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Spelling fixes in our doc
Found using `mdspell -a -n --en-us "**/*.md"`. At some point we might want to have a built in ignore dictionary to avoid false positives. (cherry picked from commit 7b87070)
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docs/developers/README.md

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@@ -163,7 +163,7 @@ TaskRun Pods. Without intervention sidecars will typically run for the entire
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lifetime of a Pod but in Tekton's case it's desirable for the sidecars to run
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only as long as Steps take to complete. There's also a need for Tekton to
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schedule the sidecars to start before a Task's Steps begin, just in case the
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Steps rely on a sidecars behaviour, for example to join an Istio service mesh.
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Steps rely on a sidecars behavior, for example to join an Istio service mesh.
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To handle all of this, Tekton Pipelines implements the following lifecycle
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for sidecar containers:
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docs/install.md

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```
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See the
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[OpenShift CLI documentation](https://docs.openshift.com/container-platform/4.3/cli_reference/openshift_cli/getting-started-cli.html)
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for more inforomation on the `oc` command.
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for more information on the `oc` command.
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1. Monitor the installation using the following command until all components show a `Running` status:
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docs/pipelineruns.md

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### Specifying a pipeline
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Since a `PipelineRun` is an invocation of a [`Pipeline`](pipelines.md), you must sepcify
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Since a `PipelineRun` is an invocation of a [`Pipeline`](pipelines.md), you must specify
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what `Pipeline` to invoke.
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You can do this by providing a reference to an existing `Pipeline`:
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be run with different `PipelineResources` in cases such as:
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- When triggering the run of a `Pipeline` against a pull request, the triggering
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system must specify the commitish of a git `PipelineResource` to use
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system must specify the commit-ish of a git `PipelineResource` to use
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- When invoking a `Pipeline` manually against one's own setup, one will need to
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ensure one's own GitHub fork (via the git `PipelineResource`), image
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registry (via the image `PipelineResource`) and Kubernetes cluster (via the

docs/pipelines.md

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with name "pipeline-ws1" and wire it into the "output" workspace expected by
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the gen-code task. The same workspace will then also be wired into the "src" workspace
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expected by the commit task. If the workspace provided by the PipelineRun is a
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persitent volume claim then we have successfully shared files between the two tasks!
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persistent volume claim then we have successfully shared files between the two tasks!
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#### Workspaces Don't Imply Task Ordering (Yet)
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One usecase for workspaces in `Pipeline`s is to provide a PVC to multiple `Task`s
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and have one or some write to it before the others read from it. This kind of behaviour
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and have one or some write to it before the others read from it. This kind of behavior
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relies on the order of the `Task`s - one writes, the next reads, and so on - but this
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ordering is not currently enforced by Tekton. This means that `Task`s which write to a
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PVC may be run at the same time as `Task`s expecting to read that data. In the worst case
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this can result in deadlock behaviour where multiple `Task`'s pods are all attempting
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this can result in deadlock behavior where multiple `Task`'s pods are all attempting
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to mount a PVC for writing at the same time.
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To avoid this situation `Pipeline` authors can explicitly declare the ordering of `Task`s

docs/podtemplates.md

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This allows to customize some Pod specific field per `Task` execution, aka `TaskRun`.
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Alternatively, you can also define a default pod template in tekton config, see [here](./install.md)
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When a pod template is specified for a `PipelineRun` or `TaskRun`, the default pod template is ignored, ie
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When a pod template is specified for a `PipelineRun` or `TaskRun`, the default pod template is ignored, i.e.
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both templates are **NOT** merged, it's always one or the other.
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---
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- `schedulerName` the name of the
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[scheduler](https://kubernetes.io/docs/tasks/administer-cluster/configure-multiple-schedulers/)
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to use when dispatching the Pod. This can be used when workloads of specific types need specific schedulers,
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eg: If you are using volcano.sh for Machine Learning Workloads, you can pass the schedulerName and have Tasks be
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e.g.: If you are using volcano.sh for Machine Learning Workloads, you can pass the schedulerName and have Tasks be
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dispatched by the volcano.sh scheduler.
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docs/resources.md

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### Variable substitution
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`Task` and `Condition` specs can refer resource params as well as pre-defined
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`Task` and `Condition` specs can refer resource params as well as predefined
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variables such as `path` using the variable substitution syntax below where
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`<name>` is the resource's `name` and `<key>` is one of the resource's `params`:
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#### Accessing local path to resource
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The `path` key is pre-defined and refers to the local path to a resource on the
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The `path` key is predefined and refers to the local path to a resource on the
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mounted volume `shell $(resources.inputs.<name>.path)`
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### Controlling where resources are mounted
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#### Pull Request
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The `pullRequest` resource will look for GitHub or Gitlab OAuth authentication
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The `pullRequest` resource will look for GitHub or GitLab OAuth authentication
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tokens in spec secrets with a field name called `authToken`.
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URLs should be of the form: https://github.com/tektoncd/pipeline/pull/1

docs/taskruns.md

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In the following example, the Task is defined with a `volumeMount`
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(`my-cache`), that is provided by the TaskRun, using a
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PersistenceVolumeClaim. The SchedulerName has also been provided to define which scheduler should be used to
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PersistentVolumeClaim. The SchedulerName has also been provided to define which scheduler should be used to
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dispatch the Pod. The Pod will also run as a non-root user.
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```yaml
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For each step we also include the fully-qualified image used, with the digest.
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If any pods have been [`OOMKilled`](https://kubernetes.io/docs/tasks/administer-cluster/out-of-resource/)
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by Kubernetes, the `Taskrun` will be marked as failed even if the exitcode is 0.
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by Kubernetes, the `Taskrun` will be marked as failed even if the exit code is 0.
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### Steps
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Tekton will happily work with sidecars injected into a TaskRun's
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pods but the behaviour is a bit nuanced: When TaskRun's steps are complete
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pods but the behavior is a bit nuanced: When TaskRun's steps are complete
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any sidecar containers running inside the Pod will be terminated. In
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order to terminate the sidecars they will be restarted with a new
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"nop" image that quickly exits. The result will be that your TaskRun's

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