[DOCS] Fix installation from source link some text

docs/install/from_source.rst

@@ -52,8 +52,8 @@ Build the Shared Library
 Our goal is to build the shared libraries:
 
    - On Linux the target library are `libtvm.so` and `libtvm_runtime.so`
-   - On macOS the target library are `libtvm.dylib` and `libtvm_runtime.dylib` 
-   - On Windows the target library are `libtvm.dll` and `libtvm_runtime.dll` 
+   - On macOS the target library are `libtvm.dylib` and `libtvm_runtime.dylib`
+   - On Windows the target library are `libtvm.dll` and `libtvm_runtime.dll`
 
 It is also possible to :ref:`build the runtime <deploy-and-integration>` library only.
 
@@ -74,14 +74,14 @@ linux operating systems, execute (in a terminal):
     sudo apt-get update
     sudo apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev
 
-Use Homebrew to install the required dependencies for macOS running either the Intel or M1 processors. You must follow the post-installation steps specified by 
+Use Homebrew to install the required dependencies for macOS running either the Intel or M1 processors. You must follow the post-installation steps specified by
 Homebrew to ensure the dependencies are correctly installed and configured:
 
-.. code:: bash 
+.. code:: bash
 
-    brew install gcc git cmake 
-    brew install llvm 
-    brew install python@3.8 
+    brew install gcc git cmake
+    brew install llvm
+    brew install python@3.8
 
 
 We use cmake to build the library.
@@ -303,20 +303,20 @@ like ``virtualenv``.
 
        pip3 install --user tornado psutil xgboost cloudpickle
 
-Note on M1 macs, you may have trouble installing xgboost / scipy. scipy and xgboost requires some additional dependencies to be installed, 
-including openblas and its dependencies. Use the following commands to install scipy and xgboost with the required dependencies and 
+Note on M1 macs, you may have trouble installing xgboost / scipy. scipy and xgboost requires some additional dependencies to be installed,
+including openblas and its dependencies. Use the following commands to install scipy and xgboost with the required dependencies and
 configuration. A workaround for this is to do the following commands:
 
-    .. code:: bash 
+    .. code:: bash
 
         brew install openblas gfortran
 
-        pip install pybind11 cython pythran   
-        
-        export OPENBLAS=/opt/homebrew/opt/openblas/lib/ 
-        
+        pip install pybind11 cython pythran  
+
+        export OPENBLAS=/opt/homebrew/opt/openblas/lib/
+
         pip install scipy --no-use-pep517
-        
+
         pip install xgboost
 
 Install Contrib Libraries

docs/install/index.rst

@@ -20,17 +20,15 @@
 Installing TVM
 ==============
 
-TVM is still pre-1.0 release software, meaning that it is under active
-development and is changing quickly. Because of this, it is best to
-:ref:`install TVM from source <install_from_source>` to guarantee you
-have the latest features and bug fixes. If you are interested in deploying to
-mobile or embedded devices, you do not need to install the entire TVM stack on
-your device. Instead, you only need the runtime and can install using the
+Checkout to :ref:`install TVM from source <install-from-source>`. Installing
+from source gives you the maximum flexibility to configure the build effectively from the official source releases.
+If you are interested in deploying to mobile or embedded devices, you do not need to
+install the entire TVM stack on your device. Instead, you only need the runtime and can install using the
 :ref:`deployment and integration guide <deploy-and-integration>`.
 
 If you would like to quickly try out TVM or run some demo and tutorials, you
-can :ref:`install from Docker <docker-images>`, or install using the
-third-party :doc:`TLCPack Python and Conda packages <tlcpack>`
+can checkout :ref:`install from Docker <docker-images>`. You can also
+tryout pre-built from third-party :doc:`TLCPack Python and Conda packages <tlcpack>`.
 
 .. toctree::
    :maxdepth: 2

docs/install/tlcpack.rst

@@ -20,19 +20,5 @@
 TLCPack
 =======
 
-The Tensor Learning Compiler binary Package (TLCPack) community builds and
-maintains third-party packaging based on Apache TVM. TLCPack is a community
-maintained release that does not contain any additional source code from the
-TVM source repository. It takes source code from Apache TVM and builds Pip and
-Conda binary packages with different build configurations, for both the stable
-release and the nightly release. Please note that additional licensing
-conditions may apply (for example, the CUDA EULA is enforced for the CUDA
-enabled package) when you use the binary builds.
-
-TLCPack is not part of Apache and is run by thirdparty community volunteers.
-Please refer to the official Apache TVM website for Apache source releases.
-
-Licenses for TVM and its dependencies can be found in the github repository.
-
-More information on installing with TLCPack can be found on the `TLCPack site
-<https://tlcpack.ai>`_.
+TLCPack is a third-party conda and pip packaging based on TVM.
+Checkout more information on `TLCPack site <https://tlcpack.ai>`_.