use the previous installation method or the following alternative installation method:
If you installed Watson Studio on IBM Cloud Private (as documented in Install Watson Studio on the ICP catalog), you can also add the Decision Optimization add-on to the IBM Cloud Private catalog and install it from the catalog.
To add the Decision Optimization to the IBM Cloud Private catalog, complete the following steps:
- Enter the following command to ensure that bx pr and docker are
bx pr login -a https://<cluster_ip>:8443 --skip-ssl-validation docker login <cluster_name>:8500
- In the IBM Cloud Private CLI tool, enter the following
bx pr load-ppa-archive --archive <add-on-installer>.tar.gz
- Set the Watson Studio images scope to
globalby entering the following command with
kubectlauthenticated (you must have administrator rights to do this):
for image in dd-install dd-init dd-processor dd-processor-docplex-python-2; do kubectl get image $image -o yaml | sed 's/scope: namespace/scope: global/' | kubectl apply -f -; done
You can then install the Decision Optimization add-on from the catalog as follows:
- Go to
ibm-dodschart now displays.
and verify that the
- Click Configure, enter a release name and the namespace where Watson Studio is installed.
- Click Install to install the add-on
Upgrading from earlier versions
To upgrade from earlier versions of Watson Studio and Decision Optimization for Watson Studio:
- Upgrade Watson Studio
- Install Decision Optimization for Watson Studio on top of
the migrated cluster.
For example, to upgrade from DSX Local V22.214.171.124 with Decision Optimization for Data Science Local V1.2.1, to version V1.2.1, first upgrade DSX Local to V1.2.1 and then install Decision Optimization for Data Science V1.1.
The Decision Optimization for Watson Studio environment currently supports Python V2.7.
As a Watson Studio user, you have free access to the Community Edition of the Decision Optimization engines. The Community Edition is limited to 1000 constraints and variables, and a search space of 1000 X 1000 for constraint programming problem. You can run optimization models that don't exceed this size without any additional configuration. To solve larger problems, install the Decision Optimization full capacity add-on as described above.
Modeling Assistant model types
The Modeling Assistant currently supports Resource Assignment, Scheduling, Supply & Demand Planning and Selection & Allocation models. See Selecting a Decision domain in the Modeling Assistant.