mlpack provides a simple timer interface for the timing of machine learning
methods. The results of any timers used during the program are displayed at
output by any command-line binding, when --verbose
is given:
$ mlpack_knn -r dataset.csv -n neighbors_out.csv -d distances_out.csv -k 5 -v
<...>
[INFO ] Program timers:
[INFO ] computing_neighbors: 0.010650s
[INFO ] loading_data: 0.002567s
[INFO ] saving_data: 0.001115s
[INFO ] total_time: 0.149816s
[INFO ] tree_building: 0.000534s
In C++, the mlpack::Timers
class can be used to add timers to a program. The
mlpack::Timers
class provides three simple methods:
void Timer::Start(const char* name);
void Timer::Stop(const char* name);
timeval Timer::Get(const char* name);
Every binding is called with an mlpack::Timers&
, which can be used in the body
of that binding. For the sake of this discussion, let us call that object
timers
.
Each timer is given a name, and is referenced by that name. You can call
timers.Start()
and timers.Stop()
multiple times for a particular timer name,
and the result will be the sum of the runs of the timer. Note that
timers.Stop()
must be called before timers.Start()
is called again,
otherwise a std::runtime_error
exception will be thrown.
A "total_time"
timer is run automatically for each mlpack binding.
Below is a very simple example of timer usage in code.
#include <mlpack/core.hpp>
#include <mlpack/core/util/io.hpp>
#define BINDING_TYPE BINDING_TYPE_CLI
#include <mlpack/core/util/mlpack_main.hpp>
using namespace mlpack;
void BINDING_FUNCTION(util::Params& params, util::Timers& timers)
{
// Start a timer.
timers.Start("some_timer");
// Do some things.
DoSomeStuff();
// Stop the timer.
timers.Stop("some_timer");
}
@endcode
If the `verbose` flag was given to this binding, then a command-line binding
would print the time that `"some_timer"` ran for at the end of the program's
output.