parsl.executors.HighThroughputExecutor

class parsl.executors.HighThroughputExecutor(label: str = 'HighThroughputExecutor', provider: parsl.providers.provider_base.ExecutionProvider = LocalProvider( channel=LocalChannel( envs={}, script_dir=None, userhome='/home/docs/checkouts/readthedocs.org/user_builds/parsl/checkouts/latest/docs' ), cmd_timeout=30, init_blocks=4, launcher=SingleNodeLauncher(), max_blocks=10, min_blocks=0, move_files=None, nodes_per_block=1, parallelism=1, walltime='00:15:00', worker_init='' ), launch_cmd: Optional[str] = None, address: Optional[str] = None, worker_ports: Optional[Tuple[int, int]] = None, worker_port_range: Optional[Tuple[int, int]] = (54000, 55000), interchange_port_range: Optional[Tuple[int, int]] = (55000, 56000), storage_access: Optional[List[parsl.data_provider.staging.Staging]] = None, working_dir: Optional[str] = None, worker_debug: bool = False, cores_per_worker: float = 1.0, mem_per_worker: Optional[float] = None, max_workers: Union[int, float] = inf, prefetch_capacity: int = 0, heartbeat_threshold: int = 120, heartbeat_period: int = 30, poll_period: int = 10, suppress_failure: bool = True, managed: bool = True, worker_logdir_root: Optional[str] = None)[source]

Executor designed for cluster-scale

The HighThroughputExecutor system has the following components:
  1. The HighThroughputExecutor instance which is run as part of the Parsl script.
  2. The Interchange which is acts as a load-balancing proxy between workers and Parsl
  3. The multiprocessing based worker pool which coordinates task execution over several cores on a node.
  4. ZeroMQ pipes connect the HighThroughputExecutor, Interchange and the process_worker_pool

Here is a diagram

             |  Data   |  Executor   |  Interchange  | External Process(es)
             |  Flow   |             |               |
        Task | Kernel  |             |               |
      +----->|-------->|------------>|->outgoing_q---|-> process_worker_pool
      |      |         |             | batching      |    |         |
Parsl<---Fut-|         |             | load-balancing|  result   exception
          ^  |         |             | watchdogs     |    |         |
          |  |         |   Q_mngmnt  |               |    V         V
          |  |         |    Thread<--|-incoming_q<---|--- +---------+
          |  |         |      |      |               |
          |  |         |      |      |               |
          +----update_fut-----+

Each of the workers in each process_worker_pool has access to its local rank through an environmental variable, PARSL_WORKER_RANK. The local rank is unique for each process and is an integer in the range from 0 to the number of workers per in the pool minus 1. The workers also have access to the ID of the worker pool as PARSL_WORKER_POOL_ID and the size of the worker pool as PARSL_WORKER_COUNT.

Parameters:
  • provider (ExecutionProvider) –
    Provider to access computation resources. Can be one of EC2Provider,
    Cobalt, Condor, GoogleCloud, GridEngine, Jetstream, Local, GridEngine, Slurm, or Torque.
  • label (str) – Label for this executor instance.
  • launch_cmd (str) – Command line string to launch the process_worker_pool from the provider. The command line string will be formatted with appropriate values for the following values (debug, task_url, result_url, cores_per_worker, nodes_per_block, heartbeat_period ,heartbeat_threshold, logdir). For example: launch_cmd=”process_worker_pool.py {debug} -c {cores_per_worker} –task_url={task_url} –result_url={result_url}”
  • address (string) – An address to connect to the main Parsl process which is reachable from the network in which workers will be running. This can be either a hostname as returned by hostname or an IP address. Most login nodes on clusters have several network interfaces available, only some of which can be reached from the compute nodes. By default, the executor will attempt to enumerate and connect through all possible addresses. Setting an address here overrides the default behavior. default=None
  • worker_ports ((int, int)) – Specify the ports to be used by workers to connect to Parsl. If this option is specified, worker_port_range will not be honored.
  • worker_port_range ((int, int)) – Worker ports will be chosen between the two integers provided.
  • interchange_port_range ((int, int)) – Port range used by Parsl to communicate with the Interchange.
  • working_dir (str) – Working dir to be used by the executor.
  • worker_debug (Bool) – Enables worker debug logging.
  • managed (Bool) – If this executor is managed by the DFK or externally handled.
  • cores_per_worker (float) – cores to be assigned to each worker. Oversubscription is possible by setting cores_per_worker < 1.0. Default=1
  • mem_per_worker (float) – GB of memory required per worker. If this option is specified, the node manager will check the available memory at startup and limit the number of workers such that the there’s sufficient memory for each worker. Default: None
  • max_workers (int) – Caps the number of workers launched by the manager. Default: infinity
  • prefetch_capacity (int) – Number of tasks that could be prefetched over available worker capacity. When there are a few tasks (<100) or when tasks are long running, this option should be set to 0 for better load balancing. Default is 0.
  • suppress_failure (Bool) – If set, the interchange will suppress failures rather than terminate early. Default: True
  • heartbeat_threshold (int) – Seconds since the last message from the counterpart in the communication pair: (interchange, manager) after which the counterpart is assumed to be un-available. Default: 120s
  • heartbeat_period (int) – Number of seconds after which a heartbeat message indicating liveness is sent to the counterpart (interchange, manager). Default: 30s
  • poll_period (int) – Timeout period to be used by the executor components in milliseconds. Increasing poll_periods trades performance for cpu efficiency. Default: 10ms
  • worker_logdir_root (string) – In case of a remote file system, specify the path to where logs will be kept.
__init__(label: str = 'HighThroughputExecutor', provider: parsl.providers.provider_base.ExecutionProvider = LocalProvider( channel=LocalChannel( envs={}, script_dir=None, userhome='/home/docs/checkouts/readthedocs.org/user_builds/parsl/checkouts/latest/docs' ), cmd_timeout=30, init_blocks=4, launcher=SingleNodeLauncher(), max_blocks=10, min_blocks=0, move_files=None, nodes_per_block=1, parallelism=1, walltime='00:15:00', worker_init='' ), launch_cmd: Optional[str] = None, address: Optional[str] = None, worker_ports: Optional[Tuple[int, int]] = None, worker_port_range: Optional[Tuple[int, int]] = (54000, 55000), interchange_port_range: Optional[Tuple[int, int]] = (55000, 56000), storage_access: Optional[List[parsl.data_provider.staging.Staging]] = None, working_dir: Optional[str] = None, worker_debug: bool = False, cores_per_worker: float = 1.0, mem_per_worker: Optional[float] = None, max_workers: Union[int, float] = inf, prefetch_capacity: int = 0, heartbeat_threshold: int = 120, heartbeat_period: int = 30, poll_period: int = 10, suppress_failure: bool = True, managed: bool = True, worker_logdir_root: Optional[str] = None)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(label, provider, script_dir=None, …) Initialize self.
hold_worker(worker_id) Puts a worker on hold, preventing scheduling of additional tasks to it.
initialize_scaling() Compose the launch command and call the scale_out
scale_in([blocks, block_ids]) Scale in the number of active blocks by specified amount.
scale_out([blocks]) Scales out the number of blocks by “blocks”
shutdown([hub, targets, block]) Shutdown the executor, including all workers and controllers.
start() Create the Interchange process and connect to it.
status() Return status of all blocks.
submit(func, *args, **kwargs) Submits work to the the outgoing_q.
weakref_cb([q]) We do not use this yet.

Attributes

connected_managers
connected_workers
hub_address Address to the Hub for monitoring.
hub_port Port to the Hub for monitoring.
outstanding
run_dir Path to the run directory.
scaling_enabled Specify if scaling is enabled.