{"id":21044968,"url":"https://github.com/quansight/nebari-mlflow-plugin","last_synced_at":"2025-03-13T22:11:46.030Z","repository":{"id":240298404,"uuid":"801560096","full_name":"Quansight/nebari-mlflow-plugin","owner":"Quansight","description":null,"archived":false,"fork":false,"pushed_at":"2024-10-14T17:47:11.000Z","size":125,"stargazers_count":0,"open_issues_count":2,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-10T15:17:09.992Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HCL","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Quansight.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-16T13:19:19.000Z","updated_at":"2024-10-14T17:47:15.000Z","dependencies_parsed_at":"2024-05-17T21:33:42.279Z","dependency_job_id":"dd75d3d3-7923-4538-b3bd-c43ef2516a52","html_url":"https://github.com/Quansight/nebari-mlflow-plugin","commit_stats":null,"previous_names":["quansight/nebari-mlflow-plugin"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quansight%2Fnebari-mlflow-plugin","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quansight%2Fnebari-mlflow-plugin/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quansight%2Fnebari-mlflow-plugin/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quansight%2Fnebari-mlflow-plugin/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Quansight","download_url":"https://codeload.github.com/Quansight/nebari-mlflow-plugin/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243489835,"owners_count":20299001,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-19T14:19:23.698Z","updated_at":"2025-03-13T22:11:45.991Z","avatar_url":"https://github.com/Quansight.png","language":"HCL","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Nebari MLflow Plugin\n\n**Table of Contents**\n\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [License](#license)\n\n## Introduction\nThis MLflow extension currently only supports Azure Nebari deployments, but aims to integrate into Nebari deployments utilizing the Azure, AWS, and GCP providers. It provides a robust, collaborative environment for AI/ML professionals to manage experiments, track metrics, and deploy models.\n\n### Features\n**Centralized Artifact Repository**: Store and manage all your metrics, parameters, and artifacts in a single location, accessible across the multi-tenant platform.\n\n**Experiment Tracking**: Log, query, and visualize metrics to understand and compare different runs and models.\n\n**Automated Configuration**: Simply type import mlflow in your Python script, and you're already configured to communicate with the remote multi-tenant MLflow server—no additional setup required.\n\n### Installation\nPrerequisites:\n- Nebari must be deployed using the Azure provider at the moment\n- Nebari version 2024.6.1 or later\n\nInstalling the MLflow extension is as straightforward as installing a Python package. Run the following commands:\n\n```bash\ngit clone nebari-mlflow-plugin\ncd nebari-mlflow-plugin\npip install nebari-mlflow-plugin\n```\nThis command installs the Python package and also creates the necessary infrastructure to run MLflow on the AI Platform.\n\n### Configuration\nAfter installation, the MLflow extension is automatically configured to work with the AI Platform. To access the MLflow interface, navigate to \u003chttps://[your-nebari-domain]/mlflow\u003e.\n\nFor Azure, your app registration will need RBAC permissions in addition to the typical Contributor permissons.  We recommend you create a **custom role** scoped at the resource_group (usually named \"\\\u003cproject_name\\\u003e-\\\u003cnamespace\\\u003e\" where the values are what you set in nebari-config.yaml), and add the following permissions:\n- Microsoft.Authorization/roleAssignments/read\n- Microsoft.Authorization/roleAssignments/write\n- Microsoft.Authorization/roleAssignments/delete\n\nThen create a **role assignment** of that role to the nebari app registration service principal.\n\n#### Configuring MLflow Tracking URL\nYou may set the `MLFLOW_TRACKING_URL` to configure mlflow in individual users' Nebari instances by adding or updating an additional block in your Nebari configuration file. Be sure to replace `{project_name}` and `{namespace}` with the values from your own nebari config file e.g. `http://mynebari-mlflow-tracking.dev.svc:5000`.\n\n```yaml\njupyterhub:\n  overrides:\n    singleuser:\n      extraEnv:\n        MLFLOW_TRACKING_URI: \"http://{project_name}-mlflow-tracking.{namespace}.svc:5000\" \n```\n\n### Usage\nGetting started with the MLflow extension is incredibly simple. To track an experiment:\n\nNavigate to the MLFLow extension URL and create a new experiment.\nIn your Python script, import MLflow and start logging metrics.\n```python\nimport mlflow\n\n# Start an experiment\nwith mlflow.start_run() as run:\n    mlflow.log_metric(\"accuracy\", 0.9)\n    mlflow.log_artifact(\"path/to/your/artifact\")\n```\nWith the above code, your metrics and artifacts are automatically stored and accessible via the MLFlow extension URL.\n\n\n## License\n\n`nebari-mlflow-plugin` is distributed under the terms of the [Apache](./LICENSE.md) license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquansight%2Fnebari-mlflow-plugin","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquansight%2Fnebari-mlflow-plugin","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquansight%2Fnebari-mlflow-plugin/lists"}