Merge branch 'develop' of github.com:PaddlePaddle/Paddle into overlap_send_op

.clang-format

@@ -19,7 +19,7 @@ BasedOnStyle:  Google
 IndentWidth:     2
 TabWidth:        2
 ContinuationIndentWidth: 4
-AccessModifierOffset: -2  # The private/protected/public has no indent in class
+AccessModifierOffset: -1  # The private/protected/public has no indent in class
 Standard:  Cpp11 
 AllowAllParametersOfDeclarationOnNextLine: true
 BinPackParameters: false

.pre-commit-config.yaml

@@ -34,6 +34,14 @@ repos:
         entry: bash ./tools/codestyle/cpplint_pre_commit.hook
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx)$
+-   repo: local
+    hooks:
+    -   id: pylint-doc-string
+        name: pylint
+        description: Check python docstring style using docstring_checker.
+        entry: bash ./tools/codestyle/pylint_pre_commit.hook
+        language: system
+        files: \.(py)$
 -   repo: https://github.com/PaddlePaddle/pre-commit-golang
     sha: 8337620115c25ff8333f1b1a493bd031049bd7c0
     hooks:

.travis.yml

@@ -18,6 +18,8 @@ env:
 addons:
   ssh_known_hosts: 13.229.163.131
 before_install:
+  # For pylint dockstring checker
+  - sudo pip install pylint pytest astroid isort
   - |
     function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; }
 script:

Dockerfile

@@ -79,6 +79,9 @@ RUN pip install pre-commit 'ipython==5.3.0' && \
     pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
     pip install opencv-python
 
+#For docstring checker
+RUN pip install pylint pytest astroid isort
+
 COPY ./python/requirements.txt /root/
 RUN pip install -r /root/requirements.txt
 

benchmark/fluid/README.md

@@ -24,22 +24,22 @@ Currently supported `--model` argument include:
 
 * Run the following command to start a benchmark job locally:
     ```bash
-      python fluid_benchmark.py --model mnist --parallel 1 --device GPU --with_test
+      python fluid_benchmark.py --model mnist  --device GPU
     ```
     You can choose to use GPU/CPU training. With GPU training, you can specify
-    `--parallel 1` to run multi GPU training.
+    `--gpus <gpu_num>` to run multi GPU training.
 * Run distributed training with parameter servers:
     * start parameter servers:
         ```bash
-        PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method pserver
+        PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist  --device GPU --update_method pserver
         ```
     * start trainers:
         ```bash
-        PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method pserver
+        PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist  --device GPU --update_method pserver
         ```
 * Run distributed training using NCCL2
     ```bash
-    PADDLE_PSERVER_PORT=7164 PADDLE_TRAINER_IPS=192.168.0.2,192.168.0.3  PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method nccl2
+    PADDLE_PSERVER_PORT=7164 PADDLE_TRAINER_IPS=192.168.0.2,192.168.0.3  PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --device GPU --update_method nccl2
     ```
 
 ## Run Distributed Benchmark on Kubernetes Cluster
@@ -48,7 +48,7 @@ We provide a script `kube_gen_job.py` to generate Kubernetes yaml files to submi
 distributed benchmark jobs to your cluster. To generate a job yaml, just run:
 
 ```bash
-python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --parallel 1 --device GPU --update_method pserver --with_test" --disttype pserver
+python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --parallel 1 --device GPU --update_method pserver " --disttype pserver
 ```
 
 Then the yaml files are generated under directory `myjob`, you can run: