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za|Wo7^hitN1c7Uy+93LO(afA+U;PpgBE5{Mcq`XzOM>ptv8P$tM~*QjLvLI%Lq$|a zx;+Mi{jC05S*(JqsEvF0=crRoODM(^HPg#Tg@)uOZ`k1ZMj_L$D#0V#k*lffnY}Hl6IprEwAhchO+`CdylJ1>GtcX zq^7>-TU!?&Ltdkd|Fcs5zc$SOAN=WUB12YI9l2t7spMtf3h=XUx5uuEU;Hor2X1kJ A3IG5A literal 0 HcmV?d00001 diff --git a/docs/cloud.rst b/docs/cloud.rst index 8501d09f..5d36adcd 100644 --- a/docs/cloud.rst +++ b/docs/cloud.rst @@ -504,3 +504,102 @@ to the Dask scheduler and workers. For example, ... } >>> options.environment = env >>> cluster = gateway.new_cluster(options) + + +Batch Workflows +--------------- + +Pangeo includes some support for batch-style, or non-interactive, workflows. + +.. note:: + + This feature is currently limited to the https://us-central1-b.gcp.pangeo.io/ + deployment. + +Pangeo's batch workflows build on https://mlflow.org/docs/latest/index.html, +a machine-learning lifecycle platform. MLFlow provides + +1. A directory structure for organizing projects, and a command-line-interface for executing them. See https://www.mlflow.org/docs/latest/projects.html +2. A Python API for logging things (parameters, metrics, output files, models). +3. A graphical user interface for viewing the logged output and artifacts. + +Notebook Execution Example +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In this example, we'll execute a "parametrized notebook". The example usese + +1. mlflow to kick off the jobs with different parameters +2. papermill to expose notebook parameters and to execute the notebook +3. papermill-mlflow-handler to write the results to our storage bucket + +On the pangeo deployment, we'll create a new MLFlow experiement for this example + +.. code-block:: console + + (notebook)$ mlflow experiments create --experiment-name=papermill + Created experiment 'papermill' with id 1 + +And we'll run an experiement + +.. code-block:: console + + (notebook)$ mlflow run --no-conda \ + https://github.com/TomAugspurger/papermill-mlflow-example \ + --version=main \ + --experiment-name=papermill \ + -P a=1 -P b=2 -P c=3 + 2020/11/03 15:47:08 INFO mlflow.projects.utils: === Fetching project from https://github.com/TomAugspurger/papermill-mlflow-example into /tmp/tmpuwcwbv7b === + 2020/11/03 15:47:09 INFO mlflow.projects.utils: === Created directory /tmp/tmps4ydrd9l for downloading remote URIs passed to arguments of type 'path' === + 2020/11/03 15:47:09 INFO mlflow.projects.backend.local: === Running command 'papermill -p a 1 -p b 2 -p c 3 sin.ipynb mlflow://out.ipynb' in run with ID 'f447862b375e4ce5bd52025a84259d0d' === + Input Notebook: sin.ipynb + Output Notebook: mlflow://out.ipynb + Executing notebook with kernel: python3 + Executing: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:04<00:00, 1.04s/cell] + 2020/11/03 15:47:17 INFO mlflow.projects: === Run (ID 'f447862b375e4ce5bd52025a84259d0d') succeeded === + +Our run as been logged by MLFlow under the "papermill" experiement. + +.. image:: _static/papermill-experiment.png + +And the outputs (the executed notebook) are available as an artifact. + +.. image:: _static/papermill-artifact.png + +Downloading that, we can see the executed notebook. + +.. image:: _static/papermill-output.png + +**Understanding the example** + +The ``papermill-mlflow-example`` directory is structured to support this workflow. + +The notebook ``sin.ipynb`` includes a papermill parameters cell, a cell that's +been tagged with "parameters". See https://papermill.readthedocs.io/en/latest/usage-parameterize.html. + +.. image:: _static/papermill-parameters.png + +Next, the ``MLproject`` YAML file, for MLFlow, contains two important pieces: + +.. code-block:: yaml + + name: mlflow-papermill-example + + entry_points: + main: + parameters: + a: float + b: float + c: float + command: "papermill -p a {a} -p b {b} -p c {c} sin.ipynb mlflow://out.ipynb" + +1. The ``parameters`` section exposes the parameters from the notebook to MLFlow. + This lets us view the parameters in the MLFlow UI, and ovverride them when calling + ``mlflow run``. +2. The output file passed to ``papermill`` starts with the protocol ``mlflow://``. + We've registered a custom writer with papermill that writes the executed + notebook to our MLFlow artifact store. + +There are many limitations + +1. All MLFlow logs and artifacts are public and visible to all users of the hub. +2. ...