diff --git a/launch/client.py b/launch/client.py
index 1bae2350..adbd1401 100644
--- a/launch/client.py
+++ b/launch/client.py
@@ -39,7 +39,7 @@
     SyncEndpoint,
 )
 from launch.request_validation import validate_task_request
-from launch.utils import trim_kwargs
+from launch.utils import infer_env_params, trim_kwargs
 
 DEFAULT_NETWORK_TIMEOUT_SEC = 120
 
@@ -198,9 +198,10 @@ def create_model_bundle_from_dirs(
         model_bundle_name: str,
         base_paths: List[str],
         requirements_path: str,
-        env_params: Dict[str, str],
         load_predict_fn_module_path: str,
         load_model_fn_module_path: str,
+        env_params: Optional[Dict[str, str]],
+        env_selector: Optional[str],
         app_config: Optional[Union[Dict[str, Any], str]] = None,
     ) -> ModelBundle:
         """
@@ -275,6 +276,9 @@ def create_model_bundle_from_dirs(
         with open(requirements_path, "r", encoding="utf-8") as req_f:
             requirements = req_f.read().splitlines()
 
+        if env_params is None:
+            env_params = infer_env_params(env_selector)
+
         tmpdir = tempfile.mkdtemp()
         try:
             zip_path = os.path.join(tmpdir, "bundle.zip")
@@ -331,7 +335,8 @@ def create_model_bundle_from_dirs(
     def create_model_bundle(  # pylint: disable=too-many-statements
         self,
         model_bundle_name: str,
-        env_params: Dict[str, str],
+        env_params: Optional[Dict[str, str]],
+        env_selector: Optional[str],
         *,
         load_predict_fn: Optional[
             Callable[[LaunchModel_T], Callable[[Any], Any]]
@@ -435,6 +440,9 @@ def create_model_bundle(  # pylint: disable=too-many-statements
             )
         # TODO should we try to catch when people intentionally pass both model and load_model_fn as None?
 
+        if env_params is None:
+            env_params = infer_env_params(env_selector)
+
         if requirements is None:
             # TODO explore: does globals() actually work as expected? Should we use globals_copy instead?
             requirements_inferred = find_packages_from_imports(globals())
diff --git a/launch/utils.py b/launch/utils.py
index 08d4fb75..7b596399 100644
--- a/launch/utils.py
+++ b/launch/utils.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 
 def trim_kwargs(kwargs_dict: Dict[Any, Any]):
@@ -7,3 +7,52 @@ def trim_kwargs(kwargs_dict: Dict[Any, Any]):
     """
     dict_copy = {k: v for k, v in kwargs_dict.items() if v is not None}
     return dict_copy
+
+
+def infer_env_params(env_selector: Optional[str]):
+    """
+    Returns an env_params dict from the env_selector.
+
+    env_selector: str - Either "pytorch" or "tensorflow"
+    """
+    if env_selector == "pytorch":
+        import torch
+
+        try:
+            ver = torch.__version__.split("+")
+            torch_version = ver[0]
+            cuda_version = ver[1][2:] if len(ver) > 1 else "113"
+            cudnn_available = torch.backends.cudnn.is_available()
+            cudnn_version = (
+                torch.backends.cudnn.version()[:1]
+                if cudnn_available is not None
+                else "8"
+            )
+
+            if (
+                len(cuda_version) < 3
+            ):  # we can only parse cuda versions in the double digits
+                raise ValueError(
+                    "PyTorch version parsing does not support CUDA versions below 10.0"
+                )
+            tag = f"{torch_version}-cuda{cuda_version[:2]}.{cuda_version[2:]}-cudnn{cudnn_version}-runtime"
+            return {
+                "framework_type": "pytorch",
+                "pytorch_image_tag": tag,
+            }
+        except:
+            raise ValueError(
+                f"Failed to parse PyTorch version {torch.__version__}, try setting your own env_params."
+            )
+    elif env_selector == "tensorflow":
+        import tensorflow as tf
+
+        ver = tf.__version__
+        return {
+            "framework_type": "tensorflow",
+            "tensorflow_version": ver,
+        }
+    else:
+        raise ValueError(
+            "Unsupported env_selector, please set to pytorch or tensorflow, or set your own env_params."
+        )