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Updates to Authoring guide and multiple README (GoogleCloudPlatform#10038)
- AUTHORING_GUIDE.md - container/snippets/README.md - error_reporting/fluent_on_compute/README.md - kms/snippets/README.md - people-and-planet-ai/README.md - people-and-planet-ai/timeseries-classification/README.md - pubsublite/spark-connector/README.md
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AUTHORING_GUIDE.md

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```
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All temporary resources should be explicitly deleted when testing is
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complete. Use pytest's fixture for cleaning up these resouces instead
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complete. Use pytest's fixture for cleaning up these resources instead
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of doing it in test itself.
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We recommend using `finally` to ensure that resource deletion occurs even if there is an error on creation. For example, this fixture creates a Dataproc cluster and tears it down regardless of errors during creation.

container/snippets/README.md

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# Samples
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All the samples are self contained unless they are placed inside their own folders. The samples use [Application Default Credentails (ADC)](https://cloud.google.com/docs/authentication/production#automatically) to authenticate with GCP. So make sure ADC is setup correctly _(i.e. `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set)_ before running the samples. Some sample might require additional python modules to be installed.
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All the samples are self contained unless they are placed inside their own folders. The samples use [Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/production#automatically) to authenticate with GCP. So make sure ADC is setup correctly _(i.e. `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set)_ before running the samples. Some sample might require additional python modules to be installed.
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You can run samples as follows:
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then use the id of the returned operation to **poll** the [`get_operation()`](https://cloud.google.com/python/docs/reference/container/latest/google.cloud.container_v1.services.cluster_manager.ClusterManagerClient#google_cloud_container_v1_services_cluster_manager_ClusterManagerClient_get_operation) API to check for it's status. You can see the
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different statuses it can be in, in [this proto definition](https://github.com/googleapis/googleapis/blob/master/google/container/v1/cluster_service.proto#L1763-L1778).
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- [**create_cluster.py**](create_cluster.py): An example of creating a GKE cluster _(with mostly the defaults)_. This example shows how to handle responses of type [`Operation`](https://cloud.google.com/python/docs/reference/container/latest/google.cloud.container_v1.types.Operation) that reperesents a long running operation. The example uses the python module [`backoff`](https://github.com/litl/backoff) to handle a graceful exponential backoff retry mechanism to check if the `Operation` has completed.
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- [**create_cluster.py**](create_cluster.py): An example of creating a GKE cluster _(with mostly the defaults)_. This example shows how to handle responses of type [`Operation`](https://cloud.google.com/python/docs/reference/container/latest/google.cloud.container_v1.types.Operation) that represents a long running operation. The example uses the python module [`backoff`](https://github.com/litl/backoff) to handle a graceful exponential backoff retry mechanism to check if the `Operation` has completed.
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- [**delete_cluster.py**](delete_cluster.py): An example of deleting a GKE cluster. This example shows how to handle responses of type [`Operation`](https://cloud.google.com/python/docs/reference/container/latest/google.cloud.container_v1.types.Operation) that reperesents a long running operation.
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- [**delete_cluster.py**](delete_cluster.py): An example of deleting a GKE cluster. This example shows how to handle responses of type [`Operation`](https://cloud.google.com/python/docs/reference/container/latest/google.cloud.container_v1.types.Operation) that represents a long running operation.

error_reporting/fluent_on_compute/README.md

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This section contains samples for [Google Cloud Error Reporting](https://cloud.google.com/error-reporting).
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A startup script has been provided to demonstrated how to properly provision a GCE
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instance with fluentd configured. Note the intallation of fluentd, the addition of the config file,
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instance with fluentd configured. Note the installation of fluentd, the addition of the config file,
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and the restarting of the fluetnd service. You can start an instance using
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it like this:
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kms/snippets/README.md

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## Python Samples for Google Cloud Key Management Service
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This directory contains samples for Google Cloud Key Management Service, which may be used as a refererence for how to use this product.
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This directory contains samples for Google Cloud Key Management Service, which may be used as a reference for how to use this product.
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The Google Cloud KMS API is a service that allows you to keep encryption keys centrally in the cloud, for direct use by cloud services. More info about Cloud KMS can be found at https://cloud.google.com/kms/docs/
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Samples, quickstarts, and other documentation are available at <a href="https://cloud.google.com/kms">cloud.google.com</a>.
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To run this sample:
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1. If this is your first time working with GCP products, you will need to set up [the Cloud SDK][cloud_sdk] or utilize [Google Cloud Shell][gcloud_shell]. This sample may [require authetication][authentication] and you will need to [enable billing][enable_billing].
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1. If this is your first time working with GCP products, you will need to set up [the Cloud SDK][cloud_sdk] or utilize [Google Cloud Shell][gcloud_shell]. This sample may [require authentication][authentication] and you will need to [enable billing][enable_billing].
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1. Make a fork of this repo and clone the branch locally, then navigate to the sample directory you want to use.
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people-and-planet-ai/README.md

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> [Watch the video in YouTube<br> ![thumbnail](http://img.youtube.com/vi/LnEhSVEJUuY/0.jpg)](https://youtu.be/LnEhSVEJUuY)
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This model is trained to classify if a ship is fishing or not every hour from their
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[_Maritime Mobile Service Identitiy_ (MMSI)](https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity)
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[_Maritime Mobile Service Identity_ (MMSI)](https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity)
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location data.
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* **Model**: 1D Fully Convolutional Network in [Keras]

people-and-planet-ai/timeseries-classification/README.md

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> [Watch the video in YouTube<br> ![thumbnail](http://img.youtube.com/vi/LnEhSVEJUuY/0.jpg)](https://youtu.be/LnEhSVEJUuY)
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This model is trained to classify if a ship is fishing or not every hour from their
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[_Maritime Mobile Service Identitiy_ (MMSI)](https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity)
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[_Maritime Mobile Service Identity_ (MMSI)](https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity)
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location data.
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* **Model**: 1D Fully Convolutional Network in [Keras]

pubsublite/spark-connector/README.md

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[Create Cluster]: https://pantheon.corp.google.com/dataproc/clustersAdd
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[Dataproc Image Version 1.5]: https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-release-1.5
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[Dataproc Image Version 2.0]: https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-release-2.0
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[compatability]: gs://spark-lib/pubsublite/pubsublite-spark-sql-streaming-LATEST-with-dependencies.jar
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[compatibility]: gs://spark-lib/pubsublite/pubsublite-spark-sql-streaming-LATEST-with-dependencies.jar

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