Stan Models in CmdStanPy

The: CmdStanModel class manages the Stan program and its corresponding compiled executable. It provides properties and functions to inspect the model code and filepaths. By default, the Stan program is compiled on instantiation.

import os
from cmdstanpy import cmdstan_path, CmdStanModel

bernoulli_stan = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan')
bernoulli_model = CmdStanModel(stan_file=bernoulli_stan)

A model object can be instantiated by specifying either the Stan program file path or the compiled executable file path or both. If both are specified, the constructor will check the timestamps on each and will only re-compile the program if the Stan file has a later timestamp which indicates that the program may have been edited.

Model compilation

Model compilation is carried out via the GNU Make build tool. The CmdStan makefile contains a set of general rules which specify the dependencies between the Stan program and the Stan platform components and low-level libraries. Optional behaviors can be specified by use of variables which are passed in to the make command as name, value pairs.

Model compilation is done in two steps:

  • The stanc compiler translates the Stan program to C++.

  • The C++ compiler compiles the generated code and links in the necessary supporting libraries.

Therefore, both the constructor and the compile method allow optional arguments stanc_options and cpp_options which specify options for each compilation step. Options are specified as a Python dictionary mapping compiler option names to appropriate values.

To use Stan’s parallelization features, Stan programs must be compiled with the appropriate C++ compiler flags. If you are running GPU hardware and wish to use the OpenCL framework to speed up matrix operations, you must set the C++ compiler flag STAN_OPENCL. For high-level within-chain parallelization using the Stan language reduce_sum function, it’s necessary to set the C++ compiler flag STAN_THREADS. While any value can be used, we recommend the value True.

For example, given Stan program named ‘proc_parallel.stan’, you can take advantage of both kinds of parallelization by specifying the compiler options when instantiating the model:

proc_parallel_model = CmdStanModel(
    cpp_options={"STAN_THREADS": True, "STAN_OPENCL": True},

Specifying a custom Make tool

To use custom Make-tool use set_make_env function.

from cmdstanpy import set_make_env
set_make_env("mingw32-make.exe") # On Windows with mingw32-make