Metadata-Version: 2.1
Name: kfp-notebook
Version: 0.3.0
Summary: Jupyter Notebook operator for Kubeflow Pipeline.
Home-page: https://github.ibm.com/ai-workspace/kfp-notebook
Author: IBM - CODAIT
License: Apache License, Version 2.0
Description: <!--
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        Copyright 2018-2019 IBM Corporation
        
        Licensed under the Apache License, Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at
        
        http://www.apache.org/licenses/LICENSE-2.0
        
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        See the License for the specific language governing permissions and
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        KFP-Notebook is an Notebook op to enable running notebooks as part of a Kubeflow Pipeline.
         
        
        ## Building kfp-notebook
        
        ```bash
        make clean install
        ```
        
        ## Usage
        
        The example below can easily be added to a `python script` or `jupyter notebook` for testing purposes.
        
        ```python
        import os
        import kfp
        from notebook.pipeline._notebook_op import NotebookOp
        from kubernetes.client.models import V1EnvVar, V1SecretKeySelector
        
        url = 'http://weakish1.fyre.ibm.com:32488/pipeline'
        
        # configures artifact location
        notebook_location = kfp.dsl.ArtifactLocation.s3(
                bucket="oscon",
                endpoint="weakish1.fyre.ibm.com:30427",
                insecure=True,
                access_key_secret=V1SecretKeySelector(name="mlpipeline-minio-artifact", key="accesskey"),
                secret_key_secret=V1SecretKeySelector(name="mlpipeline-minio-artifact", key="secretkey"))
        
        def run_notebook_op(op_name, notebook_path):    
            op= NotebookOp(
                name=op_name,
                notebook=notebook_path,
                cos_endpoint='http://weakish1.fyre.ibm.com:30427',
                cos_user='minio',
                cos_password='minio123',
                image='lresende/notebook-kubeflow-pipeline:dev',
                artifact_location=notebook_location,
            )
            op.container.set_image_pull_policy('Always')
            
            return op
            
        def demo_pipeline():
            stats_op = run_notebook_op('stats', 'generate-community-overview')
            contributions_op = run_notebook_op('contributions', 'generate-community-contributions')
            run_notebook_op('overview', 'overview').after(stats_op, contributions_op)
            
        # Compile the new pipeline
        kfp.compiler.Compiler().compile(demo_pipeline,'pipelines/oscon_pipeline.tar.gz')
        
        # Upload the compiled pipeline
        client = kfp.Client(host=url)
        client.upload_pipeline('pipelines/oscon_pipeline.tar.gz',pipeline_name='oscon-pipeline')
        #experiment = client.create_experiment(name='oscon-community-stats')
        #run = client.run_pipeline(experiment.id, 'oscon-community-stats', 'pipelines/community_pipeline.tar.gz')
        
        ```
        
        ## Generated Kubeflow Pipelines
        
        ![Kubeflow Pipeline Example](docs/source/images/kfp-pipeline-example.png)
        
        ## Building and publishing a new release to IBM internal PyPi repository
        Publishing a package requires the "twine" python package.
        
        You can run: 
        ``` 
        twine upload --repository-url https://na.artifactory.swg-devops.com/artifactory/api/pypi/dbg-aiworkspace-team-pypi-local dist/* 
        ``` 
        and enter your IBM credentials to upload to a new release
        
        ```
        $ twine upload -r local dist/* --verbose
        Uploading distributions to https://na.artifactory.swg-devops.com/artifactory/api/pypi/dbg-aiworkspace-team-pypi-local
        Uploading kfp_notebook-<version>-py3-none-any.whl
        100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.2k/15.2k [00:01<00:00, 11.0kB/s]
        ```
        
        Alternatively, if you want to be able to run without user input :
        
        Edit your ~/.pypirc file to include a "local" repository to push to: 
        ```
        [distutils]
        index-servers =
                local
                pypi
                
        [local]
        repository: https://na.artifactory.swg-devops.com/artifactory/api/pypi/dbg-aiworkspace-team-pypi-local
        username : YOURNAME@us.ibm.com
        password: YOUR_ARTIFACTORY_API_TOKEN  ### This is opt. only if you don't want to enter your IBM password at publish time
        
        [pypi]
        repository: https://pypi.python.org/pypi
        ```
        Run the following to publish the package: 
        ```
        twine upload -r local dist/* --verbose
        ```
        
        
Keywords: jupyter,kubeflow,pipeline
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
