Merge branch V0.2 to Master

README.md

@@ -35,7 +35,7 @@ pip Installation Prerequisites
 * git, wget
 
 ```
-python3 -m pip install -v --user git+https://github.com/Microsoft/nni.git@v0.1
+python3 -m pip install -v --user git+https://github.com/Microsoft/nni.git@v0.2
 source ~/.bashrc
 ```
 
@@ -64,6 +64,7 @@ To learn more about how this example was constructed and how to analyze the expe
 * [Tuners supported by NNI.](src/sdk/pynni/nni/README.md)
 * [How to enable early stop (i.e. assessor) in an experiment?](docs/EnableAssessor.md)
 * [How to run an experiment on multiple machines?](docs/RemoteMachineMode.md)
+* [How to run an experiment on OpenPAI?](docs/PAIMode.md)
 * [How to write a customized tuner?](docs/CustomizedTuner.md)
 * [How to write a customized assessor?](examples/assessors/README.md)
 * [How to resume an experiment?](docs/NNICTLDOC.md)

deployment/Dockerfile

@@ -10,4 +10,6 @@ RUN pip3 --no-cache-dir install tensorflow-gpu==1.10.0
 #
 #Keras 2.1.6
 #
-RUN pip3 --no-cache-dir install Keras==2.1.6
+RUN pip3 --no-cache-dir install Keras==2.1.6
+
+WORKDIR /root

deployment/Dockerfile.build.base

@@ -22,27 +22,62 @@ FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
 
 LABEL maintainer='Microsoft NNI Team<nni@microsoft.com>'
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    sudo apt-utils git curl vim unzip openssh-client wget \
-    build-essential cmake \
-    libopenblas-dev
+ENV HADOOP_VERSION=2.7.2
+LABEL HADOOP_VERSION=2.7.2
 
-#
-# Python 3.5
-#
-RUN apt-get install -y --no-install-recommends python3.5 python3.5-dev python3-pip python3-tk && \
-    pip3 install --no-cache-dir --upgrade pip setuptools && \
-    echo "alias python='python3'" >> /root/.bash_aliases && \
-    echo "alias pip='pip3'" >> /root/.bash_aliases
+RUN DEBIAN_FRONTEND=noninteractive && \
+    apt-get -y update && \
+    apt-get -y install sudo \
+    apt-utils \
+    git \
+    curl \
+    vim \
+    unzip \
+    wget \
+    build-essential \ 
+    cmake \
+    libopenblas-dev \
+    automake \
+    openjdk-8-jdk \
+    openssh-client \
+    openssh-server \
+    lsof \
+    python3.5 \
+    python3-dev \
+    python3-pip \
+    python3-tk \
+    libcupti-dev && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
 
 # numpy 1.14.3  scipy 1.1.0 
 RUN pip3 --no-cache-dir install \
     numpy==1.14.3 scipy==1.1.0 
 
 #
-#Install node 10.10.0, yarn 1.9.4, NNI v0.1
+#Install hadoop
+#
+RUN wget -qO- http://archive.apache.org/dist/hadoop/common/hadoop-${HADOOP_VERSION}/hadoop-${HADOOP_VERSION}.tar.gz | \
+    tar xz -C /usr/local && \
+    mv /usr/local/hadoop-${HADOOP_VERSION} /usr/local/hadoop
+
+#
+#Install NNI
 #
-RUN git clone -b v0.1 https://github.com/Microsoft/nni.git
-RUN cd nni && sh install.sh
-RUN echo 'PATH=~/.local/node/bin:~/.local/yarn/bin:~/.local/bin:$PATH' >> ~/.bashrc
-RUN cd .. && rm -rf nni
+RUN pip3 install -v --user git+https://github.com/Microsoft/nni.git@v0.2
+
+ENV JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64 \
+    HADOOP_INSTALL=/usr/local/hadoop \
+    NVIDIA_VISIBLE_DEVICES=all
+
+ENV HADOOP_PREFIX=${HADOOP_INSTALL} \
+    HADOOP_BIN_DIR=${HADOOP_INSTALL}/bin \
+    HADOOP_SBIN_DIR=${HADOOP_INSTALL}/sbin \
+    HADOOP_HDFS_HOME=${HADOOP_INSTALL} \
+    HADOOP_COMMON_LIB_NATIVE_DIR=${HADOOP_INSTALL}/lib/native \
+    HADOOP_OPTS="-Djava.library.path=${HADOOP_INSTALL}/lib/native"
+
+ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/root/.local/bin:/usr/bin:/sbin:/bin:${HADOOP_BIN_DIR}:${HADOOP_SBIN_DIR} \
+    LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/targets/x86_64-linux/lib/stubs:${JAVA_HOME}/jre/lib/amd64/server
+
+WORKDIR /root

docs/GetStarted.md

@@ -14,12 +14,12 @@
 
 * __Install NNI through pip__
 
-      python3 -m pip install -v --user git+https://github.com/Microsoft/nni.git@v0.1
+      python3 -m pip install -v --user git+https://github.com/Microsoft/nni.git@v0.2
       source ~/.bashrc
 
 * __Install NNI through source code__
 
-      git clone -b v0.1 https://github.com/Microsoft/nni.git
+      git clone -b v0.2 https://github.com/Microsoft/nni.git
       cd nni
       chmod +x install.sh
       source install.sh

docs/PAIMode.md

@@ -0,0 +1,79 @@
+**Run an Experiment on OpenPAI**
+===
+NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai) (aka pai), called pai mode. Before starting to use NNI pai mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In pai mode, your trial program will run in pai's container created by Docker. 
+
+## Setup environment
+Install NNI, follow the install guide [here](GetStarted.md).
+
+## Run an experiment
+Use `examples/trials/mnist-annotation` as an example. The nni config yaml file's content is like: 
+```
+authorName: your_name
+experimentName: auto_mnist
+# how many trials could be concurrently running
+trialConcurrency: 2
+# maximum experiment running duration
+maxExecDuration: 3h
+# empty means never stop
+maxTrialNum: 100
+# choice: local, remote, pai
+trainingServicePlatform: pai
+# choice: true, false  
+useAnnotation: true
+tuner:
+  builtinTunerName: TPE
+  classArgs:
+    optimize_mode: maximize
+trial:
+  command: python3 mnist.py
+  codeDir: ~/nni/examples/trials/mnist-annotation
+  gpuNum: 0
+  cpuNum: 1
+  memoryMB: 8196
+  image: openpai/pai.example.tensorflow
+  dataDir: hdfs://10.1.1.1:9000/nni
+  outputDir: hdfs://10.1.1.1:9000/nni
+# Configuration to access OpenPAI Cluster
+paiConfig:
+  userName: your_pai_nni_user
+  passWord: your_pai_password
+  host: 10.1.1.1
+```
+Note: You should set `trainingServicePlatform: pai` in nni config yaml file if you want to start experiment in pai mode. 
+
+Compared with LocalMode and [RemoteMachineMode](RemoteMachineMode.md), trial configuration in pai mode have five additional keys:
+* cpuNum
+    * Required key. Should be positive number based on your trial program's CPU  requirement
+* memoryMB
+    * Required key. Should be positive number based on your trial program's memory requirement
+* image
+    * Required key. In pai mode, your trial program will be scheduled by OpenPAI to run in [Docker container](https://www.docker.com/). This key is used to specify the Docker image used to create the container in which your traill will run. 
+* dataDir
+    * Optional key. It specifies the HDFS data direcotry for trial to download data. The format should be something like hdfs://{your HDFS host}:9000/{your data directory}
+* outputDir 
+    * Optional key. It specifies the HDFS output direcotry for trial. Once the trial is completed (either succeed or fail), trial's stdout, stderr will be copied to this directory by NNI sdk automatically. The format should be something like hdfs://{your HDFS host}:9000/{your output directory}
+
+Once complete to fill nni experiment config file and save (for example, save as exp_pai.yaml), then run the following command
+```
+nnictl create --config exp_pai.yaml
+```
+to start the experiment in pai mode. NNI will create OpanPAI job for each trial, and the job name format is something like `nni_exp_{experiment_id}_trial_{trial_id}`. 
+You can see the pai jobs created by NNI in your OpenPAI cluster's web portal, like:
+![](./nni_pai_joblist.jpg)
+
+Notice: In pai mode, NNIManager will start a rest server and listen on `51189` port, to receive metrics from trial job running in PAI container. So you should `enable 51189` TCP port in your firewall rule to allow incoming traffic. 
+
+Once a trial job is completed, you can goto NNI WebUI's overview page (like http://localhost:8080/oview) to check trial's information. 
+
+Expand a trial information in trial list view, click the logPath link like:
+![](./nni_webui_joblist.jpg)
+
+And you will be redirected to HDFS web portal to browse the output files of that trial in HDFS:
+![](./nni_trial_hdfs_output.jpg)
+
+You can see there're three fils in output folder: stderr, stdout, and trial.log
+
+If you also want to save trial's other output into HDFS, like model files, you can use environment variable `NNI_OUTPUT_DIR` in your trial code to save your own output files, and NNI SDK will copy all the files in `NNI_OUTPUT_DIR` from trial's container to HDFS. 
+
+Any problems when using NNI in pai mode, plesae create issues on [NNI github repo](https://github.com/Microsoft/nni), or send mail to nni@microsoft.com
+

docs/WebUI.md

@@ -0,0 +1,54 @@
+# WebUI
+
+## View summary page
+
+Click the tab "Overview".
+
+* See the experiment parameters.
+* See search_space json.
+* See good performance trial.
+
+![](./img/overview.jpg)
+
+## View job accuracy
+
+Click the tab "Optimization Progress" to see the point graph of all trials. Hover every point to see its specific accuracy.
+
+![](./img/accuracy.jpg)
+
+## View hyper parameter
+
+Click the tab "Hyper Parameter" to see the parallel graph.
+
+* You can select the percentage to see top trials.
+* Choose two axis to swap its positions
+
+![](./img/searchspace.jpg)
+
+## View trial status 
+
+Click the tab "Trial Status" to see the status of the all trials. Specifically:
+
+* Trial duration: trial's duration in the bar graph.
+* Trial detail: trial's id, trial's duration, start time, end time, status, accuracy and search space file.
+
+![](./img/openRow.jpg)
+
+* Kill: you can kill a job that status is running.
+* Tensor: you can see a job in the tensorflow graph, it will link to the Tensorboard page.
+
+![](./img/trialStatus.jpg)
+
+* Intermediate Result Graph.
+
+![](./img/intermediate.jpg)
+
+## Control 
+
+Click the tab "Control" to add a new trial or update the search_space file and some experiment parameters.
+
+![](./img/control.jpg)
+
+## Feedback
+
+[Known Issues](https://github.com/Microsoft/nni/issues).

examples/trials/auto-gbdt/config.yml

@@ -9,12 +9,13 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution,
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize
     optimize_mode: minimize
 trial:
   command: python3 main.py
   codeDir: .
-  gpuNum: 0
+  gpuNum: 0

examples/trials/mnist-annotation/config.yml

@@ -8,12 +8,13 @@ trainingServicePlatform: local
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution,
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize
     optimize_mode: maximize
 trial:
   command: python3 mnist.py
   codeDir: .
-  gpuNum: 0
+  gpuNum: 0

examples/trials/mnist-batch-tune-keras/config.yml

@@ -10,6 +10,7 @@ searchSpacePath: search_space.json
 useAnnotation: false
 tuner:
   #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: BatchTuner
   classArgs:
     #choice: maximize, minimize

examples/trials/mnist-keras/config.yml

@@ -9,12 +9,13 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution,
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize
     optimize_mode: maximize
 trial:
   command: python3 mnist-keras.py
   codeDir: .
-  gpuNum: 0
+  gpuNum: 0

examples/trials/mnist-smartparam/config.yml

@@ -8,12 +8,13 @@ trainingServicePlatform: local
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution,
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize
     optimize_mode: maximize
 trial:
   command: python3 mnist.py
   codeDir: .
-  gpuNum: 0
+  gpuNum: 0

examples/trials/mnist/config.yml

@@ -9,12 +9,13 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution,
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize
     optimize_mode: maximize
 trial:
   command: python3 mnist.py
   codeDir: .
-  gpuNum: 0
+  gpuNum: 0

examples/trials/mnist/config_assessor.yml

@@ -9,7 +9,8 @@ searchSpacePath: ~/nni/examples/trials/mnist/search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution,
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize
@@ -23,4 +24,4 @@ assessor:
 trial:
   command: python3 mnist.py
   codeDir: ~/nni/examples/trials/mnist
-  gpuNum: 0
+  gpuNum: 0

examples/trials/pytorch_cifar10/config.yml

@@ -9,7 +9,8 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution,
+  #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize

install.sh

@@ -1,5 +1,7 @@
 #!/bin/bash
-make build
 make install-dependencies
+make build
 make dev-install
+make install-examples
+make update-bash-config
 source ~/.bashrc

src/nni_manager/training_service/pai/hdfsClientUtility.ts

@@ -131,7 +131,10 @@ export namespace HDFSClientUtility {
         const deferred : Deferred<boolean> = new Deferred<boolean>();
         hdfsClient.exists(hdfsPath, (exist : boolean ) => {
              deferred.resolve(exist);
-        })
+        });
+
+        // Set timeout and reject the promise once reach timeout (5 seconds)
+        setTimeout(() => deferred.reject(`Check HDFS path ${hdfsPath} exists timeout`), 5000);
 
         return deferred.promise;
     }