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| <h1>Apache TVM</h1> |
| <p class="subHeadingOne">An End to End Machine Learning Compiler Framework for CPUs, GPUs and accelerators</p> |
| <a href="/#about" class="btn btn-dark">Learn More</a> |
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| <p class="subHeadingTwo">Apache TVM is an open source machine learning compiler framework for CPUs, |
| GPUs, and machine learning accelerators. It aims to enable machine learning engineers to optimize and run |
| computations efficiently on any hardware backend.</p> |
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| <h4 id="about-apache-tvm">About Apache TVM</h4> |
| <p>The vision of the Apache TVM Project is to host a diverse community of experts and practitioners |
| in machine learning, compilers, and systems architecture to build an accessible, extensible, and |
| automated open-source framework that optimizes current and emerging machine learning models for |
| any hardware platform. TVM provides the following main features:</p> |
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| <ul> |
| <li>Compilation of deep learning models into minimum deployable modules.</li> |
| <li>Infrastructure to automatic generate and optimize models on more backend with better performance.</li> |
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| <h2 class="key-title-text" id="key-features--capabilities">Key Features & Capabilities</h2> |
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| <li class="key-block"><img src="/assets/images/speed.svg" alt="Performance" title="speed" /> |
| <h3 id="performance">Performance</h3> |
| |
| <p class="mb-3">Compilation and minimal runtimes commonly unlock ML workloads on existing hardware.</p> |
| </li> |
| <li class="key-block"><img src="/assets/images/run.svg" alt="Run Everywhere" title="run" /> |
| <h3 id="run-everywhere">Run Everywhere</h3> |
| <p>CPUs, GPUs, browsers, microcontrollers, FPGAs and more.</p> |
| |
| <p class="mt-0 mt-lg-3">Automatically generate and optimize tensor operators on more backends.</p> |
| </li> |
| <li class="key-block"><img src="/assets/images/Flexibility.svg" alt="Flexibility" title="Flexibility" /> |
| <h3 id="flexibility">Flexibility</h3> |
| <p>Need support for block sparsity, quantization (1,2,4,8 bit integers, posit), random forests/classical ML, memory planning, MISRA-C compatibility, Python prototyping or all of the above?</p> |
| |
| <p class="mt-0 mt-lg-3">TVM’s flexible design enables all of these things and more.</p> |
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| <li class="key-block"><img src="/assets/images/use.svg" alt="Ease of Use" title="Ease of Use" /> |
| <h3 id="ease-of-use">Ease of Use</h3> |
| <p>Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet and more. Start using TVM with Python today, build out production stacks using C++, Rust, or Java the next day.</p> |
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| <h3 id="docs">Docs</h3> |
| <p><a href="https://tvm.apache.org/docs/">Written with care <br /> & love for you.</a></p> |
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| <h3 id="community">Community</h3> |
| <p><a href="/community">Join the TVM <br /> community</a></p> |
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| <h3 id="blog">Blog</h3> |
| <p><a href="/blog">Read more about TVM <br /> and our thinking</a></p> |
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