def run_training(
config=None,
tuner=None,
logdir=None,
trial_name=None, # pylint: disable=unused-argument
is_chief=True):
"""Do all training runs.
This is the top level training function for policy gradient based models.
Run this from the main function.
Args:
config: config_lib.Config instance containing global config (agent and
environment hparams). If None, config will be parsed from FLAGS.config.
tuner: (unused) A tuner instance. Leave as None if not tuning.
logdir: Parent directory where all data from all runs will be written. If
None, FLAGS.logdir will be used.
trial_name: (unused) If tuning, set this to a unique string that identifies
this trial. If `tuner` is not None, this also must be set.
is_chief: True if this worker is the chief.
Returns:
List of results dicts which were written to disk. Each training run gets a
results dict. Results dict contains metrics, i.e. (name, value) pairs which
give information about the training run.
Raises:
ValueError: If FLAGS.num_workers does not divide FLAGS.num_repetitions.
ValueError: If results dicts read from disk contain invalid data.
"""
if not config:
# If custom config is not given, get it from flags.
config = defaults.default_config_with_updates(FLAGS.config)
if not logdir:
logdir = FLAGS.logdir
if FLAGS.num_repetitions % FLAGS.num_workers != 0:
raise ValueError('Number of workers must divide number of repetitions')
num_local_reps = FLAGS.num_repetitions // FLAGS.num_workers
logging.info('Running %d reps globally.', FLAGS.num_repetitions)
logging.info('This worker will run %d local reps.', num_local_reps)
if FLAGS.max_npe:
max_generations = FLAGS.max_npe // config.batch_size
logging.info('Max samples per rep: %d', FLAGS.max_npe)
logging.info('Max generations per rep: %d', max_generations)
else:
max_generations = sys.maxint
logging.info('Running unlimited generations.')
assert FLAGS.num_workers > 0
logging.info('Starting experiment. Directory: "%s"', logdir)
results = results_lib.Results(logdir, FLAGS.task_id)
local_results_list = results.read_this_shard()
if local_results_list:
if local_results_list[0]['max_npe'] != FLAGS.max_npe:
raise ValueError(
'Cannot resume training. Max-NPE changed. Was %s, now %s',
local_results_list[0]['max_npe'], FLAGS.max_npe)
if local_results_list[0][
'max_global_repetitions'] != FLAGS.num_repetitions:
raise ValueError(
'Cannot resume training. Number of repetitions changed. Was %s, '
'now %s', local_results_list[0]['max_global_repetitions'],
FLAGS.num_repetitions)
start_rep = len(local_results_list)
for rep in xrange(start_rep, num_local_reps):
global_rep = num_local_reps * FLAGS.task_id + rep
logging.info('Starting repetition: Rep = %d. (global rep = %d)', rep,
global_rep)
# Save data for each rep, like checkpoints, goes into separate folders.
run_dir = os.path.join(logdir, 'run_%d' % global_rep)
if not tf.gfile.IsDirectory(run_dir):
tf.gfile.MakeDirs(run_dir)
checkpoint_writer = CheckpointWriter(run_dir,
population_size=config.batch_size)
data_manager = data.DataManager(config, run_number=global_rep)
task_eval_fn = ga_lib.make_task_eval_fn(data_manager.rl_task)
if config.agent.algorithm == 'rand':
logging.info('Running random search.')
assert FLAGS.max_npe
result = run_random_search(FLAGS.max_npe, run_dir, task_eval_fn,
config.timestep_limit)
else:
assert config.agent.algorithm == 'ga'
logging.info('Running genetic algorithm.')
pop = ga_lib.make_population(ga_lib.random_individual(
config.timestep_limit),
n=config.batch_size)
hof = utils.MaxUniquePriorityQueue(2) # Hall of fame.
result = ga_lib.ga_loop(pop,
cxpb=config.agent.crossover_rate,
mutpb=config.agent.mutation_rate,
task_eval_fn=task_eval_fn,
ngen=max_generations,
halloffame=hof,
checkpoint_writer=checkpoint_writer)
logging.info('Finished rep. Num gens: %d', result.generations)
results_dict = {
'max_npe': FLAGS.max_npe,
'batch_size': config.batch_size,
'max_batches': FLAGS.max_npe // config.batch_size,
'npe': result.num_programs,
'max_global_repetitions': FLAGS.num_repetitions,
'max_local_repetitions': num_local_reps,
'code_solution': result.best_code if result.solution_found else '',
'best_reward': result.reward,
'num_batches': result.generations,
'found_solution': result.solution_found,
'task': data_manager.task_name,
'global_rep': global_rep
}
logging.info('results_dict: %s', results_dict)
results.append(results_dict)
if is_chief:
logging.info(
'Worker is chief. Waiting for all workers to finish so that results '
'can be reported to the tuner.')
global_results_list, shard_stats = results.read_all(
num_shards=FLAGS.num_workers)
while not all(s.finished for s in shard_stats):
logging.info(
'Still waiting on these workers: %s', ', '.join([
'%d (%d reps left)' %
(i, s.max_local_reps - s.num_local_reps_completed)
for i, s in enumerate(shard_stats) if not s.finished
]))
sleep(60)
global_results_list, shard_stats = results.read_all(
num_shards=FLAGS.num_workers)
logging.info(
'%d results obtained. Chief worker is exiting the experiment.',
len(global_results_list))
return global_results_list
def run_training(config=None, tuner=None, logdir=None, trial_name=None, # pylint: disable=unused-argument
is_chief=True):
"""Do all training runs.
This is the top level training function for policy gradient based models.
Run this from the main function.
Args:
config: config_lib.Config instance containing global config (agent and
environment hparams). If None, config will be parsed from FLAGS.config.
tuner: (unused) A tuner instance. Leave as None if not tuning.
logdir: Parent directory where all data from all runs will be written. If
None, FLAGS.logdir will be used.
trial_name: (unused) If tuning, set this to a unique string that identifies
this trial. If `tuner` is not None, this also must be set.
is_chief: True if this worker is the chief.
Returns:
List of results dicts which were written to disk. Each training run gets a
results dict. Results dict contains metrics, i.e. (name, value) pairs which
give information about the training run.
Raises:
ValueError: If FLAGS.num_workers does not divide FLAGS.num_repetitions.
ValueError: If results dicts read from disk contain invalid data.
"""
if not config:
# If custom config is not given, get it from flags.
config = defaults.default_config_with_updates(FLAGS.config)
if not logdir:
logdir = FLAGS.logdir
if FLAGS.num_repetitions % FLAGS.num_workers != 0:
raise ValueError('Number of workers must divide number of repetitions')
num_local_reps = FLAGS.num_repetitions // FLAGS.num_workers
logging.info('Running %d reps globally.', FLAGS.num_repetitions)
logging.info('This worker will run %d local reps.', num_local_reps)
if FLAGS.max_npe:
max_generations = FLAGS.max_npe // config.batch_size
logging.info('Max samples per rep: %d', FLAGS.max_npe)
logging.info('Max generations per rep: %d', max_generations)
else:
max_generations = sys.maxint
logging.info('Running unlimited generations.')
assert FLAGS.num_workers > 0
logging.info('Starting experiment. Directory: "%s"', logdir)
results = results_lib.Results(logdir, FLAGS.task_id)
local_results_list = results.read_this_shard()
if local_results_list:
if local_results_list[0]['max_npe'] != FLAGS.max_npe:
raise ValueError(
'Cannot resume training. Max-NPE changed. Was %s, now %s',
local_results_list[0]['max_npe'], FLAGS.max_npe)
if local_results_list[0]['max_global_repetitions'] != FLAGS.num_repetitions:
raise ValueError(
'Cannot resume training. Number of repetitions changed. Was %s, '
'now %s',
local_results_list[0]['max_global_repetitions'],
FLAGS.num_repetitions)
start_rep = len(local_results_list)
for rep in xrange(start_rep, num_local_reps):
global_rep = num_local_reps * FLAGS.task_id + rep
logging.info(
'Starting repetition: Rep = %d. (global rep = %d)',
rep, global_rep)
# Save data for each rep, like checkpoints, goes into separate folders.
run_dir = os.path.join(logdir, 'run_%d' % global_rep)
if not tf.gfile.IsDirectory(run_dir):
tf.gfile.MakeDirs(run_dir)
checkpoint_writer = CheckpointWriter(run_dir,
population_size=config.batch_size)
data_manager = data.DataManager(config, run_number=global_rep)
task_eval_fn = ga_lib.make_task_eval_fn(data_manager.rl_task)
if config.agent.algorithm == 'rand':
logging.info('Running random search.')
assert FLAGS.max_npe
result = run_random_search(
FLAGS.max_npe, run_dir, task_eval_fn, config.timestep_limit)
else:
assert config.agent.algorithm == 'ga'
logging.info('Running genetic algorithm.')
pop = ga_lib.make_population(
ga_lib.random_individual(config.timestep_limit),
n=config.batch_size)
hof = utils.MaxUniquePriorityQueue(2) # Hall of fame.
result = ga_lib.ga_loop(
pop,
cxpb=config.agent.crossover_rate, mutpb=config.agent.mutation_rate,
task_eval_fn=task_eval_fn,
ngen=max_generations, halloffame=hof,
checkpoint_writer=checkpoint_writer)
logging.info('Finished rep. Num gens: %d', result.generations)
results_dict = {
'max_npe': FLAGS.max_npe,
'batch_size': config.batch_size,
'max_batches': FLAGS.max_npe // config.batch_size,
'npe': result.num_programs,
'max_global_repetitions': FLAGS.num_repetitions,
'max_local_repetitions': num_local_reps,
'code_solution': result.best_code if result.solution_found else '',
'best_reward': result.reward,
'num_batches': result.generations,
'found_solution': result.solution_found,
'task': data_manager.task_name,
'global_rep': global_rep}
logging.info('results_dict: %s', results_dict)
results.append(results_dict)
if is_chief:
logging.info(
'Worker is chief. Waiting for all workers to finish so that results '
'can be reported to the tuner.')
global_results_list, shard_stats = results.read_all(
num_shards=FLAGS.num_workers)
while not all(s.finished for s in shard_stats):
logging.info(
'Still waiting on these workers: %s',
', '.join(
['%d (%d reps left)'
% (i, s.max_local_reps - s.num_local_reps_completed)
for i, s in enumerate(shard_stats)
if not s.finished]))
sleep(60)
global_results_list, shard_stats = results.read_all(
num_shards=FLAGS.num_workers)
logging.info(
'%d results obtained. Chief worker is exiting the experiment.',
len(global_results_list))
return global_results_list