[Security] load --> safe_load (#1987)

* load --> safe_load

* retrigger ci

* Update setup.py

* fix for fairscale

* up-pin fairscale version

* Update test_core.sh

(cherry picked from commit 5f4bb24)

.github/workflow_scripts/test_core.sh

@@ -6,6 +6,7 @@ source $(dirname "$0")/env_setup.sh
 
 setup_build_env
 install_core_all_tests
+python3 -m pip install ray_lightning==0.2.0  # TODO Change this line once we support ray_lightning 0.3.0
 
 cd core/
 python3 -m pytest --junitxml=results.xml --runslow tests

core/setup.py

@@ -49,7 +49,7 @@
 
 tests_require = [
     'pytest',
-    'ray_lightning>=0.2.0,<0.3.0',  # test ray lightning resource calculation
+    # TODO(Re-enable ray_lightning once it released 0.3.0) 'ray_lightning>=0.2.0,<0.3.0'
 ]
 
 all_requires = []

multimodal/setup.py

@@ -30,7 +30,7 @@
     'torch>=1.9,<1.13',
     'torchvision<0.14.0',
     'torchtext<0.14.0',
-    'fairscale>=0.4.5,<0.5.0',
+    'fairscale>=0.4.5,<=0.4.6',
     'scikit-image>=0.19.1,<0.20.0',
     'smart_open>=5.2.1,<5.3.0',
     'pytorch_lightning>=1.6.0,<1.7.0',

multimodal/src/autogluon/multimodal/predictor.py

@@ -1121,7 +1121,8 @@ def _top_k_average(
         best_k_models_yaml_path = os.path.join(save_path, BEST_K_MODELS_FILE)
         if os.path.exists(best_k_models_yaml_path):
             with open(best_k_models_yaml_path, "r") as f:
-                best_k_models = yaml.load(f, Loader=yaml.Loader)
+                best_k_models = yaml.safe_load(f)
+
         else:
             # In some cases, the training ends up too early (e.g., due to time_limit) so that there is
             # no saved best_k model checkpoints. In that scenario, we won't perform any model averaging.