work_queue.WorkQueue Class Reference

Python Work Queue object. More...

Inheritance diagram for work_queue.WorkQueue:

Public Member Functions
def	__init__
	Create a new work queue. More...
def	name
	Get the project name of the queue. More...
def	port
	Get the listening port of the queue. More...
def	stats
	Get queue statistics. More...
def	stats_hierarchy
	Get worker hierarchy statistics. More...
def	stats_category
	Get the task statistics for the given category. More...
def	status
	Get queue information as list of dictionaries. More...
def	workers_summary
	Get resource statistics of workers connected. More...
def	specify_category_mode
	Turn on or off first-allocation labeling for a given category. More...
def	specify_category_autolabel_resource
	Turn on or off first-allocation labeling for a given category and resource. More...
def	task_state
	Get current task state. More...
def	enable_monitoring
	Enables resource monitoring of tasks in the queue, and writes a summary per task to the directory given. More...
def	enable_monitoring_full
	As enable_monitoring, but it also generates a time series and a debug file. More...
def	activate_fast_abort
	Turn on or off fast abort functionality for a given queue for tasks in the "default" category, and for task which category does not set an explicit multiplier. More...
def	activate_fast_abort_category
	Turn on or off fast abort functionality for a given queue. More...
def	specify_draining_by_hostname
	Turn on or off draining mode for workers at hostname. More...
def	empty
	Determine whether there are any known tasks queued, running, or waiting to be collected. More...
def	hungry
	Determine whether the queue can support more tasks. More...
def	specify_algorithm
	Set the worker selection algorithm for queue. More...
def	specify_task_order
	Set the order for dispatching submitted tasks in the queue. More...
def	specify_name
	Change the project name for the given queue. More...
def	specify_manager_preferred_connection
	Set the preference for using hostname over IP address to connect. More...
def	specify_master_preferred_connection
	See specify_manager_preferred_connection. More...
def	specify_min_taskid
	Set the minimum taskid of future submitted tasks. More...
def	specify_priority
	Change the project priority for the given queue. More...
def	specify_num_tasks_left
	Specify the number of tasks not yet submitted to the queue. More...
def	specify_manager_mode
	Specify the manager mode for the given queue. More...
def	specify_master_mode
	See specify_manager_mode. More...
def	specify_catalog_server
	Specify the catalog server the manager should report to. More...
def	specify_log
	Specify a log file that records the cummulative stats of connected workers and submitted tasks. More...
def	specify_transactions_log
	Specify a log file that records the states of tasks. More...
def	specify_password
	Add a mandatory password that each worker must present. More...
def	specify_password_file
	Add a mandatory password file that each worker must present. More...
def	specify_max_resources
	Specifies the maximum resources allowed for the default category. More...
def	specify_min_resources
	Specifies the minimum resources allowed for the default category. More...
def	specify_category_max_resources
	Specifies the maximum resources allowed for the given category. More...
def	specify_category_min_resources
	Specifies the minimum resources allowed for the given category. More...
def	specify_category_first_allocation_guess
	Specifies the first-allocation guess for the given category. More...
def	initialize_categories
	Initialize first value of categories. More...
def	cancel_by_taskid
	Cancel task identified by its taskid and remove from the given queue. More...
def	cancel_by_tasktag
	Cancel task identified by its tag and remove from the given queue. More...
def	cancel_by_category
	Cancel all tasks of the given category and remove them from the queue. More...
def	shutdown_workers
	Shutdown workers connected to queue. More...
def	block_host
	Block workers running on host from working for the manager. More...
def	blacklist
	Replaced by block_host. More...
def	block_host_with_timeout
	Block workers running on host for the duration of the given timeout. More...
def	blacklist_with_timeout
	See block_host_with_timeout. More...
def	unblock_host
	Unblock given host, of all hosts if host not given. More...
def	blacklist_clear
	See unblock_host. More...
def	invalidate_cache_file
	Delete file from workers's caches. More...
def	specify_keepalive_interval
	Change keepalive interval for a given queue. More...
def	specify_keepalive_timeout
	Change keepalive timeout for a given queue. More...
def	estimate_capacity
	Turn on manager capacity measurements. More...
def	tune
	Tune advanced parameters for work queue. More...
def	submit
	Submit a task to the queue. More...
def	wait
	Wait for tasks to complete. More...
def	wait_for_tag
	Similar to wait, but guarantees that the returned task has the specified tag. More...
def	map
	Maps a function to elements in a sequence using work_queue. More...
def	pair
	Returns the values for a function of each pair from 2 sequences. More...
def	tree_reduce
	Reduces a sequence until only one value is left, and then returns that value. More...
def	application_info
	Should return a dictionary with information for the status display. More...

Detailed Description

Python Work Queue object.

This class uses a dictionary to map between the task pointer objects and the work_queue.Task.

Constructor & Destructor Documentation

def work_queue.WorkQueue.__init__	(		self,
			port = WORK_QUEUE_DEFAULT_PORT,
			name = None,
			shutdown = False,
			stats_log = None,
			transactions_log = None,
			debug_log = None,
			ssl = None,
			status_display_interval = None
	)

Create a new work queue.

Parameters

self	Reference to the current work queue object.
port	The port number to listen on. If zero, then a random port is chosen. A range of possible ports (low, hight) can be also specified instead of a single integer.
name	The project name to use.
stats_log	The name of a file to write the queue's statistics log.
transactions_log	The name of a file to write the queue's transactions log.
debug_log	The name of a file to write the queue's debug log.
shutdown	Automatically shutdown workers when queue is finished. Disabled by default.
ssl	A tuple of filenames (ssl_key, ssl_cert) in pem format, or True. If not given, then TSL is not activated. If True, a self-signed temporary key and cert are generated.
status_display_interval	Number of seconds between updates to the jupyter status display. None, or less than 1 disables it.

See Also

work_queue_create - For more information about environmental variables that affect the behavior this method.

References work_queue.WorkQueue._free_queue(), work_queue.WorkQueue._info_widget, work_queue.WorkQueue._setup_ssl(), work_queue.WorkQueue._shutdown, work_queue.WorkQueue._stats, work_queue.WorkQueue._stats_hierarchy, work_queue.WorkQueue._task_table, work_queue.WorkQueue._update_status_display(), work_queue.WorkQueue._work_queue, work_queue.WorkQueue.shutdown_workers(), work_queue.WorkQueue.specify_log(), work_queue.WorkQueue.specify_transactions_log(), work_queue_delete(), work_queue_specify_name(), and work_queue_ssl_create().

Member Function Documentation

def work_queue.WorkQueue.name

(

self

)

Get the project name of the queue.

>>> print(q.name)

References work_queue.WorkQueue._work_queue, and work_queue_name().

def work_queue.WorkQueue.port

(

self

)

Get the listening port of the queue.

>>> print(q.port)

References work_queue.WorkQueue._work_queue, and work_queue_port().

def work_queue.WorkQueue.stats

(

self

)

Get queue statistics.

>>> print(q.stats)

The fields in stats can also be individually accessed through this call. For example:

>>> print(q.stats.workers_busy)

References work_queue.WorkQueue._stats, work_queue.WorkQueue._work_queue, and work_queue_get_stats().

def work_queue.WorkQueue.stats_hierarchy

(

self

)

Get worker hierarchy statistics.

>>> print(q.stats_hierarchy)

The fields in stats_hierarchy can also be individually accessed through this call. For example:

>>> print(q.stats_hierarchy.workers_busy)

References work_queue.WorkQueue._stats_hierarchy, work_queue.WorkQueue._work_queue, and work_queue_get_stats_hierarchy().

def work_queue.WorkQueue.stats_category	(		self,
			category
	)

Get the task statistics for the given category.

Parameters

self	Reference to the current work queue object.
category	A category name. For example: s = q.stats_category("my_category") >>> print(s) The fields in work_queue_stats can also be individually accessed through this call. For example: >>> print(s.tasks_waiting)

References work_queue.WorkQueue._work_queue, and work_queue_get_stats_category().

def work_queue.WorkQueue.status	(		self,
			request
	)

Get queue information as list of dictionaries.

Parameters

self	Reference to the current work queue object
request	One of: "queue", "tasks", "workers", or "categories" For example: import json tasks_info = q.status("tasks")

References work_queue.WorkQueue._work_queue, and work_queue_status().

def work_queue.WorkQueue.workers_summary

(

self

)

Get resource statistics of workers connected.

Parameters

self	Reference to the current work queue object.

Returns

A list of dictionaries that indicate how many .workers connected with a certain number of .cores, .memory, and disk. For example:

workers = q.worker_summary()
>>> for w in workers:
>>>    print("{} workers with: {} cores, {} MB memory, {} MB disk".format(w.workers, w.cores, w.memory, w.disk)

References work_queue.WorkQueue._work_queue, and work_queue_workers_summary().

def work_queue.WorkQueue.specify_category_mode	(		self,
			category,
			mode
	)

Turn on or off first-allocation labeling for a given category.

By default, only cores, memory, and disk are labeled, and gpus are unlabeled. NOTE: autolabeling is only meaningfull when task monitoring is enabled (enable_monitoring). When monitoring is enabled and a task exhausts resources in a worker, mode dictates how work queue handles the exhaustion:

Parameters

self	Reference to the current work queue object.
category	A category name. If None, sets the mode by default for newly created categories.
mode	One of: WORK_QUEUE_ALLOCATION_MODE_FIXED Task fails (default). WORK_QUEUE_ALLOCATION_MODE_MAX If maximum values are specified for cores, memory, disk, and gpus (e.g. via specify_category_max_resources or Task.specify_memory), and one of those resources is exceeded, the task fails. Otherwise it is retried until a large enough worker connects to the manager, using the maximum values specified, and the maximum values so far seen for resources not specified. Use Task.specify_max_retries to set a limit on the number of times work queue attemps to complete the task. WORK_QUEUE_ALLOCATION_MODE_MIN_WASTE As above, but work queue tries allocations to minimize resource waste. WORK_QUEUE_ALLOCATION_MODE_MAX_THROUGHPUT As above, but work queue tries allocations to maximize throughput.

References work_queue.WorkQueue._work_queue, and work_queue_specify_category_mode().

def work_queue.WorkQueue.specify_category_autolabel_resource	(		self,
			category,
			resource,
			autolabel
	)

Turn on or off first-allocation labeling for a given category and resource.

This function should be use to fine-tune the defaults from specify_category_mode.

Parameters

self	Reference to the current work queue object.
category	A category name.
resource	A resource name.
autolabel	True/False for on/off.

Returns

1 if resource is valid, 0 otherwise.

References work_queue.WorkQueue._work_queue, and work_queue_enable_category_resource().

def work_queue.WorkQueue.task_state	(		self,
			taskid
	)

Get current task state.

See work_queue_task_state_t for possible values.

>>> print(q.task_state(taskid))

References work_queue.WorkQueue._work_queue, and work_queue_task_state().

def work_queue.WorkQueue.enable_monitoring	(		self,
			dirname = None,
			watchdog = True
	)

Enables resource monitoring of tasks in the queue, and writes a summary per task to the directory given.

Additionally, all summaries are consolidate into the file all_summaries-PID.log

Returns 1 on success, 0 on failure (i.e., monitoring was not enabled).

Parameters

self	Reference to the current work queue object.
dirname	Directory name for the monitor output.
watchdog	If True (default), kill tasks that exhaust their declared resources.

References work_queue.WorkQueue._work_queue, and work_queue_enable_monitoring().

def work_queue.WorkQueue.enable_monitoring_full	(		self,
			dirname = None,
			watchdog = True
	)

As enable_monitoring, but it also generates a time series and a debug file.

WARNING: Such files may reach gigabyte sizes for long running tasks.

Returns 1 on success, 0 on failure (i.e., monitoring was not enabled).

Parameters

self	Reference to the current work queue object.
dirname	Directory name for the monitor output.
watchdog	If True (default), kill tasks that exhaust their declared resources.

References work_queue.WorkQueue._work_queue, and work_queue_enable_monitoring_full().

def work_queue.WorkQueue.activate_fast_abort	(		self,
			multiplier
	)

Turn on or off fast abort functionality for a given queue for tasks in the "default" category, and for task which category does not set an explicit multiplier.

Parameters

self	Reference to the current work queue object.
multiplier	The multiplier of the average task time at which point to abort; if negative (the default) fast_abort is deactivated.

References work_queue.WorkQueue._work_queue, and work_queue_activate_fast_abort().

def work_queue.WorkQueue.activate_fast_abort_category	(		self,
			name,
			multiplier
	)

Turn on or off fast abort functionality for a given queue.

Parameters

self	Reference to the current work queue object.
name	Name of the category.
multiplier	The multiplier of the average task time at which point to abort; if zero, deacticate for the category, negative (the default), use the one for the "default" category (see activate_fast_abort)

References work_queue.WorkQueue._work_queue, and work_queue_activate_fast_abort_category().

def work_queue.WorkQueue.specify_draining_by_hostname	(		self,
			hostname,
			drain_mode = True
	)

Turn on or off draining mode for workers at hostname.

Parameters

self	Reference to the current work queue object.
hostname	The hostname the host running the workers.
drain_mode	If True, no new tasks are dispatched to workers at hostname, and empty workers are shutdown. Else, workers works as usual.

References work_queue.WorkQueue._work_queue, and work_queue_specify_draining_by_hostname().

def work_queue.WorkQueue.empty

(

self

)

Determine whether there are any known tasks queued, running, or waiting to be collected.

Returns 0 if there are tasks remaining in the system, 1 if the system is "empty".

Parameters

self	Reference to the current work queue object.

References work_queue.WorkQueue._work_queue, and work_queue_empty().

Referenced by work_queue.WorkQueue.map(), work_queue.WorkQueue.pair(), work_queue.WorkQueue.tree_reduce(), and work_queue.WorkQueue.wait_for_tag().

def work_queue.WorkQueue.hungry

(

self

)

Determine whether the queue can support more tasks.

Returns the number of additional tasks it can support if "hungry" and 0 if "sated".

Parameters

self	Reference to the current work queue object.

References work_queue.WorkQueue._work_queue, and work_queue_hungry().

def work_queue.WorkQueue.specify_algorithm	(		self,
			algorithm
	)

Set the worker selection algorithm for queue.

Parameters

self	Reference to the current work queue object.
algorithm	One of the following algorithms to use in assigning a task to a worker. See work_queue_schedule_t for possible values.

References work_queue.WorkQueue._work_queue, and work_queue_specify_algorithm().

def work_queue.WorkQueue.specify_task_order	(		self,
			order
	)

Set the order for dispatching submitted tasks in the queue.

Parameters

self	Reference to the current work queue object.
order	One of the following algorithms to use in dispatching submitted tasks to workers: WORK_QUEUE_TASK_ORDER_FIFO WORK_QUEUE_TASK_ORDER_LIFO

References work_queue.WorkQueue._work_queue, and work_queue_specify_task_order().

def work_queue.WorkQueue.specify_name	(		self,
			name
	)

Change the project name for the given queue.

Parameters

self	Reference to the current work queue object.
name	The new project name.

References work_queue.WorkQueue._work_queue, and work_queue_specify_name().

def work_queue.WorkQueue.specify_manager_preferred_connection	(		self,
			mode
	)

Set the preference for using hostname over IP address to connect.

'by_ip' uses IP addresses from the network interfaces of the manager (standard behavior), 'by_hostname' to use the hostname at the manager, or 'by_apparent_ip' to use the address of the manager as seen by the catalog server.

Parameters

self	Reference to the current work queue object.
mode	An string to indicate using 'by_ip', 'by_hostname' or 'by_apparent_ip'.

References work_queue.WorkQueue._work_queue, and work_queue_manager_preferred_connection().

def work_queue.WorkQueue.specify_master_preferred_connection	(		self,
			mode
	)

See specify_manager_preferred_connection.

References work_queue.WorkQueue._work_queue, and work_queue_manager_preferred_connection().

def work_queue.WorkQueue.specify_min_taskid	(		self,
			minid
	)

Set the minimum taskid of future submitted tasks.

Further submitted tasks are guaranteed to have a taskid larger or equal to minid. This function is useful to make taskids consistent in a workflow that consists of sequential managers. (Note: This function is rarely used). If the minimum id provided is smaller than the last taskid computed, the minimum id provided is ignored.

Parameters

self	Reference to the current work queue object.
minid	Minimum desired taskid

Returns

Returns the actual minimum taskid for future tasks.

References work_queue.WorkQueue._work_queue, and work_queue_specify_min_taskid().

def work_queue.WorkQueue.specify_priority	(		self,
			priority
	)

Change the project priority for the given queue.

Parameters

self	Reference to the current work queue object.
priority	An integer that presents the priorty of this work queue manager. The higher the value, the higher the priority.

References work_queue.WorkQueue._work_queue, and work_queue_specify_priority().

def work_queue.WorkQueue.specify_num_tasks_left	(		self,
			ntasks
	)

Specify the number of tasks not yet submitted to the queue.

It is used by work_queue_factory to determine the number of workers to launch. If not specified, it defaults to 0. work_queue_factory considers the number of tasks as: num tasks left + num tasks running + num tasks read.

Parameters

self	Reference to the current work queue object.
ntasks	Number of tasks yet to be submitted.

References work_queue.WorkQueue._work_queue, and work_queue_specify_num_tasks_left().

def work_queue.WorkQueue.specify_manager_mode	(		self,
			mode
	)

Specify the manager mode for the given queue.

(Kept for compatibility. It is no-op.)

Parameters

self	Reference to the current work queue object.
mode	This may be one of the following values: WORK_QUEUE_MASTER_MODE_STANDALONE or WORK_QUEUE_MASTER_MODE_CATALOG.

References work_queue.WorkQueue._work_queue, and work_queue_specify_manager_mode().

def work_queue.WorkQueue.specify_master_mode	(		self,
			mode
	)

See specify_manager_mode.

References work_queue.WorkQueue._work_queue, and work_queue_specify_manager_mode().

def work_queue.WorkQueue.specify_catalog_server	(		self,
			hostname,
			port
	)

Specify the catalog server the manager should report to.

Parameters

self	Reference to the current work queue object.
hostname	The hostname of the catalog server.
port	The port the catalog server is listening on.

References work_queue.WorkQueue._work_queue, and work_queue_specify_catalog_server().

def work_queue.WorkQueue.specify_log	(		self,
			logfile
	)

Specify a log file that records the cummulative stats of connected workers and submitted tasks.

Parameters

self	Reference to the current work queue object.
logfile	Filename.

References work_queue.WorkQueue._work_queue, and work_queue_specify_log().

Referenced by work_queue.WorkQueue.__init__().

def work_queue.WorkQueue.specify_transactions_log	(		self,
			logfile
	)

Specify a log file that records the states of tasks.

Parameters

self	Reference to the current work queue object.
logfile	Filename.

References work_queue.WorkQueue._work_queue, and work_queue_specify_transactions_log().

Referenced by work_queue.WorkQueue.__init__().

def work_queue.WorkQueue.specify_password	(		self,
			password
	)

Add a mandatory password that each worker must present.

Parameters

self	Reference to the current work queue object.
password	The password.

References work_queue.WorkQueue._work_queue, and work_queue_specify_password().

def work_queue.WorkQueue.specify_password_file	(		self,
			file
	)

Add a mandatory password file that each worker must present.

Parameters

self	Reference to the current work queue object.
file	Name of the file containing the password.

References work_queue.WorkQueue._work_queue, and work_queue_specify_password_file().

def work_queue.WorkQueue.specify_max_resources	(		self,
			rmd
	)

Specifies the maximum resources allowed for the default category.

Parameters

self	Reference to the current work queue object.
rmd	Dictionary indicating maximum values. See Task.resources_measured for possible fields. For example: >>> # A maximum of 4 cores is found on any worker: >>> q.specify_max_resources({'cores': 4}) >>> # A maximum of 8 cores, 1GB of memory, and 10GB disk are found on any worker: >>> q.specify_max_resources({'cores': 8, 'memory': 1024, 'disk': 10240})

References work_queue.WorkQueue._work_queue, and work_queue_specify_max_resources().

def work_queue.WorkQueue.specify_min_resources	(		self,
			rmd
	)

Specifies the minimum resources allowed for the default category.

Parameters

self	Reference to the current work queue object.
rmd	Dictionary indicating minimum values. See Task.resources_measured for possible fields. For example: >>> # A minimum of 2 cores is found on any worker: >>> q.specify_min_resources({'cores': 2}) >>> # A minimum of 4 cores, 512MB of memory, and 1GB disk are found on any worker: >>> q.specify_min_resources({'cores': 4, 'memory': 512, 'disk': 1024})

References work_queue.WorkQueue._work_queue, and work_queue_specify_min_resources().

def work_queue.WorkQueue.specify_category_max_resources	(		self,
			category,
			rmd
	)

Specifies the maximum resources allowed for the given category.

Parameters

self	Reference to the current work queue object.
category	Name of the category.
rmd	Dictionary indicating maximum values. See Task.resources_measured for possible fields. For example: >>> # A maximum of 4 cores may be used by a task in the category: >>> q.specify_category_max_resources("my_category", {'cores': 4}) >>> # A maximum of 8 cores, 1GB of memory, and 10GB may be used by a task: >>> q.specify_category_max_resources("my_category", {'cores': 8, 'memory': 1024, 'disk': 10240})

References work_queue.WorkQueue._work_queue, and work_queue_specify_category_max_resources().

def work_queue.WorkQueue.specify_category_min_resources	(		self,
			category,
			rmd
	)

Specifies the minimum resources allowed for the given category.

Parameters

self	Reference to the current work queue object.
category	Name of the category.
rmd	Dictionary indicating minimum values. See Task.resources_measured for possible fields. For example: >>> # A minimum of 2 cores is found on any worker: >>> q.specify_category_min_resources("my_category", {'cores': 2}) >>> # A minimum of 4 cores, 512MB of memory, and 1GB disk are found on any worker: >>> q.specify_category_min_resources("my_category", {'cores': 4, 'memory': 512, 'disk': 1024})

References work_queue.WorkQueue._work_queue, and work_queue_specify_category_min_resources().

def work_queue.WorkQueue.specify_category_first_allocation_guess	(		self,
			category,
			rmd
	)

Specifies the first-allocation guess for the given category.

Parameters

self	Reference to the current work queue object.
category	Name of the category.
rmd	Dictionary indicating maximum values. See Task.resources_measured for possible fields. For example: >>> # Tasks are first tried with 4 cores: >>> q.specify_category_first_allocation_guess("my_category", {'cores': 4}) >>> # Tasks are first tried with 8 cores, 1GB of memory, and 10GB: >>> q.specify_category_first_allocation_guess("my_category", {'cores': 8, 'memory': 1024, 'disk': 10240})

References work_queue.WorkQueue._work_queue, and work_queue_specify_category_first_allocation_guess().

def work_queue.WorkQueue.initialize_categories	(		self,
			filename,
			rm
	)

Initialize first value of categories.

Parameters

self	Reference to the current work queue object.
rm	Dictionary indicating maximum values. See Task.resources_measured for possible fields.
filename	JSON file with resource summaries.

References work_queue.WorkQueue._work_queue, and work_queue_initialize_categories().

def work_queue.WorkQueue.cancel_by_taskid	(		self,
			id
	)

Cancel task identified by its taskid and remove from the given queue.

Parameters

self	Reference to the current work queue object.
id	The taskid returned from submit.

References work_queue.WorkQueue._work_queue, and work_queue_cancel_by_taskid().

Referenced by work_queue.WorkQueue.cancel_by_category().

def work_queue.WorkQueue.cancel_by_tasktag	(		self,
			tag
	)

Cancel task identified by its tag and remove from the given queue.

Parameters

self	Reference to the current work queue object.
tag	The tag assigned to task using specify_tag.

References work_queue.WorkQueue._work_queue, and work_queue_cancel_by_tasktag().

def work_queue.WorkQueue.cancel_by_category	(		self,
			category
	)

Cancel all tasks of the given category and remove them from the queue.

Parameters

self	Reference to the current work queue object.
tag	The tag assigned to task using specify_tag.

References work_queue.WorkQueue.cancel_by_taskid().

def work_queue.WorkQueue.shutdown_workers	(		self,
			n
	)

Shutdown workers connected to queue.

Gives a best effort and then returns the number of workers given the shutdown order.

Parameters

self	Reference to the current work queue object.
n	The number to shutdown. To shut down all workers, specify "0".

References work_queue.WorkQueue._work_queue, and work_queue_shut_down_workers().

Referenced by work_queue.WorkQueue.__init__().

def work_queue.WorkQueue.block_host	(		self,
			host
	)

Block workers running on host from working for the manager.

Parameters

self	Reference to the current work queue object.
host	The hostname the host running the workers.

References work_queue.WorkQueue._work_queue, and work_queue_block_host().

Referenced by work_queue.WorkQueue.blacklist().

def work_queue.WorkQueue.blacklist	(		self,
			host
	)

Replaced by block_host.

References work_queue.WorkQueue.block_host().

def work_queue.WorkQueue.block_host_with_timeout	(		self,
			host,
			timeout
	)

Block workers running on host for the duration of the given timeout.

Parameters

self	Reference to the current work queue object.
host	The hostname the host running the workers.
timeout	How long this block entry lasts (in seconds). If less than 1, block indefinitely.

References work_queue.WorkQueue._work_queue, and work_queue_block_host_with_timeout().

Referenced by work_queue.WorkQueue.blacklist_with_timeout().

def work_queue.WorkQueue.blacklist_with_timeout	(		self,
			host,
			timeout
	)

See block_host_with_timeout.

References work_queue.WorkQueue.block_host_with_timeout().

def work_queue.WorkQueue.unblock_host	(		self,
			host = None
	)

Unblock given host, of all hosts if host not given.

Parameters

self	Reference to the current work queue object.
host	The of the hostname the host.

References work_queue.WorkQueue._work_queue, work_queue_unblock_all(), and work_queue_unblock_host().

Referenced by work_queue.WorkQueue.blacklist_clear().

def work_queue.WorkQueue.blacklist_clear	(		self,
			host = None
	)

See unblock_host.

References work_queue.WorkQueue.unblock_host().

def work_queue.WorkQueue.invalidate_cache_file	(		self,
			local_name
	)

Delete file from workers's caches.

Parameters

self	Reference to the current work queue object.
local_name	Name of the file as seen by the manager.

References work_queue.WorkQueue._work_queue, and work_queue_invalidate_cached_file().

def work_queue.WorkQueue.specify_keepalive_interval	(		self,
			interval
	)

Change keepalive interval for a given queue.

Parameters

self	Reference to the current work queue object.
interval	Minimum number of seconds to wait before sending new keepalive checks to workers.

References work_queue.WorkQueue._work_queue, and work_queue_specify_keepalive_interval().

def work_queue.WorkQueue.specify_keepalive_timeout	(		self,
			timeout
	)

Change keepalive timeout for a given queue.

Parameters

self	Reference to the current work queue object.
timeout	Minimum number of seconds to wait for a keepalive response from worker before marking it as dead.

References work_queue.WorkQueue._work_queue, and work_queue_specify_keepalive_timeout().

def work_queue.WorkQueue.estimate_capacity

(

self

)

Turn on manager capacity measurements.

Parameters

self	Reference to the current work queue object.

References work_queue.WorkQueue._work_queue, and work_queue_specify_estimate_capacity_on().

def work_queue.WorkQueue.tune	(		self,
			name,
			value
	)

Tune advanced parameters for work queue.

Parameters

self	Reference to the current work queue object.
name	The name fo the parameter to tune. Can be one of following: "resource-submit-multiplier" Treat each worker as having ({cores,memory,gpus} * multiplier) when submitting tasks. This allows for tasks to wait at a worker rather than the manager. (default = 1.0) "min-transfer-timeout" Set the minimum number of seconds to wait for files to be transferred to or from a worker. (default=10) "foreman-transfer-timeout" Set the minimum number of seconds to wait for files to be transferred to or from a foreman. (default=3600) "transfer-outlier-factor" Transfer that are this many times slower than the average will be aborted. (default=10x) "default-transfer-rate" The assumed network bandwidth used until sufficient data has been collected. (1MB/s) "fast-abort-multiplier" Set the multiplier of the average task time at which point to abort; if negative or zero fast_abort is deactivated. (default=0) "keepalive-interval" Set the minimum number of seconds to wait before sending new keepalive checks to workers. (default=300) "keepalive-timeout" Set the minimum number of seconds to wait for a keepalive response from worker before marking it as dead. (default=30) "short-timeout" Set the minimum timeout when sending a brief message to a single worker. (default=5s) "long-timeout" Set the minimum timeout when sending a brief message to a foreman. (default=1h) "category-steady-n-tasks" Set the number of tasks considered when computing category buckets. "hungry-minimum" Mimimum number of tasks to consider queue not hungry. (default=10) "wait-for-workers" Mimimum number of workers to connect before starting dispatching tasks. (default=0) "wait_retrieve_many" Parameter to alter how work_queue_wait works. If set to 0, work_queue_wait breaks out of the while loop whenever a task changes to WORK_QUEUE_TASK_DONE (wait_retrieve_one mode). If set to 1, work_queue_wait does not break, but continues recieving and dispatching tasks. This occurs until no task is sent or recieved, at which case it breaks out of the while loop (wait_retrieve_many mode). (default=0)
value	The value to set the parameter to.

Returns

0 on succes, -1 on failure.

References work_queue.WorkQueue._work_queue, and work_queue_tune().

def work_queue.WorkQueue.submit	(		self,
			task
	)

Submit a task to the queue.

It is safe to re-submit a task returned by wait.

Parameters

self	Reference to the current work queue object.
task	A task description created from work_queue.Task.

References work_queue.WorkQueue._task_table, work_queue.WorkQueue._work_queue, and work_queue_submit().

Referenced by work_queue.WorkQueue.map(), work_queue.WorkQueue.pair(), and work_queue.WorkQueue.tree_reduce().

def work_queue.WorkQueue.wait	(		self,
			timeout = WORK_QUEUE_WAITFORTASK
	)

Wait for tasks to complete.

This call will block until the timeout has elapsed

Parameters

self	Reference to the current work queue object.
timeout	The number of seconds to wait for a completed task before returning. Use an integer to set the timeout or the constant WORK_QUEUE_WAITFORTASK to block until a task has completed.

References work_queue.WorkQueue.wait_for_tag().

def work_queue.WorkQueue.wait_for_tag	(		self,
			tag,
			timeout = WORK_QUEUE_WAITFORTASK
	)

Similar to wait, but guarantees that the returned task has the specified tag.

This call will block until the timeout has elapsed.

Parameters

self	Reference to the current work queue object.
tag	Desired tag. If None, then it is equivalent to self.wait(timeout)
timeout	The number of seconds to wait for a completed task before returning.

References work_queue.WorkQueue._task_table, work_queue.WorkQueue._update_status_display(), work_queue.WorkQueue._work_queue, work_queue.WorkQueue.empty(), and work_queue_wait_for_tag().

Referenced by work_queue.WorkQueue.map(), work_queue.WorkQueue.pair(), work_queue.WorkQueue.tree_reduce(), and work_queue.WorkQueue.wait().

def work_queue.WorkQueue.map	(		self,
			fn,
			array,
			chunk_size = 1
	)

Maps a function to elements in a sequence using work_queue.

Similar to regular map function in python

Parameters

self	Reference to the current work queue object.
fn	The function that will be called on each element
seq	The sequence that will call the function
chunk_size	The number of elements to process at once

References work_queue.WorkQueue.empty(), batch_queue_module.submit, batch_queue_module::@3.submit, work_queue.WorkQueue.submit(), and work_queue.WorkQueue.wait_for_tag().

def work_queue.WorkQueue.pair	(		self,
			fn,
			seq1,
			seq2,
			chunk_size = 1
	)

Returns the values for a function of each pair from 2 sequences.

The pairs that are passed into the function are generated by itertools

Parameters

self	Reference to the current work queue object.
fn	The function that will be called on each element
seq1	The first seq that will be used to generate pairs
seq2	The second seq that will be used to generate pairs

References work_queue.WorkQueue.empty(), batch_queue_module.submit, batch_queue_module::@3.submit, work_queue.WorkQueue.submit(), and work_queue.WorkQueue.wait_for_tag().

def work_queue.WorkQueue.tree_reduce	(		self,
			fn,
			seq,
			chunk_size = 2
	)

Reduces a sequence until only one value is left, and then returns that value.

The sequence is reduced by passing a pair of elements into a function and then stores the result. It then makes a sequence from the results, and reduces again until one value is left.

If the sequence has an odd length, the last element gets reduced at the end.

Parameters

self	Reference to the current work queue object.
fn	The function that will be called on each element
seq	The seq that will be reduced
chunk_size	The number of elements per Task (for tree reduc, must be greater than 1)

References work_queue.WorkQueue.empty(), batch_queue_module.submit, batch_queue_module::@3.submit, work_queue.WorkQueue.submit(), and work_queue.WorkQueue.wait_for_tag().

def work_queue.WorkQueue.application_info

(

self

)

Should return a dictionary with information for the status display.

This method is meant to be overriden by custom applications.

The dictionary should be of the form:

{ "application_info" : {"values" : dict, "units" : dict} }

where "units" is an optional dictionary that indicates the units of the corresponding key in "values".

Parameters

self	Reference to the current work queue object.

For example:

>>> myapp.application_info()
{'application_info': {'values': {'size_max_output': 0.361962, 'current_chunksize': 65536}, 'units': {'size_max_output': 'MB'}}}

---

The documentation for this class was generated from the following file:

• work_queue.binding.py