DataVisualizer


The DataVisualizer component performs visualization on the data.

Pre-requisite: Explored dataset object saved into a path (in-path) or pushed into a repo.

Name

data_visualizer

Purpose

To visualize data using xpresso Visualization Libaries

Usage Scenarios

It can be used to perform visualization on data. (refer Data Visualization Library

Created By

xpresso.ai Team

Support e-mail

support@xpresso.ai

Binary / Source / Both Versions

Binary

Docker Image Reference

  • For non-Abzooba instances: dockerregistry.xpresso.ai/library/data_visualizer:2.2

  • For Abzooba sandbox instance: dockerregistrysb.xpresso.ai/library/data_visualizer:2.2

  • For Abzooba QA instance: xpresso.ai/library/data_visualizer:2.2

  • For Abzooba PROD instance: dockerregistryprod.xpresso.ai/library/data_visualizer:2.2

Type of component

pipeline_job

Usage Instructions

When Deploying Components:
  1. Specify Mount Path (Mount Path is a shared directory between components in a pipeline which is used for reading/writing data)

  2. Specify the Docker image referred above in the ‘Custom Docker Image’ textbox

  3. Specify arguments as specified in the ‘Deploy Solution Arguments’ section below

Result

Stores the visualization report into the location specified by out-path. Note: For saving data into NFS specify the out-path within mount_path

Example

Assume that a dataset has to be fetched from the data repository and visualized. Create a component of type ‘pipeline_job’, and deploy it using the Custom Docker image specified above. Create a pipeline using the component To visualize the dataset, the following parameters must be specified when an experiment is run on the pipeline: Run Name: <provide a unique run name> Pipeline Version: <select the version of the pipeline you want to run> repo-name: <name of data repository> (usually, the same as the solution name) branch-name: <name of branch from which to fetch data for exploration> commit-id: <commit ID of data to be fetched for exploration> target-attribute: <specify the name of the target attribute in your dataset, if any> out-path: <path in NFS mount where you want to store the results, e.g., ‘/data’> The visualization report will be stored here

Deploy Solution Arguments:

  • Using explored data from mount path

Field

Parameter key (refer run-parameters below)

Description

Mandatory?

Dynamic arg required?

Comments

-component-name

component_name

The component name in the solution

Yes

Yes

-visualizer-output-path

visualizer_output_path

The path where visualization results are saved

Yes

Yes

Visualization results are saved here

-visualizer-input-path

visualizer_input_path

Path of the file to load explored data for visualization

Yes

Yes

-target-attribute

target_attribute

Name of the target variable in the dataset

No

Yes

Applicable only to structured datasets (refer to Data Visualization Library

  • Fetching explored data from the data versioning system

Field

Parameter key (refer run-parameters below)

Description

Mandatory?

Dynamic arg required?

Comments

-component-name

component_name

The component name in the solution

Yes

Yes

-visualizer-output-path

visualizer_output_path

The path where visualization results are saved

Yes

Yes

Visualization results are saved here

-repo-name

repo_name

Name of the data versioning repository (usually, the same as the solution name)

Yes

Yes

-branch-name

branch_name

Name of branch from which to fetch data for exploration

Yes

Yes

-commit-id

commit_id

Commit ID of data to be fetched for exploration

Yes

Yes

-target-attribute

target_attribute

Name of the target variable in the dataset

No

Yes

Applicable only to structured datasets (refer to Data Visualization Library

Dynamic-args:

Specify dynamic argument right after its static argument and check the “Dynamic” checkbox. The value of this dynamic arg should be a placeholder string. This string will appear on Run Experiment form where an actual run-time value for its static argument should be filled in.

Eg: If the static argument is -out-path, then it’s dynamic arg could be out_path. This out_path will be reflected as an input field in the Run Experiment form. Value to this input field can be a string-valued path which is the expected value for -out-path arg.

Run-parameters (file or commit ID):

While loading parameters from a file or data versioning repository use mentioned keys. For more details refer Guide For Dynamically Loading Run Parameters From File Or Data Versioning Repository