revert to 8f80fe3

.travis.yml

@@ -1,31 +1,108 @@
-sudo: true
+sudo: false
 
 language: cpp
 
-cache: ccache
-
 os:
+  # - linux
   - osx
 
-osx_image: xcode9.4
+osx_image: xcode8
+
+env:
+  # code analysis
+  # - TASK=lint
+  # build mxnet.so with CUDA
+  # - TASK=build
+  # run tests/cpp
+  - TASK=cpp_test
+  # run tests/python
+  - TASK=python_test
+  - TASK=r_test
+  # - TASK=julia JULIA_VER=0.4
+  # - TASK=scala_test
+
+  # TODO, R test, distributed test, clang, more g++ versions
 
 matrix:
   include:
+    - # os: linux
+      # dist: trusty
+      # env: TASK=perl_test
     - os: osx
+      ## sudo is required because
+      ## prexexisting packages conflict
+      ## with new ones.
+      ## would be nice to have macports
+      ## on travis osx, it has all needed perl packages
+      sudo: required
+      env: TASK=perl_test
+#       env: TASK=julia JULIA_VER=0.4
+#     - os: linux
+#       env: TASK=build
+#     - os: linux
+#       env: TASK=cpp_test
+#     - os: linux
+#       env: TASK=python_test
+#     - os: linux
+#       env: TASK=r_test
+#     - os: linux
+#       env: TASK=scala_test
+
+# dependent apt packages
+addons:
+  apt:
+    sources:
+      - ubuntu-toolchain-r-test
+    packages:
+      - doxygen
+      - wget
+      - git
+      - libcurl4-openssl-dev
+      - unzip
+      - libatlas-dev
+      - libopencv-dev
+      - gcc-4.8
+      - g++-4.8
+      - python-numpy
+      - python-nose
+      - python3-numpy
+      - python3-dev
+      - python3-nose
+      - python-h5py
+      - python3-h5py
+      - graphviz
+      - libmouse-perl
+      - pdl
+      - cpanminus
+      - swig
+      - libgraphviz-perl
 
 before_install:
+  - export NVCC_PREFIX=${HOME}
+  - source dmlc-core/scripts/travis/travis_setup_env.sh
   - export PYTHONPATH=${PYTHONPATH}:${PWD}/python
+  - export MAVEN_SKIP_RC=true
+  - export MAVEN_OPTS="-Xmx512m -XX:MaxPermSize=256m -XX:-UseGCOverheadLimit -XX:+CMSClassUnloadingEnabled -XX:+UseConcMarkSweepGC"
 
 install:
-  - brew install ccache
-  - export PATH="/usr/local/opt/ccache/libexec:$PATH"
-  - source ci/travis/install.sh
-
-# We build with 2 concurrent jobs to match the number of cores present on MacOS virutal machines.
-# nproc does not report the correct number of cores reliably in Travis, so using nproc is not
-# recommended.
-# https://docs.travis-ci.com/user/reference/overview/
+  - source tests/travis/setup.sh
+
 script:
-  - export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
-  - mv make/osx.mk config.mk
-  - make -j 2
+  - tests/travis/run_test.sh
+
+cache:
+  directories:
+    - ${HOME}/.cache/usr
+
+before_cache:
+  - dmlc-core/scripts/travis/travis_before_cache.sh
+
+after_failure:
+  - tests/travis/travis_after_failure.sh
+
+notifications:
+# Emails are sent to the committer's git-configured email address by default,
+  email:
+    on_success: change
+    on_failure: always
+  #slack: dmlc:NmroCzntCiWOuxUZpii40USd

CONTRIBUTORS.md

@@ -178,4 +178,3 @@ List of Contributors
 * [Aaron Markham](https://github.com/aaronmarkham)
 * [Sam Skalicky](https://github.com/samskalicky)
 * [Per Goncalves da Silva](https://github.com/perdasilva)
-* [Cheng-Che Lee](https://github.com/stu1130)

Jenkinsfile

@@ -900,10 +900,6 @@ core_logic: {
         }
       }
     },
-    /*  Disabled due to master build failure:
-     *  http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/master/1221/pipeline/
-     *  https://github.com/apache/incubator-mxnet/issues/11801
-
     'dist-kvstore tests CPU': {
       node(NODE_LINUX_CPU) {
         ws('workspace/it-dist-kvstore') {
@@ -915,7 +911,7 @@ core_logic: {
           }
         }
       }
-    }, */
+    },
     'Scala: GPU': {
       node(NODE_LINUX_GPU) {
         ws('workspace/ut-scala-gpu') {

R-package/R/initializer.R

@@ -3,7 +3,7 @@
 #' @param name the name of the variable.
 #' @param shape the shape of the array to be generated.
 #'
-mx.init.internal.default <- function(name, shape, ctx, allow.unknown = FALSE) {
+mx.init.internal.default <- function(name, shape, ctx, allow.unknown=FALSE) {
   if (endsWith(name, "bias")) return (mx.nd.zeros(shape))
   if (endsWith(name, "gamma")) return (mx.nd.ones(shape))
   if (endsWith(name, "beta")) return (mx.nd.zeros(shape))
@@ -19,7 +19,7 @@ mx.init.internal.default <- function(name, shape, ctx, allow.unknown = FALSE) {
 #'
 #' @export
 mx.init.uniform <- function(scale) {
-  function(name, shape, ctx, allow.unknown = FALSE) {
+  function(name, shape, ctx, allow.unknown=FALSE) {
     if (!endsWith(name, "weight")) {
       return (mx.init.internal.default(name = name, shape = shape, allow.unknown = allow.unknown))
     }
@@ -33,7 +33,7 @@ mx.init.uniform <- function(scale) {
 #'
 #' @export
 mx.init.normal <- function(sd) {
-  function(name, shape, ctx, allow.unknown = FALSE) {
+  function(name, shape, ctx, allow.unknown=FALSE) {
     if (!endsWith(name, "weight")) {
       return (mx.init.internal.default(name = name, shape = shape, allow.unknown = allow.unknown))
     }
@@ -59,15 +59,15 @@ mx.init.Xavier <- function(rnd_type = "uniform", factor_type = "avg",
       return (mx.init.internal.default(name = name, shape = shape, allow.unknown = allow.unknown))
     }
 
-    fan_out <- shape[length(shape)]
-    fan_in <- prod(shape[-length(shape)])
+    fan_out = shape[length(shape)]
+    fan_in  = prod(shape[-length(shape)])
     factor_val <- switch(factor_type,
                          "avg" = (fan_in + fan_out) / 2,
                          "in" = fan_in,
                          "out" = fan_out,
                          stop("Not supported factor type. See usage of function mx.init.Xavier"))
 
-    scale <- sqrt(magnitude / factor_val)
+    scale = sqrt(magnitude / factor_val)
 
     if (rnd_type == "uniform"){
       return(mx.nd.random.uniform(low = -scale, high = scale, shape = shape))
@@ -83,16 +83,14 @@ mx.init.Xavier <- function(rnd_type = "uniform", factor_type = "avg",
 #' Create initialization of argument  like arg.array
 #'
 #' @param initializer The initializer.
-#' @param shape.array A named list that represents the shape of the weights
+#' @param shape.array named-list The shape of the weights
 #' @param ctx mx.context The context of the weights
 #' @param skip.unknown Whether skip the unknown weight types
 #' @export
-mx.init.create <- function(initializer, shape.array, ctx = NULL, skip.unknown = TRUE) {
+mx.init.create <- function(initializer, shape.array, ctx=NULL, skip.unknown=TRUE) {
   if (length(shape.array) == 0) return(list())
-  names <- names(shape.array)
-  ret <- lapply(
-    seq_along(names),
-    function(i) initializer(names[[i]], shape.array[[i]], ctx, allow.unknown = skip.unknown))
+  names = names(shape.array)
+  ret <- lapply(seq_along(names), function(i) initializer(names[[i]], shape.array[[i]], ctx, allow.unknown=skip.unknown))
   names(ret) <- names
   if (skip.unknown) {
     ret <- mx.util.filter.null(ret)

ci/docker/install/tensorrt.sh

@@ -30,12 +30,9 @@ apt-get install -y automake libtool
 git clone --recursive -b 3.5.1.1 https://github.com/google/protobuf.git
 cd protobuf
 ./autogen.sh
-./configure --disable-shared CXXFLAGS=-fPIC
+./configure
 make -j$(nproc)
 make install
-rm -rf /usr/local/lib/libprotobuf-lite.so*
-rm -rf /usr/local/lib/libprotobuf.so*
-rm -rf /usr/local/lib/libprotoc.so*
 ldconfig
 popd
 

ci/docker/install/ubuntu_mklml.sh

@@ -21,5 +21,5 @@
 # the whole docker cache for the image
 
 set -ex
-wget -q --no-check-certificate -O /tmp/mklml.tgz https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz
-tar -zxf /tmp/mklml.tgz && cp -rf mklml_*/* /usr/local/ && rm -rf mklml_*
+wget --no-check-certificate -O /tmp/mklml.tgz https://github.com/intel/mkl-dnn/releases/download/v0.14/mklml_lnx_2018.0.3.20180406.tgz
+tar -zxvf /tmp/mklml.tgz && cp -rf mklml_*/* /usr/local/ && rm -rf mklml_*

ci/docker/install/ubuntu_tvm.sh

@@ -25,14 +25,14 @@ cd tvm
 # This is a stable tag that support MXNet TVM bridge.
 # We use this since support for mxnet bridge just checked
 # into master and there is yet a version tag
-git checkout v0.4
-
-cp cmake/config.cmake .
-echo set\(USE_CUDA /usr/local/cuda\) >> config.cmake
-echo set\(USE_LLVM llvm-config-5.0\) >> config.cmake
-echo set\(USE_RPC ON\) >> config.cmake
-echo set\(USE_GRAPH_RUNTIME ON\) >> config.cmake
+git checkout 30eaf463e34d7c301357c31a010945d11df16537
 
+cp make/config.mk
+echo USE_CUDA=1 >> config.mk
+echo LLVM_CONFIG=llvm-config-5.0 >> config.mk
+echo USE_RPC=1 >> config.mk
+echo USE_GRAPH_RUNTIME=1 >> config.mk
+echo CUDA_PATH=/usr/local/cuda >> config.mk
 make -j$(nproc)
 
 cd python

ci/util.py

@@ -17,7 +17,6 @@
 
 import os
 import contextlib
-import logging
 import requests
 
 def get_mxnet_root() -> str:

docs/_static/js/options.js

@@ -8,7 +8,7 @@ $(document).ready(function () {
     function label(lbl) {
         return lbl.replace(/[ .]/g, '-').toLowerCase();
     }
-
+   
     function urlSearchParams(searchString) {
         let urlDict = new Map();
         let searchParams = searchString.substring(1).split("&");
@@ -45,11 +45,11 @@ $(document).ready(function () {
         showContent();
         if (window.location.href.indexOf("/install/index.html") >= 0) {
             if (versionSelect.indexOf(defaultVersion) >= 0) {
-                history.pushState(null, null, 'index.html?platform=' + platformSelect + '&language=' + languageSelect + '&processor=' + processorSelect);
+                history.pushState(null, null, '/install/index.html?platform=' + platformSelect + '&language=' + languageSelect + '&processor=' + processorSelect);
             } else {
-                history.pushState(null, null, 'index.html?version=' + versionSelect + '&platform=' + platformSelect + '&language=' + languageSelect + '&processor=' + processorSelect);
+                history.pushState(null, null, '/install/index.html?version=' + versionSelect + '&platform=' + platformSelect + '&language=' + languageSelect + '&processor=' + processorSelect);
             }
-        }
+        } 
     }
 
     function showContent() {
@@ -73,22 +73,22 @@ $(document).ready(function () {
             $('.current-version').html( $(this).text() + ' <span class="caret"></span></button>' );
             if ($(this).text().indexOf(defaultVersion) < 0) {
                 if (window.location.search.indexOf("version") < 0) {
-                    history.pushState(null, null, 'index.html' + window.location.search.concat( '&version=' + $(this).text() ));
+                    history.pushState(null, null, '/install/index.html' + window.location.search.concat( '&version=' + $(this).text() ));
                 } else {
-                    history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('version'), $(this).text() ));
+                    history.pushState(null, null, '/install/index.html' + window.location.search.replace( urlParams.get('version'), $(this).text() ));
                 }
             } else if (window.location.search.indexOf("version") >= 0) {
-                  history.pushState(null, null, 'index.html' + window.location.search.replace( 'version', 'prev' ));
+                  history.pushState(null, null, '/install/index.html' + window.location.search.replace( 'version', 'prev' ));
               }
         }
         else if ($(this).hasClass("platforms")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('platform'), $(this).text() ));
+            history.pushState(null, null, '/install/index.html' + window.location.search.replace( urlParams.get('platform'), $(this).text() ));
         }
         else if ($(this).hasClass("languages")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('language'), $(this).text() ));
+            history.pushState(null, null, '/install/index.html' + window.location.search.replace( urlParams.get('language'), $(this).text() ));
         }
         else if ($(this).hasClass("processors")) {
-            history.pushState(null, null, 'index.html' + window.location.search.replace( urlParams.get('processor'), $(this).text() ));
+            history.pushState(null, null, '/install/index.html' + window.location.search.replace( urlParams.get('processor'), $(this).text() ));
         }
         showContent();
         //window.location.search = window.location.search.replace( urlParams.get('version'), $(this).text() );

docs/_static/searchtools_custom.js

@@ -8,14 +8,14 @@
  * Redistribution and use in source and binary forms, with or without
  * modification, are permitted provided that the following conditions are
  * met:
- *
+ * 
  * * Redistributions of source code must retain the above copyright
  *   notice, this list of conditions and the following disclaimer.
- *
+ * 
  * * Redistributions in binary form must reproduce the above copyright
  *   notice, this list of conditions and the following disclaimer in the
  *   documentation and/or other materials provided with the distribution.
- *
+ * 
  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
@@ -523,7 +523,7 @@ var Search = {
             displayNextItem();
           });
         } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
-          $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[0] + '.md.txt',
+          $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[0] + '.txt',
                   dataType: "text",
                   complete: function(jqxhr, textstatus) {
                     var data = jqxhr.responseText;

docs/api/clojure/index.md

@@ -1,20 +1,9 @@
 # MXNet - Clojure API
-
 MXNet supports the Clojure programming language. The MXNet Clojure package brings flexible and efficient GPU
 computing and state-of-art deep learning to Clojure. It enables you to write seamless tensor/matrix computation with multiple GPUs in Clojure. It also lets you construct and customize the state-of-art deep learning models in Clojure, and apply them to tasks, such as image classification and data science challenges.
 
 See the [MXNet Clojure API Documentation](docs/index.html) for detailed API information.
 
-```eval_rst
-.. toctree::
-   :maxdepth: 1
-
-   kvstore.md
-   module.md
-   ndarray.md
-   symbol_in_pictures.md
-   symbol.md
-```
 
 ## Tensor and Matrix Computations
 You can perform tensor or matrix computation in pure Clojure: