import os
import json
from math import ceil, log
from pprint import pprint, pformat
from mpi4py import MPI
import math
from nas4candle.nasapi.evaluator import Evaluator
from nas4candle.nasapi.search import util, Search
from nas4candle.nasapi.search.nas.agent import nas_ppo_async_a3c_emb
logger = util.conf_logger('nas4candle.nasapi.search.nas.ppo_a3c_async')
def print_logs(runner):
logger.debug('num_episodes = {}'.format(runner.global_episode))
logger.debug(' workers = {}'.format(runner.workers))
def key(d):
return json.dumps(dict(arch_seq=d['arch_seq']))
LAUNCHER_NODES = int(os.environ.get('BALSAM_LAUNCHER_NODES', 1))
WORKERS_PER_NODE = int(os.environ.get('nas4candle.nasapi_WORKERS_PER_NODE', 1))
class NasPPOAsyncA3C(Search):
def __init__(self, problem, run, evaluator, **kwargs):
self.rank = MPI.COMM_WORLD.Get_rank()
from random import random
import traceback
import numpy as np
from tensorflow import keras
from nas4candle.nasapi.search import util
from nas4candle.nasapi.search.nas.model.trainer.classifier_train_valid import \
TrainerClassifierTrainValid
from nas4candle.nasapi.search.nas.model.trainer.regressor_train_valid import \
TrainerRegressorTrainValid
logger = util.conf_logger('nas4candle.nasapi.search.nas.run')
def run(config):
# load functions
load_data = util.load_attr_from(config['load_data']['func'])
config['load_data']['func'] = load_data
config['create_structure']['func'] = util.load_attr_from(
config['create_structure']['func'])
# Loading data
kwargs = config['load_data'].get('kwargs')
data = load_data() if kwargs is None else load_data(**kwargs)
logger.info(f'Data loaded with kwargs: {kwargs}')
# Set data shape
if type(data) is tuple:
if len(data) != 2:
raise RuntimeError(
import tensorflow as tf
from tensorflow import keras
import numpy as np
import math
from sklearn.metrics import mean_squared_error
import nas4candle.nasapi.search.nas.model.arch as a
import nas4candle.nasapi.search.nas.model.train_utils as U
from nas4candle.nasapi.search import util
from nas4candle.nasapi.search.nas.utils._logging import JsonMessage as jm
logger = util.conf_logger('nas4candle.nasapi.model.trainer')
class TrainerTrainValid:
def __init__(self, config, model):
self.cname = self.__class__.__name__
self.config = config
self.model = model
self.callbacks = []
self.data = self.config[a.data]
self.config_hp = self.config[a.hyperparameters]
self.optimizer_name = self.config_hp[a.optimizer]
self.loss_metric_name = self.config_hp[a.loss_metric]
self.metrics_name = [U.selectMetric(m) for m in self.config_hp[a.metrics]]
self.batch_size = self.config_hp[a.batch_size]
self.learning_rate = self.config_hp[a.learning_rate]
self.num_epochs = self.config_hp[a.num_epochs]
import json
import os.path as osp
import numpy as np
import tensorflow as tf
from mpi4py import MPI
import nas4candle.nasapi.search.nas.utils.common.tf_util as U
from nas4candle.nasapi.evaluator import Evaluator
from nas4candle.nasapi.search.nas.env import NasEnv
from nas4candle.nasapi.search.nas.utils import bench, logger
from nas4candle.nasapi.search.nas.utils.common import set_global_seeds
from nas4candle.nasapi.search import util
from nas4candle.nasapi.search.nas.utils._logging import JsonMessage as jm
dh_logger = util.conf_logger('nas4candle.nasapi.search.nas.agent.nas_random')
def traj_segment_generator(env, horizon):
t = 0
ac = env.action_space.sample() # not used, just so we have the datatype
new = True # marks if we're on first timestep of an episode
ob = env.reset()
cur_ep_ret = 0 # return in current episode
cur_ep_len = 0 # len of current episode
ep_rets = [] # returns of completed episodes in this segment
ep_lens = [] # lengths of ...
ts_i2n_ep = {}
import numpy as np
import tensorflow as tf
from mpi4py import MPI
import nas4candle.nasapi.search.nas.utils.common.tf_util as U
from nas4candle.nasapi.search import util
from nas4candle.nasapi.search.nas.agent.utils import (
reward_for_final_timestep, traj_segment_generator)
from nas4candle.nasapi.search.nas.utils._logging import JsonMessage as jm
from nas4candle.nasapi.search.nas.utils.common import (Dataset,
explained_variance,
fmt_row, zipsame)
from nas4candle.nasapi.search.nas.utils.common.mpi_adam_async import MpiAdamAsync
from nas4candle.nasapi.search.nas.utils.common.mpi_moments import mpi_moments
dh_logger = util.conf_logger('nas4candle.nasapi.search.nas.agent.pposgd_async')
def add_vtarg_and_adv(seg, gamma, lam):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
"""
new = np.append(
seg["new"], 0
) # last element is only used for last vtarg, but we already zeroed it if last new = 1
vpred = np.append(seg["vpred"], seg["nextvpred"])
T = len(seg["rew"])
seg["adv"] = gaelam = np.empty(T, 'float32')
rew = seg["rew"]
lastgaelam = 0
for t in reversed(range(T)):
* ``acq-func`` : Acquisition function
* ``LCB`` :
* ``EI`` :
* ``PI`` :
* ``gp_hedge`` : (default)
"""
import signal
from nas4candle.nasapi.search.hps.optimizer import Optimizer
from nas4candle.nasapi.search import Search
from nas4candle.nasapi.search import util
logger = util.conf_logger('nas4candle.nasapi.search.hps.ambs')
SERVICE_PERIOD = 2 # Delay (seconds) between main loop iterations
CHECKPOINT_INTERVAL = 10 # How many jobs to complete between optimizer checkpoints
EXIT_FLAG = False
def on_exit(signum, stack):
global EXIT_FLAG
EXIT_FLAG = True
class AMBS(Search):
def __init__(self, problem, run, evaluator, **kwargs):
super().__init__(problem, run, evaluator, **kwargs)
logger.info("Initializing AMBS")
import time
import numpy as np
import tensorflow as tf
from mpi4py import MPI
import nas4candle.nasapi.search.nas.utils.common.tf_util as U
from nas4candle.nasapi.search import util
from nas4candle.nasapi.search.nas.utils._logging import JsonMessage as jm
TAG_UPDATE_START = 1
TAG_UPDATE_DONE = 2
dh_logger = util.conf_logger(
'nas4candle.nasapi.baselines.common.mpi_adam_async')
class MpiAdamAsync(object):
def __init__(self,
var_list,
*,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
scale_grad_by_procs=True,
comm=None):
self.var_list = var_list
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
self.scale_grad_by_procs = scale_grad_by_procs
