fix bug and update readme

python/paddle_fl/README.md

+## Compilation and Installation
+
+### Docker Installation 
+
+```sh
+#Pull and run the docker
+docker pull hub.baidubce.com/paddlefl/paddle_mpc:latest
+docker run --name <docker_name> --net=host -it -v $PWD:/root <image id> /bin/bash
+
+#Install paddle_fl
+pip install paddle_fl
+```
+
+### Compile From Source Code
+
+#### Environment preparation
+
+* CentOS 6 or CentOS 7 (64 bit)
+* Python 2.7.15+/3.5.1+/3.6/3.7 ( 64 bit) or above 
+* pip or pip3 9.0.1+ (64 bit)
+* PaddlePaddle release 1.6.3
+* Redis 5.0.8 (64 bit)
+* GCC or G++ 4.8.3+
+* cmake 3.15+
+
+#### Clone the source code, compile and install
+
+Fetch the source code and checkout stable release
+```sh
+git clone https://github.com/PaddlePaddle/PaddleFL
+cd /path/to/PaddleFL
+
+# Checkout stable release
+mkdir build && cd build
+```
+
+Execute compile commands, where `PYTHON_EXECUTABLE` is path to the python binary where the PaddlePaddle is installed, and `PYTHON_INCLUDE_DIRS` is the corresponding python include directory. You can get the `PYTHON_INCLUDE_DIRS` via the following command:
+
+```sh
+${PYTHON_EXECUTABLE} -c "from distutils.sysconfig import get_python_inc;print(get_python_inc())"
+```
+Then you can put the directory in the following command and make:
+```sh
+cmake ../ -DPYTHON_EXECUTABLE=${PYTHON_EXECUTABLE} -DPYTHON_INCLUDE_DIRS=${python_include_dir}
+make -j$(nproc)
+```
+
+Install the package:
+
+```sh
+make install
+cd /path/to/PaddleFL/python
+${PYTHON_EXECUTABLE} setup.py sdist bdist_wheel
+pip or pip3 install dist/***.whl -U
+```
+
+
+
+

python/paddle_fl/mpc/README.md

@@ -50,69 +50,11 @@ A PE program is exactly a PaddlePaddle program, and will be executed as normal P
 
 Upon completion of the secure training (or inference) job, the models (or prediction results) will be output by CPs in encrypted form. Result Parties can collect the encrypted results, decrypt them using the tools in PE, and deliver the plaintext results to users.
 
-## Compilation and Installation
-
-### Docker Installation 
-
-```sh
-#Pull and run the docker
-docker pull hub.baidubce.com/paddlefl/paddle_mpc:latest
-docker run --name <docker_name> --net=host -it -v $PWD:/root <image id> /bin/bash
-
-#Install paddle_fl
-pip install paddle_fl
-```
-
-### Compile From Source Code
-
-#### Environment preparation
-
-* CentOS 6 or CentOS 7 (64 bit)
-* Python 2.7.15+/3.5.1+/3.6/3.7 ( 64 bit) or above 
-* pip or pip3 9.0.1+ (64 bit)
-* PaddlePaddle release 1.6.3
-* Redis 5.0.8 (64 bit)
-* GCC or G++ 4.8.3+
-* cmake 3.15+
-
-#### Clone the source code, compile and install
-
-Fetch the source code and checkout stable release
-```sh
-git clone https://github.com/PaddlePaddle/PaddleFL
-cd /path/to/PaddleFL
-
-# Checkout stable release
-mkdir build && cd build
-```
-
-Execute compile commands, where `PYTHON_EXECUTABLE` is path to the python binary where the PaddlePaddle is installed, and `PYTHON_INCLUDE_DIRS` is the corresponding python include directory. You can get the `PYTHON_INCLUDE_DIRS` via the following command:
-
-```sh
-${PYTHON_EXECUTABLE} -c "from distutils.sysconfig import get_python_inc;print(get_python_inc())"
-```
-Then you can put the directory in the following command and make:
-```sh
-cmake ../ -DPYTHON_EXECUTABLE=${PYTHON_EXECUTABLE} -DPYTHON_INCLUDE_DIRS=${python_include_dir}
-make -j$(nproc)
-```
-
-Install the package:
-
-```sh
-make install
-cd /path/to/PaddleFL/python
-${PYTHON_EXECUTABLE} setup.py sdist bdist_wheel
-pip or pip3 install dist/***.whl -U
-```
-
-Validate the installation by running the `python` or `python3`, then runs `import paddle_encrypted as pe` and `pe.version()`. The installation succeeds if you see `Paddle Encrypted Version: 1.0.0`.
-
 ## Example
 
 #### Build your model
 
-In Paddle Encrypted, you can build models as it is in PaddlePaddle, but using the variables and operators over encrypted data. First, prepare a training script as the example below. It is worth to note that the operators and variables are created using the `paddle.fluid_encrypted` package.
+In Paddle Encrypted, you can build models as it is in PaddlePaddle, but using the variables and operators over encrypted data. First, prepare a training script as the example below. It is worth to note that the operators and variables are created using the `paddle_fl.mpc` package.
 
 ```python
 # An example to build an LR model, named train.py (USE THE HOUSE PRICE CASE)
@@ -127,7 +69,7 @@ role, addr, port = sys.argv[1], sys.argv[2], sys.argv[3]
 # init the MPC environment
 pfl_mpc.init("aby3", (int)role, net_server_addr=addr, net_server_port=(int)port)
 
-#data processing
+# data processing
 BATCH_SIZE = 10
 
 feature_reader = aby3.load_aby3_shares("/tmp/house_feature", id=role, shape=(13, ))

python/paddle_fl/mpc/examples/mnist_demo/mnist_demo.py

@@ -86,9 +86,9 @@ step = 0
 for epoch_id in range(epoch_num):
     # feed data via loader
     for sample in loader():
-        exe.run(feed=sample)
+        loss = exe.run(feed=sample, fetch_list=[cost.name])
         if step % 50 == 0:
-            print('Epoch={}, Step={}'.format(epoch_id, step))
+            print('Epoch={}, Step={}, loss={}'.format(epoch_id, step, loss))
         step += 1
 
 end_time = time.time()

python/paddle_fl/paddle_fl/examples/dpsgd_demo/fl_server.py

@@ -14,8 +14,8 @@
 
 import paddle_fl.paddle_fl as fl
 import paddle.fluid as fluid
-from paddle_fl.core.server.fl_server import FLServer
-from paddle_fl.core.master.fl_job import FLRunTimeJob
+from paddle_fl.paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
 server = FLServer()
 server_id = 0
 job_path = "fl_job_config"

python/paddle_fl/paddle_fl/examples/femnist_demo/fl_trainer.py

@@ -14,7 +14,7 @@
 
 from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
-import paddle_fl.paddle_fl.dataset.femnist
+import paddle_fl.paddle_fl.dataset.femnist as femnist
 import numpy
 import sys
 import paddle
@@ -76,7 +76,7 @@ while not trainer.stop():
     #train_data,test_data= data_generater(trainer_id,inner_step=trainer._step,batch_size=64,count_by_step=count_by_step)
     train_reader = paddle.batch(
         paddle.reader.shuffle(
-            paddle_fl.dataset.femnist.train(
+            femnist.train(
                 trainer_id,
                 inner_step=trainer._step,
                 batch_size=64,
@@ -85,7 +85,7 @@ while not trainer.stop():
         batch_size=64)
 
     test_reader = paddle.batch(
-        paddle_fl.dataset.femnist.test(
+        femnist.test(
             trainer_id,
             inner_step=trainer._step,
             batch_size=64,