diff --git a/.github/workflow_scripts/env_setup.sh b/.github/workflow_scripts/env_setup.sh
index 64ce1021fb5..51409f1902a 100644
--- a/.github/workflow_scripts/env_setup.sh
+++ b/.github/workflow_scripts/env_setup.sh
@@ -21,7 +21,7 @@ function setup_mxnet_gpu {
 }
 
 function setup_torch_gpu {
-    python3 -m pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
+    python3 -m pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
 }
 
 function install_common {
diff --git a/multimodal/setup.py b/multimodal/setup.py
index c7c0088a337..84cd4a4370c 100644
--- a/multimodal/setup.py
+++ b/multimodal/setup.py
@@ -32,24 +32,25 @@
     "seqeval<=1.2.2",
     "evaluate<=0.3.0",
     "accelerate>=0.9,<0.14",
+    "tensorboard<2.12.0",
     "timm<0.7.0",
-    "torch>=1.9,<1.13",
-    "torchvision<0.14.0",
-    "torchtext<0.14.0",
+    "torch>=1.9,<1.14",
+    "torchvision<0.15.0",
+    "torchtext<0.15.0",
     "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>=1.7.4,<1.8.0",
+    "pytorch_lightning>=1.7.4,<1.9.0",
     "text-unidecode<=1.3",
-    "torchmetrics>=0.8.0,<0.9.0",
-    "transformers>=4.23.0,<4.24.0",
+    "torchmetrics>=0.8.0,<0.11.0",
+    "transformers>=4.23.0,<4.26.0",
     "nptyping>=1.4.4,<1.5.0",
     "omegaconf>=2.1.1,<2.2.0",
     "sentencepiece>=0.1.95,<0.2.0",
     f"autogluon.core[raytune]=={version}",
     f"autogluon.features=={version}",
     f"autogluon.common=={version}",
-    "pytorch-metric-learning>=1.3.0,<1.4.0",
+    "pytorch-metric-learning>=1.3.0,<1.7.0",
     "nlpaug>=1.1.10,<=1.1.10",
     "nltk>=3.4.5,<4.0.0",
     "openmim>0.1.5,<=0.2.1",
diff --git a/multimodal/src/autogluon/multimodal/data/templates.py b/multimodal/src/autogluon/multimodal/data/templates.py
index df539d77d56..7e55716a916 100644
--- a/multimodal/src/autogluon/multimodal/data/templates.py
+++ b/multimodal/src/autogluon/multimodal/data/templates.py
@@ -640,7 +640,8 @@ def read_from_file(self) -> Dict:
                 "Please ignore this warning if you are creating new prompts for this dataset."
             )
             return {}
-        yaml_dict = yaml.safe_load(open(self.yaml_path, "r"))
+        with open(self.yaml_path, "r") as f:
+            yaml_dict = yaml.safe_load(f)
         return yaml_dict[self.TEMPLATES_KEY]
 
     def write_to_file(self) -> None:
diff --git a/multimodal/src/autogluon/multimodal/predictor.py b/multimodal/src/autogluon/multimodal/predictor.py
index 8d3cbe957b4..39402386c2f 100644
--- a/multimodal/src/autogluon/multimodal/predictor.py
+++ b/multimodal/src/autogluon/multimodal/predictor.py
@@ -892,8 +892,9 @@ def _hyperparameter_tune(self, hyperparameter_tune_kwargs, resources, **_fit_arg
         )
 
         ray_tune_adapter = AutommRayTuneAdapter()
-        if try_import_ray_lightning():
-            ray_tune_adapter = AutommRayTuneLightningAdapter()
+        # Do not use ray lightning.
+        # if try_import_ray_lightning():
+        #     ray_tune_adapter = AutommRayTuneLightningAdapter()
         search_space = _fit_args.get("hyperparameters", dict())
         metric = "val_" + _fit_args.get("validation_metric_name")
         mode = _fit_args.get("minmax_mode")
diff --git a/multimodal/src/autogluon/multimodal/utils/checkpoint.py b/multimodal/src/autogluon/multimodal/utils/checkpoint.py
index 14ec1dfee0a..74bda2995fc 100644
--- a/multimodal/src/autogluon/multimodal/utils/checkpoint.py
+++ b/multimodal/src/autogluon/multimodal/utils/checkpoint.py
@@ -2,6 +2,7 @@
 import os
 import re
 import shutil
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import pytorch_lightning as pl
@@ -10,13 +11,19 @@
 from pytorch_lightning.utilities.cloud_io import atomic_save, get_filesystem
 from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.rank_zero import rank_zero_warn
-from pytorch_lightning.utilities.types import _METRIC, _PATH
+from torch import Tensor
+from torchmetrics import Metric
 
 from ..constants import AUTOMM, DEEPSPEED_STRATEGY
 
 logger = logging.getLogger(AUTOMM)
 
 
+_PATH = Union[str, Path]
+_NUMBER = Union[int, float]
+_METRIC = Union[Metric, Tensor, _NUMBER]
+
+
 def average_checkpoints(
     checkpoint_paths: List[str],
 ):