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-<img src=https://raw.githubusercontent.com/tqchen/tvm.ai/master/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
+<img src=https://raw.githubusercontent.com/apache/incubator-tvm-site/master/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
==============================================
[Documentation](https://docs.tvm.ai) |
[Contributors](CONTRIBUTORS.md) |
-[Community](https://tvm.ai/community.html) |
+[Community](https://tvm.apache.org/community) |
[Release Notes](NEWS.md)
-[![Build Status](http://ci.tvm.ai:8080/buildStatus/icon?job=tvm/master)](http://ci.tvm.ai:8080/job/tvm/job/master/)
+[![Build Status](https://ci.tvm.ai/buildStatus/icon?job=tvm/master)](https://ci.tvm.ai/job/tvm/job/master/)
[![Azure Pipeline](https://dev.azure.com/tvmai/tvm/_apis/build/status/windows_mac_build?branchName=master)](https://dev.azure.com/tvmai/tvm/_build/latest?definitionId=2&branchName=master)
Apache TVM (incubating) is a compiler stack for deep learning systems. It is designed to close the gap between the
- Read the VTA `release blog post`_.
- Read the VTA tech report: `An Open Hardware Software Stack for Deep Learning`_.
-.. _release blog post: https://tvm.ai/2018/07/12/vta-release-announcement.html
+.. _release blog post: https://tvm.apache.org/2018/07/12/vta-release-announcement
.. _An Open Hardware Software Stack for Deep Learning: https://arxiv.org/abs/1807.04188
\ No newline at end of file
<url>https://github.com/apache/incubator-tvm/tree/master/jvm</url>
<description>TVM4J Package</description>
<organization>
- <name>Distributed (Deep) Machine Learning Community</name>
- <url>http://dmlc.ml</url>
+ <name>Apache Software Foundation</name>
+ <url>https://apache.org</url>
</organization>
<licenses>
<license>
</license>
</licenses>
<scm>
- <connection>scm:git:git@github.com:dmlc/tvm.git</connection>
- <developerConnection>scm:git:git@github.com:dmlc/tvm.git</developerConnection>
+ <connection>scm:git:git@github.com:apache/incubator-tvm.git</connection>
+ <developerConnection>scm:git:git@github.com:apache/incubator-tvm.git</developerConnection>
<url>https://github.com/apache/incubator-tvm</url>
</scm>
# There are plenty of useful schedule primitives in tvm. You can also find
# some tutorials that describe them in more details, such as
# (1). :ref:`opt-conv-gpu`
-# (2). `Optimizing DepthwiseConv on NVIDIA GPU <https://tvm.ai/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html>`_
+# (2). `Optimizing DepthwiseConv on NVIDIA GPU <https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example>`_
#
# However, their implementations are manually tuned for some special input
# shapes. In this section, we build a large enough space to cover