[DOCKER] Add demo-gpu image (#1407)

docker/Dockerfile.demo_cpu

 # Minimum docker image for demo purposes
-# prebuilt-image: tvmai/demo_cpu
+# prebuilt-image: tvmai/demo-cpu
 FROM ubuntu:16.04
 
 RUN apt-get update --fix-missing

docker/Dockerfile.demo_gpu

+# Minimum docker image for demo purposes
+# prebuilt-image: tvmai/demo-gpu
+FROM nvidia/cuda:8.0-cudnn7-devel
+
+RUN apt-get update --fix-missing
+
+COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
+RUN bash /install/ubuntu_install_core.sh
+
+# Python: basic dependencies
+RUN apt-get update && apt-get install -y python3-dev python3-pip
+RUN pip3 install numpy nose-timer cython decorator scipy
+
+# LLVM
+RUN echo deb http://apt.llvm.org/xenial/ llvm-toolchain-xenial-6.0 main \
+     >> /etc/apt/sources.list.d/llvm.list && \
+     wget -O - http://apt.llvm.org/llvm-snapshot.gpg.key|sudo apt-key add - && \
+     apt-get update && apt-get install -y --force-yes llvm-6.0
+
+# Jupyter notebook.
+RUN pip3 install matplotlib Image Pillow jupyter[notebook]
+
+# Deep learning frameworks
+RUN pip3 install mxnet tensorflow keras
+
+# Build TVM
+COPY install/install_tvm_gpu.sh /install/install_tvm_gpu.sh
+RUN bash /install/install_tvm_gpu.sh
+
+# Environment variables
+ENV PYTHONPATH=/usr/tvm/python:/usr/tvm/topi/python:/usr/tvm/nnvm/python/:/usr/tvm/vta/python:${PYTHONPATH}
+ENV PATH=/usr/local/nvidia/bin:${PATH}
+ENV PATH=/usr/local/cuda/bin:${PATH}
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/nvidia/lib64:${LD_LIBRARY_PATH}

docker/README.md

@@ -24,10 +24,10 @@ The helper bash script can be useful to build demo sessions.
 ## Prebuilt Docker Images
 
 We provide several pre-built images for doing quick exploration with TVM installed.
-For example, you can run the following command to get ```tvmai/demo_cpu``` image.
+For example, you can run the following command to get ```tvmai/demo-cpu``` image.
 
 ```bash
-/path/to/tvm/docker/bash.sh tvmai/demo_cpu
+/path/to/tvm/docker/bash.sh tvmai/demo-cpu
 ```
 
 Then inside the docker container, you can type the following command to start the jupyter notebook

docker/build.sh

@@ -2,7 +2,7 @@
 #
 # Execute command within a docker container
 #
-# Usage: ci_build.sh <CONTAINER_TYPE> [--dockerfile <DOCKERFILE_PATH>] [-it]
+# Usage: build.sh <CONTAINER_TYPE> [--dockerfile <DOCKERFILE_PATH>] [-it]
 #                    <COMMAND>
 #
 # CONTAINER_TYPE: Type of the docker container used the run the build: e.g.,

docker/install/install_tvm_gpu.sh

+cd /usr
+git clone https://github.com/dmlc/tvm --recursive
+cd /usr/tvm
+echo set\(USE_LLVM llvm-config-6.0\) >> config.cmake
+echo set\(USE_CUDA ON\) >> config.cmake
+echo set\(USE_CUDNN ON\) >> config.cmake
+echo set\(USE_RPC ON\) >> config.cmake
+echo set\(USE_SORT ON\) >> config.cmake
+echo set\(USE_GRAPH_RUNTIME ON\) >> config.cmake
+echo set\(USE_BLAS openblas\) >> config.cmake
+mkdir -p build
+cd build
+cmake ..
+make -j10

docs/install/docker.rst

@@ -3,7 +3,7 @@
 Docker Images
 =============
 We provide several prebuilt docker images to quickly try out tvm.
-These images are also helpful to do demo/tutorials of TVM.
+These images are also helpful run through TVM demo and tutorials.
 You can get the docker images via the following steps.
 We need `docker <https://docs.docker.com/engine/installation/>`_ and
 `nvidia-docker <https://github.com/NVIDIA/nvidia-docker/>`_ if we want to use cuda.
@@ -15,14 +15,14 @@ First, clone tvm repo to get the auxiliary scripts
     git clone --recursive https://github.com/dmlc/tvm
 
 
-We can then use the following command to launch a `tvmai/demo_cpu` image.
+We can then use the following command to launch a `tvmai/demo-cpu` image.
 
 .. code:: bash
 
     /path/to/tvm/docker/bash.sh tvmai/demo_cpu
 
 .. note::
-    You can find all the prebuilt images in `https://hub.docker.com/r/tvmai/`_
+    You can find all the prebuilt images in `<https://hub.docker.com/r/tvmai/>`_
 
 
 This auxiliary script does the following things:
@@ -41,5 +41,5 @@ Then you can start a jupyter notebook by typing
 
 Docker Source
 -------------
-Check out `https://github.com/dmlc/tvm/tree/master/docker`_ if you are interested in
+Check out `<https://github.com/dmlc/tvm/tree/master/docker>`_ if you are interested in
 building your own docker images.

docs/install/from_source.rst

@@ -84,8 +84,7 @@ The configuration of tvm can be modified by `config.cmake`.
       cmake ..
       make -j4
 
-If everything goes well, we can go to the specific language installation section.
-
+If everything goes well, we can go to :ref:`python-package-installation`_
 
 Building on Windows
 ~~~~~~~~~~~~~~~~~~~
@@ -114,6 +113,8 @@ Currently, ROCm is supported only on linux, so all the instructions are written
 - You need to first install HIP runtime from ROCm. Make sure the installation system has ROCm installed in it.
 - Install latest stable version of LLVM (v6.0.1), and LLD, make sure ``ld.lld`` is available via command line.
 
+.. _python-package-installation:
+
 Python Package Installation
 ---------------------------