From 1806647dfedc7f92b88d08cbbbee2a5511a032d3 Mon Sep 17 00:00:00 2001 From: tqchen Date: Sun, 10 Oct 2021 09:43:59 -0400 Subject: [PATCH] [DOCS] Fix installation from source link some text Fix install from source link(pointed to matplotlib). Updated some wording. Move description of tlcpack to just a link so it can be kept from tlcpack side. --- docs/install/from_source.rst | 30 +++++++++++++++--------------- docs/install/index.rst | 14 ++++++-------- docs/install/tlcpack.rst | 18 ++---------------- 3 files changed, 23 insertions(+), 39 deletions(-) diff --git a/docs/install/from_source.rst b/docs/install/from_source.rst index 5c3a2544c578..b28c18162437 100644 --- a/docs/install/from_source.rst +++ b/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 ` 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 diff --git a/docs/install/index.rst b/docs/install/index.rst index 5759b1eb9186..0082fa338665 100644 --- a/docs/install/index.rst +++ b/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 ` 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 `. 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 `. If you would like to quickly try out TVM or run some demo and tutorials, you -can :ref:`install from Docker `, or install using the -third-party :doc:`TLCPack Python and Conda packages ` +can checkout :ref:`install from Docker `. You can also +tryout pre-built from third-party :doc:`TLCPack Python and Conda packages `. .. toctree:: :maxdepth: 2 diff --git a/docs/install/tlcpack.rst b/docs/install/tlcpack.rst index 541cffb0c2c2..c903e30b68bf 100644 --- a/docs/install/tlcpack.rst +++ b/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 -`_. +TLCPack is a third-party conda and pip packaging based on TVM. +Checkout more information on `TLCPack site `_.