Source code for tensorlayer.distributed

#! /usr/bin/python
# -*- coding: utf-8 -*-

import json, os, time
import tensorflow as tf
from tensorflow.python.training import session_run_hook

__all__ = ['TaskSpecDef', 'TaskSpec', 'DistributedSession', 'StopAtTimeHook', 'LoadCheckpoint']


[docs]class TaskSpecDef(object): """Specification for a distributed task. It contains the job name, index of the task, the parameter servers and the worker servers. If you want to use the last worker for continuous evaluation you can call the method `use_last_worker_as_evaluator` which returns a new :class:`TaskSpecDef` object without the last worker in the cluster specification. Parameters ---------- task_type : str Task type. One of `master`, `worker` or `ps`. index : int The zero-based index of the task. Distributed training jobs will have a single master task, one or more parameter servers, and one or more workers. trial : int The identifier of the trial being run. ps_hosts : str OR list of str A string with a coma separate list of hosts for the parameter servers or a list of hosts. worker_hosts : str OR list of str A string with a coma separate list of hosts for the worker servers or a list of hosts. master : str A string with the master hosts Notes ---------- master might not be included in TF_CONFIG and can be None. The shard_index is adjusted in any case to assign 0 to master and >= 1 to workers. This implementation doesn't support sparse arrays in the `TF_CONFIG` variable as the official TensorFlow documentation shows, as it is not a supported by the json definition. References ---------- - `ML-engine trainer considerations <https://cloud.google.com/ml-engine/docs/trainer-considerations#use_tf_config>`__ """ def __init__(self, task_type='master', index=0, trial=None, ps_hosts=None, worker_hosts=None, master=None): self.type = task_type self._index = int(index) self._cluster_spec = None self.num_workers = 1 self.num_ps = 0 self.shard_index = int(index) self._master = True self.trial = trial self.ps_hosts = ps_hosts self.worker_hosts = worker_hosts self.master = master self._server = None if ps_hosts and worker_hosts: self.ps_hosts = ps_hosts if isinstance(ps_hosts, list) else ps_hosts.split(',') self.num_ps = len(self.ps_hosts) self.worker_hosts = worker_hosts if isinstance(worker_hosts, list) else worker_hosts.split(',') if master is not None and len(master) > 0: self._cluster_spec = tf.train.ClusterSpec({'ps': self.ps_hosts, 'worker': self.worker_hosts, 'master': master}) # master is a worker too self.num_workers = len(self.worker_hosts) + 1 if self.type == 'worker': self.shard_index = self._index + 1 self._master = self.type == 'master' else: self._cluster_spec = tf.train.ClusterSpec({'ps': self.ps_hosts, 'worker': self.worker_hosts}) self.num_workers = len(self.worker_hosts) if self.type == 'worker': self.shard_index = self._index self._master = self.type == 'worker' and self._index == 0 def is_ps(self): """Returns true if this server is a parameter server""" return self.type == 'ps' def is_worker(self): """Returns true if this server is a worker server""" return self.type == 'worker' def is_master(self): """Returns true if this server is the master server""" return self._master def is_evaluator(self): """Returns true if this server is the evaluator server""" return self.type == 'worker' and self.num_workers == self._index def device_fn(self): """Returns the function with the specification to create the graph in this server""" current_device = '/job:{}/task:{}'.format(self.type, self._index) ps_devices = '/job:ps' return tf.train.replica_device_setter(ps_device=ps_devices, worker_device=current_device, cluster=self._cluster_spec) def create_server(self): if self._server is None and self.ps_hosts and self.worker_hosts and not self.is_evaluator(): # create server and join if it is a parameter server self._server = tf.train.Server(self._cluster_spec, job_name=self.type, task_index=self._index) if self.is_ps(): self._server.join() def target(self): if self._server is None: self.create_server() if self._server is not None: return self._server.target else: return None def use_last_worker_as_evaluator(self): """Returns a new :class:`TaskSpecDef` where the last worker has been removed from the list of worker_hosts, so it is not used for training anymore. You can call is_evaluator to know whether this server is the evaluator one or not. In case there is only one server for training this method raises an exception, as you cannot use any server for evaluation. """ if self.num_workers <= 1: raise Exception('You need more than one worker instance to use one as evaluator') return TaskSpecDef(
task_type=self.type, index=self._index, trial=self.trial, ps_hosts=self.ps_hosts, worker_hosts=self.worker_hosts[:-1], master=self.master) def create_task_spec_def(): """Returns the a :class:`TaskSpecDef` based on the environment variables for distributed training. References ---------- - `ML-engine trainer considerations <https://cloud.google.com/ml-engine/docs/trainer-considerations#use_tf_config>`__ - `TensorPort Distributed Computing <https://www.tensorport.com/documentation/code-details/>`__ """ if 'TF_CONFIG' in os.environ: # TF_CONFIG is used in ML-engine env = json.loads(os.environ.get('TF_CONFIG', '{}')) task_data = env.get('task', None) or {'type': 'master', 'index': 0} cluster_data = env.get('cluster', None) or {'ps': None, 'worker': None, 'master': None} return TaskSpecDef( task_type=task_data['type'], index=task_data['index'], trial=task_data['trial'] if 'trial' in task_data else None, ps_hosts=cluster_data['ps'], worker_hosts=cluster_data['worker'], master=cluster_data['master'] if 'master' in cluster_data else None) elif 'JOB_NAME' in os.environ: # JOB_NAME, TASK_INDEX, PS_HOSTS, WORKER_HOSTS and MASTER_HOST are used in TensorPort return TaskSpecDef( task_type=os.environ['JOB_NAME'], index=os.environ['TASK_INDEX'], ps_hosts=os.environ.get('PS_HOSTS', None), worker_hosts=os.environ.get('WORKER_HOSTS', None), master=os.environ.get('MASTER_HOST', None)) else: raise Exception('You need to setup TF_CONFIG or JOB_NAME to define the task.') def create_distributed_session(task_spec=None, checkpoint_dir=None, scaffold=None, hooks=None, chief_only_hooks=None, save_checkpoint_secs=600, save_summaries_steps=object(), save_summaries_secs=object(), config=None, stop_grace_period_secs=120, log_step_count_steps=100): """Creates a distributed session. It calls `MonitoredTrainingSession` to create a :class:`MonitoredSession` for distributed training. Parameters ---------- task_spec : :class:`TaskSpecDef`. The task spec definition from create_task_spec_def() checkpoint_dir : str. Optional path to a directory where to restore variables. scaffold : ``Scaffold`` A `Scaffold` used for gathering or building supportive ops. If not specified, a default one is created. It's used to finalize the graph. hooks : list of ``SessionRunHook`` objects. Optional chief_only_hooks : list of ``SessionRunHook`` objects. Activate these hooks if `is_chief==True`, ignore otherwise. save_checkpoint_secs : int The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If `save_checkpoint_secs` is set to `None`, then the default checkpoint saver isn't used. save_summaries_steps : int The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If both `save_summaries_steps` and `save_summaries_secs` are set to `None`, then the default summary saver isn't used. Default 100. save_summaries_secs : int The frequency, in secs, that the summaries are written to disk using a default summary saver. If both `save_summaries_steps` and `save_summaries_secs` are set to `None`, then the default summary saver isn't used. Default not enabled. config : ``tf.ConfigProto`` an instance of `tf.ConfigProto` proto used to configure the session. It's the `config` argument of constructor of `tf.Session`. stop_grace_period_secs : int Number of seconds given to threads to stop after `close()` has been called. log_step_count_steps : int The frequency, in number of global steps, that the global step/sec is logged. Examples -------- A simple example for distributed training where all the workers use the same dataset: >>> task_spec = TaskSpec() >>> with tf.device(task_spec.device_fn()): >>> tensors = create_graph() >>> with tl.DistributedSession(task_spec=task_spec, ... checkpoint_dir='/tmp/ckpt') as session: >>> while not session.should_stop(): >>> session.run(tensors) An example where the dataset is shared among the workers (see https://www.tensorflow.org/programmers_guide/datasets): >>> task_spec = TaskSpec() >>> # dataset is a :class:`tf.data.Dataset` with the raw data >>> dataset = create_dataset() >>> if task_spec is not None: >>> dataset = dataset.shard(task_spec.num_workers, task_spec.shard_index) >>> # shuffle or apply a map function to the new sharded dataset, for example: >>> dataset = dataset.shuffle(buffer_size=10000) >>> dataset = dataset.batch(batch_size) >>> dataset = dataset.repeat(num_epochs) >>> # create the iterator for the dataset and the input tensor >>> iterator = dataset.make_one_shot_iterator() >>> next_element = iterator.get_next() >>> with tf.device(task_spec.device_fn()): >>> # next_element is the input for the graph >>> tensors = create_graph(next_element) >>> with tl.DistributedSession(task_spec=task_spec, ... checkpoint_dir='/tmp/ckpt') as session: >>> while not session.should_stop(): >>> session.run(tensors) References ---------- - `MonitoredTrainingSession <https://www.tensorflow.org/api_docs/python/tf/train/MonitoredTrainingSession>`__ """ target = task_spec.target() if task_spec is not None else None is_chief = task_spec.is_master() if task_spec is not None else True return tf.train.MonitoredTrainingSession( master=target, is_chief=is_chief, checkpoint_dir=checkpoint_dir, scaffold=scaffold, save_checkpoint_secs=save_checkpoint_secs, save_summaries_steps=save_summaries_steps, save_summaries_secs=save_summaries_secs, log_step_count_steps=log_step_count_steps, stop_grace_period_secs=stop_grace_period_secs, config=config, hooks=hooks, chief_only_hooks=chief_only_hooks)
[docs]class StopAtTimeHook(session_run_hook.SessionRunHook): """Hook that requests stop after a specified time. Parameters ---------- time_running: int Maximum time running in seconds """ def __init__(self, time_running): self._time_running = time_running self._end_time = 0 def begin(self): self._end_time = time.time() + self._time_running def after_run(self, run_context, run_values): if time.time() > self._end_time:
run_context.request_stop()
[docs]class LoadCheckpoint(session_run_hook.SessionRunHook): """Hook that loads a checkpoint after the session is created. >>> from tensorflow.python.ops import variables as tf_variables >>> from tensorflow.python.training.monitored_session import SingularMonitoredSession >>> >>> tensors = create_graph() >>> saver = tf.train.Saver(var_list=tf_variables.trainable_variables()) >>> checkpoint_hook = LoadCheckpoint(saver, my_checkpoint_file) >>> with tf.SingularMonitoredSession(hooks=[checkpoint_hook]) as session: >>> while not session.should_stop(): >>> session.run(tensors) """ def __init__(self, saver, checkpoint): self._saver = saver self._checkpoint = checkpoint self._loaded = False def after_create_session(self, session, coord): if not self._loaded: self._loaded = True
self._saver.restore(self._checkpoint) # Alias TaskSpec = create_task_spec_def DistributedSession = create_distributed_session