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TensorRT-LLMs/features/auto_deploy/auto-deploy.html
Kaiyu Xie 259faa6ee7 Update GitHub pages in root to v1.2.0rc6
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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>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deployment Guide</span></p>
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<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/curl_responses_client.html">Curl Responses Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
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</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-on-trtllm.html">Deployment Guide for Qwen3 on TensorRT LLM - Blackwell &amp; Hopper 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>
<li class="toctree-l2"><a class="reference internal" href="../../deployment-guide/deployment-guide-for-kimi-k2-thinking-on-trtllm.html">Deployment Guide for Kimi K2 Thinking on TensorRT LLM - Blackwell</a></li>
</ul>
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<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/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>
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<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>
</details></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">API Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../llm-api/index.html">LLM API Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../llm-api/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="current nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../feature-combination-matrix.html">Feature Combination Matrix</a></li>
<li class="toctree-l1"><a class="reference internal" href="../attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../disagg-serving.html">Disaggregated Serving</a></li>
<li class="toctree-l1"><a class="reference internal" href="../kvcache.html">KV Cache System</a></li>
<li class="toctree-l1"><a class="reference internal" href="../long-sequence.html">Long Sequences</a></li>
<li class="toctree-l1"><a class="reference internal" href="../lora.html">LoRA (Low-Rank Adaptation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../multi-modality.html">Multimodal Support in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../overlap-scheduler.html">Overlap Scheduler</a></li>
<li class="toctree-l1"><a class="reference internal" href="../paged-attention-ifb-scheduler.html">Paged Attention, IFB, and Request Scheduling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../parallel-strategy.html">Parallelism in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../sampling.html">Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../additional-outputs.html">Additional Outputs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../guided-decoding.html">Guided Decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="../speculative-decoding.html">Speculative Decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="../checkpoint-loading.html">Checkpoint Loading</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">AutoDeploy (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ray-orchestrator.html">Ray Orchestrator (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../torch_compile_and_piecewise_cuda_graph.html">Torch Compile &amp; Piecewise CUDA Graph</a></li>
<li class="toctree-l1"><a class="reference internal" href="../helix.html">Helix Parallelism</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
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<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>
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<section class="tex2jax_ignore mathjax_ignore" id="autodeploy-prototype">
<h1>AutoDeploy (Prototype)<a class="headerlink" href="#autodeploy-prototype" title="Link to this heading">#</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This project is under active development and is currently in a prototype stage. The code is a prototype, subject to change, and may include backward-incompatible updates. While we strive for correctness, there are no guarantees regarding functionality, stability, or reliability.</p>
</div>
<section id="seamless-model-deployment-from-pytorch-to-tensorrt-llm">
<h2>Seamless Model Deployment from PyTorch to TensorRT LLM<a class="headerlink" href="#seamless-model-deployment-from-pytorch-to-tensorrt-llm" title="Link to this heading">#</a></h2>
<p>AutoDeploy is a prototype designed to simplify and accelerate the deployment of PyTorch models, including off-the-shelf models such as those from the Hugging Face Transformers library, to TensorRT LLM.</p>
<p><img alt="AutoDeploy overview" src="../../_images/ad_overview.png" />
<sub><em>AutoDeploy overview and relation with TensorRT LLM’s LLM API</em></sub></p>
<p>AutoDeploy provides an alternative method for deploying models using the LLM API without requiring code changes to the source model (for example, Hugging Face Transformers models) or manual implementation of inference optimizations, such as KV-caches, multi-GPU parallelism, or quantization. Instead, AutoDeploy extracts a computation graph from the source model and applies inference optimizations through a series of automated graph transformations. AutoDeploy generates an inference-optimized graph that can be directly executed in the TensorRT LLM PyTorch runtime and leverages various runtime optimizations including in-flight batching, paging, and overlap scheduling.</p>
</section>
<section id="key-features">
<h2>Key Features<a class="headerlink" href="#key-features" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><strong>Seamless Model Translation:</strong> Automatically converts PyTorch/Hugging Face models to TensorRT LLM without manual rewrites.</p></li>
<li><p><strong>Unified Model Definition:</strong> Maintain a single source of truth with your original PyTorch/Hugging Face model.</p></li>
<li><p><strong>Optimized Inference:</strong> Built-in transformations for sharding, quantization, KV-cache integration, MHA fusion, and CudaGraph optimization.</p></li>
<li><p><strong>Immediate Deployment:</strong> Day-0 support for models with continuous performance enhancements.</p></li>
<li><p><strong>Quick Setup &amp; Prototyping:</strong> Lightweight pip package for easy installation with a demo environment for fast testing.</p></li>
</ul>
</section>
<section id="get-started">
<h2>Get Started<a class="headerlink" href="#get-started" title="Link to this heading">#</a></h2>
<ol class="arabic simple">
<li><p><strong>Install AutoDeploy:</strong></p></li>
</ol>
<p>AutoDeploy is included with the TRT-LLM installation.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>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>--upgrade<span class="w"> </span>pip<span class="w"> </span>setuptools<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>You can refer to <a class="reference internal" href="../../installation/linux.html"><span class="std std-doc">TRT-LLM installation guide</span></a> for more information.</p>
<ol class="arabic simple" start="2">
<li><p><strong>Run Llama Example:</strong></p></li>
</ol>
<p>You are now ready to run an in-framework LLama Demo.</p>
<p>The general entry point for running the AutoDeploy demo is the <code class="docutils literal notranslate"><span class="pre">build_and_run_ad.py</span></code> script, Checkpoints are loaded directly from Huggingface (HF) or a local HF-like directory:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>examples/auto_deploy
python<span class="w"> </span>build_and_run_ad.py<span class="w"> </span>--model<span class="w"> </span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span>
</pre></div>
</div>
</section>
<section id="support-matrix">
<h2>Support Matrix<a class="headerlink" href="#support-matrix" title="Link to this heading">#</a></h2>
<p>AutoDeploy streamlines the model deployment process through an automated workflow designed for efficiency and performance. The workflow begins with a PyTorch model, which is exported using <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> to generate a standard Torch graph. This graph contains core PyTorch ATen operations alongside custom attention operations, determined by the attention backend specified in the configuration.</p>
<p>The exported graph then undergoes a series of automated transformations, including graph sharding, KV-cache insertion, and GEMM fusion, to optimize model performance. After these transformations, the graph is compiled using one of the supported compile backends (like <code class="docutils literal notranslate"><span class="pre">torch-opt</span></code>), followed by deploying it via the TensorRT LLM runtime.</p>
<ul class="simple">
<li><p><a class="reference internal" href="support_matrix.html"><span class="std std-doc">Support Matrix</span></a></p></li>
</ul>
</section>
<section id="advanced-usage">
<h2>Advanced Usage<a class="headerlink" href="#advanced-usage" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="advanced/example_run.html"><span class="std std-doc">Example Run Script</span></a></p></li>
<li><p><a class="reference internal" href="advanced/logging.html"><span class="std std-doc">Logging Level</span></a></p></li>
<li><p><a class="reference internal" href="advanced/workflow.html"><span class="std std-doc">Incorporating AutoDeploy into Your Own Workflow</span></a></p></li>
<li><p><a class="reference internal" href="advanced/expert_configurations.html"><span class="std std-doc">Expert Configurations</span></a></p></li>
<li><p><a class="reference internal" href="advanced/benchmarking_with_trtllm_bench.html"><span class="std std-doc">Performance Benchmarking</span></a></p></li>
</ul>
</section>
<section id="roadmap">
<h2>Roadmap<a class="headerlink" href="#roadmap" title="Link to this heading">#</a></h2>
<p>We are actively expanding AutoDeploy to support a broader range of model architectures and inference features.</p>
<p><strong>Upcoming Model Support:</strong></p>
<ul class="simple">
<li><p>Vision-Language Models (VLMs)</p></li>
<li><p>Structured State Space Models (SSMs) and Linear Attention architectures</p></li>
</ul>
<p><strong>Planned Features:</strong></p>
<ul class="simple">
<li><p>Low-Rank Adaptation (LoRA)</p></li>
<li><p>Speculative Decoding for accelerated generation</p></li>
</ul>
<p>To track development progress and contribute, visit our <a class="reference external" href="https://github.com/orgs/NVIDIA/projects/83/views/13">Github Project Board</a>.
We welcome community contributions, see <code class="docutils literal notranslate"><span class="pre">examples/auto_deploy/CONTRIBUTING.md</span></code> for guidelines.</p>
</section>
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