resource_monitor Namespace Reference

Data Structures
class	Categories
	Class to encapsule all the categories in a workflow. More...
class	Category
class	ResourceExhaustion
	Exception raised when a function goes over the resources limits. More...
class	ResourceInternalError

Functions
def	monitored (limits=None, callback=None, interval=1, return_resources=True)
	Create a monitored version of a function. More...

Detailed Description

Python resource_monitor bindings.

The objects and methods provided by this package correspond to the native C API in category.h, rmonitor_poll.h, and rmsummary.h

The SWIG-based Python bindings provide a higher-level interface that revolves around the following function/decorator and objects:

• resource_monitor.monitored
• resource_monitor.ResourceExhaustion
• resource_monitor.Category

Function Documentation

monitored()

def resource_monitor.monitored	(		limits = None,
			callback = None,
			interval = 1,
			return_resources = True
	)

Create a monitored version of a function.

It can be used as a decorator, or called by itself.

Parameters

limits	Dictionary of resource limits to set. Available limits are: wall_time: time spent during execution (seconds) cpu_time: user + system time of the execution (seconds) cores: peak number of cores used cores_avg: number of cores computed as cpu_time/wall_time max_concurrent_processes: the maximum number of processes running concurrently total_processes: count of all of the processes created virtual_memory: maximum virtual memory across all processes (megabytes) memory: maximum resident size across all processes (megabytes) swap_memory: maximum swap usage across all processes (megabytes) bytes_read: number of bytes read from disk bytes_written: number of bytes written to disk bytes_received: number of bytes read from the network bytes_sent: number of bytes written to the network bandwidth: maximum network bits/s (average over one minute) total_files: total maximum number of files and directories of all the working directories in the tree disk: size of all working directories in the tree (megabytes)
callback	Function to call every time a measurement is done. The arguments given to the function are id: Unique identifier for the function and its arguments. name: Name of the original function. step: Measurement step. It is -1 for the last measurement taken. resources: Current resources measured.
interval	Maximum time in seconds between measurements.
return_resources	Whether to modify the return value of the function to a tuple of the original result and a dictionary with the final measurements. # Decorating a function: @monitored() def my_sum_a(lst): return sum(lst) @monitored(return_resources = False, callback = lambda id, name, step, res: print('memory used', res['memory'])) def my_sum_b(lst): return sum(lst) >>> (result_a, resources) = my_sum_a([1,2,3]) >>> print(result, resources['memory']) 6, 66 >>> result_b = my_sum_b([1,2,3]) memory used: 66 >>> assert(result_a == result_b) # Wrapping the already defined function 'sum', adding limits: my_sum_monitored = monitored(limits = {'memory': 1024})(sum) try: # original_result = sum(...) (original_result, resources_used) = my_sum_monitored(...) except ResourceExhaustion as e: print(e) ... # Defining a function with a callback and a decorator. # In this example, we record the time series of resources used: import multiprocessing results_series = multiprocessing.Queue() def my_callback(id, name, step, resources): results_series.put((step, resources)) @monitored(callback = my_callback, return_resources = False): def my_function(...): ... result = my_function(...) # print the time series while not results_series.empty(): try: step, resources = results_series.get(False) print(step, resources) except multiprocessing.Empty: pass