[Tutorial] Add sse tutorial & MXNet Tutorial CI (dmlc#252)

* add sse tutorial

* add mxnet tutorial ci

* fix ci

* fix ci

* fix ci

* fix ci

* fix ci

* fix ci

* Fix ci

* Fix ci

* Fix ci

* fix ci

* fix ci

* fix ci

* fix ci

* fix ci

* fix ci

* Fix CI

Fix CI image

* permission fix

* fix a bug in the code.

* small fix

* fix doc

* fix ci

* shorten the iters

* fix

* remove extra file

* add load_backend api to dynamically switch to another backend

* try fix

* fix tutorial

* fix tutorial

* fix bug in tutorial

Jenkinsfile

@@ -40,27 +40,34 @@ def mxnet_unit_test(dev) {
 def example_test(dev) {
   withEnv(["DGL_LIBRARY_PATH=${env.WORKSPACE}/build", "PYTHONPATH=${env.WORKSPACE}/python"]) {
     dir ("tests/scripts") {
-      sh "./task_example_test.sh ${dev}"
+      sh "bash task_example_test.sh ${dev}"
     }
   }
 }
 
 def pytorch_tutorials() {
   withEnv(["DGL_LIBRARY_PATH=${env.WORKSPACE}/build", "PYTHONPATH=${env.WORKSPACE}/python"]) {
     dir ("tests/scripts") {
-      sh "./task_tutorial_test.sh"
+      sh "bash task_pytorch_tutorial_test.sh"
     }
   }
 }
 
+def mxnet_tutorials() {
+  withEnv(["DGL_LIBRARY_PATH=${env.WORKSPACE}/build", "PYTHONPATH=${env.WORKSPACE}/python"]) {
+    dir("tests/scripts") {
+      sh "bash task_mxnet_tutorial_test.sh"
+    }
+  }
+}
 pipeline {
   agent none
   stages {
     stage("Lint Check") {
       agent { docker { image "dgllib/dgl-ci-lint" } }
       steps {
         init_git_submodule()
-        sh "tests/scripts/task_lint.sh"
+        sh "bash tests/scripts/task_lint.sh"
       }
     }
     stage("Build") {
@@ -144,9 +151,19 @@ pipeline {
       }
     }
     stage("Doc") {
-      agent { docker { image "dgllib/dgl-ci-cpu" } }
-      steps {
-        pytorch_tutorials()
+      parallel {
+        stage("TH Tutorial") {
+          agent { docker { image "dgllib/dgl-ci-cpu" } }
+          steps {
+            pytorch_tutorials()
+          }
+        }
+        stage("MX Tutorial") {
+          agent { docker { image "dgllib/dgl-ci-mxnet-cpu" } }
+          steps {
+            mxnet_tutorials()
+          }
+        }
       }
     }
   }

docs/source/api/python/sampler.rst

@@ -1,11 +1,6 @@
 .. apisampler
 
-Sampling subgraphs from a large graph
-=====================================
+Graph samplers
+==============
 
-.. currentmodule:: dgl.contrib.sampling
-
-NeighborSampler
----------------
-
-A function that generates a subgraph loader for sampling subgraphs.
+.. autofunction:: dgl.contrib.sampling.sampler.NeighborSampler

python/dgl/__init__.py

@@ -1,4 +1,3 @@
-from . import backend
 # One has to manually import dgl.data; fixes #125
 #from . import data
 from . import function
@@ -10,6 +9,7 @@
 from ._ffi.base import DGLError, __version__
 
 from .base import ALL
+from .backend import load_backend
 from .batched_graph import *
 from .graph import DGLGraph
 from .subgraph import DGLSubGraph

python/dgl/backend/__init__.py

@@ -14,11 +14,13 @@ def _missing_api(*args, **kwargs):
                           ' the DGLBACKEND environment.' % (api, mod_name))
     return _missing_api
 
-def _load_backend():
-    mod_name = os.environ.get('DGLBACKEND', 'pytorch').lower()
+def load_backend(mod_name):
     mod = importlib.import_module('.%s' % mod_name, __name__)
     thismod = sys.modules[__name__]
     for api in backend.__dict__.keys():
+        if api.startswith('__'):
+            # ignore python builtin attributes
+            continue
         if api == 'data_type_dict':
             # load data type
             if api not in mod.__dict__:
@@ -41,7 +43,7 @@ def _load_backend():
             else:
                 setattr(thismod, api, _gen_missing_api(api, mod_name))
 
-_load_backend()
+load_backend(os.environ.get('DGLBACKEND', 'pytorch').lower())
 
 def is_enabled(api):
     """Return true if the api is enabled by the current backend.

python/dgl/contrib/sampling/sampler.py

@@ -81,7 +81,11 @@ def NeighborSampler(g, batch_size, expand_factor, num_hops=1,
                     neighbor_type='in', node_prob=None, seed_nodes=None,
                     shuffle=False, num_workers=1, max_subgraph_size=None,
                     return_seed_id=False):
-    '''
+    '''Create a sampler that samples neighborhood.
+
+    .. note:: This method currently only supports MXNet backend. Set
+        "DGLBACKEND" environment variable to "mxnet".
+
     This creates a subgraph data loader that samples subgraphs from the input graph
     with neighbor sampling. This simpling method is implemented in C and can perform
     sampling very efficiently.
@@ -127,7 +131,7 @@ def NeighborSampler(g, batch_size, expand_factor, num_hops=1,
     Returns
     -------
     A subgraph loader that returns a list of batched subgraphs and a dictionary of
-        additional infomration about the subgraphs.
+        additional information about the subgraphs.
     '''
     return NSSubgraphLoader(g, batch_size, expand_factor, num_hops, neighbor_type, node_prob,
                             seed_nodes, shuffle, num_workers, max_subgraph_size, return_seed_id)

tests/scripts/task_lint.sh

@@ -1,4 +1,4 @@
-#!/bin/sh
+#!/bin/bash
 
 # cpplint
 echo 'Checking code style of C++ codes...'

tests/scripts/task_tutorial_test.sh → tests/scripts/task_mxnet_tutorial_test.sh

@@ -14,7 +14,7 @@ pip3 install -r requirements.txt || fail "installing requirements"
 
 # Test
 export MPLBACKEND=Agg
-for f in $(find . -name "*.py")
+for f in $(find . -name "*_mx.py")
 do
     echo "Running tutorial ${f} ..."
     python3 $f || fail "run ${f}"

tests/scripts/task_pytorch_tutorial_test.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+# The working directory for this script will be "tests/scripts"
+
+TUTORIAL_ROOT="../../tutorials"
+
+function fail {
+    echo FAIL: $@
+    exit -1
+}
+
+pushd ${TUTORIAL_ROOT} > /dev/null
+# Install requirements
+pip3 install -r requirements.txt || fail "installing requirements"
+
+# Test
+export MPLBACKEND=Agg
+for f in $(find . -name "*.py" ! -name "*_mx.py")
+do
+    echo "Running tutorial ${f} ..."
+    python3 $f || fail "run ${f}"
+done
+
+popd > /dev/null