[Docker] update docker image (#575)

* update docker image

* specify lint version

* rm torch import from unified tests

docker/Dockerfile.ci_cpu

@@ -7,8 +7,14 @@ RUN apt-get update --fix-missing
 COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
 RUN bash /install/ubuntu_install_core.sh
 
+COPY install/ubuntu_install_build.sh /install/ubuntu_install_build.sh
+RUN bash /install/ubuntu_install_build.sh
+
 COPY install/ubuntu_install_python.sh /install/ubuntu_install_python.sh
 RUN bash /install/ubuntu_install_python.sh
 
 COPY install/ubuntu_install_python_package.sh /install/ubuntu_install_python_package.sh
 RUN bash /install/ubuntu_install_python_package.sh
+
+COPY install/ubuntu_install_torch.sh /install/ubuntu_install_torch.sh
+RUN bash /install/ubuntu_install_torch.sh

docker/Dockerfile.ci_cpu_mxnet

@@ -7,6 +7,9 @@ RUN apt-get update --fix-missing
 COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
 RUN bash /install/ubuntu_install_core.sh
 
+COPY install/ubuntu_install_build.sh /install/ubuntu_install_build.sh
+RUN bash /install/ubuntu_install_build.sh
+
 COPY install/ubuntu_install_python.sh /install/ubuntu_install_python.sh
 RUN bash /install/ubuntu_install_python.sh
 

docker/Dockerfile.ci_gpu

@@ -7,12 +7,18 @@ RUN apt-get update --fix-missing
 COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
 RUN bash /install/ubuntu_install_core.sh
 
+COPY install/ubuntu_install_build.sh /install/ubuntu_install_build.sh
+RUN bash /install/ubuntu_install_build.sh
+
 COPY install/ubuntu_install_python.sh /install/ubuntu_install_python.sh
 RUN bash /install/ubuntu_install_python.sh
 
 COPY install/ubuntu_install_python_package.sh /install/ubuntu_install_python_package.sh
 RUN bash /install/ubuntu_install_python_package.sh
 
+COPY install/ubuntu_install_torch.sh /install/ubuntu_install_torch.sh
+RUN bash /install/ubuntu_install_torch.sh
+
 # Environment variables
 ENV PATH=/usr/local/nvidia/bin:${PATH}
 ENV PATH=/usr/local/cuda/bin:${PATH}

docker/Dockerfile.ci_gpu_mxnet

@@ -7,6 +7,9 @@ RUN apt-get update --fix-missing
 COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
 RUN bash /install/ubuntu_install_core.sh
 
+COPY install/ubuntu_install_build.sh /install/ubuntu_install_build.sh
+RUN bash /install/ubuntu_install_build.sh
+
 COPY install/ubuntu_install_python.sh /install/ubuntu_install_python.sh
 RUN bash /install/ubuntu_install_python.sh
 

docker/Dockerfile.ci_lint

@@ -11,4 +11,4 @@ RUN bash /install/ubuntu_install_python.sh
 
 RUN apt-get install -y doxygen graphviz
 
-RUN pip3 install cpplint pylint mypy
+RUN pip3 install cpplint==1.3.0 pylint==2.2.2 mypy

docker/README.md

 ## Build docker image for CI
 
 ### CPU image
+```bash
 docker build -t dgl-cpu -f Dockerfile.ci_cpu .
+```
 
 ### GPU image
+```bash
 docker build -t dgl-gpu -f Dockerfile.ci_gpu .
+```
 
 ### Lint image
+```bash
 docker build -t dgl-lint -f Dockerfile.ci_lint .
+```
 
 ### CPU MXNet image
+```bash
 docker build -t dgl-mxnet-cpu -f Dockerfile.ci_cpu_mxnet .
+```
 
 ### GPU MXNet image
+```bash
 docker build -t dgl-mxnet-gpu -f Dockerfile.ci_gpu_mxnet .
+```

docker/install/ubuntu_install_build.sh

+# install cmake 3.9
+wget https://cmake.org/files/v3.9/cmake-3.9.0.tar.gz
+tar xvf cmake-3.9.0.tar.gz
+cd cmake-3.9.0
+./configure
+make -j4
+make install
+cd ..

docker/install/ubuntu_install_core.sh

 # install libraries for building c++ core on ubuntu
 apt update && apt install -y --no-install-recommends --force-yes \
-        apt-utils git build-essential make cmake wget unzip sudo \
+        apt-utils git build-essential make wget unzip sudo \
         libz-dev libxml2-dev libopenblas-dev libopencv-dev \
         graphviz graphviz-dev libgraphviz-dev ca-certificates

docker/install/ubuntu_install_mxnet_cpu.sh

-pip3 install --pre mxnet
+pip3 install mxnet==1.4.1

docker/install/ubuntu_install_mxnet_gpu.sh

-git clone --recursive -b dgl_graph https://github.com/zheng-da/incubator-mxnet.git
-cd incubator-mxnet
-make -j4 USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda \
-  USE_CUDNN=1 USE_MKLDNN=1 USE_DIST_KVSTORE=1
-pip3 install -e python
+pip3 install mxnet-cu90==1.4.1

docker/install/ubuntu_install_python_package.sh

 # install libraries for python package on ubuntu
 pip2 install nose numpy cython scipy networkx matplotlib nltk requests[security]
 pip3 install nose numpy cython scipy networkx matplotlib nltk requests[security]
-
-# install DL Framework
-pip2 install torch torchvision
-pip3 install torch torchvision

docker/install/ubuntu_install_torch.sh

+#!/bin/bash
+# install torch
+pip2 install torch==1.0.1 torchvision==0.2.2
+pip3 install torch==1.0.1 torchvision==0.2.2

tests/compute/test_basics.py

-# Currently readonly graph construction only accepts sparse tensor in MXNet,
-# and pytorch doesn't support readonly graph or graph creation from sparse
-# tensor.  For now, readonly graph test is postponed until we have better
-# readonly graph support.
 import backend as F
 import dgl
 import networkx as nx

tests/compute/test_filter.py

-import torch as th
 from dgl.graph import DGLGraph
 import backend as F
 

tests/compute/test_index.py

@@ -68,7 +68,7 @@ def test_index():
     assert np.allclose(ans, y2)
     assert np.allclose(ans, y3)
 
-    # from torch
+    # from dl tensor
     data = F.ones((10,), dtype=F.int64) * 10
     idx = toindex(data)
     y1 = idx.tonumpy()

tests/compute/test_pickle.py

@@ -9,8 +9,6 @@ import dgl.function as fn
 import pickle
 import io
 
-import torch
-
 def _assert_is_identical(g, g2):
     assert g.is_multigraph == g2.is_multigraph
     assert g.is_readonly == g2.is_readonly