This is an automated email from the ASF dual-hosted git repository. tqchen pushed a commit to branch main in repository https://gitbox.apache.org/repos/asf/tvm-site.git
commit 17f1c1ca32ec03b91e7ccacc5430e2a177265415 Author: tqchen <[email protected]> AuthorDate: Mon Sep 1 17:11:51 2025 -0400 Remove less active links --- _data/community.yml | 18 +++--------------- _data/menus.yml | 4 ---- _includes/header_index.html | 8 ++++---- index.md | 24 ++++++++---------------- 4 files changed, 15 insertions(+), 39 deletions(-) diff --git a/_data/community.yml b/_data/community.yml index 831d042e5b..ea884d7711 100644 --- a/_data/community.yml +++ b/_data/community.yml @@ -7,19 +7,7 @@ des: We use discuss forum for general discussions and usage trouble shooting. We welcome all topics related to the TVM stack. buttonname: Join The Discussion link: https://discuss.tvm.apache.org/ -- cardname: Github Issues +- cardname: Github des: We use our Github issue tracker for developer RFCs and roadmap discussion. - buttonname: Github Issue Tracker - link: https://github.com/apache/tvm/issues/ -- cardname: Contributing - des: As a community project, we welcome contributions! The package is developed and used by the community. - buttonname: Contribute - link: https://tvm.apache.org/docs/contribute/ -- cardname: Calendar - des: The TVM Community conducts a number of public events, including monthly general meetings, project sub-meetings, and the annual TVM Conference. You can subscribe to the public events calendar here. - buttonname: Calendar - link: https://calendar.google.com/calendar/embed?src=071aaettatchrj779v0k8jsmcc%40group.calendar.google.com -- cardname: Discord - des: Connect directly with TVM community members in the TVM Discord server. - buttonname: Discord - link: https://discord.gg/77Hh4jVhbM + buttonname: Github + link: https://github.com/apache/tvm/ diff --git a/_data/menus.yml b/_data/menus.yml index 2f70d9517d..c535446e33 100644 --- a/_data/menus.yml +++ b/_data/menus.yml @@ -2,11 +2,7 @@ link: /community - name: Download link: /download -- name: Blog - link: /blog - name: Docs link: https://tvm.apache.org/docs/ -- name: Conference - link: https://tvmcon.org/ - name: Github link: https://github.com/apache/tvm/ diff --git a/_includes/header_index.html b/_includes/header_index.html index e8196a71d3..f411db5b0b 100644 --- a/_includes/header_index.html +++ b/_includes/header_index.html @@ -6,13 +6,13 @@ <div class="bannerInner"> <div class="bannerDetails"> <h1>Apache TVM</h1> - <p class="subHeadingOne">An End to End Machine Learning Compiler Framework for CPUs, GPUs and accelerators</p> + <p class="subHeadingOne">An Open Machine Learning Compiler Framework</p> <a href="/#about" class="btn btn-dark">Learn More</a> </div> <div class="rightAlignDetails"> - <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> + <p class="subHeadingTwo"> + Apache TVM is a machine learning compilation framework, following the principle of Python-first development and universal deployment. It takes in pre-trained machine learning models, compiles and generates deployable modules that can be embedded and run everywhere. + </p> </div> </div> </div> diff --git a/index.md b/index.md index d72714f379..1f9da6e77f 100644 --- a/index.md +++ b/index.md @@ -14,12 +14,12 @@ title: "Apache TVM" * {:.aboutDetailsCol} #### About Apache TVM 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 +in machine learning, compilers, and systems architecture to build an accessible, flexible, and +efficient open-source framework that optimizes current and emerging machine learning models for any hardware platform. TVM provides the following main features: - * Compilation of deep learning models into minimum deployable modules. - * Infrastructure to automatic generate and optimize models on more backend with better performance. + * Python-first development that enables quick customization of machine learning compiler pipelines. + * Universal deployment to bring models into minimum deployable modules. </div> </section> @@ -38,23 +38,19 @@ any hardware platform. TVM provides the following main features: * {:.key-block}  ### Run Everywhere - CPUs, GPUs, browsers, microcontrollers, FPGAs and more. + From data center GPUs to edge environments. - {:.mt-0.mt-lg-3} - Automatically generate and optimize tensor operators on more backends. * {:.key-block}  ### Flexibility - 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? - - {:.mt-0.mt-lg-3} - TVM’s flexible design enables all of these things and more. + Flexible design that enables easy customization of compilation pipelines. * {:.key-block}  ### Ease of Use -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. + Easy to use python first compiler API that brings universal deployment. + </div> </section> @@ -72,10 +68,6 @@ Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML ### Community [Join the TVM <br/> community](/community) -* {:.doc-link-block} -### Blog -[Read more about TVM <br/> and our thinking](/blog) - </div> </section>
