Alien-XGBoost

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xgboost/dmlc-core/src/io/s3_filesys.h  view on Meta::CPAN

namespace dmlc {
namespace io {
/*! \brief AWS S3 filesystem */
class S3FileSystem : public FileSystem {
 public:
  /*! \brief destructor */
  virtual ~S3FileSystem() {}

  /*!
   * \brief Sets AWS access credentials
   * \param aws_access_id The AWS Access Key ID
   * \param aws_secret_key The AWS Secret Key
   * \return the information about the file
   */
  void SetCredentials(const std::string& aws_access_id,
                      const std::string& aws_secret_key);

  /*!
   * \brief get information about a path
   * \param path the path to the file

xgboost/dmlc-core/tracker/yarn/src/main/java/org/apache/hadoop/yarn/dmlc/ApplicationMaster.java  view on Meta::CPAN

    private int numServer = 0;
    // total number of tasks
    private int numTasks;
    // maximum number of attempts to try in each task
    private int maxNumAttempt = 3;
    // command to launch
    private String command = "";

    // username
    private String userName = "";
    // user credentials
    private Credentials credentials = null;
    // application tracker hostname
    private String appHostName = "";
    // tracker URL to do
    private String appTrackerUrl = "";
    // tracker port
    private int appTrackerPort = 0;

    // whether we start to abort the application, due to whatever fatal reasons
    private boolean startAbort = false;
    // worker resources

xgboost/doc/tutorials/aws_yarn.md  view on Meta::CPAN

===============================
This is a step-by-step tutorial on how to setup and run distributed [XGBoost](https://github.com/dmlc/xgboost)
on an AWS EC2 cluster. Distributed XGBoost runs on various platforms such as MPI, SGE and Hadoop YARN.
In this tutorial, we use YARN as an example since this is a widely used solution for distributed computing.

Prerequisite
------------
We need to get a [AWS key-pair](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html)
to access the AWS services. Let us assume that we are using a key ```mykey``` and  the corresponding permission file ```mypem.pem```.

We also need [AWS credentials](http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html),
which includes an `ACCESS_KEY_ID` and a `SECRET_ACCESS_KEY`.

Finally, we will need a S3 bucket to host the data and the model, ```s3://mybucket/```

Setup a Hadoop YARN Cluster
---------------------------
This sections shows how to start a Hadoop YARN cluster from scratch.
You can skip this step if you have already have one.
We will be using [yarn-ec2](https://github.com/tqchen/yarn-ec2) to start the cluster.

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