#
# AUTO-TUPLE
#
@timemory.util.auto_tuple(get_timemory_config(), key="")
def bar():
"""
Demonstrate decorator and context-manager with auto_tuple
Run fibonacci calculation and then sleep for 2 seconds within context-manager
"""
print("fibonacci({}) = {}".format(nfib, fibonacci(nfib)))
with timemory.util.auto_tuple(get_timemory_config(), key="[sleep]"):
time.sleep(2)
#
# EXECUTE
#
if __name__ == "__main__":
# do some work
foo()
bar()
# get the dictionary
data = timemory.get()
# print the dictionary
print("TIMEMORY_DATA:\n{}".format(
json.dumps(data, indent=4, sort_keys=True)))
def from_timemory(input=None, select=None):
"""Read in timemory data.
Links:
https://github.com/NERSC/timemory
https://timemory.readthedocs.io
Arguments:
input (str or file-stream or dict or None):
Valid argument types are:
1. Filename for a timemory JSON tree file
2. Open file stream to one of these files
3. Dictionary from timemory JSON tree
Currently, timemory supports two JSON layouts: flat and tree.
The former is a 1D-array representation of the hierarchy which
represents the hierarchy via indentation schemes in the labels
and is not compatible with hatchet. The latter is a hierarchical
representation of the data and is the required JSON layout when
using hatchet. Timemory JSON tree files typically have the
extension ".tree.json".
If input is None, this assumes that timemory has been
recording data within the application that is using hatchet.
In this situation, this method will attempt to import the
data directly from timemory.
At the time of this writing, the direct data import will:
1. Stop any currently collecting components
2. Aggregate child thread data of the calling thread
3. Clear all data on the child threads
4. Aggregate the data from any MPI and/or UPC++ ranks.
Thus, if MPI or UPC++ is used, every rank must call this routine.
The zeroth rank will have the aggregation and all the other non-zero
ranks will only have the rank-specific data.
Whether or not the per-thread and per-rank data itself is
combined is controlled by the `collapse_threads` and `collapse_processes`
attributes in the `timemory.settings` submodule.
In the C++ API, it is possible for only #1 to be applied and data
can be obtained for an individual thread and/or rank without aggregation.
This is not currently available to Python, however, it can be made
available upon request via a GitHub Issue.
select (list of str):
A list of strings which match the component enumeration names
"""
from .readers.timemory_reader import TimemoryReader
if input is not None:
try:
return TimemoryReader(input, select).read()
except IOError:
pass
else:
try:
import timemory
TimemoryReader(timemory.get(hierarchy=True), select).read()
except ImportError:
print(
"Error! timemory could not be imported. Provide filename, file stream, or dict."
)
raise
def get_results(report=False):
# Get the results as dictionary
data = timemory.get()
if report is True:
print("\n{}".format(json.dumps(data, indent=4, sort_keys=True)))
return data