Hello from magnus

Magnus is a thin layer of abstraction over the underlying infrastructure to enable data scientist and machine learning engineers. It provides:

• A way to execute Jupyter notebooks/python functions in local or remote platforms.
• A framework to define complex pipelines via YAML or Python SDK.
• Robust and automatic logging to ensure maximum reproducibility of experiments.
• A framework to interact with secret managers ranging from environment variables to other vendors.
• Interactions with various experiment tracking tools.

What does thin mean?

• We really have no say in what happens within your notebooks or python functions.
• We do not dictate how the infrastructure should be configured as long as it satisfies some basic criteria.
  • The underlying infrastructure should support container execution and an orchestration framework.
  • Some way to handle secrets either via environment variables or secrets manager.
  • A blob storage or some way to store your intermediate artifacts.
  • A database or blob storage to store logs.
• We have no opinion of how your structure your project.
• We do not creep into your CI/CD practices but it is your responsibility to provide the same environment where ever the execution happens. This is usually via git, virtual environment manager and docker.
• We transpile to the orchestration framework that is used by your teams to do the heavy lifting.

What does it do?

Shift Left

Magnus provides patterns typically used in production environments even in the development phase.

• Reduces the need for code refactoring during production phase of the project.
• Enables best practices and understanding of infrastructure patterns.
• Run the same code on your local machines or in production environments.

:sparkles::sparkles:Happy Experimenting!!:sparkles::sparkles:

Documentation

More details about the project and how to use it available here.

Extensions

All the services of magnus are extendable by design, please refer to magnus extensions

Installation

The minimum python version that magnus supports is 3.8

pip

magnus is a python package and should be installed as any other.

pip install magnus

We recommend that you install magnus in a virtual environment specific to the project and also poetry for your application development.

The command to install in a poetry managed virtual environment

poetry add magnus

Example Run

To give you a flavour of how magnus works, lets create a simple pipeline.

Copy the contents of this yaml into getting-started.yaml or alternatively in a python file if you are using the SDK.

!!! Note

The below execution would create a folder called 'data' in the current working directory. The command as given should work in linux/macOS but for windows, please change accordingly.

dag:
  description: Getting started
  start_at: step parameters
  steps:
    step parameters:
      type: task
      command_type: python-lambda
      command: "lambda x: {'x': int(x) + 1}"
      next: step shell
    step shell:
      type: task
      command_type: shell
      command: mkdir data ; env >> data/data.txt # For Linux/macOS
      next: success
      catalog:
        put:
          - "*"
    success:
      type: success
    fail:
      type: fail

The same could also be defined via a Python SDK.

#in pipeline.py
from magnus import Pipeline, Task

def pipeline():
    first = Task(name='step parameters', command="lambda x: {'x': int(x) + 1}", command_type='python-lambda',
                next_node='step shell')
    second = Task(name='step shell', command='mkdir data ; env >> data/data.txt',
                  command_type='shell', catalog={'put': '*'})

    pipeline = Pipeline(name='getting_started')
    pipeline.construct([first, second])
    pipeline.execute(parameters_file='parameters.yaml')

if __name__ == '__main__':
    pipeline()

Since the pipeline expects a parameter x, lets provide that using parameters.yaml

x: 3

And let's run the pipeline using:

 magnus execute --file getting-started.yaml --parameters-file parameters.yaml

If you are using the python SDK:

poetry run python pipeline.py

You should see a list of warnings but your terminal output should look something similar to this:

{
    "run_id": "20230131195647",
    "dag_hash": "",
    "use_cached": false,
    "tag": "",
    "original_run_id": "",
    "status": "SUCCESS",
    "steps": {
        "step parameters": {
            "name": "step parameters",
            "internal_name": "step parameters",
            "status": "SUCCESS",
            "step_type": "task",
            "message": "",
            "mock": false,
            "code_identities": [
                {
                    "code_identifier": "e15d1374aac217f649972d11fe772e61b5a2478d",
                    "code_identifier_type": "git",
                    "code_identifier_dependable": true,
                    "code_identifier_url": "INTENTIONALLY REMOVED",
                    "code_identifier_message": ""
                }
            ],
            "attempts": [
                {
                    "attempt_number": 0,
                    "start_time": "2023-01-31 19:56:55.007931",
                    "end_time": "2023-01-31 19:56:55.009273",
                    "duration": "0:00:00.001342",
                    "status": "SUCCESS",
                    "message": ""
                }
            ],
            "user_defined_metrics": {},
            "branches": {},
            "data_catalog": []
        },
        "step shell": {
            "name": "step shell",
            "internal_name": "step shell",
            "status": "SUCCESS",
            "step_type": "task",
            "message": "",
            "mock": false,
            "code_identities": [
                {
                    "code_identifier": "e15d1374aac217f649972d11fe772e61b5a2478d",
                    "code_identifier_type": "git",
                    "code_identifier_dependable": true,
                    "code_identifier_url": "INTENTIONALLY REMOVED",
                    "code_identifier_message": ""
                }
            ],
            "attempts": [
                {
                    "attempt_number": 0,
                    "start_time": "2023-01-31 19:56:55.128697",
                    "end_time": "2023-01-31 19:56:55.150878",
                    "duration": "0:00:00.022181",
                    "status": "SUCCESS",
                    "message": ""
                }
            ],
            "user_defined_metrics": {},
            "branches": {},
            "data_catalog": [
                {
                    "name": "data/data.txt",
                    "data_hash": "7e91b0a9ff8841a3b5bf2c711f58bcc0cbb6a7f85b9bc92aa65e78cdda59a96e",
                    "catalog_relative_path": "20230131195647/data/data.txt",
                    "catalog_handler_location": ".catalog",
                    "stage": "put"
                }
            ]
        },
        "success": {
            "name": "success",
            "internal_name": "success",
            "status": "SUCCESS",
            "step_type": "success",
            "message": "",
            "mock": false,
            "code_identities": [
                {
                    "code_identifier": "e15d1374aac217f649972d11fe772e61b5a2478d",
                    "code_identifier_type": "git",
                    "code_identifier_dependable": true,
                    "code_identifier_url": "INTENTIONALLY REMOVED",
                    "code_identifier_message": ""
                }
            ],
            "attempts": [
                {
                    "attempt_number": 0,
                    "start_time": "2023-01-31 19:56:55.239877",
                    "end_time": "2023-01-31 19:56:55.240116",
                    "duration": "0:00:00.000239",
                    "status": "SUCCESS",
                    "message": ""
                }
            ],
            "user_defined_metrics": {},
            "branches": {},
            "data_catalog": []
        }
    },
    "parameters": {
        "x": 4
    },
    "run_config": {
        "executor": {
            "type": "local",
            "config": {
                "enable_parallel": false,
                "placeholders": {}
            }
        },
        "run_log_store": {
            "type": "buffered",
            "config": {}
        },
        "catalog": {
            "type": "file-system",
            "config": {
                "compute_data_folder": "data",
                "catalog_location": ".catalog"
            }
        },
        "secrets": {
            "type": "do-nothing",
            "config": {}
        },
        "experiment_tracker": {
            "type": "do-nothing",
            "config": {}
        },
        "variables": {},
        "pipeline": {
            "start_at": "step parameters",
            "name": "getting_started",
            "description": "",
            "max_time": 86400,
            "steps": {
                "step parameters": {
                    "mode_config": {},
                    "next_node": "step shell",
                    "command": "lambda x: {'x': int(x) + 1}",
                    "command_type": "python-lambda",
                    "command_config": {},
                    "catalog": {},
                    "retry": 1,
                    "on_failure": "",
                    "type": "task"
                },
                "step shell": {
                    "mode_config": {},
                    "next_node": "success",
                    "command": "mkdir data ; env >> data/data.txt",
                    "command_type": "shell",
                    "command_config": {},
                    "catalog": {
                        "put": "*"
                    },
                    "retry": 1,
                    "on_failure": "",
                    "type": "task"
                },
                "success": {
                    "mode_config": {},
                    "type": "success"
                },
                "fail": {
                    "mode_config": {},
                    "type": "fail"
                }
            }
        }
    }
}

You should see that data folder being created with a file called data.txt in it. This is according to the command in step shell.

You should also see a folder .catalog being created with a single folder corresponding to the run_id of this run.

To understand more about the input and output, please head over to the documentation.