[DOCS] Update main website to tvm.apache.org (#4429)

* [DOCS] Update main website to tvm.apache.org

* Update jvm pom repo loc

* Change the org to asf

* Update ci addr to new one

README.md

@@ -15,14 +15,14 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-<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

docs/vta/index.rst

@@ -49,5 +49,5 @@ Literature
 - 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

jvm/pom.xml

@@ -10,8 +10,8 @@
   <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>
@@ -20,8 +20,8 @@
     </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>
 

tutorials/autotvm/tune_conv2d_cuda.py

@@ -60,7 +60,7 @@ from tvm import autotvm
 # 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