#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
import itertools
from guild import batch_util
def gen_trials(flags, _batch=None):
flag_list = [
_trial_flags(name, val) for name, val in sorted(flags.items())
]
return [dict(trial) for trial in itertools.product(*flag_list)]
def _trial_flags(flag_name, flag_val):
if isinstance(flag_val, list):
return [(flag_name, trial_val) for trial_val in flag_val]
return [(flag_name, flag_val)]
if __name__ == "__main__":
batch_util.default_main(gen_trials)
def _validate_min_max(val, dist_name, flag_name):
if dist_name not in (None, "uniform"):
raise batch_util.BatchError(
"unsupported distribution %r for flag %s - must be 'uniform'" %
(dist_name, flag_name))
if len(val) != 2:
raise batch_util.BatchError(
"unexpected arguemt list %r for flag %s - expected 2 arguments" %
(val, flag_name))
def _gen_trial_vals(dims, num_trials, random_seed):
import skopt
res = skopt.dummy_minimize(lambda *args: 0,
dims,
n_calls=num_trials,
random_state=random_seed)
return res.x_iters
def _patch_numpy_deprecation_warnings():
warnings.filterwarnings("ignore", category=Warning)
# pylint: disable=unused-variable
import numpy.core.umath_tests
if __name__ == "__main__":
_patch_numpy_deprecation_warnings()
batch_util.default_main(_gen_batch_trials)
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
import itertools
from guild import batch_util
def gen_trials(flags, _batch=None):
flag_list = [
_trial_flags(name, val) for name, val in sorted(flags.items())
]
return [dict(trial) for trial in itertools.product(*flag_list)]
def _trial_flags(flag_name, flag_val):
if isinstance(flag_val, list):
return [(flag_name, trial_val) for trial_val in flag_val]
return [(flag_name, flag_val)]
if __name__ == "__main__":
batch_util.default_main(gen_trials, default_max_trials=None)