Deploying models

After all the hard work you did to train your tree ensemble model, you now have to deploy the model. Deployment refers to distributing your model to other machines and devices so as to make predictions on them. To facilitate the coming discussions, let us define a few terms.

  • Host machine : the machine running treelite.

  • Target machine : the machine on which predictions will be made. The host machine may or may not be identical to the target machine. In cases where it’s infeasible to install treelite on the target machine, the host and target machines will be necessarily distinct.

  • Shared library : a blob of executable subroutines that can be imported by other native applications. Shared libraries will often have file extensions .dll, .so, or .dylib. Going back to the particular context of tree deployment, treelite will produce a shared library containing the prediction subroutine (compiled to native machine code).

  • Runtime package : a tiny fraction of the full treelite package, consisting of a few helper functions that lets you easily load shared libraries and make predictions. The runtime is good to have, but on systems lacking Python we can do without it.

In this document, we will document three options for deployment. We will present the programming interface each deployment option presents, as well as its dependencies and requirements.

Option 1: Install treelite on the target machine

If feasible, this option is probably the most convenient. On the target machine, install treelite by running pip:

pip3 install treelite --user

Once treelite is installed, it suffices to follow instructions in First tutorial.

Dependencies and Requirements

The target machine shall meet the following conditions:

  • Treelite is installed (either by pip install or by manual compilation, see below)

  • One of the following C compiler is available: gcc, clang, Microsoft Visual C++.

  • The C compiler supports the following features of the C99 standard: inline functions; declaration of loop variables inside for loop; the expf function in <math.h>; the <stdint.h> header. Recent versions of gcc and clang should work, as well as Microsoft Visual Studio 2013 or newer.

  • Python is installed, with version 2.7 or >= 3.4.

  • The following Python packages are available: numpy, scipy.sparse.

In addition, if you are using operating systems other than Windows, Mac OS X, and Linux, you would need to compile treelite from the source. To do this, you’ll need git, CMake (>= 3.1), and a C++ compiler that complies with the C++11 standard.

Option 2: Deploy prediction code with the runtime package

With this option, you no longer have to install the full treelite package on the target machine. Only the runtime package and the prediction library will need to be copied to the target machine.

Dependencies and Requirements

The target machine shall meet the following conditions:

  • One of the following C++ compiler is available: gcc, clang, Microsoft Visual C++.

  • The C++ compiler complies with the C++11 standard. Recent versions of gcc and clang qualifies, as well as Microsoft Visual C++ 2015 or newer.

  • When compiling pure C program, the C++ compiler supports the following features of the C99 standard: inline functions; declaration of loop variables inside for loop; the expf function in <math.h>; the <stdint.h> header.

  • Python is installed, with version 2.7 or >= 3.4.

  • The following Python packages are available: numpy, scipy.sparse.

  • CMake 3.1 or newer is installed.

  • An archive utility exists that can open a .zip archive.

Deployment instructions

1. On the host machine, install treelite and import your tree ensemble model. You should end up with the model object of type Model.

### Run this block on the **host** machine

import treelite
model = treelite.Model.load('your_model.model', 'xgboost')
# You may also use `from_xgboost` method or the builder class

2. Export your model as a source package by calling the method export_srcpkg() of the Model object. The source package will contain C code representation of the prediction subroutine.

### Continued from the previous code block

# Operating system of the target machine
platform = 'unix'
# C compiler to use to compile prediction code on the target machine
toolchain = 'gcc'
# Save the source package as a zip archive named mymodel.zip
# Later, we'll use this package to produce the library mymodel.so.
model.export_srcpkg(platform=platform, toolchain=toolchain,
                    pkgpath='./mymodel.zip', libname='mymodel.so',
                    verbose=True)

After calling export_srcpkg(), you should be able to find the zip archive named mymodel.zip inside the current working directory.

john.doe@host-machine:/home/john.doe/$ ls .
mymodel.zip   your_model.model

The content of mymodel.zip consists of the header and source files, as well as the Makefile:

john.doe@host-machine:/home/john.doe/$ unzip -l mymodel.zip
Archive:  mymodel.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
        0  11-01-2017 23:11   mymodel/
      167  11-01-2017 23:11   mymodel/Makefile
  4831036  11-01-2017 23:11   mymodel/mymodel.c
      311  11-01-2017 23:11   mymodel/mymodel.h
      109  11-01-2017 23:11   mymodel/recipe.json
---------                     -------
  4831623                     5 files

3. Export the runtime package using the method save_runtime_package():

### Continued from the previous code block
treelite.save_runtime_package(destdir='.')

This command produces the zip archived named treelite_runtime.zip. This archive contains all the necessary files for prediction task.

john.doe@host-machine:/home/john.doe/$ ls .
mymodel.zip   your_model.model   treelite_runtime.zip
john.doe@host-machine:/home/john.doe/$ unzip -l treelite_runtime.zip
Archive:  treelite_runtime.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
        0  10-30-2017 12:23   runtime/
.................full output not shown here.................
      709  09-26-2017 17:58   runtime/src/common/math.h
---------                     -------
    71353                     39 files

4. Now you are ready to deploy the model to the target machine. Copy to the target machine the two archives mymodel.zip (source package) and treelite_runtime.zip (runtime package):

john.doe@host-machine:/home/john.doe/$ sftp john.doe@target-machine
Connected to target-machine.
sftp> put mymodel.zip
Uploading mymodel.zip to /home/john.doe/mymodel.zip
mymodel.zip                             100%  410KB 618.2KB/s   00:00
sftp> put treelite_runtime.zip
Uploading treelite_runtime.zip to /home/john.doe/treelite_runtime.zip
treelite_runtime.zip                    100%   28KB 618.0KB/s   00:00
sftp> quit

5. It is time to move to the target machine. On the target machine, extract both archives mymodel.zip and treelite_runtime.zip:

john.doe@host-machine:/home/john.doe/$ ssh john.doe@target-machine
Last login: Tue Oct 31 00:43:36 2017 from host-machine

john.doe@target-machine:/home/john.doe/$ unzip mymodel.zip
Archive:  mymodel.zip
   creating: mymodel/
  inflating: mymodel/Makefile
  inflating: mymodel/mymodel.c
  inflating: mymodel/mymodel.h
  inflating: mymodel/recipe.json
john.doe@target-machine:/home/john.doe/$ unzip treelite_runtime.zip
Archive:  treelite_runtime.zip
   creating: runtime/
.................full output not shown here.................
  inflating: runtime/src/common/math.h

6. Now build the runtime package: cmake followed by make.

john.doe@target-machine:/home/john.doe/$ cd runtime/build
john.doe@target-machine:/home/john.doe/runtime/build/$ cmake ..
-- The C compiler identification is GNU 7.2.0
-- The CXX compiler identification is GNU 7.2.0
.................full output not shown here.................
-- Configuring done
-- Generating done
-- Build files have been written to: /home/john.doe/runtime/build
john.doe@target-machine:/home/john.doe/runtime/build/$ make
Scanning dependencies of target objtreelite_runtime
[ 50%] Building CXX object CMakeFiles/objtreelite_runtime.dir/src/c_api/c_api_common.cc.o
[ 50%] Building CXX object CMakeFiles/objtreelite_runtime.dir/src/c_api/c_api_error.cc.o
[ 50%] Building CXX object CMakeFiles/objtreelite_runtime.dir/src/c_api/c_api_runtime.cc.o
[ 66%] Building CXX object CMakeFiles/objtreelite_runtime.dir/src/logging.cc.o
[ 83%] Building CXX object CMakeFiles/objtreelite_runtime.dir/src/predictor.cc.o
[ 83%] Built target objtreelite_runtime
Scanning dependencies of target treelite_runtime
[100%] Linking CXX shared library ../lib/libtreelite_runtime.so
[100%] Built target treelite_runtime
john.doe@target-machine:/home/john.doe/runtime/build/$ cd ../..
john.doe@target-machine:/home/john.doe/$

Note

Building the runtime package on Windows

The example shown assumes the target was UNIX. On Windows, CMake will create a Visual Studio solution file named treelite_runtime.sln. Open it and compile the solution by selecting Build > Build Solution from the menu.

Note

Building the runtime package on Mac OS X

The default clang installation on Mac OS X does not support OpenMP, the language construct for multithreading. To enable multithreading in treelite, we recommend that you install gcc 7.x using Homebrew:

john.doe@target-machine:/home/john.doe/runtime/build/$ brew install gcc@7

After g++ is installed, run CMake again with gcc as the C++ compiler:

john.doe@target-machine:/home/john.doe/runtime/build/$ cmake .. \
                         -DCMAKE_CXX_COMPILER=g++-7 -DCMAKE_C_COMPILER=gcc-7

7. Build the source package. Now only make will do. (On Windows, run NMake instead.)

john.doe@target-machine:/home/john.doe/$ cd mymodel
john.doe@target-machine:/home/john.doe/mymodel/$ make
gcc -c -O3 -o mymodel.o mymodel.c -fPIC -std=c99 -flto -fopenmp
gcc -shared -O3 -o mymodel.so mymodel.o -std=c99 -flto -fopenmp
john.doe@target-machine:/home/john.doe/mymodel/$ ls
Makefile       mymodel.c      mymodel.so
mymodel.h      mymodel.o      recipe.json

Note

Parallel compilation with GNU Make

If you used parallel_comp option to split the model into multiple source files, you can take advantage of parallel compilation. Simply replace make with make -jN, where N is replaced with the number of workers to launch. Setting N too high may result into memory shortage.

8. Temporarily set the environment variable PYTHONPATH to the directory runtime/python, so that the runtime package can be found by the Python interpreter. Then launch an interactive Python session and import the module treelite.runtime. If no error occurs, we are done.

john.doe@target-machine:/home/john.doe/$ set PYTHONPATH=./runtime/python/
john.doe@target-machine:/home/john.doe/$ ipython
Python 3.6.2 (default, Jul 17 2017, 16:44:45)
Type 'copyright', 'credits' or 'license' for more information
IPython 6.1.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import treelite.runtime

Prediction instructions

Finally, we are ready to make predictions, per instructions given in First tutorial. Don’t forget to set PYTHONPATH before running the following script:

import treelite.runtime
import numpy as np
# Load data
X = np.load('data.npy')
batch = Batch.from_npy2d(X)
# Load predictor
predictor = treelite.runtime.Predictor('./mymodel/mymodel.so', verbose=True)
out_pred = predictor.predict(batch)

Option 3: Deploy prediciton code only

With this option, neither Python nor a C++ compiler is required. You should be able to adopt this option using any basic installation of UNIX-like operating systems.

Dependencies and Requirements

The target machine shall meet the following conditions:

  • A C compiler is available.

  • The C compiler supports the following features of the C99 standard: inline functions; declaration of loop variables inside for loop; the expf function in <math.h>; the <stdint.h> header.

  • GNU Make or Microsoft NMake is installed.

  • An archive utility exists that can open a .zip archive.

Deployment instructions

1. On the host machine, install treelite and import your tree ensemble model. You should end up with the model object of type Model.

### Run this block on the **host** machine

import treelite
model = treelite.Model.load('your_model.model', 'xgboost')
# You may also use `from_xgboost` method or the builder class

2. Export your model as a source package by calling the method export_srcpkg() of the Model object. The source package will contain C code representation of the prediction subroutine.

### Continued from the previous code block

# Operating system of the target machine
platform = 'unix'
# C compiler to use to compile prediction code on the target machine
toolchain = 'gcc'
# Save the source package as a zip archive named mymodel.zip
# Later, we'll use this package to produce the library mymodel.so.
model.export_srcpkg(platform=platform, toolchain=toolchain,
                    pkgpath='./mymodel.zip', libname='mymodel.so',
                    verbose=True)

Note

On the value of toolchain

Treelite supports only three toolchain configurations (‘msvc’, ‘gcc’, ‘clang’) for which it generates Makefiles. If you are using a compiler other than these three, you will have to write your own Makefile. For now, just set toolchain='gcc' and move on.

After calling export_srcpkg(), you should be able to find the zip archive named mymodel.zip inside the current working directory.

john.doe@host-machine:/home/john.doe/$ ls .
mymodel.zip   your_model.model

The content of mymodel.zip consists of the header and source files, as well as the Makefile:

john.doe@host-machine:/home/john.doe/$ unzip -l mymodel.zip
Archive:  mymodel.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
        0  11-01-2017 23:11   mymodel/
      167  11-01-2017 23:11   mymodel/Makefile
  4831036  11-01-2017 23:11   mymodel/mymodel.c
      311  11-01-2017 23:11   mymodel/mymodel.h
      109  11-01-2017 23:11   mymodel/recipe.json
---------                     -------
  4831623                     5 files

3. Now you are ready to deploy the model to the target machine. Copy to the target machine the archive mymodel.zip (source package).

john.doe@host-machine:/home/john.doe/$ sftp john.doe@target-machine
Connected to target-machine.
sftp> put mymodel.zip
Uploading mymodel.zip to /home/john.doe/mymodel.zip
mymodel.zip                             100%  410KB 618.2KB/s   00:00
sftp> quit

4. It is time to move to the target machine. On the target machine, extract the archive mymodel.zip:

john.doe@host-machine:/home/john.doe/$ ssh john.doe@target-machine
Last login: Tue Oct 31 00:43:36 2017 from host-machine

john.doe@target-machine:/home/john.doe/$ unzip mymodel.zip
Archive:  mymodel.zip
   creating: mymodel/
  inflating: mymodel/Makefile
  inflating: mymodel/mymodel.c
  inflating: mymodel/mymodel.h
  inflating: mymodel/recipe.json

5. Build the source package (using GNU Make or NMake).

john.doe@target-machine:/home/john.doe/$ cd mymodel
john.doe@target-machine:/home/john.doe/mymodel/$ make
gcc -c -O3 -o mymodel.o mymodel.c -fPIC -std=c99 -flto -fopenmp
gcc -shared -O3 -o mymodel.so mymodel.o -std=c99 -flto -fopenmp
john.doe@target-machine:/home/john.doe/mymodel/$ ls
Makefile       mymodel.c      mymodel.so
mymodel.h      mymodel.o      recipe.json

Note

Parallel compilation with GNU Make

If you used parallel_comp option to split the model into multiple source files, you can take advantage of parallel compilation. Simply replace make with make -jN, where N is replaced with the number of workers to launch. Setting N too high may result into memory shortage.

Note

Using other compilers

If you are using a compiler other than gcc, clang, or Microsoft Visual C++, you will need to compose your own Makefile. Open the Makefile and make necessary changes.

Prediction instructions

The prediction library provides the function predict with the following signature:

float predict(union Entry* data, int pred_margin);

Here, the argument data must be an array of length M, where M is the number of features used in the tree ensemble. The data array stores all the feature values of a single row. To indicate presence or absence of a feature value, we use the union type Entry, which defined as

union Entry {
  int missing;
  float fvalue;
};

For missing values, we set the missing field to -1. For non-missing ones, we set the fvalue field to the feature value. The total number of features is given by the function

size_t get_num_feature(void);

Let’s look at an example. We’d start by initializing the array inst, a dense aray to hold feature values of a single data row:

/* number of features */
const size_t num_feature = get_num_feature();
/* inst: dense vector storing feature values */
union Entry* inst = malloc(sizeof(union Entry) * num_feature);
/* clear inst with all missing values */
for (i = 0; i < num_feature; ++i) {
  inst[i].missing = -1;
}

Before calling the function predict, the array inst needs to be initialized with missing and present feature values. The following peudocode illustrates the idea:

For each data row rid:
  inst[i].missing == -1 for every i, assuming all features lack values

  For each feature i for which the data row in fact has a feature value:
    Set inst[i].fvalue = [feature value], to indicate presence

  Call predict(inst, 0) and get prediction for the data row rid

  For each feature i for which the row has a feature value:
    Set inst[i].missing = -1, to prepare for next row (rid + 1)

The task is not too difficult as long as the input data is given as a particular form of sparse matrix: the Compressed Sparse Row format. The sparse matrix consists of three arrays:

  • val stores nonzero entries in row-major order.

  • col_ind stores column indices of the entries in val. The expression col_ind[i] indicates the column index of the i th entry val[i].

  • row_ptr stores the locations in val that start and end data rows. The i th data row is given by the array slice val[row_ptr[i]:row_ptr[i+1]].

/* nrow : number of data rows */
for (rid = 0; rid < nrow; ++rid) {
  ibegin = row_ptr[rid];
  iend = row_ptr[rid + 1];
  /* Fill nonzeros */
  for (i = ibegin; i < iend; ++i) {
    inst[col_ind[i]].fvalue = val[i];
  }
  out_pred[rid] = predict(inst, 0);
  /* Drop nonzeros */
  for (i = ibegin; i < iend; ++i) {
    inst[col_ind[i]].missing = -1;
  }
}

It only remains to create three arrays val, col_ind, and row_ptr. You may want to use a third-pary library here to read from a SVMLight format. For now, we’ll punt the issue of loading the input data and write it out as constants in the program:

#include <stdio.h>
#include <stdlib.h>
#include "mymodel.h"

int main(void) {
  /* 5x13 "sparse" matrix, in CSR format
     [[ 0.  ,  0.  ,  0.68,  0.99,  0.  ,  0.11,  0.  ,  0.82,  0.  ,
        0.  ,  0.  ,  0.  ,  0.  ],
      [ 0.  ,  0.  ,  0.99,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,
        0.61,  0.  ,  0.  ,  0.  ],
      [ 0.02,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,
        0.  ,  0.  ,  0.  ,  0.  ],
      [ 0.  ,  0.  ,  0.36,  0.  ,  0.82,  0.  ,  0.  ,  0.57,  0.  ,
        0.  ,  0.  ,  0.  ,  0.75],
      [ 0.47,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,
        0.  ,  0.  ,  0.45,  0.  ]]
  */
  const float val[] = {0.68, 0.99, 0.11, 0.82, 0.99, 0.61, 0.02, 0.36, 0.82,
                       0.57, 0.75, 0.47, 0.45};
  const size_t col_ind[] = {2, 3, 5, 7, 2, 9, 0, 2, 4, 7, 12, 0, 11};
  const size_t row_ptr[] = {0, 4, 6, 7, 11, 13};
  const size_t nrow = 5;
  const size_t ncol = 13;

  /* number of features */
  const size_t num_feature = get_num_feature();
  /* inst: dense vector storing feature values */
  union Entry* inst = malloc(sizeof(union Entry) * num_feature);
  float* out_pred = malloc(sizeof(float) * nrow);
  size_t rid, ibegin, iend, i;

  /* clear inst with all missing */
  for (i = 0; i < num_feature; ++i) {
    inst[i].missing = -1;
  }

  for (rid = 0; rid < nrow; ++rid) {
    ibegin = row_ptr[rid];
    iend = row_ptr[rid + 1];
    /* Fill nonzeros */
    for (i = ibegin; i < iend; ++i) {
      inst[col_ind[i]].fvalue = val[i];
    }
    out_pred[rid] = predict(inst, 0);
    /* Drop nonzeros */
    for (i = ibegin; i < iend; ++i) {
      inst[col_ind[i]].missing = -1;
    }
    printf("pred[%zu] = %f\n", rid, out_pred[rid]);
  }
  free(inst);
  free(out_pred);
  return 0;
}

Save the program as a .c file and put it in the same directory mymodel/. To link the program against the prediction library mymodel.so, simply run

gcc -o myprog myprog.c mymodel.so -I. -std=c99

As long as the program myprog is in the same directory of the prediction library mymodel.so, we’ll be good to go.

A sample output:

pred[0] = 44.880001
pred[1] = 44.880001
pred[2] = 44.880001
pred[3] = 42.670002
pred[4] = 44.880001