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<sectionid="configuration-guide">
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<h1>Configuration Guide<aclass="headerlink" href="#configuration-guide" title="Link to this heading"></a></h1>
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<p>The following guide will help you configure <codeclass="docutils literal notranslate"><spanclass="pre">values.yaml</span></code> file for a SuperSONIC deployment.</p>
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<p>The following guide will help you configure <codeclass="docutils literal notranslate"><spanclass="pre">values.yaml</span></code> file for a SuperSONIC deployment.
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The full list of parameters can be found in the <aclass="reference external" href="configuration-reference">Configuration Reference</a>.</p>
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<sectionid="configure-triton-model-repository">
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<h3>2. Configure Triton model repository.<aclass="headerlink" href="#configure-triton-model-repository" title="Link to this heading"></a></h3>
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<ul>
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<li><p>To learn about the structure of model repositories, refer to the
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<aclass="reference external" href="https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_repository.html">NVIDIA Model Repository Guide</a>.</p></li>
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<li><p>Model repositories are specified in the <codeclass="docutils literal notranslate"><spanclass="pre">triton.args</span></code> parameter in the values file.
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The parameter contains the full command that launches a Triton server; you can specify
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one or multiple model repositories via the <codeclass="docutils literal notranslate"><spanclass="pre">--model-repository</span></code> flag.</p></li>
<li><p>Make sure that the model repository paths exist. You can load models from a volume mounted to the Triton container.
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The following options for model repository mouning are provided via <codeclass="docutils literal notranslate"><spanclass="pre">triton.modelRepository</span></code> parameter in <codeclass="docutils literal notranslate"><spanclass="pre">values.yaml</span></code>:</p>
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<divclass="highlight-yaml notranslate"><divclass="highlight"><pre><span></span><spanclass="c1"># -- Model repository configuration</span>
The following options for model repository mouning are provided via <codeclass="docutils literal notranslate"><spanclass="pre">triton.modelRepository</span></code> parameter in <codeclass="docutils literal notranslate"><spanclass="pre">values.yaml</span></code>:</p></li>
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</ul>
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<details>
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<summary>Model repository configuration</summary>
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<spanclass="w"></span><spanclass="c1"># -- Model repository mount path (e.g /cvmfs/)</span>
## Option 2: mount CVMFS as PersistentVolumeClaim (CVMFS StorageClass must be installed at the cluster)
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storageType: "cvmfs-pvc"
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## -- OR --
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## Option 3: mount CVMFS via hostPath (CVMFS must be already mounted on the nodes)
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storageType: "cvmfs"
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## -- OR --
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## Option 4: mount an NFS storage volume
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storageType: "nfs"
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nfs:
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server:
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path:
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</details></section>
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<sectionid="select-resources-for-triton-pods">
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<h3>1. Select resources for Triton pods.<aclass="headerlink" href="#select-resources-for-triton-pods" title="Link to this heading"></a></h3>
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<h3>3. Select resources for Triton pods.<aclass="headerlink" href="#select-resources-for-triton-pods" title="Link to this heading"></a></h3>
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<ul>
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<li><p>You can configure CPU, memory, and GPU resources for Triton pods via the <codeclass="docutils literal notranslate"><spanclass="pre">triton.resources</span></code> parameter in the values file.</p>
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