Install a Standalone Analytics Container

You can install a standalone deployment of the analytics container to set up your data files. The accuracy of the data files are important for a proper analysis of your entitlements.

The deployment uses the Docker deployer machine to set up a standalong analytics container on the target node. The analytics container is automatically installed at the node where the Spark master is running.

Review the hardware and software specifications as outlined in the Release Notes (LINK)
Ensure your deployers have the appropriate system access to the ForgeRock Google Cloud Repository repository site to access the Docker image. The fully qualified image is: gcr.io/forgerock-autoid//autoid/dev-compact:deployer-rel-31-spark2.4.4-analytics-19-elektra
- The image contains release 31 of the microservices
- Apache Spark 2.4.4
- Analytics container version 19
On both the control and target nodes, make sure you have CentOS 7 and Python installed on all nodes. Python is packaged with CentOS 7. Autonomous Identity works with the following versions:
- CentOS 7 uses Python 2.7.5 in its base package repository.
- CentOS 7.7 uses Python 3.7.2 in its base package repository.
Ensure that you have access to the ForgeRock git repository. The ForgeRock Git repository has some scripts that you can clone to your system.

Getting Started

On the Target node, download the registry_key.json to access the Google Cloud Repository (gcr.io) from here.
Access the Google Cloud Repository using the registry_key.json.
```
$ [../../resources/install.bash:#access-gcr]
```

Download the analytics container.

$ docker pull gcr.io/forgerock-autoid/analytics:v14

Start the Analytics Container

To start the analytics container, replace the following values for your environment:

SPARK_MASTER_URL
SPARK_HOST
DB_HOST
DB_PORT
DB_CERT_PASSWORD
DB_SSL_ENABLED
DB_USER
DB_PASSWORD
CONFIGURATION_SERVICE_URL
CONFIGURATION_SERVICE_USER
CONFIGURATION_SERVICE_PASSWORD
1. Run the analytics container with the variables for your environment.
  $ [../../resources/install.bash:#start-standalone-analytics]
2. Create a symlink to the Python runtime on the host.
  $ sudo ln -s /opt/zoran/python-3.6/bin/python3.6 /usr/local/bin/python3.6

Configure the Template

Run the create-template function on the Analytics container. The command creates the /data/conf/analytics_init_config.yml.
```
$ docker exec -it analytics bash analytics create-template
```
Edit the /data/conf/analytics_init_config.yml file with the data directory and user column descriptions.
Make sure the csv data files are placed in the path as specified in the /data/conf/analytics_init_config.yml.

Apply the template.

$ docker exec -it analytics bash analytics apply-template

Run the Analytics Pipeline

Analytics Container Commands
Command	Description
analytics create-template	When you run this command, the analytics container creates the `analytics_init_config.yml` . You want to edit the `user_column_descriptions` and the `spark.total.cores` that represents the number of cores that you want to allocate to the analytics.
analytics apply-template	Make sure to copy the .csv files to the `/data/input/` folder. Then run `analytics apply-template`.
analytics create-ui-config	Creates the `analytics_config.yml` file that is used later for various analytic jobs.
analytics apply-ui-config	Applies the `analytics_config.yml` .
analytics validate	Runs data validation.
analytics ingest	Ingests data into Autonomous Identity.
analytics audit	Runs a data audit to ensure if meets the specifications.
analytics train	Runs an analytics training run.
analytics predict-as-is	Runs an as-is predictions.
analytics predict-recommendation	Runs recommendations.
analytics publish	Publishes the analytics.

Run the Analytics pipeline using one of the above commands.
```
$ docker exec -it analytics bash analytics <command>
```

Autonomous Identity 2021.8.2

Install a Standalone Analytics Container

Getting Started

Start the Analytics Container

Configure the Template

Run the Analytics Pipeline