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doc:fix llm api reference blank issue. (#2669)
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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 id="installing-on-grace-hopper">
<span id="grace-hopper"></span><h1>Installing on Grace Hopper<a class="headerlink" href="#installing-on-grace-hopper" title="Link to this heading"></a></h1>
<ol class="arabic">
<li><p>Install TensorRT-LLM (tested on Ubuntu 22.04).</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip3<span class="w"> </span>install<span class="w"> </span><span class="nv">torch</span><span class="o">==</span><span class="m">2</span>.5.1<span class="w"> </span>torchvision<span class="w"> </span>torchaudio<span class="w"> </span>--index-url<span class="w"> </span>https://download.pytorch.org/whl/cu124
sudo<span class="w"> </span>apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>libopenmpi-dev<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>pip3<span class="w"> </span>install<span class="w"> </span>tensorrt_llm
</pre></div>
</div>
<p>If using the <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">PyTorch NGC Container</a> image, the prerequisite step for installing CUDA-enabled PyTorch package is not required.</p>
</li>
<li><p>Sanity check the installation by running the following in Python (tested on Python 3.10):</p>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorrt_llm</span> <span class="kn">import</span> <span class="n">LLM</span><span class="p">,</span> <span class="n">SamplingParams</span>
<span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">&quot;Hello, my name is&quot;</span><span class="p">,</span>
<span class="s2">&quot;The president of the United States is&quot;</span><span class="p">,</span>
<span class="s2">&quot;The capital of France is&quot;</span><span class="p">,</span>
<span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="p">]</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="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="n">outputs</span> <span class="o">=</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="c1"># Print the outputs.</span>
<span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">:</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">prompt</span>
<span class="n">generated_text</span> <span class="o">=</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="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Prompt: </span><span class="si">{</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </span><span class="si">{</span><span class="n">generated_text</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">)</span>
</pre></div>
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