fix icons

docs-gen/content/docs/getting-started/introduction.md

@@ -49,4 +49,4 @@ Currently, we support the following eventsources:
 
 ## Architecture Overview
 
-![architecture](/images/architecture.drawio.svg)
+![architecture](/images/architecture.svg)

docs/docs/getting-started/index.xml

@@ -59,7 +59,7 @@ the link between CD and CT to provide Level 2 of the <a href="https://cloud.g
 <li><a href="../../reference/run-completion">Run Completion Eventsource</a></li>
 </ul>
 <h2 id="architecture-overview">Architecture Overview</h2>
-&lt;p>&lt;img src="https://sky-uk.github.io/kfp-operator/images/architecture.drawio.svg" alt="architecture">&lt;/p></description></item><item><title>Docs: Installation</title><link>https://sky-uk.github.io/kfp-operator/docs/getting-started/installation/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sky-uk.github.io/kfp-operator/docs/getting-started/installation/</guid><description>
+&lt;p>&lt;img src="https://sky-uk.github.io/kfp-operator/images/architecture.svg" alt="architecture">&lt;/p></description></item><item><title>Docs: Installation</title><link>https://sky-uk.github.io/kfp-operator/docs/getting-started/installation/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sky-uk.github.io/kfp-operator/docs/getting-started/installation/</guid><description>
 &lt;p>We recommend the installation using Helm as it allows a declarative approach to managing Kubernetes resources.&lt;/p>
 &lt;p>This guide assumes you are familiar with &lt;a href="https://helm.sh/">Helm&lt;/a>.&lt;/p>
 &lt;h2 id="prerequisites">Prerequisites&lt;/h2>

docs/docs/getting-started/introduction/index.html

@@ -9,7 +9,7 @@
 <a href=https://github.com/sky-uk/kfp-operator/edit/master/docs-gen/content/en/docs/getting-started/introduction.md class=td-page-meta--edit target=_blank rel=noopener><i class="fa fa-edit fa-fw"></i> Edit this page</a>
 <a href="https://github.com/sky-uk/kfp-operator/new/master/docs-gen/content/en/docs/getting-started/introduction.md?filename=change-me.md&value=---%0Atitle%3A+%22Long+Page+Title%22%0AlinkTitle%3A+%22Short+Nav+Title%22%0Aweight%3A+100%0Adescription%3A+%3E-%0A+++++Page+description+for+heading+and+indexes.%0A---%0A%0A%23%23+Heading%0A%0AEdit+this+template+to+create+your+new+page.%0A%0A%2A+Give+it+a+good+name%2C+ending+in+%60.md%60+-+e.g.+%60getting-started.md%60%0A%2A+Edit+the+%22front+matter%22+section+at+the+top+of+the+page+%28weight+controls+how+its+ordered+amongst+other+pages+in+the+same+directory%3B+lowest+number+first%29.%0A%2A+Add+a+good+commit+message+at+the+bottom+of+the+page+%28%3C80+characters%3B+use+the+extended+description+field+for+more+detail%29.%0A%2A+Create+a+new+branch+so+you+can+preview+your+new+file+and+request+a+review+via+Pull+Request.%0A" class=td-page-meta--child target=_blank rel=noopener><i class="fa fa-edit fa-fw"></i> Create child page</a>
 <a href="https://github.com/sky-uk/kfp-operator/issues/new?title=Introduction" class=td-page-meta--issue target=_blank rel=noopener><i class="fab fa-github fa-fw"></i> Create documentation issue</a>
-<a href=https://github.com/sky-uk/kfp-operator/issues/new class=td-page-meta--project-issue target=_blank rel=noopener><i class="fas fa-tasks fa-fw"></i> Create project issue</a></div><div class=td-toc><nav id=TableOfContents><ul><li><a href=#compatibility>Compatibility</a></li><li><a href=#tfx-pipelines-and-components>TFX Pipelines and Components</a><ul><li><a href=#lifecycle-phases-and-parameter-types>Lifecycle phases and Parameter types</a></li></ul></li><li><a href=#eventing-support>Eventing Support</a></li><li><a href=#architecture-overview>Architecture Overview</a></li></ul></nav></div></aside><main class="col-12 col-md-9 col-xl-8 pl-md-5" role=main><nav aria-label=breadcrumb class=td-breadcrumbs><ol class=breadcrumb><li class=breadcrumb-item><a href=https://sky-uk.github.io/kfp-operator/docs/>Documentation</a></li><li class=breadcrumb-item><a href=https://sky-uk.github.io/kfp-operator/docs/getting-started/>Getting Started</a></li><li class="breadcrumb-item active" aria-current=page><a href=https://sky-uk.github.io/kfp-operator/docs/getting-started/introduction/>Introduction</a></li></ol></nav><div class=td-content><h1>Introduction</h1><header class=article-meta></header><h2 id=compatibility>Compatibility</h2><p>The operator currently supports</p><ul><li>TFX Pipelines with Python 3.7 and 3.9 - pipelines created using the KFP DSL are not supported yet</li><li>KFP standalone (a full KFP installation is not supported yet) and Vertex AI</li></ul><h2 id=tfx-pipelines-and-components>TFX Pipelines and Components</h2><p>Unlike imperative Kubeflow Pipelines deployments, the operator takes care of providing all environment-specific configuration and setup for the pipelines. Pipeline creators therefore don&rsquo;t have to provide DAG runners, metadata configs, serving directories, etc. Furthermore, pusher is not required and the operator can extend the pipeline with this very environment-specific component.</p><p>For running a pipeline using the operator, only the list of TFX components needs to be returned. Everything else is done by the operator. See the <a href=https://github.com/sky-uk/kfp-operator/blob/master/docs-gen/includes/quickstart/penguin_pipeline/pipeline.py>penguin pipeline</a> for an example.</p><h3 id=lifecycle-phases-and-parameter-types>Lifecycle phases and Parameter types</h3><p>TFX Pipelines go through certain lifecycle phases that are unique to this technology. It is helpful to understand where these differ and where they are executed.</p><p><strong>Development:</strong> Creating the components definition as code.</p><p><strong>Compilation:</strong> Applying compile-time parameters and defining the execution runtime (aka DAG runner) for the pipeline to be compiled into a deployable artifact.</p><p><strong>Deployment:</strong> Creating a pipeline representation in the target environment.</p><p><strong>Running:</strong> Instantiating the pipeline, applying runtime parameters and running all pipeline steps involved to completion.</p><p><em>Note:</em> Local runners usually skip compilation and deployment and run the pipeline straight away.</p><p>TFX allows the parameterization of Pipelines in most lifecycle stages:</p><table><thead><tr><th>Parameter type</th><th>Description</th><th>Example</th></tr></thead><tbody><tr><td>Named Constants</td><td>Code constants</td><td>ANN layer size</td></tr><tr><td>Compile-time parameter</td><td>Parameters that are unlikely to change between pipeline runs supplied as environment variabels to the pipeline function</td><td>Bigquery dataset</td></tr><tr><td>Runtime parameter</td><td>Parameters exposed as TFX <a href="https://www.tensorflow.org/tfx/api_docs/python/tfx/v1/dsl/experimental/RuntimeParameter?hl=en">RuntimeParameter</a> which can be overridden at runtime allow simplified experimentation without having to recompile the pipeline</td><td>Number of training runs</td></tr></tbody></table><p>The pipeline operator supports the application of compile time and runtime parameters through its custom resources. We strongly encourage the usage of both of these parameter types to speed up development and experimentation lifecycles. Note that Runtime parameters can initialised to default values from both constants and compile-time parameters</p><h2 id=eventing-support>Eventing Support</h2><p>The Kubeflow Pipelines operator can optionally be installed with <a href=https://argoproj.github.io/argo-events/>Argo-Events</a> eventsources which lets users react to events.</p><p>Currently, we support the following eventsources:</p><ul><li><a href=../../reference/run-completion>Run Completion Eventsource</a></li></ul><h2 id=architecture-overview>Architecture Overview</h2><p><img src=https://sky-uk.github.io/kfp-operator/images/architecture.drawio.svg alt=architecture></p></div></main></div></div><footer class="bg-dark py-5 row d-print-none"><div class="container-fluid mx-sm-5"><div class=row><div class="col-6 col-sm-4 text-xs-center order-sm-2"></div><div class="col-6 col-sm-4 text-right text-xs-center order-sm-3"></div><div class="col-12 col-sm-4 text-center py-2 order-sm-2"></div></div></div></footer></div><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js integrity=sha384-9/reFTGAW83EW2RDu2S0VKaIzap3H66lZH81PoYlFhbGU+6BZp6G7niu735Sk7lN crossorigin=anonymous></script>
+<a href=https://github.com/sky-uk/kfp-operator/issues/new class=td-page-meta--project-issue target=_blank rel=noopener><i class="fas fa-tasks fa-fw"></i> Create project issue</a></div><div class=td-toc><nav id=TableOfContents><ul><li><a href=#compatibility>Compatibility</a></li><li><a href=#tfx-pipelines-and-components>TFX Pipelines and Components</a><ul><li><a href=#lifecycle-phases-and-parameter-types>Lifecycle phases and Parameter types</a></li></ul></li><li><a href=#eventing-support>Eventing Support</a></li><li><a href=#architecture-overview>Architecture Overview</a></li></ul></nav></div></aside><main class="col-12 col-md-9 col-xl-8 pl-md-5" role=main><nav aria-label=breadcrumb class=td-breadcrumbs><ol class=breadcrumb><li class=breadcrumb-item><a href=https://sky-uk.github.io/kfp-operator/docs/>Documentation</a></li><li class=breadcrumb-item><a href=https://sky-uk.github.io/kfp-operator/docs/getting-started/>Getting Started</a></li><li class="breadcrumb-item active" aria-current=page><a href=https://sky-uk.github.io/kfp-operator/docs/getting-started/introduction/>Introduction</a></li></ol></nav><div class=td-content><h1>Introduction</h1><header class=article-meta></header><h2 id=compatibility>Compatibility</h2><p>The operator currently supports</p><ul><li>TFX Pipelines with Python 3.7 and 3.9 - pipelines created using the KFP DSL are not supported yet</li><li>KFP standalone (a full KFP installation is not supported yet) and Vertex AI</li></ul><h2 id=tfx-pipelines-and-components>TFX Pipelines and Components</h2><p>Unlike imperative Kubeflow Pipelines deployments, the operator takes care of providing all environment-specific configuration and setup for the pipelines. Pipeline creators therefore don&rsquo;t have to provide DAG runners, metadata configs, serving directories, etc. Furthermore, pusher is not required and the operator can extend the pipeline with this very environment-specific component.</p><p>For running a pipeline using the operator, only the list of TFX components needs to be returned. Everything else is done by the operator. See the <a href=https://github.com/sky-uk/kfp-operator/blob/master/docs-gen/includes/quickstart/penguin_pipeline/pipeline.py>penguin pipeline</a> for an example.</p><h3 id=lifecycle-phases-and-parameter-types>Lifecycle phases and Parameter types</h3><p>TFX Pipelines go through certain lifecycle phases that are unique to this technology. It is helpful to understand where these differ and where they are executed.</p><p><strong>Development:</strong> Creating the components definition as code.</p><p><strong>Compilation:</strong> Applying compile-time parameters and defining the execution runtime (aka DAG runner) for the pipeline to be compiled into a deployable artifact.</p><p><strong>Deployment:</strong> Creating a pipeline representation in the target environment.</p><p><strong>Running:</strong> Instantiating the pipeline, applying runtime parameters and running all pipeline steps involved to completion.</p><p><em>Note:</em> Local runners usually skip compilation and deployment and run the pipeline straight away.</p><p>TFX allows the parameterization of Pipelines in most lifecycle stages:</p><table><thead><tr><th>Parameter type</th><th>Description</th><th>Example</th></tr></thead><tbody><tr><td>Named Constants</td><td>Code constants</td><td>ANN layer size</td></tr><tr><td>Compile-time parameter</td><td>Parameters that are unlikely to change between pipeline runs supplied as environment variabels to the pipeline function</td><td>Bigquery dataset</td></tr><tr><td>Runtime parameter</td><td>Parameters exposed as TFX <a href="https://www.tensorflow.org/tfx/api_docs/python/tfx/v1/dsl/experimental/RuntimeParameter?hl=en">RuntimeParameter</a> which can be overridden at runtime allow simplified experimentation without having to recompile the pipeline</td><td>Number of training runs</td></tr></tbody></table><p>The pipeline operator supports the application of compile time and runtime parameters through its custom resources. We strongly encourage the usage of both of these parameter types to speed up development and experimentation lifecycles. Note that Runtime parameters can initialised to default values from both constants and compile-time parameters</p><h2 id=eventing-support>Eventing Support</h2><p>The Kubeflow Pipelines operator can optionally be installed with <a href=https://argoproj.github.io/argo-events/>Argo-Events</a> eventsources which lets users react to events.</p><p>Currently, we support the following eventsources:</p><ul><li><a href=../../reference/run-completion>Run Completion Eventsource</a></li></ul><h2 id=architecture-overview>Architecture Overview</h2><p><img src=https://sky-uk.github.io/kfp-operator/images/architecture.svg alt=architecture></p></div></main></div></div><footer class="bg-dark py-5 row d-print-none"><div class="container-fluid mx-sm-5"><div class=row><div class="col-6 col-sm-4 text-xs-center order-sm-2"></div><div class="col-6 col-sm-4 text-right text-xs-center order-sm-3"></div><div class="col-12 col-sm-4 text-center py-2 order-sm-2"></div></div></div></footer></div><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js integrity=sha384-9/reFTGAW83EW2RDu2S0VKaIzap3H66lZH81PoYlFhbGU+6BZp6G7niu735Sk7lN crossorigin=anonymous></script>
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docs/docs/index.xml

@@ -533,7 +533,7 @@ In this example we are updating a serving component with the location of the new
 <li><a href="../../reference/run-completion">Run Completion Eventsource</a></li>
 </ul>
 <h2 id="architecture-overview">Architecture Overview</h2>
-&lt;p>&lt;img src="https://sky-uk.github.io/kfp-operator/images/architecture.drawio.svg" alt="architecture">&lt;/p></description></item><item><title>Docs: Pipeline Dependencies</title><link>https://sky-uk.github.io/kfp-operator/docs/examples/pipeline_dependencies/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sky-uk.github.io/kfp-operator/docs/examples/pipeline_dependencies/</guid><description>
+&lt;p>&lt;img src="https://sky-uk.github.io/kfp-operator/images/architecture.svg" alt="architecture">&lt;/p></description></item><item><title>Docs: Pipeline Dependencies</title><link>https://sky-uk.github.io/kfp-operator/docs/examples/pipeline_dependencies/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sky-uk.github.io/kfp-operator/docs/examples/pipeline_dependencies/</guid><description>
 &lt;p>Pipeline dependencies allow splitting up larger machine learning pipelines into sub-pipelines. This is particularly useful when:&lt;/p>
 &lt;ul>
 &lt;li>The data of an earlier step changes at a lower frequency than the data for subsequent steps&lt;/li>