├── .gitignore ├── prep_data.py ├── project.yaml ├── kflow.py ├── .github └── workflows │ └── mlrun.yml ├── README.md ├── Jenkinsfile └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | .aws-secrets 3 | .idea/ 4 | .ipynb_checkpoints 5 | .pytest_cache/ 6 | .vscode/ 7 | build 8 | chart 9 | chart.html 10 | dask-worker-space 11 | dask-worker-space/ 12 | dataset.csv 13 | dist 14 | Dockerfile.mlrun-test-nb 15 | docs/_build 16 | docs/external/*.html 17 | docs/external/*.md 18 | iteration_results.csv 19 | mlrun.egg-info/ 20 | model.txt 21 | result*.html 22 | tests/test_results/ 23 | venv 24 | mlrun/utils/version/version.json 25 | mlrun/api/migrations_sqlite/mlrun.db 26 | -------------------------------------------------------------------------------- /prep_data.py: -------------------------------------------------------------------------------- 1 | import mlrun 2 | 3 | def prep_data(context, source_url: mlrun.DataItem, label_column='label'): 4 | # Convert the DataItem to a pandas DataFrame 5 | df = source_url.as_df() 6 | print("data url:", source_url.url) 7 | df[label_column] = df[label_column].astype('category').cat.codes 8 | 9 | # Record the DataFrane length after the run 10 | context.log_result('num_rows', df.shape[0]) 11 | 12 | # Store the data set in your artifacts database 13 | context.log_dataset('cleaned_data', df=df, index=False, format='csv') -------------------------------------------------------------------------------- /project.yaml: -------------------------------------------------------------------------------- 1 | kind: project 2 | metadata: 3 | name: pipe 4 | spec: 5 | description: test 6 | params: 7 | label_column: label 8 | functions: 9 | - url: prep_data.py 10 | name: prep-data 11 | image: mlrun/mlrun 12 | handler: prep_data 13 | with_repo: true 14 | - url: hub://sklearn_classifier 15 | name: train 16 | - url: hub://test_classifier 17 | name: test 18 | - url: hub://v2_model_server 19 | name: serve 20 | workflows: 21 | - name: main 22 | path: ./kflow.py 23 | engine: kfp 24 | artifacts: 25 | - kind: '' 26 | target_path: https://s3.wasabisys.com/iguazio/data/iris/iris.data.raw.csv 27 | key: data 28 | source: ./ 29 | subpath: '' 30 | origin_url: '' 31 | load_source_on_run: true 32 | desired_state: online 33 | -------------------------------------------------------------------------------- /kflow.py: -------------------------------------------------------------------------------- 1 | import mlrun 2 | from kfp import dsl 3 | 4 | funcs = {} 5 | project = mlrun.projects.pipeline_context.project 6 | iris_data = 'https://s3.wasabisys.com/iguazio/data/iris/iris.data.raw.csv' 7 | default_pkg_class = "sklearn.linear_model.LogisticRegression" 8 | 9 | @dsl.pipeline( 10 | name="Demo training pipeline", 11 | description="Shows how to use mlrun." 12 | ) 13 | def kfpipeline(model_pkg_class=default_pkg_class, build=0): 14 | 15 | # if build=True, build the function image before the run 16 | with dsl.Condition(build==1) as build_cond: 17 | funcs["prep-data"].deploy_step(skip_deployed=True) 18 | 19 | # run a local data prep function 20 | prep_data = funcs["prep-data"].as_step(name='prep_data', 21 | inputs={'source_url': project.get_artifact_uri("data")}, 22 | outputs=["cleaned_data"]).after(build_cond) 23 | -------------------------------------------------------------------------------- /.github/workflows/mlrun.yml: -------------------------------------------------------------------------------- 1 | #name: mlrun-project-workflow 2 | #on: [issue_comment] 3 | # 4 | #jobs: 5 | # submit-project: 6 | # if: github.event.issue.pull_request != null && startsWith(github.event.comment.body, '/run') 7 | # runs-on: ubuntu-latest 8 | # 9 | # steps: 10 | # - uses: actions/checkout@v2 11 | # - name: Set up Python 3.7 12 | # uses: actions/setup-python@v1 13 | # with: 14 | # python-version: '3.7' 15 | # architecture: 'x64' 16 | # 17 | # - name: Install mlrun 18 | # run: python -m pip install pip install mlrun 19 | # - name: Submit project 20 | # run: python -m mlrun project ./ -w -r main ${CMD:5} 21 | # env: 22 | # V3IO_USERNAME: ${{ secrets.V3IO_USERNAME }} 23 | # V3IO_API: ${{ secrets.V3IO_API }} 24 | # V3IO_ACCESS_KEY: ${{ secrets.V3IO_ACCESS_KEY }} 25 | # MLRUN_DBPATH: ${{ secrets.MLRUN_DBPATH }} 26 | # GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} 27 | # SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }} 28 | # CMD: ${{ github.event.comment.body}} 29 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # project-demo 2 | 3 | demo the use with project object 4 | 5 | see documentation in: https://docs.mlrun.org/en/latest/projects/overview.html 6 | 7 | ## Load & Run using MLRun SDK 8 | 9 | When our project is already created and stored in a git archive we can quickly load and use it with the 10 | `mlrun.load_project()` method. `load_project` will use a local context directory (with initialized `git`) 11 | or clone a remote repo into the local dir and return a project object. 12 | 13 | Users need to provide the path to the `context` dir and the git/zip/tar archive `url`, the `name` can be specified or taken 14 | from the project object, they can also specify `secrets` (repo credentials), `init_git` flag (to initialize git in the context dir), 15 | `clone` flag (indicating we must clone and ignore/remove local copy), and `user_project` flag (indicate the project name is unique to the user). 16 | 17 | example, load a project from git and run the `main` workflow: 18 | 19 | ```python 20 | project = mlrun.load_project("./", "git://github.com/mlrun/project-demo.git") 21 | project.run("main", arguments={'data': data_url}) 22 | ``` 23 | 24 | ## Load & run using MLRun CLI 25 | 26 | Loading a project from `git` into `./project` : 27 | 28 | ``` 29 | mlrun project -n myproj -u "git://github.com/mlrun/project-demo.git" ./project 30 | ``` 31 | 32 | Running a specific workflow (`main`) from the project stored in `.` (current dir): 33 | 34 | ``` 35 | mlrun project -r main -w ./project 36 | ``` 37 | 38 | -------------------------------------------------------------------------------- /Jenkinsfile: -------------------------------------------------------------------------------- 1 | @Library('pipelinex@development') _ 2 | import com.iguazio.pipelinex.DockerRepo 3 | 4 | workDir = '/home/jenkins' 5 | podLabel = 'mlrun-ltm' 6 | 7 | properties([ 8 | parameters([ 9 | string(defaultValue: 'mustprovide', description: 'MLRun API URL', name: 'MLRUN_DBPATH'), 10 | string(defaultValue: 'mustprovide', description: 'V3IO API URL', name: 'V3IO_API'), 11 | string(defaultValue: 'mustprovide', description: 'V3IO username', name: 'V3IO_USERNAME'), 12 | string(defaultValue: 'mustprovide', description: 'V3IO access key', name: 'V3IO_ACCESS_KEY'), 13 | string(defaultValue: 'mustprovide', description: 'MLRun project name', name: 'PROJECT_NAME'), 14 | ]) 15 | ]) 16 | 17 | podTemplate( 18 | label: podLabel, 19 | containers: [ 20 | containerTemplate(name: 'base-build', image: 'mlrun/mlrun:0.9.0-rc2' , ttyEnabled: true, command: 'cat'), 21 | ], 22 | volumes: [ 23 | hostPathVolume(mountPath: '/var/run/docker.sock', hostPath: '/var/run/docker.sock'), 24 | ], 25 | ) { 26 | node(podLabel) { 27 | common.notify_slack { 28 | withCredentials([ 29 | string(credentialsId: 'iguazio-prod-git-user-token', variable: 'GIT_TOKEN') 30 | ]) { 31 | 32 | container('base-build') { 33 | stage("git clone") { 34 | checkout scm 35 | } 36 | 37 | stage("build pipeline for ${env.user_input}") { 38 | sh """ MLRUN_DBPATH=${env.MLRUN_DBPATH} V3IO_API=${env.V3IO_API} V3IO_USERNAME=${env.V3IO_USERNAME} V3IO_ACCESS_KEY=${env.V3IO_ACCESS_KEY} mlrun project -r main -n ${env.PROJECT_NAME} -w ./ """ 39 | 40 | } 41 | 42 | } 43 | } 44 | } 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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