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docs/sdk-examples.md
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| ```python | ||
| # define a custom script | ||
| custom_script = aztk.spark.models.CustomScript( |
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Since custom scripts are going away in favor of plugins, we should probably leave this out.
docs/sdk-examples.md
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| status = client.get_application_status(cluster_config.cluster_id, app2.name) | ||
| ``` | ||
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| ## stream logs of app, print to console as it runs |
docs/sdk-examples.md
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| ## Run application against cluster |
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I feel like "Run an application on the cluster" is better than "against".
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| Welcome to aztk's documentation! | |||
| ================================ | |||
| Azure Distributed Data Engineering Toolkit (AZTK) is a python CLI application for provisioning on-demand Spark on Docker clusters in Azure. It's a cheap and easy way to get up and running with a Spark cluster, and a great tool for Spark users who want to experiment and start testing at scale. | |||
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We should pick between aztk and AZTK and standardize in our docs.
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In the SDK docs, I think the only package we should have is the edit: and the aztk.error module |
| Each Job has one or more applications given as a List in Job.yaml. Applications are defined using the following properties: | ||
| ```yaml | ||
| applications: | ||
| - name: |
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I thought we decided to leave in white spaces in yaml since it is required.
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Hhm i guess its the auto formatting that removed those
| pytest==3.1.3 | ||
| pytest-xdist==1.22.0 | ||
| twine==1.9.1 | ||
| docker==3.2.1 |
3 participants
fix #496
http://aztk.readthedocs.io/en/feature-readthedocs/