Skip to content

Latest commit

 

History

History
executable file
·
289 lines (206 loc) · 11.2 KB

README.md

File metadata and controls

executable file
·
289 lines (206 loc) · 11.2 KB
Raw

LaS-VPE Platform

AUR

By Ken Yu, Yang Zhou, Da Li, Dangwei Li and Houjing Huang, under guidance of Dr. Zhang Zhang and Prof. Kaiqi Huang.

LaS-VPE Platform is a large-scale distributed video parsing and evaluation platform under the Intelligent Scene Exploration and Evaluation(iSEE) research platform of the Center for Research on Intelligent Perception and Computing(CRIPAC), Institute of Automation, Chinese Academy of Science.

The platform is powered by Spark Streaming and Kafka.

The documentation is published on Github Pages.

License

LaS-VPE Platform is released under the GPL License.

Contents

  1. Requirements
  2. How to run
  3. How to monitor
  4. Basic concepts in this project
  5. How to add a new module
  6. How to add a new native algorithm
  7. [How to deploy a new version](#How to deploy a new version of an application)

Requirements

  1. Use Maven to build the project.

    For Ubuntu:

    sudo apt-get install maven

    For CentOS:

    sudo yum install maven

  2. Increase open files limit in Linux.

    Paste following towards end of /etc/security/limits.conf:

       *       hard    nofile  500000
   *       soft    nofile  500000
   root    hard    nofile  500000
   root    soft    nofile  500000

  3. Deploy Kafka(>=0.10.2.0), Spark(>=2.0.2), HDFS(>=2.7.2) and YARN(>=2.7.2) properly on your cluster.

    • To enable multi-appications running concurrently, see Job-Scheduling and configure your environment.

    • Disable virtual memory checking of YARN in "yarn-site.xml" to enable GPU applications:

      <property>
  <name>yarn.nodemanager.vmem-check-enabled</name>
  <value>false</value>
</property>

  4. If you choose to run algorithms based on Caffe or Caffe2, no matter the algorithms use CPU-only or GPUs, you must have the latest CUDA(>=8.0) and CUDNN(>=v6.0) installed on all nodes in your cluster.

    Note that the libraries of CUDA and CUDNN should be contained in the directories that JVM finds for java.library.path. By default, directories like "/usr/local/lib(64)" or "/usr/local/cuda/lib(64)" are not found by JVM. You must have them installed to "/usr/lib64" or "${HADOOP_HOME}/lib/native". Header files are not needed on nodes where you do not perform building.

How to run

  1. Clone the project to your cluster:

    # Make sure to clone with --recursive
git clone --recursive https://github.com/kyu-sz/LaS-VPE-Platform

  2. Retrieve additional model files such as DeepMAR.caffemodel from algorithm providers. Put them in appropriate locations. For example, the DeepMAR.caffemodel should be put in ${PROJECT_DIR}/models/DeepMAR/. See models.

  3. Configure the environment and running properties in the files in conf. Specially, modify the cluster-env.sh in conf to adapt to your cluster address.

  4. Build and pack the system into a JAR:

    mvn package

    • Tests are enabled by default. Use mvn package -Dmaven.test.skip.exec to skip them when your machine is not able to run the tests or you are sure the tests will pass and need to save time.

    • If your maven resolves dependencies at a low speed, try mvn -Dmaven.artifact.threads=100 package or add export MAVEN_OPTS=-Dmaven.artifact.threads=100 to your ~/.bashrc.

    • You may also try a Maven mirror. For users in China, the Aliyun mirror is recommended.

  5. Install all the dependencies required by the native libraries on all nodes in your cluster.

    Especially, we use OpenBLAS for Caffe2 by default for best efficiency. Both Eigen3 and OpenBLAS should be installed by package management tools like yum (CentOS) or apt (Ubuntu) in addition to the packages listed on Caffe2's installation guide:

    For CentOS:

    sudo yum install openblas-devel eigen3-devel
sudo ln -s /usr/lib64/libopenblas.so /usr/lib64/libcblas.so

    For Ubuntu:

    sudo apt install openblas-dev eigen3-dev
sudo ln -s /usr/lib/libopenblas.so /usr/lib/libcblas.so

    Note that on CentOS, the libgflags-devel is too old for the latest GLog. It should be compiled from the latest version on GitHub. However, these libraries might cause compilation failure of Caffe in old versions. Therefore, you need not install them on every node, since these nodes might be shared by other users who might use old Caffe. The Caffe2 installing script can automatically copy the libraries to the project library folder, so that they can be distributed to the Hadoop native directories.

    Use the following commands to install gflags and glog (using a sudoer account):

    git clone https://github.com/gflags/gflags.git && \
cd gflags && \
mkdir build && cd build && \
cmake3 -DBUILD_SHARED_LIBS=ON -DCMAKE_CXX_FLAGS='-fPIC' .. && \
make -j 8 && sudo make install && cd ../.. && \
git clone https://github.com/google/glog && \
cd glog && \
mkdir build && cd build && \
cmake3 -DBUILD_SHARED_LIBS=ON -DCMAKE_CXX_FLAGS='-fPIC' .. && \
make -j 8 && sudo make install && cd ../..

  6. Build and deliver the native libraries to worker nodes using install.sh in sbin.

    ./sbin/install.sh

    Note that this script requires the HADOOP_HOME environment variable.

    • Trouble shooting:

      • libcblas.so is not found (-lcblas fails):

        Create a link from the OpenBLAS library:

        sudo ln -s /usr/lib64/libopenblas.so /usr/lib64/libcblas.so

      • Built once and failed, then reconfigure the environment and dependencies but still fails:

        Call the cleaning script, then build again.

      • Eigen/Core not found:

        Ensure you have installed Eigen3 on your computer and its headers are in "/usr/include" or "/usr/local/include".

        For CentOS, use yum install eigen3-devel.

  7. Finally, you can start the applications by invoking the scripts in the home directory by command like "./sbin/run-*.sh".

    • It is recommended to last start the run-command-generating-app.sh, which is the debugging tool to simulate commands to the message handling application.

Welcome to Ken Yu's Chinese blog on experiences gained during the development.

How to monitor

To briefly monitor, some informations are printed to the console that starts each module. However, to use this function, the console terminal must be able to access the Kafka cluster.

To fully monitor your Spark application, you might need to access the log files in the slave nodes. However, if your application runs on a cluster without desktop, and you connect remotely to the cluster, you might not be able to access the web pages loaded from the slave nodes.

To solve this problem, first add the address address of the slave nodes to the /etc/hosts in the master node. Make sure the master node can access the pages on slave nodes by terminal browsers like w3m or lynx. In Ubuntu, they can be installed by sudo apt-get install w3m or sudo apt-get install lynx.

Then, configure your master node to be a proxy server using Squid. Tutorials can be found in websites like Help-Ubuntu-Squid.

Finally, configure your browser to use the proxy provided by you master node. Then it would be able to access pages on slave nodes.

For Firefox, it is recommended to use the AutoProxy plugin for enabling proxy. Beside obvious configurations, you need to first access about:config, then set network.proxy.socks_remote_dns as true.

Basic concepts in this project

  • Application: Same as that in YARN.

  • Stream: A flow of DStreams. Each stream may take in more than one kind of data, but outputs at most one kind of data. An Application may contains multiple streams.

  • Node: An execution of a Stream. A pack of input data and parameters are input into the stream.

  • ExecutionPlan: A flow graph of Node.

How to add a new module

Before starting your work on developing this platform, choosing a suitable IDE will definitely improve your efficiency! Here we strongly recommend the Intellij IDEA, since our project contains a mixture of Java codes and Scala codes, while is organized with Maven. Intellij IDEA is a much better choice than Eclipse.

  1. See an application such as PedestrianTrackingApp for example of how to write an application module.

  2. Write your own module then add it to this project.

  3. Register its class name to the AppManager by adding a line in the static block, similar to the existing lines.

  4. Extend the CommandGeneratingApp, MessageHandlingApp and DataManagingApp for support of the module.

How to add a new native algorithm

You may want to run algorithms written in other languages like C/C++ on this platform. Here is an example: ISEE-Basic-Pedestrian-Tracker.

  1. Wrap your algorithm with JNI. It is recommended to implement this in another GitHub repository, and import it as a submodule.

  2. Add the corresponding Java class to the platform. Be careful to put it in a suitable package.

  3. Build your algorithm project, and copy the resulting shared JNI library and those it depends on into the library folder directory.

  4. If the new algorithm requires extra configuration files, register them to the CongigFileManager.

How to deploy a new version of an application

  1. Kill the particular old application.

  2. If checkpointing is enabled, clean the old checkpoint directory for that application or use a new one.

  3. Build the new version of project using mvn package -Dmaven.test.skip.exec.

  4. Run the new application with a script. You do not need to stop other applications~