Benchmarks are stored in a Python package, i.e. collection of .py
files in the benchmark suite’s benchmark directory (as defined by
benchmark_dir in the asv.conf.json file). The package may
contain arbitrarily nested subpackages, contents of which will also be
used, regardless of the file names.
Within each .py file, each benchmark is a function or method. The
name of the function must have a special prefix, depending on the type
of benchmark. asv understands how to handle the prefix in either
CamelCase or lowercase with underscores. For example, to create a
timing benchmark, the following are equivalent:
def time_range():
for i in range(1000):
pass
def TimeRange():
for i in range(1000):
pass
Benchmarks may be organized into methods of classes if desired:
class Suite:
def time_range(self):
for i in range(1000):
pass
def time_xrange(self):
for i in xrange(1000):
pass
There are some options to asv run that may be useful when writing
benchmarks.
You may find that asv run spends a lot of time setting up the
environment each time. You can have asv run use an existing
Python environment that already has the benchmarked project and all of
its dependencies installed. Use the --python argument to specify
a Python environment to use:
asv run --python=python
If you don’t care about getting accurate timings, but just want to
ensure the code is running, you can add the --quick argument,
which will run each benchmark only once:
asv run --quick
In order to display the standard error output (this includes exception tracebacks)
that your benchmarks may produce, pass the --show-stderr flag:
asv run --show-stderr
Finally, there is a special command, asv dev, that uses all of
these features and is equivalent to:
asv run --python=same --quick --show-stderr --dry-run
If initialization needs to be performed that should not be included in
the timing of the benchmark, include that code in a setup method
on the class, or add an attribute called setup to a free function.
For example:
class Suite:
def setup(self):
# load data from a file
with open("/usr/share/words.txt", "r") as fd:
self.words = fd.readlines()
def time_upper(self):
for word in self.words:
word.upper()
# or equivalently...
words = []
def my_setup():
global words
with open("/usr/share/words.txt", "r") as fd:
words = fd.readlines()
def time_upper():
for word in words:
word.upper()
time_upper.setup = my_setup
You can also include a module-level setup function, which will be
run for every benchmark within the module, prior to any setup
assigned specifically to each function.
Similarly, benchmarks can also have a teardown function that is
run after the benchmark. This is useful if, for example, you need to
clean up any changes made to the filesystem.
Note that although different benchmarks run in separate processes, for
a given benchmark repeated measurement (cf. repeat attribute) and
profiling occur within the same process. For these cases, the setup
and teardown routines are run multiple times in the same process.
If setup raises a NotImplementedError, the benchmark is marked
as skipped.
The setup method is run multiple times, for each benchmark and for
each repeat. If the setup is especially expensive, the
setup_cache method may be used instead, which only performs the
setup calculation once and then caches the result to disk. It is run
only once also for repeated benchmarks and profiling, unlike
setup. setup_cache can persist the data for the benchmarks it
applies to in two ways:
asv pickles to disk, and
then loads and passes it as the first argument to each benchmark.asv) which are then explicitly
loaded in each benchmark process. It is probably best to load
the data in a setup method so the loading time is not
included in the timing of the benchmark.A separate cache is used for each environment and each commit of the project begin tested and is thrown out between benchmark runs.
For example, caching data in a pickle:
class Suite:
def setup_cache(self):
fib = [1, 1]
for i in range(100):
fib.append(fib[-2] + fib[-1])
return fib
def track_fib(self, fib):
return fib[-1]
As another example, explicitly saving data in a file:
class Suite:
def setup_cache(self):
with open("test.dat", "wb") as fd:
for i in range(100):
fd.write('{0}\n'.format(i))
def setup(self):
with open("test.dat", "rb") as fd:
self.data = [int(x) for x in fd.readlines()]
def track_numbers(self):
return len(self.data)
The setup_cache timeout can be specified by setting the
.timeout attribute of the setup_cache function. The default
value is the maximum of the timeouts of the benchmarks using it.
Each benchmark can have a number of arbitrary attributes assigned to
it. The attributes that asv understands depends on the type of
benchmark and are defined below. For free functions, just assign the
attribute to the function. For methods, include the attribute at the
class level. For example, the following are equivalent:
def time_range():
for i in range(1000):
pass
time_range.timeout = 120.0
class Suite:
timeout = 120.0
def time_range(self):
for i in range(1000):
pass
For the list of attributes, see Benchmark types and attributes.
You might want to run a single benchmark for multiple values of some
parameter. This can be done by adding a params attribute to the
benchmark object:
def time_range(n):
for i in range(n):
pass
time_range.params = [0, 10, 20, 30]
This will also make the setup and teardown functions parameterized:
class Suite:
params = [0, 10, 20]
def setup(self, n):
self.obj = range(n)
def teardown(self, n):
del self.obj
def time_range_iter(self, n):
for i in self.obj:
pass
If setup raises a NotImplementedError, the benchmark is marked
as skipped for the parameter values in question.
The parameter values can be any Python objects. However, it is often best to use only strings or numbers, because these have simple unambiguous text representations.
When you have multiple parameters, the test is run for all of their combinations:
def time_ranges(n, func_name):
f = {'range': range, 'arange': numpy.arange}[func_name]
for i in f(n):
pass
time_ranges.params = ([10, 1000], ['range', 'arange'])
The test will be run for parameters (10, 'range'), (10, 'arange'),
(1000, 'range'), (1000, 'arange').
You can also provide informative names for the parameters:
time_ranges.param_names = ['n', 'function']
These will appear in the test output; if not provided you get default names such as “param1”, “param2”.
Note that setup_cache is not parameterized.
Timing benchmarks have the prefix time.
The timing itself is based on the Python standard library’s timeit
module, with some extensions for automatic heuristics shamelessly
stolen from IPython’s %timeit
magic function. This means that in most cases the benchmark function
itself will be run many times to achieve accurate timing.
The default timing function is time.process_time (POSIX
CLOCK_PROCESS_CPUTIME), which measures the CPU time used only by
the current process. You can change the timer by setting the
benchmark’s timer attribute, for example to timeit.default_timer
to measure wall clock time.
Note
One consequence of using time.process_time is that the time
spent in child processes of the benchmark is not included.
Multithreaded benchmarks also return the total CPU time
counting all CPUs. In these cases you may want to measure the
wall clock time, by setting the
timer = timeit.default_timer benchmark attribute.
For best results, the benchmark function should contain as little as
possible, with as much extraneous setup moved to a setup function:
class Suite:
def setup(self):
# load data from a file
with open("/usr/share/words.txt", "r") as fd:
self.words = fd.readlines()
def time_upper(self):
for word in self.words:
word.upper()
How setup and teardown behave for timing benchmarks
is similar to the Python timeit module, and the behavior is controlled
by the number and repeat attributes.
For the list of attributes, see Benchmark types and attributes.
Memory benchmarks have the prefix mem.
Memory benchmarks track the size of Python objects. To write a memory benchmark, write a function that returns the object you want to track:
def mem_list():
return [0] * 256
The asizeof module
is used to determine the size of Python objects. Since asizeof
includes the memory of all of an object’s dependencies (including the
modules in which their classes are defined), a memory benchmark
instead calculates the incremental memory of a copy of the object,
which in most cases is probably a more useful indicator of how much
space each additional object will use. If you need to do something
more specific, a generic Tracking (Generic) benchmark can be used
instead.
For details, see Benchmark types and attributes.
Note
The memory benchmarking feature is still experimental.
asizeof may not be the most appropriate metric to use.
Note
The memory benchmarks are not supported on PyPy.
Peak memory benchmarks have the prefix peakmem.
Peak memory benchmark tracks the maximum resident size (in bytes) of the process in memory. This does not necessarily count memory paged on-disk, or that used by memory-mapped files. To write a peak memory benchmark, write a function that does the operation whose maximum memory usage you want to track:
def peakmem_list():
[0] * 165536
Note
The peak memory benchmark also counts memory usage during the
setup routine, which may confound the benchmark results. One
way to avoid this is to use setup_cache instead.
For details, see Benchmark types and attributes.
It is also possible to use asv to track any arbitrary numerical
value. “Tracking” benchmarks can be used for this purpose and use the
prefix track. These functions simply need to return a numeric
value. For example, to track the number of objects known to the
garbage collector at a given state:
import gc
def track_num_objects():
return len(gc.get_objects())
track_num_objects.unit = "objects"
For details, see Benchmark types and attributes.
When you edit benchmark’s code in the benchmark suite, this often changes what is measured, and previously measured results should be discarded.
Airspeed Velocity records with each benchmark measurement a “version
number” for the benchmark. By default, it is computed by hashing the
benchmark source code text, including any setup and
setup_cache routines. If there are changes in the source code of
the benchmark in the benchmark suite, the version number changes, and
asv will ignore results whose version number is different from the
current one.
It is also possible to control the versioning of benchmark results
manually, by setting the .version attribute for the benchmark. The
version number, i.e. content of the attribute, can be any Python
string. asv only checks whether the version recorded with a
measurement matches the current version, so you can use any versioning
scheme.
See Benchmark types and attributes for reference documentation.