not using master for doc build until bugs are fixed

Author: Sheng Zha

ci/install_dep.sh

@@ -34,12 +34,12 @@ utils.assign_node_labels(linux_gpu: 'linux-gpu', linux_cpu: 'linux-cpu')
 utils.main_wrapper(
 core_logic: {
   utils.parallel_stage('Doc Test', [
-    build_steps.test_doctest('gluon-nlp-gpu-py3-master', 'gpu/py3-master',
+    build_steps.test_doctest('gluon-nlp-cpu-py3', 'cpu/py3',
                              'src/gluonnlp', 'src/gluonnlp', 4)
   ])
 
   utils.parallel_stage('Documentation', [
-    build_steps.create_website('gluon-nlp-gpu-py3-master', 'gpu/py3-master')
+    build_steps.create_website('gluon-nlp-gpu-py3', 'gpu/py3')
   ])
 
   utils.parallel_stage('Deploy', [

ci/jenkins/Jenkinsfile_py3-master_gpu_doc

@@ -14,8 +14,21 @@ dependencies:
   - pytest-cov=2.6.0
   - mock<3
   - pytest-xdist<2
+  - recommonmark
+  - pandoc=1.19.2
+  - notedown
+  - numba>=v0.40.0
+  - nbsphinx>=0.3.4,<0.4
+  - nbconvert=5.4.0
+  - tornado=5.1.1
+  - ipython
+  - ipykernel
   - pip:
     - pylint-quotes<0.2
     - mxnet-mkl>=1.4.0
     - sacremoses
     - sentencepiece<0.2
+    - https://github.com/mli/mx-theme/archive/0.3.1.tar.gz
+    - seaborn
+    - jieba
+    - sphinx-autorun

ci/step_documentation.sh

@@ -14,8 +14,21 @@ dependencies:
   - pytest-cov=2.6.0
   - mock<3
   - pytest-xdist<2
+  - recommonmark
+  - pandoc=1.19.2
+  - notedown
+  - numba>=v0.40.0
+  - nbsphinx>=0.3.4,<0.4
+  - nbconvert=5.4.0
+  - tornado=5.1.1
+  - ipython
+  - ipykernel
   - pip:
     - pylint-quotes<0.2
     - mxnet-cu92mkl>=1.4.0
     - sacremoses
     - sentencepiece<0.2
+    - https://github.com/mli/mx-theme/archive/0.3.1.tar.gz
+    - seaborn
+    - jieba
+    - sphinx-autorun

ci/step_sanity_check.sh

@@ -56,26 +56,26 @@ def apply_weight_drop(block, local_param_regex, rate, axes=(),
     >>> gluonnlp.model.apply_weight_drop(net, r'.*h2h_weight', 0.5)
     >>> net.collect_params()
     lstm0_ (
-      Parameter lstm0_l0_i2h_weight (shape=(40, 0), dtype=float32)
-      WeightDropParameter lstm0_l0_h2h_weight (shape=(40, 10), dtype=float32, \
+      Parameter lstm0_l0_i2h_weight (shape=(40, 0), dtype=<class 'numpy.float32'>)
+      WeightDropParameter lstm0_l0_h2h_weight (shape=(40, 10), dtype=<class 'numpy.float32'>, \
 rate=0.5, mode=training)
-      Parameter lstm0_l0_i2h_bias (shape=(40,), dtype=float32)
-      Parameter lstm0_l0_h2h_bias (shape=(40,), dtype=float32)
-      Parameter lstm0_r0_i2h_weight (shape=(40, 0), dtype=float32)
-      WeightDropParameter lstm0_r0_h2h_weight (shape=(40, 10), dtype=float32, \
+      Parameter lstm0_l0_i2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
+      Parameter lstm0_l0_h2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
+      Parameter lstm0_r0_i2h_weight (shape=(40, 0), dtype=<class 'numpy.float32'>)
+      WeightDropParameter lstm0_r0_h2h_weight (shape=(40, 10), dtype=<class 'numpy.float32'>, \
 rate=0.5, mode=training)
-      Parameter lstm0_r0_i2h_bias (shape=(40,), dtype=float32)
-      Parameter lstm0_r0_h2h_bias (shape=(40,), dtype=float32)
-      Parameter lstm0_l1_i2h_weight (shape=(40, 20), dtype=float32)
-      WeightDropParameter lstm0_l1_h2h_weight (shape=(40, 10), dtype=float32, \
+      Parameter lstm0_r0_i2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
+      Parameter lstm0_r0_h2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
+      Parameter lstm0_l1_i2h_weight (shape=(40, 20), dtype=<class 'numpy.float32'>)
+      WeightDropParameter lstm0_l1_h2h_weight (shape=(40, 10), dtype=<class 'numpy.float32'>, \
 rate=0.5, mode=training)
-      Parameter lstm0_l1_i2h_bias (shape=(40,), dtype=float32)
-      Parameter lstm0_l1_h2h_bias (shape=(40,), dtype=float32)
-      Parameter lstm0_r1_i2h_weight (shape=(40, 20), dtype=float32)
-      WeightDropParameter lstm0_r1_h2h_weight (shape=(40, 10), dtype=float32, \
+      Parameter lstm0_l1_i2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
+      Parameter lstm0_l1_h2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
+      Parameter lstm0_r1_i2h_weight (shape=(40, 20), dtype=<class 'numpy.float32'>)
+      WeightDropParameter lstm0_r1_h2h_weight (shape=(40, 10), dtype=<class 'numpy.float32'>, \
 rate=0.5, mode=training)
-      Parameter lstm0_r1_i2h_bias (shape=(40,), dtype=float32)
-      Parameter lstm0_r1_h2h_bias (shape=(40,), dtype=float32)
+      Parameter lstm0_r1_i2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
+      Parameter lstm0_r1_h2h_bias (shape=(40,), dtype=<class 'numpy.float32'>)
     )
     >>> ones = mx.nd.ones((3, 4, 5))
     >>> net.initialize()