github/autogen
1
1
Fork 0
mirror of https://github.com/microsoft/autogen.git synced 2026-04-20 03:02:16 -04:00
Code Issues Packages Projects Releases Wiki Activity

fixing use_ray in automl.py (#531)

Browse Source 
* fixing use_ray
This commit is contained in:
Xueqing Liu
2022-05-02 11:05:23 -04:00
committed by GitHub
parent dab0a3f6e5
commit c1e1299855
1 changed files with 7 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
flaml/automl.py
View File

@@ -1891,7 +1891,7 @@ class AutoML(BaseEstimator):
for estimator in self.estimator_list:
search_state = self._search_states[estimator]
if not hasattr(search_state, "training_function"):
if self._use_ray:
if self._use_ray is not False:
from ray.tune import with_parameters
search_state.training_function = with_parameters(
@@ -2292,7 +2292,7 @@ class AutoML(BaseEstimator):
self._use_ray = use_ray or n_concurrent_trials > 1
# use the following condition if we have an estimation of average_trial_time and average_trial_overhead
# self._use_ray = use_ray or n_concurrent_trials > ( average_trail_time + average_trial_overhead) / (average_trial_time)
if self._use_ray:
if self._use_ray is not False:
import ray
n_cpus = use_ray and ray.available_resources()["CPU"] or os.cpu_count()
@@ -2358,7 +2358,7 @@ class AutoML(BaseEstimator):
self._state.retrain_final = (
retrain_full is True
and eval_method == "holdout"
and (self._state.X_val is None or self._use_ray)
and (self._state.X_val is None or self._use_ray is not False)
or eval_method == "cv"
and (max_iter > 0 or retrain_full is True)
or max_iter == 1
@@ -2557,7 +2557,9 @@ class AutoML(BaseEstimator):
if hpo_method != "auto"
else (
"bs"
if n_concurrent_trials > 1 or self._use_ray and len(estimator_list) > 1
if n_concurrent_trials > 1
or self._use_ray is not False
and len(estimator_list) > 1
else "cfo"
)
)
@@ -3114,7 +3116,7 @@ class AutoML(BaseEstimator):
if isinstance(state.init_config, dict)
else state.init_config[0]
)
elif not self._use_ray:
elif self._use_ray is False:
self._search_sequential()
else:
self._search_parallel()