def run(**kargs):
args = ParamDict(defaults)
args.from_dict(kargs)
ns = args.to_namespace()
#args = Namespace(args)
# print(dargs)
gpu = os.environ["CUDA_VISIBLE_DEVICES"]
# test exceptions
# if random.random() < 0.3:
# raise Exception("failled with params: \n{}".format(kargs))
a = tf.random_uniform([100000, 3])
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
d = tf.multiply(c, ns.x)
#d = tf.matmul(c, ns.x)
# cfg = tf.ConfigProto(log_device_placement=True)
# sess = tf.Session(config=cfg)
sess = tf.Session()
res = sess.run(d)
sess.close()
debug = "INSIDE GPU WORKER ---------------\n" \
"params: {params}\n" \
"using GPU: {env}\n " \
"result: \n {res}" \
"-----------------------------------".format(params=args, env=gpu, res=res)
tf.reset_default_graph()
return debug
'f_init': (str, "uniform", ["normal", "uniform"]),
'f_init_val': (float, 0.01),
'logit_bias': (bool, False),
# regularisation
'clip_grads': (bool, True),
# if true clips by local norm, else clip by norm of all gradients
'clip_local': (bool, True),
'clip_value': (float, 1.0),
'dropout': (bool, False),
'embed_dropout': (bool, True),
'keep_prob': (float, 0.95),
'l2_loss': (bool, True),
'l2_loss_coef': (float, 1e-6),
}
arg_dict = ParamDict(defaults)
def run(**kwargs):
arg_dict.from_dict(kwargs)
args = arg_dict.to_namespace()
# ======================================================================================
# Load Params, Prepare results assets
# ======================================================================================
# os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
# print(args.corpus)
# Experiment parameter summary
res_param_filename = os.path.join(args.out_dir,
"params_{id}.csv".format(id=args.run_id))