Deployed dcbbcae to 0.12 with MkDocs 1.5.3 and mike 2.0.0

0.12/admin/kubernetes_deployment/index.html

@@ -1234,7 +1234,7 @@ <h2 id="3-install-kserve">3. Install KServe<a class="headerlink" href="#3-instal
 <div class="tabbed-set tabbed-alternate" data-tabs="2:1"><input checked="checked" id="__tabbed_2_1" name="__tabbed_2" type="radio"><div class="tabbed-labels"><label for="__tabbed_2_1">kubectl</label></div>
 <div class="tabbed-content">
 <div class="tabbed-block">
-<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.12.0/kserve-runtimes.yaml
+<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.12.0/kserve-cluster-resources.yaml
 </code></pre></div>
 </div>
 </div>
@@ -1249,14 +1249,12 @@ <h2 id="3-install-kserve">3. Install KServe<a class="headerlink" href="#3-instal
 </div>
 </div>
 </input></div>
-<p>then modify the <code>ingressClassName</code> in <code>ingress</code> section to point to <code>IngressClass</code> name created in step 1.</p>
-<pre><code>```yaml
-ingress: |-
-{
-    "ingressClassName" : "your-ingress-class",
-}
-```
-</code></pre>
+<p>then modify the <code>ingressClassName</code> in <code>ingress</code> section to point to <code>IngressClass</code> name created in <a href="#1-install-istio">step 1</a>.
+    <div class="highlight"><pre><span></span><code><span class="nt">ingress</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">|-</span>
+<span class="p p-Indicator">{</span>
+<span class="w">    </span><span class="s">"ingressClassName"</span><span class="nt"> </span><span class="p">:</span><span class="w"> </span><span class="s">"your-ingress-class"</span><span class="p p-Indicator">,</span>
+<span class="p p-Indicator">}</span>
+</code></pre></div></p>
 </article>
 </div>
 </div>

0.12/admin/serverless/serverless/index.html

@@ -1254,7 +1254,7 @@ <h2 id="5-install-kserve-built-in-clusterservingruntimes">5. Install KServe Buil
 <div class="tabbed-set tabbed-alternate" data-tabs="2:1"><input checked="checked" id="__tabbed_2_1" name="__tabbed_2" type="radio"><div class="tabbed-labels"><label for="__tabbed_2_1">kubectl</label></div>
 <div class="tabbed-content">
 <div class="tabbed-block">
-<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.12.0/kserve-runtimes.yaml
+<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.12.0/kserve-cluster-resources.yaml
 </code></pre></div>
 </div>
 </div>

0.12/assets/_mkdocstrings.css

@@ -106,4 +106,9 @@ code.doc-symbol-module {
 
 code.doc-symbol-module::after {
   content: "mod";
-}
+}
+
+.doc-signature .autorefs {
+  color: inherit;
+  border-bottom: 1px dotted currentcolor;
+}

0.12/get_started/first_isvc/index.html

@@ -1254,6 +1254,12 @@ <h3 id="2-create-an-inferenceservice">2. Create an <code>InferenceService</code>
 </div>
 </div>
 </input></input></div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Do not deploy <code>InferenceServices</code> in control plane namespaces (i.e. namespaces with <code>control-plane</code> label). The webhook is configured
+in a way to skip these namespaces to avoid any privillage escalations. Deploying InferenceServices to these namespaces will result in the storage initializer not being 
+injected into the pod, causing the pod to fail with the error <code>No such file or directory: '/mnt/models'</code>.</p>
+</div>
 <h3 id="3-check-inferenceservice-status">3. Check <code>InferenceService</code> status.<a class="headerlink" href="#3-check-inferenceservice-status" title="Permanent link">¶</a></h3>
 <div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>get<span class="w"> </span>inferenceservices<span class="w"> </span>sklearn-iris<span class="w"> </span>-n<span class="w"> </span>kserve-test
 </code></pre></div>

0.12/get_started/index.html

@@ -1191,9 +1191,9 @@ <h3 id="install-the-kubernetes-cli">Install the Kubernetes CLI<a class="headerli
 <h2 id="install-the-kserve-quickstart-environment">Install the KServe "Quickstart" environment<a class="headerlink" href="#install-the-kserve-quickstart-environment" title="Permanent link">¶</a></h2>
 <ol>
 <li>
-<p>After having kind installed, create a <code>kind</code> cluster with:</p>
-<div class="highlight"><pre><span></span><code>kind<span class="w"> </span>create<span class="w"> </span>cluster
-</code></pre></div>
+<p>After having kind installed, create a <code>kind</code> cluster with:
+    <div class="highlight"><pre><span></span><code>kind<span class="w"> </span>create<span class="w"> </span>cluster
+</code></pre></div></p>
 </li>
 <li>
 <p>Then run:</p>
@@ -1206,8 +1206,12 @@ <h2 id="install-the-kserve-quickstart-environment">Install the KServe "Quickstar
 </li>
 <li>
 <p>You can then get started with a local deployment of KServe by using <em>KServe Quick installation script on Kind</em>:</p>
-<div class="highlight"><pre><span></span><code>curl<span class="w"> </span>-s<span class="w"> </span><span class="s2">"https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh"</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>bash
+<div class="highlight"><pre><span></span><code>curl<span class="w"> </span>-s<span class="w"> </span><span class="s2">"https://raw.githubusercontent.com/kserve/kserve/release-0.12/hack/quick_install.sh"</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>bash
 </code></pre></div>
+<p>or install via our published Helm Charts:
+   <div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve-crd<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve-crd<span class="w"> </span>--version<span class="w"> </span>v0.12.0
+helm<span class="w"> </span>install<span class="w"> </span>kserve<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve<span class="w"> </span>--version<span class="w"> </span>v0.12.0
+</code></pre></div></p>
 </li>
 </ol>
 </article>

0.12/modelserving/v1beta1/serving_runtime/index.html

@@ -1137,7 +1137,7 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 The KServe prediction protocol is noted as either "v1" or "v2". Some serving runtimes also support their own prediction protocol, these are noted with an <code>*</code>. 
 The default serving runtime version column defines the source and version of the serving runtime - MLServer, KServe or its own. 
 These versions can also be found in the <a href="https://github.com/kserve/kserve/blob/master/config/runtimes/kustomization.yaml">runtime kustomization YAML</a>. 
-All KServe native model serving runtimes use the current KServe release version (v0.11). The supported framework version column lists the <strong>major</strong> version of the model that is supported. 
+All KServe native model serving runtimes use the current KServe release version (v0.12). The supported framework version column lists the <strong>major</strong> version of the model that is supported. 
 These can also be found in the respective <a href="https://github.com/kserve/kserve/tree/master/config/runtimes">runtime YAML</a> under the <code>supportedModelFormats</code> field. 
 For model frameworks using the KServe serving runtime, the specific default version can be found in <a href="https://github.com/kserve/kserve/tree/master/python">kserve/python</a>. 
 In a given serving runtime directory the pyproject.toml file contains the exact model framework version used. For example, in <a href="https://github.com/kserve/kserve/tree/master/python/lgbserver">kserve/python/lgbserver</a> the <a href="https://github.com/kserve/kserve/blob/master/python/lgbserver/pyproject.toml">pyproject.toml</a> file sets the model framework version to 3.3.2, <code>lightgbm ~= 3.3.2</code>.</p>
@@ -1177,7 +1177,7 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html#lightgbm.Booster.save_model">Saved LightGBM Model</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.11 (KServe)</td>
+<td>v0.12 (KServe)</td>
 <td>3</td>
 <td><a href="../lightgbm/">LightGBM Iris</a></td>
 </tr>
@@ -1195,7 +1195,7 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="http://dmg.org/pmml/v4-4-1/GeneralStructure.html">PMML</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.11 (KServe)</td>
+<td>v0.12 (KServe)</td>
 <td>3, 4 (<a href="https://github.com/autodeployai/pypmml">PMML4.4.1</a>)</td>
 <td><a href="../pmml/">SKLearn PMML</a></td>
 </tr>
@@ -1213,7 +1213,7 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="https://scikit-learn.org/stable/modules/model_persistence.html">Pickled Model</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.11 (KServe)</td>
+<td>v0.12 (KServe)</td>
 <td>1.3</td>
 <td><a href="../sklearn/v2/">SKLearn Iris</a></td>
 </tr>
@@ -1231,7 +1231,7 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="https://pytorch.org/docs/master/generated/torch.save.html">Eager Model/TorchScript</a></td>
 <td>v1, v2, *torchserve</td>
 <td>*torchserve</td>
-<td>0.8.0 (TorchServe)</td>
+<td>0.8.2 (TorchServe)</td>
 <td>2</td>
 <td><a href="../torchserve/">TorchServe mnist</a></td>
 </tr>
@@ -1258,10 +1258,28 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html">Saved Model</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.11 (KServe)</td>
+<td>v0.12 (KServe)</td>
 <td>1</td>
 <td><a href="../xgboost/">XGBoost Iris</a></td>
 </tr>
+<tr>
+<td><a href="https://github.com/kserve/kserve/tree/master/python/huggingfaceserver">HuggingFace ModelServer</a></td>
+<td><a href="https://huggingface.co/docs/transformers/v4.39.2/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">Saved Model</a> / <a href="https://huggingface.co/models">Huggingface Hub Model_Id</a></td>
+<td>v1, v2</td>
+<td>--</td>
+<td>v0.12 (KServe)</td>
+<td>4 (<a href="https://pypi.org/project/transformers/4.37.2/">Transformers</a>)</td>
+<td>--</td>
+</tr>
+<tr>
+<td><a href="https://github.com/kserve/kserve/tree/master/python/huggingfaceserver">HuggingFace VLLM ModelServer</a></td>
+<td><a href="https://huggingface.co/docs/transformers/v4.39.2/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">Saved Model</a> / <a href="https://huggingface.co/models">Huggingface Hub Model_Id</a></td>
+<td>v2</td>
+<td>--</td>
+<td>v0.12 (KServe)</td>
+<td>0 (<a href="https://pypi.org/project/vllm/0.2.7/">Vllm</a>)</td>
+<td>--</td>
+</tr>
 </tbody>
 </table>
 <p>*tensorflow - Tensorflow implements its own prediction protocol in addition to KServe's. See: <a href="https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto">Tensorflow Serving Prediction API</a> documentation</p>