The $sample_mpi() method of a CmdStanModel object is
identical to the $sample() method but with support for
MPI (message passing interface). The target audience for MPI are
those with large computer clusters. For other users, the
$sample() method provides both parallelization of
chains and threading support for within-chain parallelization.
In order to use MPI with Stan, an MPI implementation must be installed. For Unix systems the most commonly used implementations are MPICH and OpenMPI. The implementations provide an MPI C++ compiler wrapper (for example mpicxx), which is required to compile the model.
An example of compiling with MPI:
mpi_options = list(STAN_MPI=TRUE, CXX="mpicxx", TBB_CXX_TYPE="gcc")
mod = cmdstan_model("model.stan", cpp_options = mpi_options)The C++ options that must be supplied to the compile call are:
STAN_MPI: Enables the use of MPI with Stan if TRUE.
CXX: The name of the MPI C++ compiler wrapper. Typically "mpicxx".
TBB_CXX_TYPE: The C++ compiler the MPI wrapper wraps. Typically "gcc"
on Linux and "clang" on macOS.
In the call to the $sample_mpi() method it is also possible to provide
the name of the MPI launcher (mpi_cmd, defaulting to "mpiexec") and any
other MPI launch arguments (mpi_args). In most cases, it is enough to
only define the number of processes. To use n_procs processes specify
mpi_args = list("n" = n_procs).
sample_mpi(
data = NULL,
mpi_cmd = "mpiexec",
mpi_args = NULL,
seed = NULL,
refresh = NULL,
init = NULL,
save_latent_dynamics = FALSE,
output_dir = NULL,
output_basename = NULL,
chains = 1,
chain_ids = seq_len(chains),
iter_warmup = NULL,
iter_sampling = NULL,
save_warmup = FALSE,
thin = NULL,
max_treedepth = NULL,
adapt_engaged = TRUE,
adapt_delta = NULL,
step_size = NULL,
metric = NULL,
metric_file = NULL,
inv_metric = NULL,
init_buffer = NULL,
term_buffer = NULL,
window = NULL,
fixed_param = FALSE,
sig_figs = NULL,
show_messages = TRUE,
show_exceptions = TRUE,
diagnostics = c("divergences", "treedepth", "ebfmi"),
validate_csv = TRUE
)(multiple options) The data to use for the variables specified in the data block of the Stan program. One of the following:
A named list of R objects with the names corresponding to variables
declared in the data block of the Stan program. Internally this list is then
written to JSON for CmdStan using write_stan_json(). See
write_stan_json() for details on the conversions performed on R objects
before they are passed to Stan.
A path to a data file compatible with CmdStan (JSON or R dump). See the appendices in the CmdStan guide for details on using these formats.
NULL or an empty list if the Stan program has no data block.
(string) The MPI launcher used for launching MPI
processes. The default launcher is "mpiexec".
(list) A list of arguments to use when launching MPI
processes. For example, mpi_args = list("n" = 4) launches the executable
as mpiexec -n 4 model_executable, followed by CmdStan arguments for the
model executable.
(positive integer(s)) A seed for the (P)RNG to pass to CmdStan.
In the case of multi-chain sampling the single seed will automatically be
augmented by the the run (chain) ID so that each chain uses a different
seed. The exception is the transformed data block, which defaults to
using same seed for all chains so that the same data is generated for all
chains if RNG functions are used. The only time seed should be specified
as a vector (one element per chain) is if RNG functions are used in
transformed data and the goal is to generate different data for each
chain.
(non-negative integer) The number of iterations between
printed screen updates. If refresh = 0, only error messages will be
printed.
(multiple options) The initialization method to use for the variables declared in the parameters block of the Stan program. One of the following:
A real number x>0. This initializes all parameters randomly between
[-x,x] on the unconstrained parameter space.;
The number 0. This initializes all parameters to 0;
A character vector of paths (one per chain) to JSON or Rdump files
containing initial values for all or some parameters. See
write_stan_json() to write R objects to JSON files compatible with
CmdStan.
A list of lists containing initial values for all or some parameters. For MCMC the list should contain a sublist for each chain. For other model fitting methods there should be just one sublist. The sublists should have named elements corresponding to the parameters for which you are specifying initial values. See Examples.
A function that returns a single list with names corresponding to the
parameters for which you are specifying initial values. The function can
take no arguments or a single argument chain_id. For MCMC, if the function
has argument chain_id it will be supplied with the chain id (from 1 to
number of chains) when called to generate the initial values. See
Examples.
(logical) Should auxiliary diagnostic information
about the latent dynamics be written to temporary diagnostic CSV files?
This argument replaces CmdStan's diagnostic_file argument and the content
written to CSV is controlled by the user's CmdStan installation and not
CmdStanR (for some algorithms no content may be written). The default
is FALSE, which is appropriate for almost every use case. To save the
temporary files created when save_latent_dynamics=TRUE see the
$save_latent_dynamics_files()
method.
(string) A path to a directory where CmdStan should write
its output CSV files. For interactive use this can typically be left at
NULL (temporary directory) since CmdStanR makes the CmdStan output
(posterior draws and diagnostics) available in R via methods of the fitted
model objects. The behavior of output_dir is as follows:
If NULL (the default), then the CSV files are written to a temporary
directory and only saved permanently if the user calls one of the $save_*
methods of the fitted model object (e.g.,
$save_output_files()). These temporary
files are removed when the fitted model object is
garbage collected (manually or automatically).
If a path, then the files are created in output_dir with names
corresponding to the defaults used by $save_output_files().
(string) A string to use as a prefix for the names of
the output CSV files of CmdStan. If NULL (the default), the basename of
the output CSV files will be comprised from the model name, timestamp, and
5 random characters.
(positive integer) The number of Markov chains to run. The default is 4.
(integer vector) A vector of chain IDs. Must contain as many
unique positive integers as the number of chains. If not set, the default
chain IDs are used (integers starting from 1).
(positive integer) The number of warmup iterations to run
per chain. Note: in the CmdStan User's Guide this is referred to as
num_warmup.
(positive integer) The number of post-warmup iterations
to run per chain. Note: in the CmdStan User's Guide this is referred to as
num_samples.
(logical) Should warmup iterations be saved? The default
is FALSE.
(positive integer) The period between saved samples. This should typically be left at its default (no thinning) unless memory is a problem.
(positive integer) The maximum allowed tree depth for the NUTS engine. See the Tree Depth section of the CmdStan User's Guide for more details.
(logical) Do warmup adaptation? The default is TRUE.
If a precomputed inverse metric is specified via the inv_metric argument
(or metric_file) then, if adapt_engaged=TRUE, Stan will use the
provided inverse metric just as an initial guess during adaptation. To turn
off adaptation when using a precomputed inverse metric set
adapt_engaged=FALSE.
(real in (0,1)) The adaptation target acceptance
statistic.
(positive real) The initial step size for the discrete approximation to continuous Hamiltonian dynamics. This is further tuned during warmup.
(string) One of "diag_e", "dense_e", or "unit_e",
specifying the geometry of the base manifold. See the Euclidean Metric
section of the CmdStan User's Guide for more details. To specify a
precomputed (inverse) metric, see the inv_metric argument below.
(character vector) The paths to JSON or
Rdump files (one per chain) compatible with CmdStan that contain
precomputed inverse metrics. The metric_file argument is inherited from
CmdStan but is confusing in that the entry in JSON or Rdump file(s) must be
named inv_metric, referring to the inverse metric. We recommend instead
using CmdStanR's inv_metric argument (see below) to specify an inverse
metric directly using a vector or matrix from your R session.
(vector, matrix) A vector (if metric='diag_e') or a
matrix (if metric='dense_e') for initializing the inverse metric. This
can be used as an alternative to the metric_file argument. A vector is
interpreted as a diagonal metric. The inverse metric is usually set to an
estimate of the posterior covariance. See the adapt_engaged argument
above for details about (and control over) how specifying a precomputed
inverse metric interacts with adaptation.
(nonnegative integer) Width of initial fast timestep adaptation interval during warmup.
(nonnegative integer) Width of final fast timestep adaptation interval during warmup.
(nonnegative integer) Initial width of slow timestep/metric adaptation interval.
(logical) When TRUE, call CmdStan with argument
"algorithm=fixed_param". The default is FALSE. The fixed parameter
sampler generates a new sample without changing the current state of the
Markov chain; only generated quantities may change. This can be useful
when, for example, trying to generate pseudo-data using the generated
quantities block. If the parameters block is empty then using
fixed_param=TRUE is mandatory. When fixed_param=TRUE the chains and
parallel_chains arguments will be set to 1.
(positive integer) The number of significant figures used
when storing the output values. By default, CmdStan represent the output
values with 6 significant figures. The upper limit for sig_figs is 18.
Increasing this value will result in larger output CSV files and thus an
increased usage of disk space.
(logical) When TRUE (the default), prints all
output during the sampling process, such as iteration numbers and elapsed times.
If the output is silenced then the $output() method of
the resulting fit object can be used to display the silenced messages.
(logical) When TRUE (the default), prints all
informational messages, for example rejection of the current proposal.
Disable if you wish to silence these messages, but this is not usually
recommended unless you are very confident that the model is correct up to
numerical error. If the messages are silenced then the
$output() method of the resulting fit object can be
used to display the silenced messages.
(character vector) The diagnostics to automatically check
and warn about after sampling. Setting this to an empty string "" or
NULL can be used to prevent CmdStanR from automatically reading in the
sampler diagnostics from CSV if you wish to manually read in the results
and validate them yourself, for example using read_cmdstan_csv(). The
currently available diagnostics are "divergences", "treedepth",
and "ebfmi" (the default is to check all of them).
These diagnostics are also available after fitting. The
$sampler_diagnostics() method provides
access the diagnostic values for each iteration and the
$diagnostic_summary() method provides
summaries of the diagnostics and can regenerate the warning messages.
Diagnostics like R-hat and effective sample size are not currently
available via the diagnostics argument but can be checked after fitting
using the $summary() method.
Deprecated. Use diagnostics instead.
A CmdStanMCMC object.
The CmdStanR website (mc-stan.org/cmdstanr) for online documentation and tutorials.
The Stan and CmdStan documentation:
Stan documentation: mc-stan.org/users/documentation
CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide
The Stan Math Library's documentation (mc-stan.org/math) for more details on MPI support in Stan.
Other CmdStanModel methods:
model-method-check_syntax,
model-method-compile,
model-method-diagnose,
model-method-expose_functions,
model-method-format,
model-method-generate-quantities,
model-method-laplace,
model-method-optimize,
model-method-pathfinder,
model-method-sample,
model-method-variables,
model-method-variational
# \dontrun{
# mpi_options <- list(STAN_MPI=TRUE, CXX="mpicxx", TBB_CXX_TYPE="gcc")
# mod <- cmdstan_model("model.stan", cpp_options = mpi_options)
# fit <- mod$sample_mpi(..., mpi_args = list("n" = 4))
# }