Scripts_stories

US CLI Application for Running AI/ML Jobs : Scripts for the CLI application

• US CLI Application for Running AI/ML Jobs : Scripts for the CLI application
  • classes relations
  • User Stories: CLI Application Management
    • 1. User Story: Parse Command-Line Arguments
    • 2. User Story: Display Settings Schema
    • 3. User Story: Read Configuration Files
    • 4. User Story: Merge Configuration Settings
    • 5. User Story: Validate Configuration Settings
    • 6. User Story: Execute the Job
    • 7. User Story: Handle No Configuration Provided
    • Common Acceptance Criteria
    • Definition of Done (DoD):
  • Code location
  • Test location

---

classes relations

classDiagram
    direction LR

    class Main {
        +main(argv: list[str] | None = None) int
    }

    class ArgumentParser {
        +parse_args(argv: list[str] | None) Namespace
    }

    class MainSettings {
        +model_json_schema() dict
        +model_validate(object_: dict) MainSettings
        +job: Job
    }

    class Configs {
        +parse_file(file: str) dict
        +parse_string(string: str) dict
        +merge_configs(configs: list[dict]) dict
        +to_object(config: dict) Any
    }

    class Job {
        +run()
    }

    Main --> ArgumentParser : "uses"
    Main --> MainSettings : "validates"
    Main --> Configs : "parses"
    MainSettings --> Job : "contains"
    Job --> Main : "executes run()"

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User Stories: CLI Application Management

---

1. User Story: Parse Command-Line Arguments

Title:
As a user, I want to provide job configuration files and options via command-line arguments, so that I can execute AI/ML jobs conveniently from the terminal.

Description:
The CLI script parses command-line arguments to take in configuration files and other options.

Acceptance Criteria:

• The script successfully retrieves job configuration files and additional strings from the command-line input.
• Help instructions are clearly available when required by the user.

---

2. User Story: Display Settings Schema

Title:
As a developer, I want to view the settings schema, so that I can understand the structure and requirements of configuration files.

Description:
The CLI application can output the settings schema as JSON when the --schema flag is used.

Acceptance Criteria:

• The schema is printed in JSON format with correct indentation when invoked.
• The application exits successfully after displaying the schema.

---

3. User Story: Read Configuration Files

Title:
As a user, I want the application to read configuration files in YAML or JSON, so that I can specify my job settings in a structured format.

Description:
The CLI script reads specified configuration files and parses their contents to configure the job properly.

Acceptance Criteria:

• The script can read and parse configuration files without errors.
• The settings gathered from files are stored correctly for further processing.

---

4. User Story: Merge Configuration Settings

Title:
As a user, I want to combine settings from both files and command-line strings, so that I can customize my job configurations more flexibly.

Description:
The CLI application merges configurations from multiple sources (files and strings) into a single config object.

Acceptance Criteria:

• The merging process combines settings appropriately without conflicts.
• The final configuration object accurately reflects the merged settings.

---

5. User Story: Validate Configuration Settings

Title:
As a user, I want to validate my configuration settings, so I can ensure they conform to the expected structure before running a job.

Description:
The application validates the combined configuration settings against a predefined model.

Acceptance Criteria:

• The settings are validated, and any discrepancies trigger clear error messages.
• Proper messages are presented if validation is successful.

---

6. User Story: Execute the Job

Title:
As a user, I want the application to execute the AI/ML job based on the provided configurations, so that I can carry out the intended operations.

Description:
The CLI invokes the job runner, executing the task as described by the gathered configurations.

Acceptance Criteria:

• The job is executed successfully when appropriate configurations are provided.
• Log messages indicate progress and completion of the job.

---

7. User Story: Handle No Configuration Provided

Title:
As a user, I want clear feedback if no configurations are provided, so that I can correct my input and try again.

Description:
The application checks if any configurations have been provided and raises an error if none are present.

Acceptance Criteria:

• A RuntimeError is raised when both configuration files and command-line strings are absent.
• The error message clearly informs the user of the missing configurations.

---

Common Acceptance Criteria

1. Implementation Requirements:

   • The CLI application parses arguments correctly, handles files, and executes tasks.
   • Clear help instructions are provided as part of the command-line interface.

2. Error Handling:

   • Meaningful error messages guide the user in case of issues like missing configurations or validation errors.

3. Testing:

   • Unit tests validate command-line parsing, configuration reading, merging, validation, and job execution.
   • Tests ensure effective handling of edge cases related to configurations.

4. Documentation:

   • Clear and comprehensive docstrings are included for each method and class.
   • Users are provided with examples to understand how to use the command-line interface effectively.

---

Definition of Done (DoD):

• The CLI script is fully functional, allowing for the running of AI/ML jobs with various configurations.
• All user stories are tested, and acceptance criteria are met.
• Documentation is complete and easy to follow.

Code location

src/regression_model_template/scripts.py

Test location

tests/cli/test_cli.py