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<div class="bd-toc-item navbar-nav"><p aria-level="2" class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../overview.html">Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../quick-start-guide.html">Quick Start Guide</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../installation/index.html">Installation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../installation/containers.html">Pre-built release container images on NGC</a></li>
<li class="toctree-l2"><a class="reference internal" href="../installation/linux.html">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="../installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
</ul>
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</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deployment Guide</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/llm_api_examples.html">LLM Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async.html">Generate text asynchronously</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate text in streaming</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_sparse_attention.html">Sparse Attention</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_speculative_decoding.html">Speculative Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_kv_cache_connector.html">KV Cache Connector</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_kv_cache_offloading.html">KV Cache Offloading</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_runtime.html">Runtime Configuration Examples</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_sampling.html">Sampling Techniques Showcase</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Run LLM-API with pytorch backend on Slurm</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Run trtllm-bench with pytorch backend on Slurm</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Run trtllm-serve with pytorch backend on Slurm</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/trtllm_serve_examples.html">Online Serving Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_chat_client.html">Curl Chat Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_chat_client_for_multimodal.html">Curl Chat Client For Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_completion_client.html">Curl Completion Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client.html">Genai Perf Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client_for_multimodal.html">Genai Perf Client For Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_chat_client.html">OpenAI Chat Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_chat_client_for_multimodal.html">OpenAI Chat Client for Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client.html">OpenAI Completion Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client_for_lora.html">Openai Completion Client For Lora</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client_json_schema.html">OpenAI Completion Client with JSON Schema</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../examples/dynamo_k8s_example.html">Dynamo K8s Example</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../deployment-guide/index.html">Model Recipes</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.html">Deployment Guide for DeepSeek R1 on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-llama3.3-70b-on-trtllm.html">Deployment Guide for Llama3.3 70B on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-llama4-scout-on-trtllm.html">Deployment Guide for Llama4 Scout 17B on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-gpt-oss-on-trtllm.html">Deployment Guide for GPT-OSS on TensorRT-LLM - Blackwell Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-qwen3-next-on-trtllm.html">Deployment Guide for Qwen3 Next on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
</ul>
</details></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Models</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../models/supported-models.html">Supported Models</a></li>
<li class="toctree-l1"><a class="reference internal" href="../models/adding-new-model.html">Adding a New Model</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">CLI Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-bench.html">trtllm-bench</a></li>
<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-eval.html">trtllm-eval</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../commands/trtllm-serve/index.html">trtllm-serve</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="../commands/trtllm-serve/run-benchmark-with-trtllm-serve.html">Run benchmarking with <code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code></a></li>
</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">API Reference</span></p>
<ul class="current nav bd-sidenav">
<li class="toctree-l1 current active"><a class="current reference internal" href="#">LLM API Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="reference.html">API Reference</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Features</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../features/feature-combination-matrix.html">Feature Combination Matrix</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/disagg-serving.html">Disaggregated Serving</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/kvcache.html">KV Cache System</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/long-sequence.html">Long Sequences</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/lora.html">LoRA (Low-Rank Adaptation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/multi-modality.html">Multimodal Support in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/overlap-scheduler.html">Overlap Scheduler</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/paged-attention-ifb-scheduler.html">Paged Attention, IFB, and Request Scheduling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/parallel-strategy.html">Parallelism in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/sampling.html">Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/additional-outputs.html">Additional Outputs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/speculative-decoding.html">Speculative Decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/checkpoint-loading.html">Checkpoint Loading</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/auto_deploy/auto-deploy.html">AutoDeploy (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/ray-orchestrator.html">Ray Orchestrator (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/torch_compile_and_piecewise_cuda_graph.html">Torch Compile &amp; Piecewise CUDA Graph</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/overview.html">Architecture Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/perf-analysis.html">Performance Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/perf-benchmarking.html">TensorRT LLM Benchmarking</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/ci-overview.html">Continuous Integration Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/dev-containers.html">Using Dev Containers</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/api-change.html">LLM API Change Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/kv-transfer.html">Introduction to KV Cache Transmission</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Blogs</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog10_ADP_Balance_Strategy.html">ADP Balance Strategy</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog11_GPT_OSS_Eagle3.html">Running GPT-OSS-120B with Eagle3 Speculative Decoding on GB200/B200 (TensorRT LLM)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog12_Combining_Guided_Decoding_and_Speculative_Decoding.html">Combining Guided Decoding and Speculative Decoding: Making CPU and GPU Cooperate Seamlessly</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="llm-api-introduction">
<h1>LLM API Introduction<a class="headerlink" href="#llm-api-introduction" title="Link to this heading">#</a></h1>
<p>The LLM API is a high-level Python API designed to streamline LLM inference workflows.</p>
<p>It supports a broad range of use cases, from single-GPU setups to multi-GPU and multi-node deployments, with built-in support for various parallelism strategies and advanced features. The LLM API integrates seamlessly with the broader inference ecosystem, including NVIDIA <a class="reference external" href="https://github.com/ai-dynamo/dynamo">Dynamo</a>.</p>
<p>While the LLM API simplifies inference workflows with a high-level interface, it is also designed with flexibility in mind. Under the hood, it uses a PyTorch-native and modular backend, making it easy to customize, extend, or experiment with the runtime.</p>
<section id="quick-start-example">
<h2>Quick Start Example<a class="headerlink" href="#quick-start-example" title="Link to this heading">#</a></h2>
<p>A simple inference example with TinyLlama using the LLM API:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLM</span><span class="p">,</span> <span class="n">SamplingParams</span>
<span class="linenos"> 2</span>
<span class="linenos"> 3</span>
<span class="linenos"> 4</span><span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
<span class="linenos"> 5</span>
<span class="linenos"> 6</span> <span class="c1"># Model could accept HF model name, a path to local HF model,</span>
<span class="linenos"> 7</span> <span class="c1"># or TensorRT Model Optimizer&#39;s quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.</span>
<span class="linenos"> 8</span> <span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span><span class="p">)</span>
<span class="linenos"> 9</span>
<span class="linenos">10</span> <span class="c1"># Sample prompts.</span>
<span class="linenos">11</span> <span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="linenos">12</span> <span class="s2">&quot;Hello, my name is&quot;</span><span class="p">,</span>
<span class="linenos">13</span> <span class="s2">&quot;The capital of France is&quot;</span><span class="p">,</span>
<span class="linenos">14</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos">15</span> <span class="p">]</span>
<span class="linenos">16</span>
<span class="linenos">17</span> <span class="c1"># Create a sampling params.</span>
<span class="linenos">18</span> <span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">temperature</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">top_p</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
<span class="linenos">19</span>
<span class="linenos">20</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">llm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompts</span><span class="p">,</span> <span class="n">sampling_params</span><span class="p">):</span>
<span class="linenos">21</span> <span class="nb">print</span><span class="p">(</span>
<span class="linenos">22</span> <span class="sa">f</span><span class="s2">&quot;Prompt: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="si">!r}</span><span class="s2">&quot;</span>
<span class="linenos">23</span> <span class="p">)</span>
<span class="linenos">24</span>
<span class="linenos">25</span> <span class="c1"># Got output like</span>
<span class="linenos">26</span> <span class="c1"># Prompt: &#39;Hello, my name is&#39;, Generated text: &#39;\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming&#39;</span>
<span class="linenos">27</span> <span class="c1"># Prompt: &#39;The president of the United States is&#39;, Generated text: &#39;likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the&#39;</span>
<span class="linenos">28</span> <span class="c1"># Prompt: &#39;The capital of France is&#39;, Generated text: &#39;Paris.&#39;</span>
<span class="linenos">29</span> <span class="c1"># Prompt: &#39;The future of AI is&#39;, Generated text: &#39;an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are&#39;</span>
<span class="linenos">30</span>
<span class="linenos">31</span>
<span class="linenos">32</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="linenos">33</span> <span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
<p>For more advanced usage including distributed inference, multimodal, and speculative decoding, please refer to this <span class="xref myst">README</span>.</p>
</section>
<section id="model-input">
<h2>Model Input<a class="headerlink" href="#model-input" title="Link to this heading">#</a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">LLM()</span></code> constructor accepts either a Hugging Face model ID or a local model path as input.</p>
<section id="using-a-model-from-the-hugging-face-hub">
<h3>1. Using a Model from the Hugging Face Hub<a class="headerlink" href="#using-a-model-from-the-hugging-face-hub" title="Link to this heading">#</a></h3>
<p>To load a model directly from the <a class="reference internal" href="#(https://huggingface.co/)"><span class="xref myst">Hugging Face Model Hub</span></a>, simply pass its model ID (i.e., repository name) to the LLM constructor. The model will be automatically downloaded:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>You can also use <a class="reference external" href="https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4">quantized checkpoints</a> (FP4, FP8, etc) of popular models provided by NVIDIA in the same way.</p>
</section>
<section id="using-a-local-hugging-face-model">
<h3>2. Using a Local Hugging Face Model<a class="headerlink" href="#using-a-local-hugging-face-model" title="Link to this heading">#</a></h3>
<p>To use a model from local storage, first download it manually:</p>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">git lfs install</span>
<span class="go">git clone https://huggingface.co/meta-llama/Meta-Llama-3.1-8B</span>
</pre></div>
</div>
<p>Then, load the model by specifying a local directory path:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=&lt;</span><span class="n">local_path_to_model</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
<blockquote>
<div><p><strong>Note:</strong> Some models require accepting specific <a class="reference internal" href="#(https://ai.meta.com/resources/models-and-libraries/llama-downloads/)"><span class="xref myst">license agreements</span></a>. Make sure you have agreed to the terms and authenticated with Hugging Face before downloading.</p>
</div></blockquote>
</section>
</section>
<section id="tips-and-troubleshooting">
<h2>Tips and Troubleshooting<a class="headerlink" href="#tips-and-troubleshooting" title="Link to this heading">#</a></h2>
<p>The following tips typically assist new LLM API users who are familiar with other APIs that are part of TensorRT-LLM:</p>
<section id="runtimeerror-only-rank-0-can-start-multi-node-session-got-1">
<h3>RuntimeError: only rank 0 can start multi-node session, got 1<a class="headerlink" href="#runtimeerror-only-rank-0-can-start-multi-node-session-got-1" title="Link to this heading">#</a></h3>
<p>There is no need to add an <code class="docutils literal notranslate"><span class="pre">mpirun</span></code> prefix for launching single node multi-GPU inference with the LLM API.</p>
<p>For example, you can run <code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">llm_inference_distributed.py</span></code> to perform multi-GPU on a single node.</p>
</section>
<section id="hang-issue-on-slurm-node">
<h3>Hang issue on Slurm Node<a class="headerlink" href="#hang-issue-on-slurm-node" title="Link to this heading">#</a></h3>
<p>If you experience a hang or other issue on a node managed with Slurm, add prefix <code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">-n</span> <span class="pre">1</span> <span class="pre">--oversubscribe</span> <span class="pre">--allow-run-as-root</span></code> to your launch script.</p>
<p>For example, try <code class="docutils literal notranslate"><span class="pre">mpirun</span> <span class="pre">-n</span> <span class="pre">1</span> <span class="pre">--oversubscribe</span> <span class="pre">--allow-run-as-root</span> <span class="pre">python</span> <span class="pre">llm_inference_distributed.py</span></code>.</p>
</section>
<section id="mpi-abort-was-invoked-on-rank-1-in-communicator-mpi-comm-world-with-errorcode-1">
<h3>MPI_ABORT was invoked on rank 1 in communicator MPI_COMM_WORLD with errorcode 1.<a class="headerlink" href="#mpi-abort-was-invoked-on-rank-1-in-communicator-mpi-comm-world-with-errorcode-1" title="Link to this heading">#</a></h3>
<p>Because the LLM API relies on the <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code> library, put the LLM class in a function and protect the main entrypoint to the program under the <code class="docutils literal notranslate"><span class="pre">__main__</span></code> namespace to avoid a <a class="reference external" href="https://mpi4py.readthedocs.io/en/stable/mpi4py.futures.html#mpipoolexecutor">recursive spawn</a> process in <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code>.</p>
<p>This limitation is applicable for multi-GPU inference only.</p>
</section>
<section id="cannot-quit-after-generation">
<h3>Cannot quit after generation<a class="headerlink" href="#cannot-quit-after-generation" title="Link to this heading">#</a></h3>
<p>The LLM instance manages threads and processes, which may prevent its reference count from reaching zero. To address this issue, there are two common solutions:</p>
<ol class="arabic simple">
<li><p>Wrap the LLM instance in a function, as demonstrated in the quickstart guide. This will reduce the reference count and trigger the shutdown process.</p></li>
<li><p>Use LLM as an contextmanager, with the following code: <code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">LLM(...)</span> <span class="pre">as</span> <span class="pre">llm:</span> <span class="pre">...</span></code>, the shutdown methed will be invoked automatically once it goes out of the <code class="docutils literal notranslate"><span class="pre">with</span></code>-statement block.</p></li>
</ol>
</section>
<section id="single-node-hanging-when-using-docker-run-net-host">
<h3>Single node hanging when using <code class="docutils literal notranslate"><span class="pre">docker</span> <span class="pre">run</span> <span class="pre">--net=host</span></code><a class="headerlink" href="#single-node-hanging-when-using-docker-run-net-host" title="Link to this heading">#</a></h3>
<p>The root cause may be related to <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code>. There is a <a class="reference external" href="https://github.com/mpi4py/mpi4py/discussions/491#discussioncomment-12660609">workaround</a> suggesting a change from <code class="docutils literal notranslate"><span class="pre">--net=host</span></code> to <code class="docutils literal notranslate"><span class="pre">--ipc=host</span></code>, or setting the following environment variables:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">OMPI_MCA_btl_tcp_if_include</span><span class="o">=</span>lo
<span class="nb">export</span><span class="w"> </span><span class="nv">OMPI_MCA_oob_tcp_if_include</span><span class="o">=</span>lo
</pre></div>
</div>
<p>Another option to improve compatibility with <code class="docutils literal notranslate"><span class="pre">mpi4py</span></code> is to launch the task using:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mpirun<span class="w"> </span>-n<span class="w"> </span><span class="m">1</span><span class="w"> </span>--oversubscribe<span class="w"> </span>--allow-run-as-root<span class="w"> </span>python<span class="w"> </span>my_llm_task.py
</pre></div>
</div>
<p>This command can help avoid related runtime issues.</p>
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