[DOCS] Update link loc (#4257)

.github/PULL_REQUEST_TEMPLATE.md

-Thanks for contributing to TVM!   Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/dmlc/tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread.
+Thanks for contributing to TVM!   Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread.

CONTRIBUTORS.md

@@ -112,7 +112,7 @@ We do encourage everyone to work anything they are interested in.
 - [Lianmin Zheng](https://github.com/merrymercy): @merrymercy
 
 ## List of Contributors
-- [Full List of Contributors](https://github.com/dmlc/tvm/graphs/contributors)
+- [Full List of Contributors](https://github.com/apache/incubator-tvm/graphs/contributors)
   - To contributors: please add your name to the list.
 - [Qiao Zhang](https://github.com/zhangqiaorjc)
 - [Haolong Zhang](https://github.com/haolongzhangm)

apps/android_deploy/README.md

@@ -34,7 +34,7 @@ Alternatively, you may execute Docker image we provide wich contains the require
 
 ### Build APK
 
-Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/dmlc/tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
+Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/apache/incubator-tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
 
 ```
 dependencies {
@@ -124,7 +124,7 @@ If everything goes well, you will find compile tools in `/opt/android-toolchain-
 
 Follow instruction to get compiled version model for android target [here.](http://docs.tvm.ai/deploy/android.html)
 
-Copied these compiled model deploy_lib.so, deploy_graph.json and deploy_param.params to apps/android_deploy/app/src/main/assets/ and modify TVM flavor changes on [java](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java#L81)
+Copied these compiled model deploy_lib.so, deploy_graph.json and deploy_param.params to apps/android_deploy/app/src/main/assets/ and modify TVM flavor changes on [java](https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java#L81)
 
 `CPU Verison flavor`
 ```

apps/android_rpc/README.md

@@ -28,7 +28,7 @@ You will need JDK, [Android NDK](https://developer.android.com/ndk) and an Andro
 
 We use [Gradle](https://gradle.org) to build. Please follow [the installation instruction](https://gradle.org/install) for your operating system.
 
-Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/dmlc/tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
+Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/apache/incubator-tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
 
 ```
 dependencies {
@@ -146,7 +146,7 @@ android   1      1     0
 ```
 
 
-Then checkout [android\_rpc/tests/android\_rpc\_test.py](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py) and run,
+Then checkout [android\_rpc/tests/android\_rpc\_test.py](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py) and run,
 
 ```bash
 # Specify the RPC tracker
@@ -157,7 +157,7 @@ export TVM_NDK_CC=/opt/android-toolchain-arm64/bin/aarch64-linux-android-g++
 python android_rpc_test.py
 ```
 
-This will compile TVM IR to shared libraries (CPU, OpenCL and Vulkan) and run vector addition on your Android device. To verify compiled TVM IR shared libraries on OpenCL target set `'test_opencl = True'` and on Vulkan target set `'test_vulkan = True'` in  [tests/android_rpc_test.py](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py), by default on CPU target will execute.
+This will compile TVM IR to shared libraries (CPU, OpenCL and Vulkan) and run vector addition on your Android device. To verify compiled TVM IR shared libraries on OpenCL target set `'test_opencl = True'` and on Vulkan target set `'test_vulkan = True'` in  [tests/android_rpc_test.py](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py), by default on CPU target will execute.
 On my test device, it gives following results.
 
 ```bash

apps/benchmark/README.md

@@ -20,7 +20,7 @@
 
 ## Results
 
-See results on wiki page https://github.com/dmlc/tvm/wiki/Benchmark
+See results on wiki page https://github.com/apache/incubator-tvm/wiki/Benchmark
 
 ## How to Reproduce
 
@@ -78,7 +78,7 @@ python3 -m tvm.exec.rpc_tracker
   `python3 -m tvm.exec.rpc_server --tracker=10.77.1.123:9190 --key=rk3399`, where 10.77.1.123 is the IP address of the tracker.
 
 * For Android device
-   * Build and install tvm RPC apk on your device [Help](https://github.com/dmlc/tvm/tree/master/apps/android_rpc).
+   * Build and install tvm RPC apk on your device [Help](https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc).
      Make sure you can pass the android rpc test. Then you have alreadly known how to register.
 
 3. Verify the device registration

apps/sgx/README.md

@@ -39,7 +39,7 @@ Check out the `/tvm/install/ubuntu_install_sgx.sh` for the commands to get these
 If using Docker, start by running
 
 ```
-git clone --recursive https://github.com/dmlc/tvm.git
+git clone --recursive https://github.com/apache/incubator-tvm.git
 docker run --rm -it -v $(pwd)/tvm:/mnt tvmai/ci-cpu /bin/bash
 ```
 then, in the container

conda/tvm-libs/meta.yaml

@@ -43,6 +43,6 @@ requirements:
     - {{ pin_compatible('cudnn', lower_bound='7.6.0', max_pin='x') }}  # [cuda]
 
 about:
-  home: https://github.com/dmlc/tvm
+  home: https://github.com/apache/incubator-tvm
   license: Apache2
   summary: a low level domain specific language for compiling tensor computation pipelines
\ No newline at end of file

conda/tvm/meta.yaml

@@ -58,7 +58,7 @@ test:
     - python -m pytest -v tests/python/integration
 
 about:
-  home: https://github.com/dmlc/tvm
+  home: https://github.com/apache/incubator-tvm
   license: Apache-2.0
   license_family: Apache
   summary: a low level domain specific language for compiling tensor computation pipelines

docker/Dockerfile.demo_android

@@ -56,7 +56,7 @@ RUN git clone https://github.com/KhronosGroup/OpenCL-Headers /usr/local/OpenCL-H
 
 # Build TVM
 RUN cd /usr && \
-    git clone --depth=1 https://github.com/dmlc/tvm --recursive && \
+    git clone --depth=1 https://github.com/apache/incubator-tvm --recursive && \
     cd /usr/tvm && \
     mkdir -p build && \
     cd build && \

docker/Dockerfile.demo_opencl

@@ -62,7 +62,7 @@ RUN echo "Cloning TVM source & submodules"
 ENV TVM_PAR_DIR="/usr"
 RUN mkdir -p TVM_PAR_DIR && \
 	cd ${TVM_PAR_DIR} && \
-	git clone --depth=1 https://github.com/dmlc/tvm --recursive
+	git clone --depth=1 https://github.com/apache/incubator-tvm --recursive
 #RUN git submodule update --init --recursive
 
 

docker/install/install_tvm_cpu.sh

@@ -21,7 +21,7 @@ set -u
 set -o pipefail
 
 cd /usr
-git clone --depth=1 https://github.com/dmlc/tvm --recursive
+git clone --depth=1 https://github.com/apache/incubator-tvm --recursive
 cd /usr/tvm
 # checkout a hash-tag
 git checkout 4b13bf668edc7099b38d463e5db94ebc96c80470

docker/install/install_tvm_gpu.sh

@@ -21,7 +21,7 @@ set -u
 set -o pipefail
 
 cd /usr
-git clone --depth=1 https://github.com/dmlc/tvm --recursive
+git clone --depth=1 https://github.com/apache/incubator-tvm --recursive
 cd /usr/tvm
 # checkout a hash-tag
 git checkout 4b13bf668edc7099b38d463e5db94ebc96c80470

docs/contribute/community.rst

@@ -20,7 +20,7 @@
 TVM Community Guideline
 =======================
 
-TVM adopts the Apache style model and governs by merit. We believe that it is important to create an inclusive community where everyone can use, contribute to, and influence the direction of the project. See `CONTRIBUTORS.md <https://github.com/dmlc/tvm/blob/master/CONTRIBUTORS.md>`_ for the current list of contributors.
+TVM adopts the Apache style model and governs by merit. We believe that it is important to create an inclusive community where everyone can use, contribute to, and influence the direction of the project. See `CONTRIBUTORS.md <https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md>`_ for the current list of contributors.
 
 
 

docs/contribute/document.rst

@@ -68,7 +68,7 @@ Be careful to leave blank lines between sections of your documents.
 In the above case, there has to be a blank line before `Parameters`, `Returns` and `Examples`
 in order for the doc to be built correctly. To add a new function to the doc,
 we need to add the `sphinx.autodoc <http://www.sphinx-doc.org/en/master/ext/autodoc.html>`_
-rules to the `docs/api/python <https://github.com/dmlc/tvm/tree/master/docs/api/python>`_).
+rules to the `docs/api/python <https://github.com/apache/incubator-tvm/tree/master/docs/api/python>`_).
 You can refer to the existing files under this folder on how to add the functions.
 
 
@@ -96,7 +96,7 @@ to add comments about code logics to improve readability.
 Write Tutorials
 ---------------
 We use the `sphinx-gallery <https://sphinx-gallery.github.io/>`_ to build python tutorials.
-You can find the source code under `tutorials <https://github.com/dmlc/tvm/tree/master/tutorials>`_ quite self explanatory.
+You can find the source code under `tutorials <https://github.com/apache/incubator-tvm/tree/master/tutorials>`_ quite self explanatory.
 One thing that worth noting is that the comment blocks are written in reStructuredText instead of markdown so be aware of the syntax.
 
 The tutorial code will run on our build server to generate the document page.

docs/deploy/android.md

@@ -38,5 +38,5 @@ deploy_lib.so, deploy_graph.json, deploy_param.params will go to android target.
 
 ## TVM Runtime for Android Target
 
-Refer [here](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/README.md#build-and-installation) to build CPU/OpenCL version flavor TVM runtime for android target.
-From android java TVM API to load model & execute can be referred at this [java](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java) sample source.
+Refer [here](https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/README.md#build-and-installation) to build CPU/OpenCL version flavor TVM runtime for android target.
+From android java TVM API to load model & execute can be referred at this [java](https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java) sample source.

docs/deploy/cpp_deploy.md

@@ -18,7 +18,7 @@
 Deploy TVM Module using C++ API
 ===============================
 
-We provide an example on how to deploy TVM modules in [apps/howto_deploy](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy)
+We provide an example on how to deploy TVM modules in [apps/howto_deploy](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy)
 
 To run the example, you can use the following command
 
@@ -34,17 +34,17 @@ The only thing we need is to link to a TVM runtime in your target platform.
 TVM provides a minimum runtime, which costs around 300K to 600K depending on how much modules we use.
 In most cases, we can use ```libtvm_runtime.so``` that comes with the build.
 
-If somehow you find it is hard to build ```libtvm_runtime```, checkout [tvm_runtime_pack.cc](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/tvm_runtime_pack.cc).
+If somehow you find it is hard to build ```libtvm_runtime```, checkout [tvm_runtime_pack.cc](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/tvm_runtime_pack.cc).
 It is an example all in one file that gives you TVM runtime.
 You can compile this file using your build system and include this into your project.
 
-You can also checkout [apps](https://github.com/dmlc/tvm/tree/master/apps/) for example applications build with TVM on iOS, Android and others.
+You can also checkout [apps](https://github.com/apache/incubator-tvm/tree/master/apps/) for example applications build with TVM on iOS, Android and others.
 
 Dynamic Library vs. System Module
 ---------------------------------
 TVM provides two ways to use the compiled library.
-You can checkout [prepare_test_libs.py](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/prepare_test_libs.py)
-on how to generate the library and [cpp_deploy.cc](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/cpp_deploy.cc) on how to use them.
+You can checkout [prepare_test_libs.py](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/prepare_test_libs.py)
+on how to generate the library and [cpp_deploy.cc](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/cpp_deploy.cc) on how to use them.
 
 - Store library as a shared library and dynamically load the library into your project.
 - Bundle the compiled library into your project in system module mode.

docs/deploy/index.rst

@@ -38,7 +38,7 @@ on a Linux based embedded system such as Raspberry Pi:
 
 .. code:: bash
 
-    git clone --recursive https://github.com/dmlc/tvm
+    git clone --recursive https://github.com/apache/incubator-tvm
     cd tvm
     mkdir build
     cp cmake/config.cmake build

docs/deploy/nnvm.md

@@ -144,7 +144,7 @@ This process need few additional options as given below to NNVM build.
 Module export require additional options for not to compile but save as ```lib.export_library (path, fcompile=False)```
 
 The output of above API is a tar compressed file containing object file ```(lib.o)``` and cpp source file ```(devc.cc)``` which embeds device blob. Thease two files should be compiled along with other files or objects while building c++ application.
-Please refer to [Makefile](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/Makefile#L32) for a reference.
+Please refer to [Makefile](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/Makefile#L32) for a reference.
 
 The c++ code to load this system module require the below change.
 

docs/dev/inferbound.rst

@@ -19,7 +19,7 @@
 InferBound Pass
 *******************************************
 
-The InferBound pass is run after normalize, and before ScheduleOps `build_module.py <https://github.com/dmlc/tvm/blob/master/python/tvm/build_module.py>`_. The main job of InferBound is to create the bounds map, which specifies a Range for each IterVar in the program. These bounds are then passed to ScheduleOps, where they are used to set the extents of For loops, see `MakeLoopNest <https://github.com/dmlc/tvm/blob/master/src/op/op_util.cc>`_, and to set the sizes of allocated buffers (`BuildRealize <https://github.com/dmlc/tvm/blob/master/src/op/compute_op.cc>`_), among other uses.
+The InferBound pass is run after normalize, and before ScheduleOps `build_module.py <https://github.com/apache/incubator-tvm/blob/master/python/tvm/build_module.py>`_. The main job of InferBound is to create the bounds map, which specifies a Range for each IterVar in the program. These bounds are then passed to ScheduleOps, where they are used to set the extents of For loops, see `MakeLoopNest <https://github.com/apache/incubator-tvm/blob/master/src/op/op_util.cc>`_, and to set the sizes of allocated buffers (`BuildRealize <https://github.com/apache/incubator-tvm/blob/master/src/op/compute_op.cc>`_), among other uses.
 
 The output of InferBound is a map from IterVar to Range:
 
@@ -50,9 +50,9 @@ Therefore, let's review the Range and IterVar classes:
    	};
    }
 
-Note that IterVarNode also contains a Range ``dom``. This ``dom`` may or may not have a meaningful value, depending on when the IterVar was created. For example, when ``tvm.compute`` is called, an `IterVar is created <https://github.com/dmlc/tvm/blob/master/src/op/compute_op.cc>`_ for each axis and reduce axis, with dom's equal to the shape supplied in the call to ``tvm.compute``.
+Note that IterVarNode also contains a Range ``dom``. This ``dom`` may or may not have a meaningful value, depending on when the IterVar was created. For example, when ``tvm.compute`` is called, an `IterVar is created <https://github.com/apache/incubator-tvm/blob/master/src/op/compute_op.cc>`_ for each axis and reduce axis, with dom's equal to the shape supplied in the call to ``tvm.compute``.
 
-On the other hand, when ``tvm.split`` is called, `IterVars are created <https://github.com/dmlc/tvm/blob/master/src/schedule/schedule_lang.cc>`_ for the inner and outer axes, but these IterVars are not given a meaningful ``dom`` value.
+On the other hand, when ``tvm.split`` is called, `IterVars are created <https://github.com/apache/incubator-tvm/blob/master/src/schedule/schedule_lang.cc>`_ for the inner and outer axes, but these IterVars are not given a meaningful ``dom`` value.
 
 In any case, the ``dom`` member of an IterVar is never modified during InferBound. However, keep in mind that the ``dom`` member of an IterVar is sometimes used as default value for the Ranges InferBound computes.
 
@@ -114,7 +114,7 @@ Tensors haven't been mentioned yet, but in the context of TVM, a Tensor represen
    	int value_index;
    };
 
-In the Operation class declaration above, we can see that each operation also has a list of InputTensors. Thus the stages of the schedule form a DAG, where each stage is a node in the graph. There is an edge in the graph from Stage A to Stage B, if the operation of Stage B has an input tensor whose source operation is the op of Stage A. Put simply, there is an edge from A to B, if B consumes a tensor produced by A. See the diagram below. This graph is created at the beginning of InferBound, by a call to `CreateReadGraph <https://github.com/dmlc/tvm/blob/master/src/schedule/bound.cc>`_.
+In the Operation class declaration above, we can see that each operation also has a list of InputTensors. Thus the stages of the schedule form a DAG, where each stage is a node in the graph. There is an edge in the graph from Stage A to Stage B, if the operation of Stage B has an input tensor whose source operation is the op of Stage A. Put simply, there is an edge from A to B, if B consumes a tensor produced by A. See the diagram below. This graph is created at the beginning of InferBound, by a call to `CreateReadGraph <https://github.com/apache/incubator-tvm/blob/master/src/schedule/bound.cc>`_.
 
 .. image:: https://raw.githubusercontent.com/tvmai/tvmai.github.io/master/images/docs/inferbound/stage_graph.png
     :align: center

docs/dev/nnvm_overview.md

@@ -19,7 +19,7 @@
 # NNVM Design Overview
 
 NNVM is a reusable graph IR stack for deep learning systems. It provides useful API to construct, represent and transform computation graphs to get most high-level optimization needed in deep learning.
-As a part of TVM stack for deep learning, NNVM also provides a shared compiler for deep learning frameworks to optimize, compile and deploy into different hardware backends via [TVM](https://github.com/dmlc/tvm)
+As a part of TVM stack for deep learning, NNVM also provides a shared compiler for deep learning frameworks to optimize, compile and deploy into different hardware backends via [TVM](https://github.com/apache/incubator-tvm)
 
 ## Key Requirements and Design Choices
 

docs/dev/relay_add_pass.rst

@@ -399,8 +399,8 @@ information about the pass manager interface can be found in :ref:`relay-pass-in
 Relay's standard passes are listed in `include/tvm/relay/transform.h`_ and implemented
 in `src/relay/pass/`_.
 
-.. _include/tvm/relay/transform.h: https://github.com/dmlc/tvm/blob/master/include/tvm/relay/transform.h
+.. _include/tvm/relay/transform.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/relay/transform.h
 
-.. _src/relay/pass: https://github.com/dmlc/tvm/tree/master/src/relay/pass
+.. _src/relay/pass: https://github.com/apache/incubator-tvm/tree/master/src/relay/pass
 
-.. _src/relay/pass/fold_constant.cc: https://github.com/dmlc/tvm/blob/master/src/relay/pass/fold_constant.cc
+.. _src/relay/pass/fold_constant.cc: https://github.com/apache/incubator-tvm/blob/master/src/relay/pass/fold_constant.cc

docs/dev/relay_pass_infra.rst

@@ -631,14 +631,14 @@ For more pass infra related examples in Python and C++, please refer to
 
 .. _Relay module: https://docs.tvm.ai/langref/relay_expr.html#module-and-global-functions 
 
-.. _include/tvm/relay/transform.h: https://github.com/dmlc/tvm/blob/master/include/tvm/relay/transform.h
+.. _include/tvm/relay/transform.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/relay/transform.h
 
-.. _src/relay/pass/pass_manager.cc: https://github.com/dmlc/tvm/blob/master/src/relay/pass/pass_manager.cc
+.. _src/relay/pass/pass_manager.cc: https://github.com/apache/incubator-tvm/blob/master/src/relay/pass/pass_manager.cc
 
-.. _src/relay/pass/fold_constant.cc: https://github.com/dmlc/tvm/blob/master/src/relay/pass/fold_constant.cc
+.. _src/relay/pass/fold_constant.cc: https://github.com/apache/incubator-tvm/blob/master/src/relay/pass/fold_constant.cc
 
-.. _python/tvm/relay/transform.py: https://github.com/dmlc/tvm/blob/master/python/tvm/relay/transform.py
+.. _python/tvm/relay/transform.py: https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/transform.py
 
-.. _tests/python/relay/test_pass_manager.py: https://github.com/dmlc/tvm/blob/master/tests/python/relay/test_pass_manager.py
+.. _tests/python/relay/test_pass_manager.py: https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_manager.py
 
-.. _tests/cpp/relay_transform_sequential.cc: https://github.com/dmlc/tvm/blob/master/tests/cpp/relay_transform_sequential.cc
+.. _tests/cpp/relay_transform_sequential.cc: https://github.com/apache/incubator-tvm/blob/master/tests/cpp/relay_transform_sequential.cc

docs/dev/runtime.rst

@@ -43,7 +43,7 @@ PackedFunc
 `PackedFunc`_ is a simple but elegant solution
 we find to solve the challenges listed. The following code block provides an example in C++
 
-.. _PackedFunc: https://github.com/dmlc/tvm/blob/master/include/tvm/runtime/packed_func.h
+.. _PackedFunc: https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/packed_func.h
 
 .. code:: c
 
@@ -129,9 +129,9 @@ which allows us to embed the PackedFunc into any languages. Besides python, so f
 `java`_ and `javascript`_.
 This philosophy of embedded API is very like Lua, except that we don't have a new language but use C++.
 
-.. _minimum C API: https://github.com/dmlc/tvm/blob/master/include/tvm/runtime/c_runtime_api.h
-.. _java: https://github.com/dmlc/tvm/tree/master/jvm
-.. _javascript: https://github.com/dmlc/tvm/tree/master/web
+.. _minimum C API: https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/c_runtime_api.h
+.. _java: https://github.com/apache/incubator-tvm/tree/master/jvm
+.. _javascript: https://github.com/apache/incubator-tvm/tree/master/web
 
 
 One fun fact about PackedFunc is that we use it for both compiler and deployment stack.
@@ -139,7 +139,7 @@ One fun fact about PackedFunc is that we use it for both compiler and deployment
 - All TVM's compiler pass functions are exposed to frontend as PackedFunc, see `here`_
 - The compiled module also returns the compiled function as PackedFunc
 
-.. _here: https://github.com/dmlc/tvm/tree/master/src/api
+.. _here: https://github.com/apache/incubator-tvm/tree/master/src/api
 
 To keep the runtime minimum, we isolated the IR Node support from the deployment runtime. The resulting runtime takes around 200K - 600K depending on how many runtime driver modules (e.g., CUDA) get included.
 
@@ -160,7 +160,7 @@ TVM defines the compiled object as `Module`_.
 The user can get the compiled function from Module as PackedFunc.
 The generated compiled code can dynamically get function from Module in runtime. It caches the function handle in the first call and reuses in subsequent calls. We use this to link device code and callback into any PackedFunc(e.g., python) from generated code.
 
-.. _Module: https://github.com/dmlc/tvm/blob/master/include/tvm/runtime/module.h
+.. _Module: https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/module.h
 
 The ModuleNode is an abstract class that can be implemented by each type of device.
 So far we support modules for CUDA, Metal, OpenCL and loading dynamic shared libraries. This abstraction makes introduction
@@ -276,17 +276,17 @@ Each argument in PackedFunc contains a union value `TVMValue`_
 and a type code. This design allows the dynamically typed language to convert to the corresponding type directly, and statically typed language to
 do runtime type checking during conversion.
 
-.. _TVMValue: https://github.com/dmlc/tvm/blob/master/include/tvm/runtime/c_runtime_api.h#L122
+.. _TVMValue: https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/c_runtime_api.h#L122
 
 The relevant files are
 
 - `packed_func.h`_ for C++ API
 - `c_runtime_api.cc`_ for C API and how to provide callback.
 
-.. _packed_func.h: https://github.com/dmlc/tvm/blob/master/include/tvm/runtime/packed_func.h
-.. _c_runtime_api.cc: https://github.com/dmlc/tvm/blob/master/src/runtime/c_runtime_api.cc#L262
+.. _packed_func.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/packed_func.h
+.. _c_runtime_api.cc: https://github.com/apache/incubator-tvm/blob/master/src/runtime/c_runtime_api.cc#L262
 
 To support extension types, we used a registry system to register type related information, like support of any
 in C++, see `Extension types`_ for more details.
 
-.. _Extension types: https://github.com/dmlc/tvm/tree/master/apps/extension
+.. _Extension types: https://github.com/apache/incubator-tvm/tree/master/apps/extension

docs/faq.md

@@ -45,5 +45,5 @@ TVM's relation to libDNN cuDNN
 TVM can incorporate these library as external calls. One goal of TVM is to be able to
 generate high performing kernels. We will evolve TVM an incremental manner as
 we learn from the technics of manual kernel crafting and add these as primitives in DSL.
-See also [TVM Operator Inventory](https://github.com/dmlc/tvm/tree/master/topi) for
+See also [TVM Operator Inventory](https://github.com/apache/incubator-tvm/tree/master/topi) for
 recipes of operators in TVM.

docs/frontend/tensorflow.rst

@@ -57,7 +57,7 @@ Export
 
 TensorFlow frontend expects a frozen protobuf (.pb) or saved model as input. It currently does not support checkpoint (.ckpt). The graphdef needed by the TensorFlow frontend can be extracted from the active session, or by using the `TFParser`_ helper class.
 
-.. _TFParser: https://github.com/dmlc/tvm/blob/master/python/tvm/relay/frontend/tensorflow_parser.py
+.. _TFParser: https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/frontend/tensorflow_parser.py
 
 The model should be exported with a number of transformations to prepare the model for inference. It is also important to set ```add_shapes=True```, as this will embed the output shapes of each node into the graph. Here is one function to export a model as a protobuf given a session:
 
@@ -97,7 +97,7 @@ Import the Model
 Explicit Shape:
 ~~~~~~~~~~~~~~~
 
-To ensure shapes can be known throughout the entire graph, pass the ```shape``` argument to ```from_tensorflow```. This dictionary maps input names to input shapes. Please refer to these `test cases <https://github.com/dmlc/tvm/blob/master/nnvm/tests/python/frontend/tensorflow/test_forward.py#L36>`_ for examples.
+To ensure shapes can be known throughout the entire graph, pass the ```shape``` argument to ```from_tensorflow```. This dictionary maps input names to input shapes. Please refer to these `test cases <https://github.com/apache/incubator-tvm/blob/master/nnvm/tests/python/frontend/tensorflow/test_forward.py#L36>`_ for examples.
 
 Data Layout
 ~~~~~~~~~~~

docs/install/docker.rst

@@ -29,7 +29,7 @@ First, clone TVM repo to get the auxiliary scripts
 
 .. code:: bash
 
-    git clone --recursive https://github.com/dmlc/tvm
+    git clone --recursive https://github.com/apache/incubator-tvm
 
 
 We can then use the following command to launch a `tvmai/demo-cpu` image.
@@ -69,5 +69,5 @@ with ``localhost`` when pasting it into browser.
 
 Docker Source
 -------------
-Check out `<https://github.com/dmlc/tvm/tree/master/docker>`_ if you are interested in
+Check out `<https://github.com/apache/incubator-tvm/tree/master/docker>`_ if you are interested in
 building your own docker images.

docs/install/from_source.rst

@@ -29,7 +29,7 @@ To get started, clone TVM repo from github. It is important to clone the submodu
 
 .. code:: bash
 
-    git clone --recursive https://github.com/dmlc/tvm
+    git clone --recursive https://github.com/apache/incubator-tvm
 
 For windows users who use github tools, you can open the git shell, and type the following command.
 

docs/install/nnpack.md

@@ -85,7 +85,7 @@ sudo ldconfig
 ## Build TVM with NNPACK support
 
 ```bash
-git clone --recursive https://github.com/dmlc/tvm
+git clone --recursive https://github.com/apache/incubator-tvm
 ```
 
 * Set `set(USE_NNPACK ON)` in config.cmake.

docs/langref/relay_expr.rst

@@ -267,7 +267,7 @@ Operators
 An operator is a primitive operation, such as :code:`add` or :code:`conv2d`, not defined in the Relay
 language. Operators are declared in the global operator
 registry in C++. Many common operators are backed by TVM's
-Tensor Operator Inventory (`TOPI <https://github.com/dmlc/tvm/tree/master/topi>`__).
+Tensor Operator Inventory (`TOPI <https://github.com/apache/incubator-tvm/tree/master/topi>`__).
 
 To register an operator a user must provide an implementation
 of the operator, its type, and any other desired metadata.

docs/vta/install.md

@@ -103,7 +103,7 @@ Because the direct board-to-computer connection prevents the board from directly
 mkdir <mountpoint>
 sshfs xilinx@192.168.2.99:/home/xilinx <mountpoint>
 cd <mountpoint>
-git clone --recursive https://github.com/dmlc/tvm
+git clone --recursive https://github.com/apache/incubator-tvm
 # When finished, you can leave the moutpoint and unmount the directory
 cd ~
 sudo umount <mountpoint>
@@ -375,7 +375,7 @@ Once the compilation completes, the generated bitstream can be found under `<tvm
 
 ### Chisel-based Custom VTA Bitstream Compilation for DE10-Nano
 
-Similar to the HLS-based design, high-level hardware parameters in Chisel-based design are listed in the VTA configuration file [Configs.scala](https://github.com/dmlc/tvm/blob/master/vta/hardware/chisel/src/main/scala/core/Configs.scala), and they can be customized by the user.
+Similar to the HLS-based design, high-level hardware parameters in Chisel-based design are listed in the VTA configuration file [Configs.scala](https://github.com/apache/incubator-tvm/blob/master/vta/hardware/chisel/src/main/scala/core/Configs.scala), and they can be customized by the user.
 
 For Intel FPGA, bitstream generation is driven by a top-level `Makefile` under `<tvmroot>/vta/hardware/intel`.
 

jvm/README.md

@@ -175,4 +175,4 @@ Server server = new Server(proxyHost, proxyPort, "key");
 server.start();
 ```
 
-You can also use `StandaloneServerProcessor` and `ConnectProxyServerProcessor` to build your own RPC server. Refer to [Android RPC Server](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/app/src/main/java/ml/dmlc/tvm/tvmrpc/RPCProcessor.java) for more details.
+You can also use `StandaloneServerProcessor` and `ConnectProxyServerProcessor` to build your own RPC server. Refer to [Android RPC Server](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/app/src/main/java/ml/dmlc/tvm/tvmrpc/RPCProcessor.java) for more details.
\ No newline at end of file

jvm/pom.xml

@@ -7,7 +7,7 @@
   <artifactId>tvm4j-parent</artifactId>
   <version>0.0.1-SNAPSHOT</version>
   <name>TVM4J Package - Parent</name>
-  <url>https://github.com/dmlc/tvm/tree/master/jvm</url>
+  <url>https://github.com/apache/incubator-tvm/tree/master/jvm</url>
   <description>TVM4J Package</description>
   <organization>
     <name>Distributed (Deep) Machine Learning Community</name>
@@ -22,7 +22,7 @@
   <scm>
     <connection>scm:git:git@github.com:dmlc/tvm.git</connection>
     <developerConnection>scm:git:git@github.com:dmlc/tvm.git</developerConnection>
-    <url>https://github.com/dmlc/tvm</url>
+    <url>https://github.com/apache/incubator-tvm</url>
   </scm>
 
   <properties>

nnvm/tutorials/deploy_model_on_mali_gpu.py

@@ -53,7 +53,7 @@ from tvm.contrib.download import download_testdata
 #
 # .. code-block:: bash
 #
-#   git clone --recursive https://github.com/dmlc/tvm
+#   git clone --recursive https://github.com/apache/incubator-tvm
 #   cd tvm
 #   cp cmake/config.cmake .
 #   sed -i "s/USE_OPENCL OFF/USE_OPENCL ON/" config.cmake

nnvm/tutorials/deploy_model_on_rasp.py

@@ -52,7 +52,7 @@ from tvm.contrib.download import download_testdata
 #
 # .. code-block:: bash
 #
-#   git clone --recursive https://github.com/dmlc/tvm
+#   git clone --recursive https://github.com/apache/incubator-tvm
 #   cd tvm
 #   make runtime -j4
 #

nnvm/tutorials/tune_nnvm_arm.py

@@ -31,7 +31,7 @@ the best knob values for all required operators. When the tvm compiler compiles
 these operators, it will query this log file to get the best knob values.
 
 We also released pre-tuned parameters for some arm devices. You can go to
-`ARM CPU Benchmark <https://github.com/dmlc/tvm/wiki/Benchmark#arm-cpu>`_
+`ARM CPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#arm-cpu>`_
 to see the results.
 """
 
@@ -157,7 +157,7 @@ def get_network(name, batch_size):
 #   (replace :code:`[HOST_IP]` with the IP address of your host machine)
 #
 # * For Android:
-#   Follow this `readme page <https://github.com/dmlc/tvm/tree/master/apps/android_rpc>`_ to
+#   Follow this `readme page <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc>`_ to
 #   install tvm rpc apk on the android device. Make sure you can pass the android rpc test.
 #   Then you have already registred your device. During tuning, you have to go to developer option
 #   and enable "Keep screen awake during changing" and charge your phone to make it stable.

nnvm/tutorials/tune_nnvm_cuda.py

@@ -31,7 +31,7 @@ the best knob values for all required operators. When the tvm compiler compiles
 these operators, it will query this log file to get the best knob values.
 
 We also released pre-tuned parameters for some NVIDIA GPUs. You can go to
-`NVIDIA GPU Benchmark <https://github.com/dmlc/tvm/wiki/Benchmark#nvidia-gpu>`_
+`NVIDIA GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#nvidia-gpu>`_
 to see the results.
 """
 

nnvm/tutorials/tune_nnvm_mobile_gpu.py

@@ -31,7 +31,7 @@ the best knob values for all required operators. When the tvm compiler compiles
 these operators, it will query this log file to get the best knob values.
 
 We also released pre-tuned parameters for some arm devices. You can go to
-`Mobile GPU Benchmark <https://github.com/dmlc/tvm/wiki/Benchmark#mobile-gpu>`_
+`Mobile GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#mobile-gpu>`_
 to see the results.
 """
 
@@ -157,7 +157,7 @@ def get_network(name, batch_size):
 #   (replace :code:`[HOST_IP]` with the IP address of your host machine)
 #
 # * For Android:
-#   Follow this `readme page <https://github.com/dmlc/tvm/tree/master/apps/android_rpc>`_ to
+#   Follow this `readme page <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc>`_ to
 #   install tvm rpc apk on the android device. Make sure you can pass the android rpc test.
 #   Then you have already registred your device. During tuning, you have to go to developer option
 #   and enable "Keep screen awake during changing" and charge your phone to make it stable.

python/setup.py

@@ -156,7 +156,7 @@ setup(name='tvm',
       package_dir={'tvm': 'tvm'},
       package_data={'tvm': get_package_data_files()},
       distclass=BinaryDistribution,
-      url='https://github.com/dmlc/tvm',
+      url='https://github.com/apache/incubator-tvm',
       ext_modules=config_cython(),
       **setup_kwargs)
 

python/tvm/_ffi/libinfo.py

@@ -54,7 +54,7 @@ def find_lib_path(name=None, search_path=None, optional=False):
     """
     use_runtime = os.environ.get("TVM_USE_RUNTIME_LIB", False)
 
-    # See https://github.com/dmlc/tvm/issues/281 for some background.
+    # See https://github.com/apache/incubator-tvm/issues/281 for some background.
 
     # NB: This will either be the source directory (if TVM is run
     # inplace) or the install directory (if TVM is installed).

rust/frontend/Cargo.toml

@@ -20,8 +20,8 @@ name = "tvm-frontend"
 version = "0.1.0"
 license = "Apache-2.0"
 description = "Rust frontend support for TVM"
-repository = "https://github.com/dmlc/tvm"
-homepage = "https://github.com/dmlc/tvm"
+repository = "https://github.com/apache/incubator-tvm"
+homepage = "https://github.com/apache/incubator-tvm"
 readme = "README.md"
 keywords = ["rust", "tvm", "nnvm"]
 categories = ["api-bindings", "science"]

rust/frontend/README.md

@@ -17,7 +17,7 @@
 
 # TVM Runtime Frontend Support
 
-This crate provides an idiomatic Rust API for [TVM](https://github.com/dmlc/tvm) runtime frontend. Currently this requires **Nightly Rust** and tested on `rustc 1.32.0-nightly`
+This crate provides an idiomatic Rust API for [TVM](https://github.com/apache/incubator-tvm) runtime frontend. Currently this requires **Nightly Rust** and tested on `rustc 1.32.0-nightly`
 
 ## What Does This Crate Offer?
 

rust/frontend/src/context.rs

@@ -50,7 +50,7 @@ use tvm_common::ffi;
 use crate::{function, TVMArgValue};
 
 /// Device type can be from a supported device name. See the supported devices
-/// in [TVM](https://github.com/dmlc/tvm).
+/// in [TVM](https://github.com/apache/incubator-tvm).
 ///
 /// ## Example
 ///

rust/frontend/src/lib.rs

@@ -17,7 +17,7 @@
  * under the License.
  */
 
-//! [TVM](https://github.com/dmlc/tvm) is a compiler stack for deep learning systems.
+//! [TVM](https://github.com/apache/incubator-tvm) is a compiler stack for deep learning systems.
 //!
 //! This crate provides an idiomatic Rust API for TVM runtime frontend.
 //!

rust/macros/Cargo.toml

@@ -20,7 +20,7 @@ name = "tvm-macros"
 version = "0.1.0"
 license = "Apache-2.0"
 description = "Proc macros used by the TVM crates."
-repository = "https://github.com/dmlc/tvm"
+repository = "https://github.com/apache/incubator-tvm"
 readme = "README.md"
 keywords = ["tvm"]
 authors = ["TVM Contributors"]

rust/runtime/Cargo.toml

@@ -20,7 +20,7 @@ name = "tvm-runtime"
 version = "0.1.0"
 license = "Apache-2.0"
 description = "A static TVM runtime"
-repository = "https://github.com/dmlc/tvm"
+repository = "https://github.com/apache/incubator-tvm"
 readme = "README.md"
 keywords = ["tvm", "nnvm"]
 categories = ["api-bindings", "science"]

rust/runtime/src/threading.rs

@@ -296,7 +296,7 @@ pub(crate) fn sgx_join_threads() {
     ocall_packed!("__sgx_thread_group_join__", 0);
 }
 
-// @see https://github.com/dmlc/tvm/issues/988 for information on why this function is used.
+// @see https://github.com/apache/incubator-tvm/issues/988 for information on why this function is used.
 #[no_mangle]
 pub extern "C" fn TVMBackendParallelBarrier(_task_id: usize, penv: *const TVMParallelGroupEnv) {
     let barrier: &Arc<Barrier> = unsafe { &*((*penv).sync_handle as *const Arc<Barrier>) };

tests/python/relay/test_op_level2.py

@@ -142,7 +142,7 @@ def test_conv2d_run():
                         x, w, (1, 1), "SAME"))
 
     # CUDA is disabled for 'direct' schedule:
-    # https://github.com/dmlc/tvm/pull/3070#issuecomment-486597553
+    # https://github.com/apache/incubator-tvm/pull/3070#issuecomment-486597553
     # group conv2d
     dshape = (1, 32, 18, 18)
     kshape = (32, 4, 3, 3)

tests/python/unittest/test_graph_tuner_core.py

@@ -18,7 +18,7 @@
 # NOTE: We name this test file to start with test_graph_tuner
 # to make it execute after zero_rank tensor test cases. This
 # helps avoid topi arithmetic operator overloading issue:
-# https://github.com/dmlc/tvm/issues/3240.
+# https://github.com/apache/incubator-tvm/issues/3240.
 # TODO: restore the file name after this issue is resolved.
 import os
 import copy

tests/python/unittest/test_graph_tuner_utils.py

@@ -18,7 +18,7 @@
 # NOTE: We name this test file to start with test_graph_tuner
 # to make it execute after zero_rank tensor test cases. This
 # helps avoid topi arithmetic operator overloading issue:
-# https://github.com/dmlc/tvm/issues/3240
+# https://github.com/apache/incubator-tvm/issues/3240
 # TODO: restore the file name after this issue is resolved.
 import tvm
 

topi/python/setup.py

@@ -115,7 +115,7 @@ setup(name='topi',
           "decorator",
       ],
       packages=find_packages(),
-      url='https://github.com/dmlc/tvm',
+      url='https://github.com/apache/incubator-tvm',
       **setup_kwargs)
 
 

topi/python/topi/x86/conv2d.py

@@ -110,7 +110,7 @@ def _declaration_conv(cfg, data, kernel, strides, padding, dilation, layout, out
     kh, kw, _, _ = get_const_tuple(kernel.shape)
     if layout == 'HWCN':
         return nn.conv2d_hwcn(data, kernel, strides, padding, dilation, out_dtype)
-    # FIXME - https://github.com/dmlc/tvm/issues/4122
+    # FIXME - https://github.com/apache/incubator-tvm/issues/4122
     # _declaration_conv_nhwc_pack expects kernel layout to be HWOI. However, the tests use HWIO
     # layout. Commenting until we have clarity about the nhwc_pack implementation from the author.
     # elif layout == 'NHWC' and kh == 1 and kw == 1 and kernel.dtype == "int8":

topi/python/topi/x86/conv2d_avx_1x1.py

@@ -251,7 +251,7 @@ def _schedule_conv_nhwc_pack_int8(s, cfg, data, conv_out, last):
     packing of weight to make the address access be friendly to int8
     intrinsic
     """
-    # FIXME - https://github.com/dmlc/tvm/issues/3598
+    # FIXME - https://github.com/apache/incubator-tvm/issues/3598
     # pylint: disable=unreachable
     return s
 

tutorials/autotvm/tune_relay_arm.py

@@ -31,7 +31,7 @@ the best knob values for all required operators. When the TVM compiler compiles
 these operators, it will query this log file to get the best knob values.
 
 We also released pre-tuned parameters for some arm devices. You can go to
-`ARM CPU Benchmark <https://github.com/dmlc/tvm/wiki/Benchmark#arm-cpu>`_
+`ARM CPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#arm-cpu>`_
 to see the results.
 """
 
@@ -149,7 +149,7 @@ def get_network(name, batch_size):
 #   (replace :code:`[HOST_IP]` with the IP address of your host machine)
 #
 # * For Android:
-#   Follow this `readme page <https://github.com/dmlc/tvm/tree/master/apps/android_rpc>`_ to
+#   Follow this `readme page <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc>`_ to
 #   install the TVM RPC APK on the android device. Make sure you can pass the android rpc test.
 #   Then you have already registred your device. During tuning, you have to go to developer option
 #   and enable "Keep screen awake during changing" and charge your phone to make it stable.

tutorials/autotvm/tune_relay_cuda.py

@@ -31,7 +31,7 @@ the best knob values for all required operators. When the TVM compiler compiles
 these operators, it will query this log file to get the best knob values.
 
 We also released pre-tuned parameters for some NVIDIA GPUs. You can go to
-`NVIDIA GPU Benchmark <https://github.com/dmlc/tvm/wiki/Benchmark#nvidia-gpu>`_
+`NVIDIA GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#nvidia-gpu>`_
 to see the results.
 """
 

tutorials/autotvm/tune_relay_mobile_gpu.py

@@ -31,7 +31,7 @@ the best knob values for all required operators. When the TVM compiler compiles
 these operators, it will query this log file to get the best knob values.
 
 We also released pre-tuned parameters for some arm devices. You can go to
-`Mobile GPU Benchmark <https://github.com/dmlc/tvm/wiki/Benchmark#mobile-gpu>`_
+`Mobile GPU Benchmark <https://github.com/apache/incubator-tvm/wiki/Benchmark#mobile-gpu>`_
 to see the results.
 """
 
@@ -150,7 +150,7 @@ def get_network(name, batch_size):
 #   (replace :code:`[HOST_IP]` with the IP address of your host machine)
 #
 # * For Android:
-#   Follow this `readme page <https://github.com/dmlc/tvm/tree/master/apps/android_rpc>`_ to
+#   Follow this `readme page <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc>`_ to
 #   install TVM RPC APK on the android device. Make sure you can pass the android RPC test.
 #   Then you have already registred your device. During tuning, you have to go to developer option
 #   and enable "Keep screen awake during changing" and charge your phone to make it stable.

tutorials/cross_compilation_and_rpc.py

@@ -49,7 +49,7 @@ and the Firefly-RK3399 for an OpenCL example.
 #
 # .. code-block:: bash
 #
-#   git clone --recursive https://github.com/dmlc/tvm
+#   git clone --recursive https://github.com/apache/incubator-tvm
 #   cd tvm
 #   make runtime -j2
 #

tutorials/frontend/deploy_model_on_android.py

@@ -46,7 +46,7 @@ from tvm.contrib.download import download_testdata
 #
 # .. code-block:: bash
 #
-#   git clone --recursive https://github.com/dmlc/tvm
+#   git clone --recursive https://github.com/apache/incubator-tvm
 #   cd tvm
 #   docker build -t tvm.demo_android -f docker/Dockerfile.demo_android ./docker
 #   docker run --pid=host -h tvm -v $PWD:/workspace \
@@ -105,7 +105,7 @@ from tvm.contrib.download import download_testdata
 # ---------------------------------------
 # Now we can register our Android device to the tracker.
 #
-# Follow this `readme page <https://github.com/dmlc/tvm/tree/master/apps/android_rpc>`_ to
+# Follow this `readme page <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc>`_ to
 # install TVM RPC APK on the android device.
 #
 # Here is an example of config.mk. I enabled OpenCL and Vulkan.
@@ -138,7 +138,7 @@ from tvm.contrib.download import download_testdata
 #
 # .. note::
 #
-#   At this time, don't forget to `create a standalone toolchain <https://github.com/dmlc/tvm/tree/master/apps/android_rpc#architecture-and-android-standalone-toolchain>`_ .
+#   At this time, don't forget to `create a standalone toolchain <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc#architecture-and-android-standalone-toolchain>`_ .
 #
 #   for example
 #

tutorials/frontend/deploy_model_on_rasp.py

@@ -52,7 +52,7 @@ from tvm.contrib.download import download_testdata
 #
 # .. code-block:: bash
 #
-#   git clone --recursive https://github.com/dmlc/tvm
+#   git clone --recursive https://github.com/apache/incubator-tvm
 #   cd tvm
 #   mkdir build
 #   cp cmake/config.cmake build

vta/apps/tsim_example/README.md

@@ -62,7 +62,7 @@ https://www.veripool.org/projects/verilator/wiki/Installing
 ## Setup in TVM
 
 1. Install `verilator` and `sbt` as described above
-2. Get tvm `git clone https://github.com/dmlc/tvm.git`
+2. Get tvm `git clone https://github.com/apache/incubator-tvm.git`
 3. Build [tvm](https://docs.tvm.ai/install/from_source.html#build-the-shared-library)
 
 ## How to run VTA TSIM examples

web/README.md

@@ -82,7 +82,7 @@ This will create ```build/libtvm_web_runtime.bc``` and ```build/libtvm_web_runti
 
 The general idea is to use TVM as normally and set target to be ```llvm -target=asmjs-unknown-emscripten -system-lib```.
 
-The following code snippet from [tests/web/prepare_test_libs.py](https://github.com/dmlc/tvm/tree/master/tests/web/prepare_test_libs.py) demonstrate
+The following code snippet from [tests/web/prepare_test_libs.py](https://github.com/apache/incubator-tvm/tree/master/tests/web/prepare_test_libs.py) demonstrate
 the compilation process.
 
 ```python
@@ -114,7 +114,7 @@ The result js library is a library that contains both TVM runtime and the compil
 
 ## Run the Generated Library
 
-The following code snippet from [tests/web/test_module_load.js](https://github.com/dmlc/tvm/tree/master/tests/web/test_module_load.js) demonstrate
+The following code snippet from [tests/web/test_module_load.js](https://github.com/apache/incubator-tvm/tree/master/tests/web/test_module_load.js) demonstrate
 how to run the compiled library.
 
 ```js