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<li class="toctree-l1"><a class="reference internal" href="../key-features.html">Key Features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Building from Source Code on Linux</a></li>
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<li class="toctree-l1"><a class="reference internal" href="grace-hopper.html">Installing on Grace Hopper</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">API Introduction</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../llm-api-examples/index.html">LLM Examples Introduction</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="../llm-api-examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_logits_processor.html">Control generated text using logits post processor</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
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</ul>
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</details></li>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api-examples/customization.html">Common Customizations</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../llm-api-examples/llm_api_examples.html">Examples</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="../llm-api-examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_customize.html">Generate text with customization</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_logits_processor.html">Control generated text using logits post processor</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_inference.html">Generate text</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../llm-api-examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
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</ul>
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</details></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.layers.html">Layers</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.functional.html">Functionals</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.models.html">Models</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.plugin.html">Plugin</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.quantization.html">Quantization</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.runtime.html">Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_gen/runtime.html">Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-build.html">trtllm-build</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/overview.html">TensorRT-LLM Architecture</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/workflow.html">TensorRT-LLM Build Workflow</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/add-model.html">Adding a Model</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced</span></p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-runtime.html">C++ GPT Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/executor.html">Executor API</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/graph-rewriting.html">Graph Rewriting Module</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/inference-request.html">Inference Request</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/lora.html">Run gpt-2b + LoRA using GptManager / cpp runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/speculative-decoding.html">Speculative Sampling</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/disaggregated-service.html">Disaggregated-Service (experimental)</a></li>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="../performance/perf-overview.html">Overview</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-benchmarking.html">Benchmarking</a></li>
|
||
<li class="toctree-l1 has-children"><a class="reference internal" href="../performance/performance-tuning-guide/index.html">Performance Tuning Guide</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="../performance/performance-tuning-guide/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/tuning-max-batch-size-and-max-num-tokens.html">Tuning Max Batch Size and Max Num Tokens</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/deciding-model-sharding-strategy.html">Deciding Model Sharding Strategy</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/fp8-quantization.html">FP8 Quantization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-runtime-flags.html">Useful Runtime Options</a></li>
|
||
</ul>
|
||
</details></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-analysis.html">Performance Analysis</a></li>
|
||
</ul>
|
||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Reference</span></p>
|
||
<ul class="nav bd-sidenav">
|
||
<li class="toctree-l1"><a class="reference internal" href="../reference/troubleshooting.html">Troubleshooting</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../reference/support-matrix.html">Support Matrix</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../reference/precision.html">Numerical Precision</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../reference/memory.html">Memory Usage of TensorRT-LLM</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/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../blogs/H200launch.html">H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../blogs/Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../blogs/quantization-in-TRT-LLM.html">Speed up inference with SOTA quantization techniques in TRT-LLM</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="../blogs/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
|
||
</ul>
|
||
</div>
|
||
</nav></div>
|
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</div>
|
||
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Building from Source Code on Linux</span></li>
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</ul>
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</nav>
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</div>
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<article class="bd-article">
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<section id="building-from-source-code-on-linux">
|
||
<span id="build-from-source-linux"></span><h1>Building from Source Code on Linux<a class="headerlink" href="#building-from-source-code-on-linux" title="Link to this heading">#</a></h1>
|
||
<p>This document provides instructions for building TensorRT-LLM from source code on Linux. Building from source code is necessary if you want the best performance or debugging capabilities, or if the <a class="reference external" href="https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html">GNU C++11 ABI</a> is required.</p>
|
||
<section id="prerequisites">
|
||
<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading">#</a></h2>
|
||
<p>Use <a class="reference external" href="https://www.docker.com">Docker</a> to build and run TensorRT-LLM. Instructions to install an environment to run Docker containers for the NVIDIA platform can be found <a class="reference external" href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html">here</a>.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># TensorRT-LLM uses git-lfs, which needs to be installed in advance.</span>
|
||
apt-get<span class="w"> </span>update<span class="w"> </span><span class="o">&&</span><span class="w"> </span>apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>git<span class="w"> </span>git-lfs
|
||
git<span class="w"> </span>lfs<span class="w"> </span>install
|
||
|
||
git<span class="w"> </span>clone<span class="w"> </span>https://github.com/NVIDIA/TensorRT-LLM.git
|
||
<span class="nb">cd</span><span class="w"> </span>TensorRT-LLM
|
||
git<span class="w"> </span>submodule<span class="w"> </span>update<span class="w"> </span>--init<span class="w"> </span>--recursive
|
||
git<span class="w"> </span>lfs<span class="w"> </span>pull
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
<section id="building-a-tensorrt-llm-docker-image">
|
||
<h2>Building a TensorRT-LLM Docker Image<a class="headerlink" href="#building-a-tensorrt-llm-docker-image" title="Link to this heading">#</a></h2>
|
||
<p>There are two options to create a TensorRT-LLM Docker image. The approximate disk space required to build the image is 63 GB.</p>
|
||
<section id="option-1-build-tensorrt-llm-in-one-step">
|
||
<h3>Option 1: Build TensorRT-LLM in One Step<a class="headerlink" href="#option-1-build-tensorrt-llm-in-one-step" title="Link to this heading">#</a></h3>
|
||
<p>TensorRT-LLM contains a simple command to create a Docker image.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>release_build
|
||
</pre></div>
|
||
</div>
|
||
<p>You can add the <code class="docutils literal notranslate"><span class="pre">CUDA_ARCHS="<list</span> <span class="pre">of</span> <span class="pre">architectures</span> <span class="pre">in</span> <span class="pre">CMake</span> <span class="pre">format>"</span></code> optional argument to specify which architectures should be supported by TensorRT-LLM. It restricts the supported GPU architectures but helps reduce compilation time:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Restrict the compilation to Ada and Hopper architectures.</span>
|
||
make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>release_build<span class="w"> </span><span class="nv">CUDA_ARCHS</span><span class="o">=</span><span class="s2">"89-real;90-real"</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>After the image is built, the Docker container can be run.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>release_run
|
||
</pre></div>
|
||
</div>
|
||
<p>The <code class="docutils literal notranslate"><span class="pre">make</span></code> command supports the <code class="docutils literal notranslate"><span class="pre">LOCAL_USER=1</span></code> argument to switch to the local user account instead of <code class="docutils literal notranslate"><span class="pre">root</span></code> inside the container. The examples of TensorRT-LLM are installed in the <code class="docutils literal notranslate"><span class="pre">/app/tensorrt_llm/examples</span></code> directory.</p>
|
||
</section>
|
||
<section id="option-2-build-tensorrt-llm-step-by-step">
|
||
<h3>Option 2: Build TensorRT-LLM Step-By-Step<a class="headerlink" href="#option-2-build-tensorrt-llm-step-by-step" title="Link to this heading">#</a></h3>
|
||
<p>If you are looking for more flexibility, TensorRT-LLM has commands to create and run a development container in which TensorRT-LLM can be built.</p>
|
||
<section id="create-the-container">
|
||
<h4>Create the Container<a class="headerlink" href="#create-the-container" title="Link to this heading">#</a></h4>
|
||
<p><strong>On systems with GNU <code class="docutils literal notranslate"><span class="pre">make</span></code></strong></p>
|
||
<ol class="arabic">
|
||
<li><p>Create a Docker image for development. The image will be tagged locally with <code class="docutils literal notranslate"><span class="pre">tensorrt_llm/devel:latest</span></code>.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>build
|
||
</pre></div>
|
||
</div>
|
||
</li>
|
||
<li><p>Run the container.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>run
|
||
</pre></div>
|
||
</div>
|
||
<p>If you prefer to work with your own user account in that container, instead of <code class="docutils literal notranslate"><span class="pre">root</span></code>, add the <code class="docutils literal notranslate"><span class="pre">LOCAL_USER=1</span></code> option.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>run<span class="w"> </span><span class="nv">LOCAL_USER</span><span class="o">=</span><span class="m">1</span>
|
||
</pre></div>
|
||
</div>
|
||
</li>
|
||
</ol>
|
||
<p><strong>On systems without GNU <code class="docutils literal notranslate"><span class="pre">make</span></code></strong></p>
|
||
<ol class="arabic">
|
||
<li><p>Create a Docker image for development.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>build<span class="w"> </span>--pull<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--target<span class="w"> </span>devel<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--file<span class="w"> </span>docker/Dockerfile.multi<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--tag<span class="w"> </span>tensorrt_llm/devel:latest<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>.
|
||
</pre></div>
|
||
</div>
|
||
</li>
|
||
<li><p>Run the container.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>-it<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--ipc<span class="o">=</span>host<span class="w"> </span>--ulimit<span class="w"> </span><span class="nv">memlock</span><span class="o">=</span>-1<span class="w"> </span>--ulimit<span class="w"> </span><span class="nv">stack</span><span class="o">=</span><span class="m">67108864</span><span class="w"> </span>--gpus<span class="o">=</span>all<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--volume<span class="w"> </span><span class="si">${</span><span class="nv">PWD</span><span class="si">}</span>:/code/tensorrt_llm<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>--workdir<span class="w"> </span>/code/tensorrt_llm<span class="w"> </span><span class="se">\</span>
|
||
<span class="w"> </span>tensorrt_llm/devel:latest
|
||
</pre></div>
|
||
</div>
|
||
<p>Note: please make sure to set <code class="docutils literal notranslate"><span class="pre">--ipc=host</span></code> as a docker run argument to avoid <code class="docutils literal notranslate"><span class="pre">Bus</span> <span class="pre">error</span> <span class="pre">(core</span> <span class="pre">dumped)</span></code>.</p>
|
||
</li>
|
||
</ol>
|
||
</section>
|
||
<section id="build-tensorrt-llm">
|
||
<h4>Build TensorRT-LLM<a class="headerlink" href="#build-tensorrt-llm" title="Link to this heading">#</a></h4>
|
||
<p>Once in the container, build TensorRT-LLM from the source.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># To build the TensorRT-LLM code.</span>
|
||
python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--trt_root<span class="w"> </span>/usr/local/tensorrt
|
||
|
||
<span class="c1"># Deploy TensorRT-LLM in your environment.</span>
|
||
pip<span class="w"> </span>install<span class="w"> </span>./build/tensorrt_llm*.whl
|
||
</pre></div>
|
||
</div>
|
||
<p>By default, <code class="docutils literal notranslate"><span class="pre">build_wheel.py</span></code> enables incremental builds. To clean the build
|
||
directory, add the <code class="docutils literal notranslate"><span class="pre">--clean</span></code> option:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--clean<span class="w"> </span>--trt_root<span class="w"> </span>/usr/local/tensorrt
|
||
</pre></div>
|
||
</div>
|
||
<p>It is possible to restrict the compilation of TensorRT-LLM to specific CUDA
|
||
architectures. For that purpose, the <code class="docutils literal notranslate"><span class="pre">build_wheel.py</span></code> script accepts a
|
||
semicolon separated list of CUDA architecture as shown in the following
|
||
example:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build TensorRT-LLM for Ampere.</span>
|
||
python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--cuda_architectures<span class="w"> </span><span class="s2">"80-real;86-real"</span><span class="w"> </span>--trt_root<span class="w"> </span>/usr/local/tensorrt
|
||
</pre></div>
|
||
</div>
|
||
<p>To use the C++ benchmark scripts under <a class="reference internal" href="#/benchmarks/cpp/"><span class="xref myst">benchmark/cpp</span></a>, for example <code class="docutils literal notranslate"><span class="pre">gptManagerBenchmark.cpp</span></code>, add the <code class="docutils literal notranslate"><span class="pre">--benchmarks</span></code> option:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--benchmarks<span class="w"> </span>--trt_root<span class="w"> </span>/usr/local/tensorrt
|
||
</pre></div>
|
||
</div>
|
||
<p>Refer to the <a class="reference internal" href="../reference/support-matrix.html#support-matrix-hardware"><span class="std std-ref">Hardware</span></a> section for a list of architectures.</p>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
<section id="building-the-python-bindings-for-the-c-runtime">
|
||
<h2>Building the Python Bindings for the C++ Runtime<a class="headerlink" href="#building-the-python-bindings-for-the-c-runtime" title="Link to this heading">#</a></h2>
|
||
<p>The C++ Runtime, in particular, <code class="docutils literal notranslate"><span class="pre">GptSession</span></code> can be exposed to Python via bindings. This feature can be turned on through the default build options.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--trt_root<span class="w"> </span>/usr/local/tensorrt
|
||
</pre></div>
|
||
</div>
|
||
<p>After installing, the resulting wheel as described above, the C++ Runtime bindings will be available in
|
||
the <code class="docutils literal notranslate"><span class="pre">tensorrt_llm.bindings</span></code> package. Running <code class="docutils literal notranslate"><span class="pre">help</span></code> on this package in a Python interpreter will provide on overview of the
|
||
relevant classes. The associated unit tests should also be consulted for understanding the API.</p>
|
||
<p>This feature will not be enabled when <a class="reference internal" href="#link-with-the-tensorrt-llm-c++-runtime"><span class="xref myst"><code class="docutils literal notranslate"><span class="pre">building</span> <span class="pre">only</span> <span class="pre">the</span> <span class="pre">C++</span> <span class="pre">runtime</span></code></span></a>.</p>
|
||
</section>
|
||
<section id="linking-with-the-tensorrt-llm-c-runtime">
|
||
<h2>Linking with the TensorRT-LLM C++ Runtime<a class="headerlink" href="#linking-with-the-tensorrt-llm-c-runtime" title="Link to this heading">#</a></h2>
|
||
<p>The <code class="docutils literal notranslate"><span class="pre">build_wheel.py</span></code> script will also compile the library containing the C++ runtime of TensorRT-LLM. If Python support and <code class="docutils literal notranslate"><span class="pre">torch</span></code> modules are not required, the script provides the option <code class="docutils literal notranslate"><span class="pre">--cpp_only</span></code> which restricts the build to the C++ runtime only.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--cuda_architectures<span class="w"> </span><span class="s2">"80-real;86-real"</span><span class="w"> </span>--cpp_only<span class="w"> </span>--clean
|
||
</pre></div>
|
||
</div>
|
||
<p>This is particularly useful to avoid linking problems which may be introduced by particular versions of <code class="docutils literal notranslate"><span class="pre">torch</span></code> related to the <a class="reference external" href="https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html">dual ABI support of GCC</a>. The option <code class="docutils literal notranslate"><span class="pre">--clean</span></code> will remove the build directory before building. The default build directory is <code class="docutils literal notranslate"><span class="pre">cpp/build</span></code>, which may be overridden using the option
|
||
<code class="docutils literal notranslate"><span class="pre">--build_dir</span></code>. Run <code class="docutils literal notranslate"><span class="pre">build_wheel.py</span> <span class="pre">--help</span></code> for an overview of all supported options.</p>
|
||
<p>The shared library can be found in the following location:</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cpp/build/tensorrt_llm/libtensorrt_llm.so
|
||
</pre></div>
|
||
</div>
|
||
<p>In addition, link against the library containing the LLM plugins for TensorRT.</p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cpp/build/tensorrt_llm/plugins/libnvinfer_plugin_tensorrt_llm.so
|
||
</pre></div>
|
||
</div>
|
||
<section id="supported-c-header-files">
|
||
<h3>Supported C++ Header Files<a class="headerlink" href="#supported-c-header-files" title="Link to this heading">#</a></h3>
|
||
<p>When using TensorRT-LLM, you need to add the <code class="docutils literal notranslate"><span class="pre">cpp</span></code> and <code class="docutils literal notranslate"><span class="pre">cpp/include</span></code> directories to the project’s include paths. Only header files contained in <code class="docutils literal notranslate"><span class="pre">cpp/include</span></code> are part of the supported API and may be directly included. Other headers contained under <code class="docutils literal notranslate"><span class="pre">cpp</span></code> should not be included directly since they might change in future versions.</p>
|
||
</section>
|
||
</section>
|
||
</section>
|
||
|
||
|
||
</article>
|
||
|
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|
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|
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