Tutorial
========
This is rabit's tutorial, a ***Reliable Allreduce and Broadcast Interface***.
All the example codes are in the [guide](https://github.com/dmlc/rabit/blob/master/guide/) folder of the project.
To run the examples locally, you will need to build them with ```make```.

**List of Topics**
* [What is Allreduce](#what-is-allreduce)
* [Common Use Case](#common-use-case)
* [Use Rabit API](#use-rabit-api)
  - [Structure of a Rabit Program](#structure-of-a-rabit-program)
  - [Allreduce and Lazy Preparation](#allreduce-and-lazy-preparation)
  - [Checkpoint and LazyCheckpoint](#checkpoint-and-lazycheckpoint)
* [Compile Programs with Rabit](#compile-programs-with-rabit)
* [Running Rabit Jobs](#running-rabit-jobs)
  - [Running Rabit on Hadoop](#running-rabit-on-hadoop)
  - [Running Rabit using MPI](#running-rabit-using-mpi)
  - [Customize Tracker Script](#customize-tracker-script)
* [Fault Tolerance](#fault-tolerance)

What is Allreduce
-----------------
The main methods provided by rabit are Allreduce and Broadcast. Allreduce performs reduction across different computation nodes,
and returns the result to every node. To understand the behavior of the function, consider the following example in [basic.cc](../guide/basic.cc) (there is a python example right after this if you are more familiar with python).
```c++
#include <rabit.h>
using namespace rabit;
const int N = 3;
int main(int argc, char *argv[]) {
  int a[N];
  rabit::Init(argc, argv);
  for (int i = 0; i < N; ++i) {
    a[i] = rabit::GetRank() + i;
  }
  printf("@node[%d] before-allreduce: a={%d, %d, %d}\n",
         rabit::GetRank(), a[0], a[1], a[2]);
  // allreduce take max of each elements in all processes
  Allreduce<op::Max>(&a[0], N);
  printf("@node[%d] after-allreduce-max: a={%d, %d, %d}\n",
         rabit::GetRank(), a[0], a[1], a[2]);
  // second allreduce that sums everything up
  Allreduce<op::Sum>(&a[0], N);
  printf("@node[%d] after-allreduce-sum: a={%d, %d, %d}\n",
         rabit::GetRank(), a[0], a[1], a[2]);
  rabit::Finalize();
  return 0;
}
```
You can run the example using the rabit_demo.py script. The following command
starts the rabit program with two worker processes.
```bash
../tracker/rabit_demo.py -n 2 basic.rabit
```
This will start two processes, one process with rank 0 and the other with rank 1, both processes run the same code.
The ```rabit::GetRank()``` function returns the rank of current process.

Before the call to Allreduce, process 0 contains the array ```a = {0, 1, 2}```, while process 1 has the array
```a = {1, 2, 3}```. After the call to Allreduce, the array contents in all processes are replaced by the
reduction result (in this case, the maximum value in each position across all the processes). So, after the
Allreduce call, the result will become ```a = {1, 2, 3}```.
Rabit provides different reduction operators, for example,  if you change ```op::Max``` to ```op::Sum```,
the reduction operation will be a summation, and the result will become ```a = {1, 3, 5}```.
You can also run the example with different processes by setting -n to different values.

If you are more familiar with python, you can also use rabit in python. The same example as before can be found in [basic.py](../guide/basic.py):

```python
import numpy as np
import rabit

rabit.init()
n = 3
rank = rabit.get_rank()
a = np.zeros(n)
for i in xrange(n):
    a[i] = rank + i

print '@node[%d] before-allreduce: a=%s' % (rank, str(a))
a = rabit.allreduce(a, rabit.MAX)
print '@node[%d] after-allreduce-max: a=%s' % (rank, str(a))
a = rabit.allreduce(a, rabit.SUM)
print '@node[%d] after-allreduce-sum: a=%s' % (rank, str(a))
rabit.finalize()
```
You can run the program using the following command
```bash
../tracker/rabit_demo.py -n 2 basic.py
```

Broadcast is another method provided by rabit besides Allreduce. This function allows one node to broadcast its
local data to all other nodes. The following code in [broadcast.cc](../guide/broadcast.cc) broadcasts a string from
node 0 to all other nodes.
```c++
#include <rabit.h>
using namespace rabit;
const int N = 3;
int main(int argc, char *argv[]) {
  rabit::Init(argc, argv);
  std::string s;
  if (rabit::GetRank() == 0) s = "hello world";
  printf("@node[%d] before-broadcast: s=\"%s\"\n",
         rabit::GetRank(), s.c_str());
  // broadcast s from node 0 to all other nodes
  rabit::Broadcast(&s, 0);
  printf("@node[%d] after-broadcast: s=\"%s\"\n",
         rabit::GetRank(), s.c_str());
  rabit::Finalize();
  return 0;
}
```
The following command starts the program with three worker processes.
```bash
../tracker/rabit_demo.py -n 3 broadcast.rabit
```
Besides strings, rabit also allows to broadcast constant size array and vectors.

The counterpart in python can be found in [broadcast.py](../guide/broadcast.py). Here is a snippet so that you can get a better sense of how simple is to use the python library:

```python
import rabit
rabit.init()
n = 3
rank = rabit.get_rank()
s = None
if rank == 0:
    s = {'hello world':100, 2:3}
print '@node[%d] before-broadcast: s=\"%s\"' % (rank, str(s))
s = rabit.broadcast(s, 0)
print '@node[%d] after-broadcast: s=\"%s\"' % (rank, str(s))
rabit.finalize()
```

Common Use Case
---------------
Many distributed machine learning algorithms involve splitting the data into different nodes,
computing statistics locally, and finally aggregating them. Such workflow is usually done repetitively through many iterations before the algorithm converges. Allreduce naturally meets the structure of such programs,
common use cases include:

* Aggregation of gradient values, which can be used in optimization methods such as L-BFGS.
* Aggregation of other statistics, which can be used in KMeans and Gaussian Mixture Models.
* Find the best split candidate and aggregation of split statistics, used for tree based models.

Rabit is a reliable and portable library for distributed machine learning programs, that allow programs to run reliably on different platforms.

Use Rabit API
-------------
This section introduces topics about how to use rabit API.
You can always refer to [API Documentation](http://homes.cs.washington.edu/~tqchen/rabit/doc) for definition of each functions.
This section trys to gives examples of different aspectes of rabit API.

#### Structure of a Rabit Program
The following code illustrates the common structure of a rabit program. This is an abstract example,
you can also refer to [wormhole](https://github.com/dmlc/wormhole/blob/master/learn/kmeans/kmeans.cc) for an example implementation of kmeans algorithm.

```c++
#include <rabit.h>
int main(int argc, char *argv[]) {
  ...
  rabit::Init(argc, argv);
  // load the latest checked model
  int version = rabit::LoadCheckPoint(&model);
  // initialize the model if it is the first version
  if (version == 0) model.InitModel();
  // the version number marks the iteration to resume
  for (int iter = version; iter < max_iter; ++iter) {
    // at this point, the model object should allow us to recover the program state
    ...
    // each iteration can contain multiple calls of allreduce/broadcast
    rabit::Allreduce<rabit::op::Max>(&data[0], n);
    ...
    // checkpoint model after one iteration finishes
    rabit::CheckPoint(&model);
  }
  rabit::Finalize();
  return 0;
}
```

Besides the common Allreduce and Broadcast functions, there are two additional functions: ```LoadCheckPoint```
and ```CheckPoint```. These two functions are used for fault-tolerance purposes.
As mentioned before, traditional machine learning programs involve several iterations. In each iteration, we start with a model, make some calls
to Allreduce or Broadcast and update the model. The calling sequence in each iteration does not need to be the same.

* When the nodes start from the beginning (i.e. iteration 0), ```LoadCheckPoint``` returns 0, so we can initialize the model.
* ```CheckPoint``` saves the model after each iteration.
  - Efficiency Note: the model is only kept in local memory and no save to disk is performed when calling Checkpoint
* When a node goes down and restarts, ```LoadCheckPoint``` will recover the latest saved model, and
* When a node goes down, the rest of the nodes will block in the call of Allreduce/Broadcast and wait for
  the recovery of the failed node until it catches up.

Please see the [Fault Tolerance](#fault-tolerance) section to understand the recovery procedure executed by rabit.

#### Allreduce and Lazy Preparation
Allreduce is one of the most important function provided by rabit. You can call allreduce by specifying the
reduction operator, pointer to the data and size of the buffer, as follows
```c++
Allreduce<operator>(pointer_of_data, size_of_data);
```
This is the basic use case of Allreduce function. It is common that user writes the code to prepare the data needed
into the data buffer, pass the data to Allreduce function, and get the reduced result. However, when a node restarts
from failure, we can directly recover the result from other nodes(see also [Fault Tolerance](#fault-tolerance)) and
the data preparation procedure no longer necessary. Rabit Allreduce add an optional parameter preparation function
to support such scenario. User can pass in a function that corresponds to the data preparation procedure to Allreduce
calls, and the data preparation function will only be called when necessary. We use [lazy_allreduce.cc](../guide/lazy_allreduce.cc)
as an example to demonstrate this feature. It is modified from [basic.cc](../guide/basic.cc), and you can compare the two codes.
```c++
#include <rabit.h>
using namespace rabit;
const int N = 3;
int main(int argc, char *argv[]) {
  int a[N] = {0};
  rabit::Init(argc, argv);
  // lazy preparation function
  auto prepare = [&]() {
    printf("@node[%d] run prepare function\n", rabit::GetRank());
    for (int i = 0; i < N; ++i) {
      a[i] = rabit::GetRank() + i;
    }
  };
  printf("@node[%d] before-allreduce: a={%d, %d, %d}\n",
         rabit::GetRank(), a[0], a[1], a[2]);
  // allreduce take max of each elements in all processes
  Allreduce<op::Max>(&a[0], N, prepare);
  printf("@node[%d] after-allreduce-sum: a={%d, %d, %d}\n",
         rabit::GetRank(), a[0], a[1], a[2]);
  // rum second allreduce
  Allreduce<op::Sum>(&a[0], N);
  printf("@node[%d] after-allreduce-max: a={%d, %d, %d}\n",
         rabit::GetRank(), a[0], a[1], a[2]);
  rabit::Finalize();
  return 0;
}
```
Here we use features of C++11 because the lambda function makes things much shorter.
There is also C++ compatible callback interface provided in the [API](http://homes.cs.washington.edu/~tqchen/rabit/doc).
You can compile the program by typing ```make lazy_allreduce.mock```. We link against the mock library so that we can see
the effect when a process goes down. You can run the program using the following command
```bash
../tracker/rabit_demo.py -n 2 lazy_allreduce.mock mock=0,0,1,0
```
The additional arguments ```mock=0,0,1,0``` will cause node 0 to kill itself before second call of Allreduce (see also [mock test](#link-against-mock-test-rabit-library)).
You will find that the prepare function's print is only executed once and node 0 will no longer execute the preparation function when it restarts from failure.

You can also find python version of the example in [lazy_allreduce.py](../guide/lazy_allreduce.py), and run it using the followin command
```bash
../tracker/rabit_demo.py -n 2 lazy_allreduce.py mock=0,0,1,0

```

Since lazy preparation function may not be called during execution. User should be careful when using this feature. For example, a possible mistake
could be putting some memory allocation code in the lazy preparation function, and the computing memory was not allocated when lazy preparation function is not called.
The example in [lazy_allreduce.cc](../guide/lazy_allreduce.cc) provides a simple way to migrate normal prepration code([basic.cc](../guide/basic.cc)) to lazy version: wrap the preparation
code with a lambda function, and pass it to allreduce.

#### Checkpoint and LazyCheckpoint
Common machine learning algorithms usually involves iterative computation. As mentioned in the section ([Structure of a Rabit Program](#structure-of-a-rabit-program)),
user can and should use Checkpoint to ```save``` the progress so far, so that when a node fails, the latest checkpointed model can be loaded.

There are two model arguments you can pass to Checkpoint and LoadCheckpoint: ```global_model``` and ```local_model```:
* ```global_model``` refers to the model that is commonly shared across all the nodes
  - For example, the centriods of clusters in kmeans is shared across all nodes
* ```local_model``` refers to the model that is specifically tied to the current node
  - For example, in topic modeling, the topic assignments of subset of documents in current node is local model

Because the different nature of the two types of models, different strategy will be used for them.
```global_model``` is simply saved in local memory of each node, while ```local_model``` will replicated to some other
nodes (selected using a ring replication strategy). The checkpoint is only saved in the memory without touching the disk which makes rabit programs more efficient.
User is encouraged to use ```global_model``` only when is sufficient for better efficiency.

To enable a model class to be checked pointed, user can implement a [serialization interface](../include/rabit_serialization.h). The serialization interface already
provide serialization functions of STL vector and string. For python API, user can checkpoint any python object that can be pickled.

There is a special Checkpoint function called [LazyCheckpoint](http://homes.cs.washington.edu/~tqchen/rabit/doc/namespacerabit.html#a99f74c357afa5fba2c80cc0363e4e459),
which can be used for ```global_model``` only cases under certain condition.
When LazyCheckpoint is called, no action is taken and the rabit engine only remembers the pointer to the model.
The serialization will only happen when another node fails and the recovery starts. So user basically pays no extra cost calling LazyCheckpoint.
To use this function, the user need to ensure the model remain unchanged until the last call of Allreduce/Broadcast in the current version finishes.
So that when recovery procedure happens in these function calls, the serialized model will be the same.

For example, consider the following calling sequence
```
LazyCheckPoint, code1, Allreduce, code2, Broadcast, code3, LazyCheckPoint
```
The user must only change the model in code3. Such condition can usually be satiesfied in many scenarios, and user can use LazyCheckpoint to further
improve the efficiency of the program.


Compile Programs with Rabit
---------------------------
Rabit is a portable library, to use it, you only need to include the rabit header file.
* You will need to add the path to [../include](../include) to the header search path of the compiler
  - Solution 1: add ```-I/path/to/rabit/include``` to the compiler flag in gcc or clang
  - Solution 2: add the path to the environment variable CPLUS_INCLUDE_PATH
* You will need to add the path to [../lib](../lib) to the library search path of the compiler
  - Solution 1: add ```-L/path/to/rabit/lib``` to the linker flag
  - Solution 2: add the path to environment variable LIBRARY_PATH AND LD_LIBRARY_PATH
* Link against lib/rabit.a
  - Add ```-lrabit``` to the linker flag

The procedure above allows you to compile a program with rabit. The following two sections contain additional
options you can use to link against different backends other than the normal one.

#### Link against MPI Allreduce
You can link against ```rabit_mpi.a``` instead of using MPI Allreduce, however, the resulting program is backed by MPI and
is not fault tolerant anymore.
* Simply change the linker flag from ```-lrabit``` to ```-lrabit_mpi```
* The final linking needs to be done by mpi wrapper compiler ```mpicxx```

#### Link against Mock Test Rabit Library
If you want to use a mock to test the program in order to see the behavior of the code when some nodes go down, you can link against ```rabit_mock.a``` .
* Simply change the linker flag from ```-lrabit``` to ```-lrabit_mock```

The resulting rabit mock program can take in additional arguments in the following format
```
mock=rank,version,seq,ndeath
```

The four integers specify an event that will cause the program to ```commit suicide```(exit with -2)
* rank specifies the rank of the node to kill
* version specifies the version (iteration) of the model where you want the process to die
* seq specifies the sequence number of the Allreduce/Broadcast call since last checkpoint, where the process will be killed
* ndeath specifies how many times this node died already

For example, consider the following script in the test case
```bash
../tracker/rabit_demo.py -n 10 test_model_recover 10000\
                         mock=0,0,1,0 mock=1,1,1,0 mock=1,1,1,1
```
* The first mock will cause node 0 to exit when calling the second Allreduce/Broadcast (seq = 1) in iteration 0
* The second mock will cause node 1 to exit when calling the second Allreduce/Broadcast (seq = 1) in iteration 1
* The third mock will cause node 1 to exit again when calling second Allreduce/Broadcast (seq = 1) in iteration 1
  - Note that ndeath = 1 means this will happen only if node 1 died once, which is our case

Running Rabit Jobs
------------------
Rabit is a portable library that can run on multiple platforms.

#### Running Rabit Locally
* You can use [../tracker/rabit_demo.py](https://github.com/dmlc/rabit/blob/master/tracker/rabit_demo.py) to start n processes locally
* This script will restart the program when it exits with -2, so it can be used for [mock test](#link-against-mock-test-library)

#### Running Rabit on Hadoop
* You can use [../tracker/rabit_yarn.py](https://github.com/dmlc/rabit/blob/master/tracker/rabit_yarn.py) to run rabit programs as Yarn application
* This will start rabit programs as yarn applications
  - This allows multi-threading programs in each node, which can be more efficient
  - An easy multi-threading solution could be to use OpenMP with rabit code
* It is also possible to run rabit program via hadoop streaming, however, YARN is highly recommended.

#### Running Rabit using MPI
* You can submit rabit programs to an MPI cluster using [../tracker/rabit_mpi.py](https://github.com/dmlc/rabit/blob/master/tracker/rabit_mpi.py).
* If you linked your code against librabit_mpi.a, then you can directly use mpirun to submit the job

#### Customize Tracker Script
You can also modify the tracker script to allow rabit to run on other platforms. To do so, refer to existing
tracker scripts, such as [../tracker/rabit_yarn.py](../tracker/rabit_yarn.py) and [../tracker/rabit_mpi.py](https://github.com/dmlc/rabit/blob/master/tracker/rabit_mpi.py) to get a sense of how it is done.

You will need to implement a platform dependent submission function with the following definition
```python
def fun_submit(nworkers, worker_args, worker_envs):
    """
      customized submit script, that submits nslave jobs,
      each must contain args as parameter
      note this can be a lambda closure
      Parameters
         nworkers number of worker processes to start
         worker_args addtiional arguments that needs to be passed to worker
         worker_envs enviroment variables that need to be set to the worker
    """
```
The submission function should start nworkers processes in the platform, and append worker_args to the end of the other arguments.
Then you can simply call ```tracker.submit``` with fun_submit to submit jobs to the target platform

Note that the current rabit tracker does not restart a worker when it dies, the restart of a node is done by the platform, otherwise we should write the fail-restart logic in the custom script.
* Fail-restart is usually provided by most platforms.
  - rabit-yarn provides such functionality in YARN

Fault Tolerance
---------------
This section introduces how fault tolerance works in rabit.
The following figure shows how rabit deals with failures.

![](http://homes.cs.washington.edu/~tqchen/rabit/fig/fault-tol.png)

The scenario is as follows:
* Node 1 fails between the first and second call of Allreduce after the second checkpoint
* The other nodes wait in the call of the second Allreduce in order to help node 1 to recover.
* When node 1 restarts, it will call ```LoadCheckPoint```, and get the latest checkpoint from one of the existing nodes.
* Then node 1 can start from the latest checkpoint and continue running.
* When node 1 calls the first Allreduce again, as the other nodes already know the result, node 1 can get it from one of them.
* When node 1 reaches the second Allreduce, the other nodes find out that node 1 has catched up and they can continue the program normally.

This fault tolerance model is based on a key property of Allreduce and
Broadcast: All the nodes get the same result after calling Allreduce/Broadcast.
Because of this property, any node can record the results of history
Allreduce/Broadcast calls.  When a node is recovered, it can fetch the lost
results from some alive nodes and rebuild its model.

The checkpoint is introduced so that we can discard the history results of
Allreduce/Broadcast calls before the latest checkpoint. This saves memory
consumption used for backup.  The checkpoint of each node is a model defined by
users and can be split into 2 parts: a global model and a local model. The
global model is shared by all nodes and can be backed up by any nodes. The
local model of a node is replicated to some other nodes (selected using a ring
replication strategy).  The checkpoint is only saved in the memory without
touching the disk which makes rabit programs more efficient.  The strategy of
rabit is different from the fail-restart strategy where all the nodes restart
from the same checkpoint when any of them fail.  In rabit, all the alive nodes
will block in the Allreduce call and help the recovery.  To catch up, the
recovered node fetches its latest checkpoint and the results of
Allreduce/Broadcast calls after the checkpoint from some alive nodes.

This is just a conceptual introduction to rabit's fault tolerance model. The actual implementation is more sophisticated,
and can deal with more complicated cases such as multiple nodes failure and node failure during recovery phase.
