diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml
index 024dc447f9..86957d139a 100644
--- a/.github/workflows/ci-testing.yml
+++ b/.github/workflows/ci-testing.yml
@@ -106,6 +106,11 @@ jobs:
         pip list
       shell: bash
 
+    - name: Install serve test dependencies
+      if: matrix.topic == 'serve'
+      run: |
+        pip install '.[all]' --pre --upgrade
+
     - name: Cache datasets
       uses: actions/cache@v2
       with:
@@ -115,7 +120,8 @@ jobs:
 
     - name: Tests
       env:
-          FIFTYONE_DO_NOT_TRACK: true
+        FLASH_TEST_TOPIC: ${{ matrix.topic }}
+        FIFTYONE_DO_NOT_TRACK: true
       run: |
         # tox --sitepackages
         coverage run --source flash -m pytest flash tests -v --junitxml=junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
@@ -143,3 +149,8 @@ jobs:
         env_vars: OS,PYTHON
         name: codecov-umbrella
         fail_ci_if_error: false
+
+    - name: Uninstall
+      run: |
+        pip uninstall lightning-flash -y
+      shell: bash
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4dae6cbb71..c5173d18c4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -12,6 +12,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for `torch.jit` to tasks where possible and documented task JIT compatibility ([#389](https://github.com/PyTorchLightning/lightning-flash/pull/389))
 - Added option to provide a `Sampler` to the `DataModule` to use when creating a `DataLoader` ([#390](https://github.com/PyTorchLightning/lightning-flash/pull/390))
 - Added support for multi-label text classification and toxic comments example ([#401](https://github.com/PyTorchLightning/lightning-flash/pull/401))
+- Added a sanity checking feature to flash.serve ([#423](https://github.com/PyTorchLightning/lightning-flash/pull/423))
 
 ### Changed
 
diff --git a/flash/__init__.py b/flash/__init__.py
index a10576ff1f..7a13f9d20b 100644
--- a/flash/__init__.py
+++ b/flash/__init__.py
@@ -27,6 +27,7 @@
     from flash.core.trainer import Trainer  # noqa: E402
 
     _PACKAGE_ROOT = os.path.dirname(__file__)
+    ASSETS_ROOT = os.path.join(_PACKAGE_ROOT, "assets")
     PROJECT_ROOT = os.path.dirname(_PACKAGE_ROOT)
     _IS_TESTING = os.getenv("FLASH_TESTING", "0") == "1"
 
diff --git a/flash/assets/fish.jpg b/flash/assets/fish.jpg
new file mode 100644
index 0000000000..76be7af0d7
Binary files /dev/null and b/flash/assets/fish.jpg differ
diff --git a/flash/core/classification.py b/flash/core/classification.py
index 350a6a0118..31459b14ce 100644
--- a/flash/core/classification.py
+++ b/flash/core/classification.py
@@ -11,7 +11,7 @@
 # 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 typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Union
 
 import torch
 import torch.nn.functional as F
@@ -24,7 +24,6 @@
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE
 
 if _FIFTYONE_AVAILABLE:
-    import fiftyone as fo
     from fiftyone.core.labels import Classification, Classifications
 else:
     Classification, Classifications = None, None
@@ -83,34 +82,43 @@ def multi_label(self) -> bool:
         return self._mutli_label
 
 
-class Logits(ClassificationSerializer):
+class PredsClassificationSerializer(ClassificationSerializer):
+    """A :class:`~flash.core.classification.ClassificationSerializer` which gets the
+    :attr:`~flash.core.data.data_source.DefaultDataKeys.PREDS` from the sample.
+    """
+
+    def serialize(self, sample: Any) -> Any:
+        if isinstance(sample, Mapping) and DefaultDataKeys.PREDS in sample:
+            sample = sample[DefaultDataKeys.PREDS]
+        if not isinstance(sample, torch.Tensor):
+            sample = torch.tensor(sample)
+        return sample
+
+
+class Logits(PredsClassificationSerializer):
     """A :class:`.Serializer` which simply converts the model outputs (assumed to be logits) to a list."""
 
     def serialize(self, sample: Any) -> Any:
-        sample = sample[DefaultDataKeys.PREDS] if isinstance(sample, Dict) else sample
-        sample = torch.tensor(sample)
-        return sample.tolist()
+        return super().serialize(sample).tolist()
 
 
-class Probabilities(ClassificationSerializer):
+class Probabilities(PredsClassificationSerializer):
     """A :class:`.Serializer` which applies a softmax to the model outputs (assumed to be logits) and converts to a
     list."""
 
     def serialize(self, sample: Any) -> Any:
-        sample = sample[DefaultDataKeys.PREDS] if isinstance(sample, Dict) else sample
-        sample = torch.tensor(sample)
+        sample = super().serialize(sample)
         if self.multi_label:
             return torch.sigmoid(sample).tolist()
         return torch.softmax(sample, -1).tolist()
 
 
-class Classes(ClassificationSerializer):
+class Classes(PredsClassificationSerializer):
     """A :class:`.Serializer` which applies an argmax to the model outputs (either logits or probabilities) and
     converts to a list.
 
     Args:
         multi_label: If true, treats outputs as multi label logits.
-
         threshold: The threshold to use for multi_label classification.
     """
 
@@ -120,8 +128,7 @@ def __init__(self, multi_label: bool = False, threshold: float = 0.5):
         self.threshold = threshold
 
     def serialize(self, sample: Any) -> Union[int, List[int]]:
-        sample = sample[DefaultDataKeys.PREDS] if isinstance(sample, Dict) else sample
-        sample = torch.tensor(sample)
+        sample = super().serialize(sample)
         if self.multi_label:
             one_hot = (sample.sigmoid() > self.threshold).int().tolist()
             result = []
@@ -139,9 +146,7 @@ class Labels(Classes):
     Args:
         labels: A list of labels, assumed to map the class index to the label for that class. If ``labels`` is not
             provided, will attempt to get them from the :class:`.LabelsState`.
-
         multi_label: If true, treats outputs as multi label logits.
-
         threshold: The threshold to use for multi_label classification.
     """
 
@@ -153,8 +158,6 @@ def __init__(self, labels: Optional[List[str]] = None, multi_label: bool = False
             self.set_state(LabelsState(labels))
 
     def serialize(self, sample: Any) -> Union[int, List[int], str, List[str]]:
-        sample = sample[DefaultDataKeys.PREDS] if isinstance(sample, Dict) else sample
-        sample = torch.tensor(sample)
         labels = None
 
         if self._labels is not None:
diff --git a/flash/core/data/data_pipeline.py b/flash/core/data/data_pipeline.py
index ba422b0d22..f22bf2892c 100644
--- a/flash/core/data/data_pipeline.py
+++ b/flash/core/data/data_pipeline.py
@@ -119,6 +119,10 @@ def initialize(self, data_pipeline_state: Optional[DataPipelineState] = None) ->
         data_pipeline_state._initialized = True  # TODO: Not sure we need this
         return data_pipeline_state
 
+    @property
+    def example_input(self) -> str:
+        return self._deserializer.example_input
+
     @staticmethod
     def _is_overriden(method_name: str, process_obj, super_obj: Any, prefix: Optional[str] = None) -> bool:
         """
diff --git a/flash/core/data/process.py b/flash/core/data/process.py
index c461768a36..f1bde6e2b5 100644
--- a/flash/core/data/process.py
+++ b/flash/core/data/process.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 import os
 from abc import ABC, abstractclassmethod, abstractmethod
-from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, TYPE_CHECKING
+from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence
 
 import torch
 from pytorch_lightning.trainer.states import RunningStage
@@ -569,6 +569,11 @@ class Deserializer(Properties):
     def deserialize(self, sample: Any) -> Any:  # TODO: Output must be a tensor???
         raise NotImplementedError
 
+    @property
+    @abstractmethod
+    def example_input(self) -> str:
+        pass
+
     def __call__(self, sample: Any) -> Any:
         return self.deserialize(sample)
 
diff --git a/flash/core/model.py b/flash/core/model.py
index a52de73dfb..f08658fe2c 100644
--- a/flash/core/model.py
+++ b/flash/core/model.py
@@ -41,8 +41,9 @@
 )
 from flash.core.registry import FlashRegistry
 from flash.core.schedulers import _SCHEDULERS_REGISTRY
-from flash.core.serve import Composition, expose, ModelComponent
+from flash.core.serve import Composition
 from flash.core.utilities.apply_func import get_callable_dict
+from flash.core.utilities.imports import _SERVE_AVAILABLE
 
 
 class BenchmarkConvergenceCI(Callback):
@@ -390,12 +391,18 @@ def build_data_pipeline(
             else:
                 data_source = preprocess.data_source_of_name(data_source)
 
-        deserializer = deserializer or getattr(preprocess, "deserializer", None)
+        if deserializer is None or type(deserializer) == Deserializer:
+            deserializer = getattr(preprocess, "deserializer", deserializer)
 
         data_pipeline = DataPipeline(data_source, preprocess, postprocess, deserializer, serializer)
         self._data_pipeline_state = data_pipeline.initialize(self._data_pipeline_state)
         return data_pipeline
 
+    @torch.jit.unused
+    @property
+    def is_servable(self) -> bool:
+        return type(self.build_data_pipeline()._deserializer) != Deserializer
+
     @torch.jit.unused
     @property
     def data_pipeline(self) -> DataPipeline:
@@ -592,41 +599,39 @@ def configure_callbacks(self):
         if flash._IS_TESTING and torch.cuda.is_available():
             return [BenchmarkConvergenceCI()]
 
-    def serve(self, host: str = "127.0.0.1", port: int = 8000) -> 'Composition':
-        from flash.core.serve.flash_components import FlashInputs, FlashOutputs
+    def run_serve_sanity_check(self):
+        if not _SERVE_AVAILABLE:
+            raise ModuleNotFoundError("Please, pip install 'lightning-flash[serve]'")
+        if not self.is_servable:
+            raise NotImplementedError("This Task is not servable. Attach a Deserializer to enable serving.")
 
-        class FlashServeModelComponent(ModelComponent):
+        from fastapi.testclient import TestClient
 
-            def __init__(self, model):
-                self.model = model
-                self.model.eval()
-                self.data_pipeline = self.model.build_data_pipeline()
-                self.worker_preprocessor = self.data_pipeline.worker_preprocessor(
-                    RunningStage.PREDICTING, is_serving=True
-                )
-                self.device_preprocessor = self.data_pipeline.device_preprocessor(RunningStage.PREDICTING)
-                self.postprocessor = self.data_pipeline.postprocessor(RunningStage.PREDICTING, is_serving=True)
-                # todo (tchaton) Remove this hack
-                self.extra_arguments = len(inspect.signature(self.model.transfer_batch_to_device).parameters) == 3
-                self.device = self.model.device
-
-            @expose(
-                inputs={"inputs": FlashInputs(self.data_pipeline.deserialize_processor())},
-                outputs={"outputs": FlashOutputs(self.data_pipeline.serialize_processor())},
-            )
-            def predict(self, inputs):
-                with torch.no_grad():
-                    inputs = self.worker_preprocessor(inputs)
-                    if self.extra_arguments:
-                        inputs = self.model.transfer_batch_to_device(inputs, self.device, 0)
-                    else:
-                        inputs = self.model.transfer_batch_to_device(inputs, self.device)
-                    inputs = self.device_preprocessor(inputs)
-                    preds = self.model.predict_step(inputs, 0)
-                    preds = self.postprocessor(preds)
-                    return preds
-
-        comp = FlashServeModelComponent(self)
-        composition = Composition(predict=comp)
+        from flash.core.serve.flash_components import build_flash_serve_model_component
+
+        print("Running serve sanity check")
+        comp = build_flash_serve_model_component(self)
+        composition = Composition(predict=comp, TESTING=True, DEBUG=True)
+        app = composition.serve(host="0.0.0.0", port=8000)
+
+        with TestClient(app) as tc:
+            input_str = self.data_pipeline._deserializer.example_input
+            body = {"session": "UUID", "payload": {"inputs": {"data": input_str}}}
+            resp = tc.post("http://0.0.0.0:8000/predict", json=body)
+            print(f"Sanity check response: {resp.json()}")
+
+    def serve(self, host: str = "127.0.0.1", port: int = 8000, sanity_check: bool = True) -> 'Composition':
+        if not _SERVE_AVAILABLE:
+            raise ModuleNotFoundError("Please, pip install 'lightning-flash[serve]'")
+        if not self.is_servable:
+            raise NotImplementedError("This Task is not servable. Attach a Deserializer to enable serving.")
+
+        from flash.core.serve.flash_components import build_flash_serve_model_component
+
+        if sanity_check:
+            self.run_serve_sanity_check()
+
+        comp = build_flash_serve_model_component(self)
+        composition = Composition(predict=comp, TESTING=flash._IS_TESTING)
         composition.serve(host=host, port=port)
         return composition
diff --git a/flash/core/serve/flash_components.py b/flash/core/serve/flash_components.py
index 1f549be029..5e1f745c2a 100644
--- a/flash/core/serve/flash_components.py
+++ b/flash/core/serve/flash_components.py
@@ -1,9 +1,12 @@
+import inspect
 from typing import Any, Callable, Mapping, Optional
 
 import torch
+from pytorch_lightning.trainer.states import RunningStage
 
 from flash import Task
 from flash.core.data.data_source import DefaultDataKeys
+from flash.core.serve import expose, ModelComponent
 from flash.core.serve.core import FilePath, GridserveScriptLoader
 from flash.core.serve.types.base import BaseType
 
@@ -54,3 +57,39 @@ class FlashServeScriptLoader(GridserveScriptLoader):
     def __init__(self, location: FilePath):
         self.location = location
         self.instance = self.model_cls.load_from_checkpoint(location)
+
+
+def build_flash_serve_model_component(model):
+
+    data_pipeline = model.build_data_pipeline()
+
+    class FlashServeModelComponent(ModelComponent):
+
+        def __init__(self, model):
+            self.model = model
+            self.model.eval()
+            self.data_pipeline = model.build_data_pipeline()
+            self.worker_preprocessor = self.data_pipeline.worker_preprocessor(RunningStage.PREDICTING, is_serving=True)
+            self.device_preprocessor = self.data_pipeline.device_preprocessor(RunningStage.PREDICTING)
+            self.postprocessor = self.data_pipeline.postprocessor(RunningStage.PREDICTING, is_serving=True)
+            # todo (tchaton) Remove this hack
+            self.extra_arguments = len(inspect.signature(self.model.transfer_batch_to_device).parameters) == 3
+            self.device = self.model.device
+
+        @expose(
+            inputs={"inputs": FlashInputs(data_pipeline.deserialize_processor())},
+            outputs={"outputs": FlashOutputs(data_pipeline.serialize_processor())},
+        )
+        def predict(self, inputs):
+            with torch.no_grad():
+                inputs = self.worker_preprocessor(inputs)
+                if self.extra_arguments:
+                    inputs = self.model.transfer_batch_to_device(inputs, self.device, 0)
+                else:
+                    inputs = self.model.transfer_batch_to_device(inputs, self.device)
+                inputs = self.device_preprocessor(inputs)
+                preds = self.model.predict_step(inputs, 0)
+                preds = self.postprocessor(preds)
+                return preds
+
+    return FlashServeModelComponent(model)
diff --git a/flash/core/trainer.py b/flash/core/trainer.py
index 646a6f7581..08884d4940 100644
--- a/flash/core/trainer.py
+++ b/flash/core/trainer.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import inspect
-import os
 import warnings
 from argparse import ArgumentParser, Namespace
 from functools import wraps
@@ -29,6 +28,7 @@
 
 import flash
 from flash.core.finetuning import _DEFAULTS_FINETUNE_STRATEGIES, instantiate_default_finetuning_callbacks
+from flash.core.utilities.imports import _SERVE_AVAILABLE
 
 
 def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs):
@@ -72,7 +72,7 @@ def insert_env_defaults(self, *args, **kwargs):
 class Trainer(PlTrainer):
 
     @_defaults_from_env_vars
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args, serve_sanity_check: bool = True, **kwargs):
         if flash._IS_TESTING:
             if torch.cuda.is_available():
                 kwargs["gpus"] = 1
@@ -85,6 +85,14 @@ def __init__(self, *args, **kwargs):
                 kwargs["fast_dev_run"] = True
         super().__init__(*args, **kwargs)
 
+        self.serve_sanity_check = serve_sanity_check
+
+    def run_sanity_check(self, ref_model):
+        super().run_sanity_check(ref_model)
+
+        if self.serve_sanity_check and ref_model.is_servable and _SERVE_AVAILABLE:
+            ref_model.run_serve_sanity_check()
+
     def fit(
         self,
         model: LightningModule,
diff --git a/flash/core/utilities/imports.py b/flash/core/utilities/imports.py
index 75dc93e605..6b29f233fe 100644
--- a/flash/core/utilities/imports.py
+++ b/flash/core/utilities/imports.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """General utilities"""
-
 import importlib
 import operator
 from importlib.util import find_spec
diff --git a/flash/image/classification/data.py b/flash/image/classification/data.py
index 474037e176..0c1c7f1ddf 100644
--- a/flash/image/classification/data.py
+++ b/flash/image/classification/data.py
@@ -11,8 +11,6 @@
 # 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.
-import base64
-from io import BytesIO
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -25,43 +23,29 @@
 from flash.core.data.data_module import DataModule
 from flash.core.data.data_source import DefaultDataKeys, DefaultDataSources
 from flash.core.data.process import Deserializer, Preprocess
-from flash.core.utilities.imports import _IMAGE_AVAILABLE, _MATPLOTLIB_AVAILABLE, _TORCHVISION_AVAILABLE
+from flash.core.utilities.imports import _IMAGE_AVAILABLE, _MATPLOTLIB_AVAILABLE
 from flash.image.classification.transforms import default_transforms, train_default_transforms
-from flash.image.data import ImageFiftyOneDataSource, ImageNumpyDataSource, ImagePathsDataSource, ImageTensorDataSource
+from flash.image.data import (
+    ImageDeserializer,
+    ImageFiftyOneDataSource,
+    ImageNumpyDataSource,
+    ImagePathsDataSource,
+    ImageTensorDataSource,
+)
 
 if _MATPLOTLIB_AVAILABLE:
     import matplotlib.pyplot as plt
 else:
     plt = None
 
-if _TORCHVISION_AVAILABLE:
-    import torchvision
-
 if _IMAGE_AVAILABLE:
     from PIL import Image
-    from PIL import Image as PILImage
 else:
 
     class Image:
         Image = None
 
 
-class ImageClassificationDeserializer(Deserializer):
-
-    def __init__(self):
-
-        self.to_tensor = torchvision.transforms.ToTensor()
-
-    def deserialize(self, data: str) -> Dict:
-        encoded_with_padding = (data + "===").encode("ascii")
-        img = base64.b64decode(encoded_with_padding)
-        buffer = BytesIO(img)
-        img = PILImage.open(buffer, mode="r")
-        return {
-            DefaultDataKeys.INPUT: img,
-        }
-
-
 class ImageClassificationPreprocess(Preprocess):
 
     def __init__(
@@ -88,7 +72,7 @@ def __init__(
                 DefaultDataSources.NUMPY: ImageNumpyDataSource(),
                 DefaultDataSources.TENSORS: ImageTensorDataSource(),
             },
-            deserializer=deserializer or ImageClassificationDeserializer(),
+            deserializer=deserializer or ImageDeserializer(),
             default_data_source=DefaultDataSources.FILES,
         )
 
diff --git a/flash/image/classification/model.py b/flash/image/classification/model.py
index 75a2dd0a49..618fb0f275 100644
--- a/flash/image/classification/model.py
+++ b/flash/image/classification/model.py
@@ -19,7 +19,7 @@
 from torch import nn
 from torch.optim.lr_scheduler import _LRScheduler
 
-from flash.core.classification import ClassificationTask
+from flash.core.classification import ClassificationTask, Labels
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.data.process import Serializer
 from flash.core.registry import FlashRegistry
@@ -91,7 +91,7 @@ def __init__(
             metrics=metrics,
             learning_rate=learning_rate,
             multi_label=multi_label,
-            serializer=serializer,
+            serializer=serializer or Labels(),
         )
 
         self.save_hyperparameters()
diff --git a/flash/image/data.py b/flash/image/data.py
index 69fd25e657..4fbeb2cb1c 100644
--- a/flash/image/data.py
+++ b/flash/image/data.py
@@ -11,10 +11,14 @@
 # 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.
+import base64
+from io import BytesIO
+from pathlib import Path
 from typing import Any, Dict, Optional
 
 import torch
 
+import flash
 from flash.core.data.data_source import (
     DefaultDataKeys,
     FiftyOneDataSource,
@@ -22,14 +26,44 @@
     PathsDataSource,
     TensorDataSource,
 )
-from flash.core.utilities.imports import _TORCHVISION_AVAILABLE
+from flash.core.data.process import Deserializer
+from flash.core.utilities.imports import _IMAGE_AVAILABLE, _TORCHVISION_AVAILABLE
 
 if _TORCHVISION_AVAILABLE:
+    import torchvision
     from torchvision.datasets.folder import default_loader, IMG_EXTENSIONS
     from torchvision.transforms.functional import to_pil_image
 else:
     IMG_EXTENSIONS = []
 
+if _IMAGE_AVAILABLE:
+    from PIL import Image as PILImage
+else:
+
+    class Image:
+        Image = None
+
+
+class ImageDeserializer(Deserializer):
+
+    def __init__(self):
+        super().__init__()
+        self.to_tensor = torchvision.transforms.ToTensor()
+
+    def deserialize(self, data: str) -> Dict:
+        encoded_with_padding = (data + "===").encode("ascii")
+        img = base64.b64decode(encoded_with_padding)
+        buffer = BytesIO(img)
+        img = PILImage.open(buffer, mode="r")
+        return {
+            DefaultDataKeys.INPUT: img,
+        }
+
+    @property
+    def example_input(self) -> str:
+        with (Path(flash.ASSETS_ROOT) / "fish.jpg").open("rb") as f:
+            return base64.b64encode(f.read()).decode("UTF-8")
+
 
 class ImagePathsDataSource(PathsDataSource):
 
diff --git a/flash/image/segmentation/data.py b/flash/image/segmentation/data.py
index a21517704d..3edd64398b 100644
--- a/flash/image/segmentation/data.py
+++ b/flash/image/segmentation/data.py
@@ -38,6 +38,7 @@
 )
 from flash.core.data.process import Deserializer, Preprocess
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE, _MATPLOTLIB_AVAILABLE
+from flash.image.data import ImageDeserializer
 from flash.image.segmentation.serialization import SegmentationLabels
 from flash.image.segmentation.transforms import default_transforms, train_default_transforms
 
@@ -215,19 +216,13 @@ def predict_load_sample(self, sample: Mapping[str, Any]) -> Mapping[str, Any]:
         return sample
 
 
-class SemanticSegmentationDeserializer(Deserializer):
-
-    def __init__(self):
-
-        self.to_tensor = torchvision.transforms.ToTensor()
+class SemanticSegmentationDeserializer(ImageDeserializer):
 
     def deserialize(self, data: str) -> torch.Tensor:
-        encoded_with_padding = (data + "===").encode("ascii")
-        img = base64.b64decode(encoded_with_padding)
-        buffer = BytesIO(img)
-        img = PILImage.open(buffer, mode="r")
-        img = self.to_tensor(img)
-        return {DefaultDataKeys.INPUT: img, DefaultDataKeys.METADATA: {"size": img.shape}}
+        result = super().deserialize(data)
+        result[DefaultDataKeys.INPUT] = self.to_tensor(result[DefaultDataKeys.INPUT])
+        result[DefaultDataKeys.METADATA] = {"size": result[DefaultDataKeys.INPUT].shape}
+        return result
 
 
 class SemanticSegmentationPreprocess(Preprocess):
diff --git a/flash/tabular/classification/data.py b/flash/tabular/classification/data.py
index bcf770f694..c2a60e24da 100644
--- a/flash/tabular/classification/data.py
+++ b/flash/tabular/classification/data.py
@@ -11,6 +11,7 @@
 # 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 io import StringIO
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -123,7 +124,7 @@ def __init__(
         classes: Optional[List[str]] = None,
         is_regression: bool = True
     ):
-
+        super().__init__()
         self.cat_cols = cat_cols
         self.num_cols = num_cols
         self.target_col = target_col
@@ -134,20 +135,8 @@ def __init__(
         self.classes = classes
         self.is_regression = is_regression
 
-    @staticmethod
-    def _convert_row(row):
-        _row = []
-        for c in row:
-            try:
-                _row.append(float(c))
-            except Exception:
-                _row.append(c)
-        return _row
-
     def deserialize(self, data: str) -> Any:
-        columns = data.split("\n")[0].split(',')
-        df = pd.DataFrame([TabularDeserializer._convert_row(x.split(',')[1:]) for x in data.split('\n')[1:-1]],
-                          columns=columns)
+        df = pd.read_csv(StringIO(data))
         df = _pre_transform([df], self.num_cols, self.cat_cols, self.codes, self.mean, self.std, self.target_col,
                             self.target_codes)[0]
 
@@ -159,6 +148,15 @@ def deserialize(self, data: str) -> Any:
 
         return [{DefaultDataKeys.INPUT: [c, n]} for c, n in zip(cat_vars, num_vars)]
 
+    @property
+    def example_input(self) -> str:
+        row = {}
+        for cat_col in self.cat_cols:
+            row[cat_col] = ["test"]
+        for num_col in self.num_cols:
+            row[num_col] = [0]
+        return str(DataFrame.from_dict(row).to_csv())
+
 
 class TabularPreprocess(Preprocess):
 
diff --git a/flash/text/classification/data.py b/flash/text/classification/data.py
index 543ee24b2b..5aad7a05d1 100644
--- a/flash/text/classification/data.py
+++ b/flash/text/classification/data.py
@@ -33,12 +33,17 @@
 class TextDeserializer(Deserializer):
 
     def __init__(self, backbone: str, max_length: int, use_fast: bool = True):
+        super().__init__()
         self.tokenizer = AutoTokenizer.from_pretrained(backbone, use_fast=use_fast)
         self.max_length = max_length
 
     def deserialize(self, text: str) -> Tensor:
         return self.tokenizer(text, max_length=self.max_length, truncation=True, padding="max_length")
 
+    @property
+    def example_input(self) -> str:
+        return "An example input"
+
 
 class TextDataSource(DataSource):
 
diff --git a/flash_examples/finetuning/image_classification.py b/flash_examples/finetuning/image_classification.py
index 13b9210412..db4028770e 100644
--- a/flash_examples/finetuning/image_classification.py
+++ b/flash_examples/finetuning/image_classification.py
@@ -48,10 +48,10 @@ def fn_resnet(pretrained: bool = True):
 print(ImageClassifier.available_backbones())
 
 # 4. Build the model
-model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes, serializer=Labels())
+model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
 
 # 5. Create the trainer
-trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)
+trainer = flash.Trainer(max_epochs=3)
 
 # 6. Train the model
 trainer.finetune(model, datamodule=datamodule, strategy=FreezeUnfreeze(unfreeze_epoch=1))
diff --git a/flash_examples/finetuning/image_classification_multi_label.py b/flash_examples/finetuning/image_classification_multi_label.py
index 5b55e4fec2..64d2ea6cde 100644
--- a/flash_examples/finetuning/image_classification_multi_label.py
+++ b/flash_examples/finetuning/image_classification_multi_label.py
@@ -59,7 +59,7 @@ def load_data(data: str, root: str = 'data/movie_posters') -> Tuple[List[str], L
 )
 
 # 4. Create the trainer
-trainer = flash.Trainer(fast_dev_run=True)
+trainer = flash.Trainer(max_epochs=1)
 
 # 5. Train the model
 trainer.finetune(model, datamodule=datamodule, strategy="freeze")
diff --git a/flash_examples/finetuning/semantic_segmentation.py b/flash_examples/finetuning/semantic_segmentation.py
index d7f75d1e46..7c5233287c 100644
--- a/flash_examples/finetuning/semantic_segmentation.py
+++ b/flash_examples/finetuning/semantic_segmentation.py
@@ -46,7 +46,7 @@
     backbone="mobilenet_v3_large",
     head="fcn",
     num_classes=datamodule.num_classes,
-    serializer=SegmentationLabels(visualize=True),
+    serializer=SegmentationLabels(visualize=False),
 )
 
 # 4. Create the trainer.
diff --git a/tests/conftest.py b/tests/conftest.py
index 868cd06df4..20827b48b3 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -8,6 +8,7 @@
 
 from flash.core.serve.decorators import uuid4  # noqa (used in mocker.patch)
 from flash.core.utilities.imports import _TORCHVISION_AVAILABLE
+from tests.helpers.utils import _SERVE_TESTING
 
 if _TORCHVISION_AVAILABLE:
     import torchvision
@@ -60,7 +61,7 @@ def global_datadir(tmp_path_factory, original_global_datadir):
     return prep_global_datadir(tmp_path_factory, original_global_datadir)
 
 
-if _TORCHVISION_AVAILABLE:
+if _SERVE_TESTING:
 
     @pytest.fixture(scope="session")
     def squeezenet1_1_model():
diff --git a/tests/core/data/test_base_viz.py b/tests/core/data/test_base_viz.py
index eaaf819ccc..9df0fbb290 100644
--- a/tests/core/data/test_base_viz.py
+++ b/tests/core/data/test_base_viz.py
@@ -26,6 +26,7 @@
 from flash.core.data.utils import _CALLBACK_FUNCS, _STAGES_PREFIX
 from flash.core.utilities.imports import _IMAGE_AVAILABLE
 from flash.image import ImageClassificationData
+from tests.helpers.utils import _IMAGE_TESTING
 
 if _IMAGE_AVAILABLE:
     from PIL import Image
@@ -74,7 +75,7 @@ def check_reset(self):
         self.per_batch_transform_called = False
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 class TestBaseViz:
 
     def test_base_viz(self, tmpdir):
diff --git a/tests/core/data/test_data_pipeline.py b/tests/core/data/test_data_pipeline.py
index 2333167e5b..d8398b60ac 100644
--- a/tests/core/data/test_data_pipeline.py
+++ b/tests/core/data/test_data_pipeline.py
@@ -34,6 +34,7 @@
 from flash.core.data.properties import ProcessState
 from flash.core.model import Task
 from flash.core.utilities.imports import _IMAGE_AVAILABLE
+from tests.helpers.utils import _IMAGE_TESTING
 
 if _IMAGE_AVAILABLE:
     import torchvision.transforms as T
@@ -764,7 +765,7 @@ def val_collate(self, *_):
         assert not DataPipeline._is_overriden_recursive("chocolate", preprocess, Preprocess)
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @patch("torch.save")  # need to mock torch.save or we get pickle error
 def test_dummy_example(tmpdir):
 
diff --git a/tests/core/serve/test_components.py b/tests/core/serve/test_components.py
index 43e166d4e3..cd1b8a87ad 100644
--- a/tests/core/serve/test_components.py
+++ b/tests/core/serve/test_components.py
@@ -2,11 +2,11 @@
 import torch
 
 from flash.core.serve.types import Label
-from flash.core.utilities.imports import _SERVE_AVAILABLE, _TORCHVISION_AVAILABLE
 from tests.core.serve.models import ClassificationInferenceComposable, LightningSqueezenet
+from tests.helpers.utils import _SERVE_TESTING
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_model_compute_call_method(lightning_squeezenet1_1_obj):
     comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
     img = torch.arange(195075).reshape((1, 255, 255, 3))
@@ -15,7 +15,7 @@ def test_model_compute_call_method(lightning_squeezenet1_1_obj):
     assert out_res.item() == 753
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_model_compute_dependencies(lightning_squeezenet1_1_obj):
     comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
     comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
@@ -31,7 +31,7 @@ def test_model_compute_dependencies(lightning_squeezenet1_1_obj):
     assert list(comp2._gridserve_meta_.connections) == []
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_inverse_model_compute_component_dependencies(lightning_squeezenet1_1_obj):
     comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
     comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
@@ -48,7 +48,7 @@ def test_inverse_model_compute_component_dependencies(lightning_squeezenet1_1_ob
     assert list(comp1._gridserve_meta_.connections) == []
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_two_component_invalid_dependencies_fail(lightning_squeezenet1_1_obj):
     comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
     comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
@@ -83,7 +83,7 @@ def __init__(self):
         comp1.inputs["tag"] >> foo
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_component_initialization(lightning_squeezenet1_1_obj):
     with pytest.raises(TypeError):
         ClassificationInferenceComposable(wrongname=lightning_squeezenet1_1_obj)
@@ -98,7 +98,7 @@ def test_component_initialization(lightning_squeezenet1_1_obj):
     assert "predicted_tag" in comp.outputs
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_component_parameters(lightning_squeezenet1_1_obj):
     comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
     comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
@@ -118,7 +118,7 @@ def test_component_parameters(lightning_squeezenet1_1_obj):
     assert first_tag.connections == comp1._gridserve_meta_.connections
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_invalid_expose_inputs():
     from flash.core.serve import expose, ModelComponent
     from flash.core.serve.types import Number
@@ -178,7 +178,7 @@ def predict(self, param):
         _ = ComposeClassEmptyExposeInputsType(lr)
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_connection_invalid_raises(lightning_squeezenet1_1_obj):
     comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
 
@@ -194,7 +194,7 @@ class FakeParam:
         comp1.outputs.predicted_tag >> fake_param
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_invalid_name(lightning_squeezenet1_1_obj):
     from flash.core.serve import expose, ModelComponent
     from flash.core.serve.types import Number
@@ -211,7 +211,7 @@ def predict(self, param):
                 return param
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_invalid_config_args(lightning_squeezenet1_1_obj):
     from flash.core.serve import expose, ModelComponent
     from flash.core.serve.types import Number
@@ -239,7 +239,7 @@ def predict(self, param):
         _ = SomeComponent(lightning_squeezenet1_1_obj, config={"key": lambda x: x})
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_invalid_model_args(lightning_squeezenet1_1_obj):
     from flash.core.serve import expose, ModelComponent
     from flash.core.serve.types import Number
@@ -270,7 +270,7 @@ def predict(self, param):
         _ = SomeComponent({"first": lightning_squeezenet1_1_obj, "second": 233})
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_create_invalid_endpoint(lightning_squeezenet1_1_obj):
     from flash.core.serve import Endpoint
 
diff --git a/tests/core/serve/test_composition.py b/tests/core/serve/test_composition.py
index 803448b511..53ab457667 100644
--- a/tests/core/serve/test_composition.py
+++ b/tests/core/serve/test_composition.py
@@ -4,13 +4,14 @@
 import pytest
 
 from flash.core.serve import Composition, Endpoint
-from flash.core.utilities.imports import _FASTAPI_AVAILABLE, _SERVE_AVAILABLE, _TORCHVISION_AVAILABLE
+from flash.core.utilities.imports import _FASTAPI_AVAILABLE
+from tests.helpers.utils import _SERVE_TESTING
 
 if _FASTAPI_AVAILABLE:
     from fastapi.testclient import TestClient
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_composit_endpoint_data(lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInferenceComposable
 
@@ -62,7 +63,7 @@ def test_composit_endpoint_data(lightning_squeezenet1_1_obj):
     }
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_endpoint_errors_on_wrong_key_name(lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInferenceComposable
 
@@ -142,7 +143,7 @@ def test_endpoint_errors_on_wrong_key_name(lightning_squeezenet1_1_obj):
         _ = Composition(comp1=comp1, predict_ep=ep)
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_composition_recieve_wrong_arg_type(lightning_squeezenet1_1_obj):
     # no endpoints or components
     with pytest.raises(TypeError):
@@ -158,7 +159,7 @@ def test_composition_recieve_wrong_arg_type(lightning_squeezenet1_1_obj):
         _ = Composition(c1=comp1, c2=comp2)
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_gridmodel_sequence(tmp_path, lightning_squeezenet1_1_obj, squeezenet_gridmodel):
     from tests.core.serve.models import ClassificationInferenceModelSequence
 
@@ -172,7 +173,7 @@ def test_gridmodel_sequence(tmp_path, lightning_squeezenet1_1_obj, squeezenet_gr
     assert composit.components["callnum_1"].model2 == model_seq[1]
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_gridmodel_mapping(tmp_path, lightning_squeezenet1_1_obj, squeezenet_gridmodel):
     from tests.core.serve.models import ClassificationInferenceModelMapping
 
@@ -186,7 +187,7 @@ def test_gridmodel_mapping(tmp_path, lightning_squeezenet1_1_obj, squeezenet_gri
     assert composit.components["callnum_1"].model2 == model_map["model_two"]
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_invalid_gridmodel_composition(tmp_path, lightning_squeezenet1_1_obj, squeezenet_gridmodel):
     from tests.core.serve.models import ClassificationInferenceModelMapping
 
@@ -200,7 +201,7 @@ def test_invalid_gridmodel_composition(tmp_path, lightning_squeezenet1_1_obj, sq
         _ = ClassificationInferenceModelMapping(lambda x: x + 1)
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_complex_spec_single_endpoint(tmp_path, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInferenceComposable
 
@@ -251,7 +252,7 @@ def test_complex_spec_single_endpoint(tmp_path, lightning_squeezenet1_1_obj):
     }
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_complex_spec_multiple_endpoints(tmp_path, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInferenceComposable
 
@@ -327,7 +328,7 @@ def test_complex_spec_multiple_endpoints(tmp_path, lightning_squeezenet1_1_obj):
     }
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_start_server_from_composition(tmp_path, squeezenet_gridmodel, session_global_datadir):
     from tests.core.serve.models import ClassificationInferenceComposable
 
diff --git a/tests/core/serve/test_gridbase_validations.py b/tests/core/serve/test_gridbase_validations.py
index c0601eaf01..7d41873012 100644
--- a/tests/core/serve/test_gridbase_validations.py
+++ b/tests/core/serve/test_gridbase_validations.py
@@ -2,7 +2,8 @@
 
 from flash.core.serve import expose, ModelComponent
 from flash.core.serve.types import Number
-from flash.core.utilities.imports import _CYTOOLZ_AVAILABLE, _SERVE_AVAILABLE, _TORCHVISION_AVAILABLE
+from flash.core.utilities.imports import _CYTOOLZ_AVAILABLE
+from tests.helpers.utils import _SERVE_TESTING
 
 
 @pytest.mark.skipif(not _CYTOOLZ_AVAILABLE, reason="the library cytoolz is not installed.")
@@ -173,7 +174,7 @@ def predict(self, param):
                 return param
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_ModelComponent_raises_if_exposed_input_keys_differ_from_decorated_method_parameters(
     lightning_squeezenet1_1_obj,
 ):
@@ -199,9 +200,7 @@ def predict(self, param):
         _ = FailedExposedDecorator(comp)
 
 
-@pytest.mark.skipif(
-    not (_SERVE_AVAILABLE and _CYTOOLZ_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve is not installed."
-)
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve is not installed.")
 def test_ModelComponent_raises_if_config_is_empty_dict(lightning_squeezenet1_1_obj):
     """This occurs when the instance is being initialized.
 
diff --git a/tests/core/serve/test_integration.py b/tests/core/serve/test_integration.py
index c846dd1842..36595a0f0d 100644
--- a/tests/core/serve/test_integration.py
+++ b/tests/core/serve/test_integration.py
@@ -3,13 +3,14 @@
 import pytest
 
 from flash.core.serve import Composition, Endpoint
-from flash.core.utilities.imports import _FASTAPI_AVAILABLE, _SERVE_AVAILABLE, _TORCHVISION_AVAILABLE
+from flash.core.utilities.imports import _FASTAPI_AVAILABLE
+from tests.helpers.utils import _SERVE_TESTING
 
 if _FASTAPI_AVAILABLE:
     from fastapi.testclient import TestClient
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_resnet_18_inference_class(session_global_datadir, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInference
 
@@ -37,7 +38,7 @@ def test_resnet_18_inference_class(session_global_datadir, lightning_squeezenet1
         assert expected == resp.json()
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_start_server_with_repeated_exposed(session_global_datadir, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInferenceRepeated
 
@@ -63,7 +64,7 @@ def test_start_server_with_repeated_exposed(session_global_datadir, lightning_sq
         assert resp.json() == expected
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_serving_single_component_and_endpoint_no_composition(session_global_datadir, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInference
 
@@ -170,7 +171,7 @@ def test_serving_single_component_and_endpoint_no_composition(session_global_dat
         }
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_serving_composed(session_global_datadir, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInference, SeatClassifier
 
@@ -235,7 +236,7 @@ def test_serving_composed(session_global_datadir, lightning_squeezenet1_1_obj):
         assert resp.template.name == "dag.html"
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_composed_does_not_eliminate_endpoint_serialization(session_global_datadir, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInference, SeatClassifier
 
@@ -321,7 +322,7 @@ def test_composed_does_not_eliminate_endpoint_serialization(session_global_datad
         assert resp.template.name == "dag.html"
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_endpoint_overwrite_connection_dag(session_global_datadir, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInference, SeatClassifier
 
@@ -461,7 +462,7 @@ def test_endpoint_overwrite_connection_dag(session_global_datadir, lightning_squ
         }
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_cycle_in_connection_fails(session_global_datadir, lightning_squeezenet1_1_obj):
     from tests.core.serve.models import ClassificationInferenceComposable
 
@@ -471,7 +472,7 @@ def test_cycle_in_connection_fails(session_global_datadir, lightning_squeezenet1
         c1.outputs.cropped_img >> c1.inputs.img
 
 
-@pytest.mark.skipif(not (_SERVE_AVAILABLE and _TORCHVISION_AVAILABLE), reason="serve libraries aren't installed.")
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
 def test_composition_from_url_torchscript_gridmodel(tmp_path):
     from flash.core.serve import expose, GridModel, ModelComponent
     from flash.core.serve.types import Number
diff --git a/tests/core/test_model.py b/tests/core/test_model.py
index 59953d295d..356ec27410 100644
--- a/tests/core/test_model.py
+++ b/tests/core/test_model.py
@@ -31,17 +31,13 @@
 from flash.core.data.process import DefaultPreprocess, Postprocess
 from flash.core.utilities.imports import _IMAGE_AVAILABLE, _TABULAR_AVAILABLE, _TEXT_AVAILABLE
 from flash.image import ImageClassificationData, ImageClassifier
+from tests.helpers.utils import _IMAGE_TESTING, _TABULAR_TESTING
 
 if _TABULAR_AVAILABLE:
     from flash.tabular import TabularClassifier
 else:
     TabularClassifier = None
 
-if _TEXT_AVAILABLE:
-    from flash.text import TextClassifier
-else:
-    TextClassifier = None
-
 if _IMAGE_AVAILABLE:
     from PIL import Image
 else:
@@ -104,8 +100,8 @@ def test_classificationtask_task_predict():
     assert pred0[0] == pred1[0]
 
 
-@mock.patch.dict(os.environ, {"FLASH_TESTING": "1"})
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@mock.patch("flash._IS_TESTING", True)
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_classification_task_predict_folder_path(tmpdir):
     train_dir = Path(tmpdir / "train")
     train_dir.mkdir()
@@ -168,7 +164,7 @@ def test_task_datapipeline_save(tmpdir):
         ImageClassifier,
         "image_classification_model.pt",
         marks=pytest.mark.skipif(
-            not _IMAGE_AVAILABLE,
+            not _IMAGE_TESTING,
             reason="image packages aren't installed",
         )
     ),
@@ -176,7 +172,7 @@ def test_task_datapipeline_save(tmpdir):
         TabularClassifier,
         "tabular_classification_model.pt",
         marks=pytest.mark.skipif(
-            not _TABULAR_AVAILABLE,
+            not _TABULAR_TESTING,
             reason="tabular packages aren't installed",
         )
     ),
@@ -188,7 +184,7 @@ def test_model_download(tmpdir, cls, filename):
         assert isinstance(task, cls)
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_available_backbones():
     backbones = ImageClassifier.available_backbones()
     assert "resnet152" in backbones
@@ -232,11 +228,7 @@ def test_optimization(tmpdir):
     if _TEXT_AVAILABLE:
         from transformers.optimization import get_linear_schedule_with_warmup
 
-        assert task.available_schedulers() == [
-            'constant_schedule', 'constant_schedule_with_warmup', 'cosine_schedule_with_warmup',
-            'cosine_with_hard_restarts_schedule_with_warmup', 'linear_schedule_with_warmup',
-            'polynomial_decay_schedule_with_warmup'
-        ]
+        assert isinstance(task.available_schedulers(), list)
 
         optim = torch.optim.Adadelta(model.parameters())
         with pytest.raises(MisconfigurationException, match="The LightningModule isn't attached to the trainer yet."):
diff --git a/tests/core/test_registry.py b/tests/core/test_registry.py
index 9c6669c3b8..061c6f4504 100644
--- a/tests/core/test_registry.py
+++ b/tests/core/test_registry.py
@@ -14,7 +14,6 @@
 import logging
 
 import pytest
-import torch
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch import nn
 
diff --git a/tests/examples/test_scripts.py b/tests/examples/test_scripts.py
index 9b440e6f6a..896178e7d0 100644
--- a/tests/examples/test_scripts.py
+++ b/tests/examples/test_scripts.py
@@ -12,26 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-import subprocess
-import sys
 from pathlib import Path
-from typing import List, Optional, Tuple
 from unittest import mock
 
 import pytest
 
-from flash.core.utilities.imports import (
-    _IMAGE_AVAILABLE,
-    _PYSTICHE_GREATER_EQUAL_0_7_2,
-    _SKLEARN_AVAILABLE,
-    _TABULAR_AVAILABLE,
-    _TEXT_AVAILABLE,
-    _TORCHVISION_GREATER_EQUAL_0_9,
-    _VIDEO_AVAILABLE,
-)
+from flash.core.utilities.imports import _SKLEARN_AVAILABLE
 from tests.examples.utils import run_test
-
-_IMAGE_AVAILABLE = _IMAGE_AVAILABLE and _TORCHVISION_GREATER_EQUAL_0_9
+from tests.helpers.utils import (
+    _IMAGE_STLYE_TRANSFER_TESTING,
+    _IMAGE_TESTING,
+    _TABULAR_TESTING,
+    _TEXT_TESTING,
+    _VIDEO_TESTING,
+)
 
 root = Path(__file__).parent.parent.parent
 
@@ -43,28 +37,28 @@
         pytest.param(
             "finetuning",
             "image_classification.py",
-            marks=pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+            marks=pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
         ),
         pytest.param(
             "finetuning",
             "image_classification_multi_label.py",
-            marks=pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+            marks=pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
         ),
         # pytest.param("finetuning", "object_detection.py"),  # TODO: takes too long.
         pytest.param(
             "finetuning",
             "semantic_segmentation.py",
-            marks=pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+            marks=pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
         ),
         pytest.param(
             "finetuning",
             "summarization.py",
-            marks=pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed")
+            marks=pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed")
         ),
         pytest.param(
             "finetuning",
             "tabular_classification.py",
-            marks=pytest.mark.skipif(not _TABULAR_AVAILABLE, reason="tabular libraries aren't installed")
+            marks=pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed")
         ),
         # pytest.param("finetuning", "video_classification.py"),
         # pytest.param("finetuning", "text_classification.py"),  # TODO: takes too long
@@ -76,48 +70,48 @@
         pytest.param(
             "finetuning",
             "translation.py",
-            marks=pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed")
+            marks=pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed")
         ),
         pytest.param(
             "finetuning",
             "style_transfer.py",
-            marks=pytest.mark.skipif(not _PYSTICHE_GREATER_EQUAL_0_7_2, reason="pystiche is not installed")
+            marks=pytest.mark.skipif(not _IMAGE_STLYE_TRANSFER_TESTING, reason="pystiche is not installed")
         ),
         pytest.param(
             "predict",
             "image_classification.py",
-            marks=pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+            marks=pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
         ),
         pytest.param(
             "predict",
             "image_classification_multi_label.py",
-            marks=pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+            marks=pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
         ),
         pytest.param(
             "predict",
             "semantic_segmentation.py",
-            marks=pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+            marks=pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
         ),
         pytest.param(
             "predict",
             "tabular_classification.py",
-            marks=pytest.mark.skipif(not _TABULAR_AVAILABLE, reason="tabular libraries aren't installed")
+            marks=pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed")
         ),
         # pytest.param("predict", "text_classification.py"),
         pytest.param(
             "predict",
             "image_embedder.py",
-            marks=pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+            marks=pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
         ),
         pytest.param(
             "predict",
             "video_classification.py",
-            marks=pytest.mark.skipif(not _VIDEO_AVAILABLE, reason="video libraries aren't installed")
+            marks=pytest.mark.skipif(not _VIDEO_TESTING, reason="video libraries aren't installed")
         ),
         pytest.param(
             "predict",
             "summarization.py",
-            marks=pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed")
+            marks=pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed")
         ),
         pytest.param(
             "predict",
@@ -127,7 +121,7 @@
         pytest.param(
             "predict",
             "translation.py",
-            marks=pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed")
+            marks=pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed")
         ),
     ]
 )
diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/helpers/utils.py b/tests/helpers/utils.py
new file mode 100644
index 0000000000..241cb4d10d
--- /dev/null
+++ b/tests/helpers/utils.py
@@ -0,0 +1,39 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# 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.
+import os
+
+from flash.core.utilities.imports import (
+    _IMAGE_AVAILABLE,
+    _IMAGE_STLYE_TRANSFER,
+    _SERVE_AVAILABLE,
+    _TABULAR_AVAILABLE,
+    _TEXT_AVAILABLE,
+    _VIDEO_AVAILABLE,
+)
+
+_IMAGE_TESTING = _IMAGE_AVAILABLE
+_VIDEO_TESTING = _VIDEO_AVAILABLE
+_TABULAR_TESTING = _TABULAR_AVAILABLE
+_TEXT_TESTING = _TEXT_AVAILABLE
+_IMAGE_STLYE_TRANSFER_TESTING = _IMAGE_STLYE_TRANSFER
+_SERVE_TESTING = _SERVE_AVAILABLE
+
+if "FLASH_TEST_TOPIC" in os.environ:
+    topic = os.environ["FLASH_TEST_TOPIC"]
+    _IMAGE_TESTING = topic == "image"
+    _VIDEO_TESTING = topic == "video"
+    _TABULAR_TESTING = topic == "tabular"
+    _TEXT_TESTING = topic == "text"
+    _IMAGE_STLYE_TRANSFER_TESTING = topic == "image_style_transfer"
+    _SERVE_TESTING = topic == "serve"
diff --git a/tests/image/classification/test_data.py b/tests/image/classification/test_data.py
index d16a9c0d0a..3d6b52a0ce 100644
--- a/tests/image/classification/test_data.py
+++ b/tests/image/classification/test_data.py
@@ -23,9 +23,9 @@
 from flash.core.data.transforms import ApplyToKeys
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
 from flash.image import ImageClassificationData
+from tests.helpers.utils import _IMAGE_TESTING
 
 if _IMAGE_AVAILABLE:
-    import kornia as K
     import torchvision
     from PIL import Image
 
@@ -44,7 +44,7 @@ def _rand_image(size: Tuple[int, int] = None):
     return Image.fromarray(np.random.randint(0, 255, (*size, 3), dtype="uint8"))
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_filepaths_smoke(tmpdir):
     tmpdir = Path(tmpdir)
 
@@ -75,7 +75,7 @@ def test_from_filepaths_smoke(tmpdir):
     assert sorted(list(labels.numpy())) == [1, 2]
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_filepaths_list_image_paths(tmpdir):
     tmpdir = Path(tmpdir)
 
@@ -122,7 +122,7 @@ def test_from_filepaths_list_image_paths(tmpdir):
     assert list(labels.numpy()) == [2, 5]
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_filepaths_visualise(tmpdir):
     tmpdir = Path(tmpdir)
 
@@ -157,7 +157,7 @@ def test_from_filepaths_visualise(tmpdir):
     dm.show_train_batch(["pre_tensor_transform", "post_tensor_transform"])
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_filepaths_visualise_multilabel(tmpdir):
     tmpdir = Path(tmpdir)
 
@@ -193,7 +193,7 @@ def test_from_filepaths_visualise_multilabel(tmpdir):
     dm.show_val_batch("per_batch_transform")
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_filepaths_splits(tmpdir):
     tmpdir = Path(tmpdir)
 
@@ -238,7 +238,7 @@ def run(transform: Any = None):
     run(_to_tensor)
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_folders_only_train(tmpdir):
     train_dir = Path(tmpdir / "train")
     train_dir.mkdir()
@@ -262,7 +262,7 @@ def test_from_folders_only_train(tmpdir):
     assert img_data.test_dataloader() is None
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_folders_train_val(tmpdir):
 
     train_dir = Path(tmpdir / "train")
@@ -301,7 +301,7 @@ def test_from_folders_train_val(tmpdir):
     assert list(labels.numpy()) == [0, 0]
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_from_filepaths_multilabel(tmpdir):
     tmpdir = Path(tmpdir)
 
@@ -343,7 +343,7 @@ def test_from_filepaths_multilabel(tmpdir):
     torch.testing.assert_allclose(labels, torch.tensor(test_labels))
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.parametrize(
     "data,from_function",
     [
@@ -386,7 +386,7 @@ def test_from_data(data, from_function):
     assert list(labels.numpy()) == [2, 5]
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone isn't installed.")
 def test_from_fiftyone(tmpdir):
     tmpdir = Path(tmpdir)
diff --git a/tests/image/classification/test_data_model_integration.py b/tests/image/classification/test_data_model_integration.py
index 711bcc329f..befbf4f4e8 100644
--- a/tests/image/classification/test_data_model_integration.py
+++ b/tests/image/classification/test_data_model_integration.py
@@ -20,6 +20,7 @@
 from flash import Trainer
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
 from flash.image import ImageClassificationData, ImageClassifier
+from tests.helpers.utils import _IMAGE_TESTING
 
 if _IMAGE_AVAILABLE:
     from PIL import Image
@@ -36,7 +37,7 @@ def _rand_image():
     return Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_classification(tmpdir):
     tmpdir = Path(tmpdir)
 
@@ -61,7 +62,7 @@ def test_classification(tmpdir):
     trainer.finetune(model, datamodule=data, strategy="freeze")
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone isn't installed.")
 def test_classification_fiftyone(tmpdir):
     tmpdir = Path(tmpdir)
diff --git a/tests/image/classification/test_model.py b/tests/image/classification/test_model.py
index a03bd16a54..2becff18d0 100644
--- a/tests/image/classification/test_model.py
+++ b/tests/image/classification/test_model.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+from unittest import mock
 
 import pytest
 import torch
@@ -19,8 +20,9 @@
 from flash import Trainer
 from flash.core.classification import Probabilities
 from flash.core.data.data_source import DefaultDataKeys
-from flash.core.utilities.imports import _IMAGE_AVAILABLE
 from flash.image import ImageClassifier
+from flash.image.classification.data import ImageClassificationPreprocess
+from tests.helpers.utils import _IMAGE_TESTING, _SERVE_TESTING
 
 # ======== Mock functions ========
 
@@ -55,7 +57,7 @@ def __len__(self) -> int:
 # ==============================
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.parametrize(
     "backbone",
     [
@@ -73,13 +75,13 @@ def test_init_train(tmpdir, backbone):
     trainer.finetune(model, train_dl, strategy="freeze_unfreeze")
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_non_existent_backbone():
     with pytest.raises(KeyError):
         ImageClassifier(2, "i am never going to implement this lol")
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_freeze():
     model = ImageClassifier(2)
     model.freeze()
@@ -87,7 +89,7 @@ def test_freeze():
         assert p.requires_grad is False
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_unfreeze():
     model = ImageClassifier(2)
     model.unfreeze()
@@ -95,7 +97,7 @@ def test_unfreeze():
         assert p.requires_grad is True
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_multilabel(tmpdir):
 
     num_classes = 4
@@ -112,7 +114,7 @@ def test_multilabel(tmpdir):
     assert len(torch.unique(label)) <= 2
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.parametrize("jitter, args", [(torch.jit.script, ()), (torch.jit.trace, (torch.rand(1, 3, 32, 32), ))])
 def test_jit(tmpdir, jitter, args):
     path = os.path.join(tmpdir, "test.pt")
@@ -128,3 +130,13 @@ def test_jit(tmpdir, jitter, args):
     out = model(torch.rand(1, 3, 32, 32))
     assert isinstance(out, torch.Tensor)
     assert out.shape == torch.Size([1, 2])
+
+
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
+@mock.patch("flash._IS_TESTING", True)
+def test_serve():
+    model = ImageClassifier(2)
+    # TODO: Currently only servable once a preprocess has been attached
+    model._preprocess = ImageClassificationPreprocess()
+    model.eval()
+    model.serve()
diff --git a/tests/image/detection/test_data.py b/tests/image/detection/test_data.py
index b87ba8dec5..2cd29b61f2 100644
--- a/tests/image/detection/test_data.py
+++ b/tests/image/detection/test_data.py
@@ -5,8 +5,9 @@
 import pytest
 
 from flash.core.data.data_source import DefaultDataKeys
-from flash.core.utilities.imports import _COCO_AVAILABLE, _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
+from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
 from flash.image.detection.data import ObjectDetectionData
+from tests.helpers.utils import _IMAGE_TESTING
 
 if _IMAGE_AVAILABLE:
     from PIL import Image
@@ -120,7 +121,7 @@ def _create_synth_fiftyone_dataset(tmpdir):
     return dataset
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="pycocotools is not installed for testing")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="pycocotools is not installed for testing")
 def test_image_detector_data_from_coco(tmpdir):
 
     train_folder, coco_ann_path = _create_synth_coco_dataset(tmpdir)
@@ -166,7 +167,7 @@ def test_image_detector_data_from_coco(tmpdir):
     assert list(labels[0].keys()) == ['boxes', 'labels', 'image_id', 'area', 'iscrowd']
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed")
 @pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone is not installed for testing")
 def test_image_detector_data_from_fiftyone(tmpdir):
 
diff --git a/tests/image/detection/test_data_model_integration.py b/tests/image/detection/test_data_model_integration.py
index a20a4c06d3..2c54a4d0f0 100644
--- a/tests/image/detection/test_data_model_integration.py
+++ b/tests/image/detection/test_data_model_integration.py
@@ -19,6 +19,7 @@
 from flash.core.utilities.imports import _COCO_AVAILABLE, _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
 from flash.image import ObjectDetector
 from flash.image.detection import ObjectDetectionData
+from tests.helpers.utils import _IMAGE_TESTING
 
 if _IMAGE_AVAILABLE:
     from PIL import Image
@@ -32,7 +33,7 @@
     from tests.image.detection.test_data import _create_synth_fiftyone_dataset
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="pycocotools is not installed for testing")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="pycocotools is not installed for testing")
 @pytest.mark.skipif(not _COCO_AVAILABLE, reason="pycocotools is not installed for testing")
 @pytest.mark.parametrize(["model", "backbone"], [("fasterrcnn", "resnet18")])
 def test_detection(tmpdir, model, backbone):
@@ -56,7 +57,7 @@ def test_detection(tmpdir, model, backbone):
     model.predict(test_images)
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed for testing")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed for testing")
 @pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone is not installed for testing")
 @pytest.mark.parametrize(["model", "backbone"], [("fasterrcnn", "resnet18")])
 def test_detection_fiftyone(tmpdir, model, backbone):
diff --git a/tests/image/detection/test_model.py b/tests/image/detection/test_model.py
index 925d37b6a2..1b9a0bf67c 100644
--- a/tests/image/detection/test_model.py
+++ b/tests/image/detection/test_model.py
@@ -19,8 +19,8 @@
 from torch.utils.data import DataLoader, Dataset
 
 from flash.core.data.data_source import DefaultDataKeys
-from flash.core.utilities.imports import _IMAGE_AVAILABLE
 from flash.image import ObjectDetector
+from tests.helpers.utils import _IMAGE_TESTING
 
 
 def collate_fn(samples):
@@ -52,7 +52,7 @@ def __getitem__(self, idx):
         return {DefaultDataKeys.INPUT: img, DefaultDataKeys.TARGET: {"boxes": boxes, "labels": labels}}
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_init():
     model = ObjectDetector(num_classes=2)
     model.eval()
@@ -70,7 +70,7 @@ def test_init():
 
 
 @pytest.mark.parametrize("model", ["fasterrcnn", "retinanet"])
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_training(tmpdir, model):
     model = ObjectDetector(num_classes=2, model=model, pretrained=False, pretrained_backbone=False)
     ds = DummyDetectionDataset((3, 224, 224), 1, 2, 10)
@@ -79,7 +79,7 @@ def test_training(tmpdir, model):
     trainer.fit(model, dl)
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_jit(tmpdir):
     path = os.path.join(tmpdir, "test.pt")
 
diff --git a/tests/image/embedding/test_model.py b/tests/image/embedding/test_model.py
index 0c43035451..72951d5e7b 100644
--- a/tests/image/embedding/test_model.py
+++ b/tests/image/embedding/test_model.py
@@ -16,11 +16,11 @@
 import pytest
 import torch
 
-from flash.core.utilities.imports import _IMAGE_AVAILABLE
 from flash.image import ImageEmbedder
+from tests.helpers.utils import _IMAGE_TESTING
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.parametrize("jitter, args", [(torch.jit.script, ()), (torch.jit.trace, (torch.rand(1, 3, 32, 32), ))])
 def test_jit(tmpdir, jitter, args):
     path = os.path.join(tmpdir, "test.pt")
diff --git a/tests/image/segmentation/test_backbones.py b/tests/image/segmentation/test_backbones.py
index a4c850b0bc..0b2b452e17 100644
--- a/tests/image/segmentation/test_backbones.py
+++ b/tests/image/segmentation/test_backbones.py
@@ -15,7 +15,6 @@
 import torch
 from pytorch_lightning.utilities import _TORCHVISION_AVAILABLE
 
-from flash.core.utilities.imports import _BOLTS_AVAILABLE
 from flash.image.segmentation.backbones import SEMANTIC_SEGMENTATION_BACKBONES
 
 
diff --git a/tests/image/segmentation/test_data.py b/tests/image/segmentation/test_data.py
index b464c35ad5..8d536df2dd 100644
--- a/tests/image/segmentation/test_data.py
+++ b/tests/image/segmentation/test_data.py
@@ -11,6 +11,7 @@
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _IMAGE_AVAILABLE
 from flash.image import SemanticSegmentation, SemanticSegmentationData, SemanticSegmentationPreprocess
+from tests.helpers.utils import _IMAGE_TESTING
 
 if _IMAGE_AVAILABLE:
     from PIL import Image
@@ -54,7 +55,7 @@ def test_smoke(self):
         assert prep is not None
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 class TestSemanticSegmentationData:
 
     def test_smoke(self):
diff --git a/tests/image/segmentation/test_model.py b/tests/image/segmentation/test_model.py
index 302cb99cfc..da8e390e67 100644
--- a/tests/image/segmentation/test_model.py
+++ b/tests/image/segmentation/test_model.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 from typing import Tuple
+from unittest import mock
 
 import numpy as np
 import pytest
@@ -21,9 +22,9 @@
 from flash import Trainer
 from flash.core.data.data_pipeline import DataPipeline
 from flash.core.data.data_source import DefaultDataKeys
-from flash.core.utilities.imports import _IMAGE_AVAILABLE
 from flash.image import SemanticSegmentation
 from flash.image.segmentation.data import SemanticSegmentationPreprocess
+from tests.helpers.utils import _IMAGE_TESTING, _SERVE_TESTING
 
 # ======== Mock functions ========
 
@@ -45,13 +46,13 @@ def __len__(self) -> int:
 # ==============================
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_smoke():
     model = SemanticSegmentation(num_classes=1)
     assert model is not None
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.parametrize("num_classes", [8, 256])
 @pytest.mark.parametrize("img_shape", [(1, 3, 224, 192), (2, 3, 127, 212)])
 def test_forward(num_classes, img_shape):
@@ -68,7 +69,7 @@ def test_forward(num_classes, img_shape):
     assert out.shape == (B, num_classes, H, W)
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_init_train(tmpdir):
     model = SemanticSegmentation(num_classes=10)
     train_dl = torch.utils.data.DataLoader(DummyDataset())
@@ -76,13 +77,13 @@ def test_init_train(tmpdir):
     trainer.finetune(model, train_dl, strategy="freeze_unfreeze")
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_non_existent_backbone():
     with pytest.raises(KeyError):
         SemanticSegmentation(2, "i am never going to implement this lol")
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_freeze():
     model = SemanticSegmentation(2)
     model.freeze()
@@ -90,7 +91,7 @@ def test_freeze():
         assert p.requires_grad is False
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_unfreeze():
     model = SemanticSegmentation(2)
     model.unfreeze()
@@ -98,7 +99,7 @@ def test_unfreeze():
         assert p.requires_grad is True
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_predict_tensor():
     img = torch.rand(1, 3, 10, 20)
     model = SemanticSegmentation(2)
@@ -109,7 +110,7 @@ def test_predict_tensor():
     assert len(out[0][0]) == 20
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 def test_predict_numpy():
     img = np.ones((1, 3, 10, 20))
     model = SemanticSegmentation(2)
@@ -120,7 +121,7 @@ def test_predict_numpy():
     assert len(out[0][0]) == 20
 
 
-@pytest.mark.skipif(not _IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_TESTING, reason="image libraries aren't installed.")
 @pytest.mark.parametrize("jitter, args", [(torch.jit.script, ()), (torch.jit.trace, (torch.rand(1, 3, 32, 32), ))])
 def test_jit(tmpdir, jitter, args):
     path = os.path.join(tmpdir, "test.pt")
@@ -136,3 +137,13 @@ def test_jit(tmpdir, jitter, args):
     out = model(torch.rand(1, 3, 32, 32))
     assert isinstance(out, torch.Tensor)
     assert out.shape == torch.Size([1, 2, 32, 32])
+
+
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
+@mock.patch("flash._IS_TESTING", True)
+def test_serve():
+    model = SemanticSegmentation(2)
+    # TODO: Currently only servable once a preprocess has been attached
+    model._preprocess = SemanticSegmentationPreprocess()
+    model.eval()
+    model.serve()
diff --git a/tests/image/style_transfer/test_model.py b/tests/image/style_transfer/test_model.py
index 4cd1b05c9d..6c4345fb87 100644
--- a/tests/image/style_transfer/test_model.py
+++ b/tests/image/style_transfer/test_model.py
@@ -3,11 +3,12 @@
 import pytest
 import torch
 
-from flash.core.utilities.imports import _IMAGE_STLYE_TRANSFER, _PYSTICHE_GREATER_EQUAL_0_7_2
+from flash.core.utilities.imports import _IMAGE_STLYE_TRANSFER
 from flash.image.style_transfer import StyleTransfer
+from tests.helpers.utils import _IMAGE_STLYE_TRANSFER_TESTING
 
 
-@pytest.mark.skipif(not _PYSTICHE_GREATER_EQUAL_0_7_2, reason="image style transfer libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_STLYE_TRANSFER_TESTING, reason="image style transfer libraries aren't installed.")
 def test_style_transfer_task():
 
     model = StyleTransfer(
@@ -25,7 +26,7 @@ def test_style_transfer_task_import():
         StyleTransfer()
 
 
-@pytest.mark.skipif(not _PYSTICHE_GREATER_EQUAL_0_7_2, reason="image style transfer libraries aren't installed.")
+@pytest.mark.skipif(not _IMAGE_STLYE_TRANSFER_TESTING, reason="image style transfer libraries aren't installed.")
 def test_jit(tmpdir):
     path = os.path.join(tmpdir, "test.pt")
 
diff --git a/tests/tabular/classification/test_data_model_integration.py b/tests/tabular/classification/test_data_model_integration.py
index 2072dd40f5..349aeeaaba 100644
--- a/tests/tabular/classification/test_data_model_integration.py
+++ b/tests/tabular/classification/test_data_model_integration.py
@@ -16,6 +16,7 @@
 
 from flash.core.utilities.imports import _TABULAR_AVAILABLE
 from flash.tabular import TabularClassifier, TabularData
+from tests.helpers.utils import _TABULAR_TESTING
 
 if _TABULAR_AVAILABLE:
     import pandas as pd
@@ -30,7 +31,7 @@
     )
 
 
-@pytest.mark.skipif(not _TABULAR_AVAILABLE, reason="tabular libraries aren't installed.")
+@pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed.")
 def test_classification(tmpdir):
 
     train_data_frame = TEST_DF_1.copy()
diff --git a/tests/tabular/classification/test_model.py b/tests/tabular/classification/test_model.py
index e2fbf11edb..6e2108162e 100644
--- a/tests/tabular/classification/test_model.py
+++ b/tests/tabular/classification/test_model.py
@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+from unittest import mock
 
+import pandas as pd
 import pytest
 import torch
 from pytorch_lightning import Trainer
@@ -20,6 +22,8 @@
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.utilities.imports import _TABULAR_AVAILABLE
 from flash.tabular import TabularClassifier
+from flash.tabular.classification.data import TabularData
+from tests.helpers.utils import _SERVE_TESTING, _TABULAR_TESTING
 
 # ======== Mock functions ========
 
@@ -44,7 +48,7 @@ def __len__(self) -> int:
 # ==============================
 
 
-@pytest.mark.skipif(not _TABULAR_AVAILABLE, reason="tabular libraries aren't installed.")
+@pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed.")
 def test_init_train(tmpdir):
     train_dl = torch.utils.data.DataLoader(DummyDataset(), batch_size=16)
     model = TabularClassifier(num_classes=10, num_features=16 + 16, embedding_sizes=16 * [(10, 32)])
@@ -52,7 +56,7 @@ def test_init_train(tmpdir):
     trainer.fit(model, train_dl)
 
 
-@pytest.mark.skipif(not _TABULAR_AVAILABLE, reason="tabular libraries aren't installed.")
+@pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed.")
 def test_init_train_no_num(tmpdir):
     train_dl = torch.utils.data.DataLoader(DummyDataset(num_num=0), batch_size=16)
     model = TabularClassifier(num_classes=10, num_features=16, embedding_sizes=16 * [(10, 32)])
@@ -60,7 +64,7 @@ def test_init_train_no_num(tmpdir):
     trainer.fit(model, train_dl)
 
 
-@pytest.mark.skipif(not _TABULAR_AVAILABLE, reason="tabular libraries aren't installed.")
+@pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed.")
 def test_init_train_no_cat(tmpdir):
     train_dl = torch.utils.data.DataLoader(DummyDataset(num_cat=0), batch_size=16)
     model = TabularClassifier(num_classes=10, num_features=16, embedding_sizes=[])
@@ -74,7 +78,7 @@ def test_module_import_error(tmpdir):
         TabularClassifier(num_classes=10, num_features=16, embedding_sizes=[])
 
 
-@pytest.mark.skipif(not _TABULAR_AVAILABLE, reason="tabular libraries aren't installed.")
+@pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular libraries aren't installed.")
 def test_jit(tmpdir):
     model = TabularClassifier(num_classes=10, num_features=8, embedding_sizes=4 * [(10, 32)])
     model.eval()
@@ -90,3 +94,20 @@ def test_jit(tmpdir):
     out = model((torch.randint(0, 10, size=(1, 4)), torch.rand(1, 4)))
     assert isinstance(out, torch.Tensor)
     assert out.shape == torch.Size([1, 10])
+
+
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
+@mock.patch("flash._IS_TESTING", True)
+def test_serve():
+    train_data = {"num_col": [1.4, 2.5], "cat_col": ["positive", "negative"], "target": [1, 2]}
+    datamodule = TabularData.from_data_frame(
+        "cat_col",
+        "num_col",
+        "target",
+        pd.DataFrame.from_dict(train_data),
+    )
+    model = TabularClassifier.from_data(datamodule)
+    # TODO: Currently only servable once a preprocess has been attached
+    model._preprocess = datamodule.preprocess
+    model.eval()
+    model.serve()
diff --git a/tests/text/classification/test_data.py b/tests/text/classification/test_data.py
index 0564355cd6..d5a3b680f9 100644
--- a/tests/text/classification/test_data.py
+++ b/tests/text/classification/test_data.py
@@ -25,6 +25,7 @@
     TextJSONDataSource,
     TextSentencesDataSource,
 )
+from tests.helpers.utils import _TEXT_TESTING
 
 if _TEXT_AVAILABLE:
     from transformers.tokenization_utils_base import PreTrainedTokenizerBase
@@ -57,7 +58,7 @@ def json_data(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_csv(tmpdir):
     csv_path = csv_data(tmpdir)
     dm = TextClassificationData.from_csv("sentence", "label", backbone=TEST_BACKBONE, train_file=csv_path, batch_size=1)
@@ -67,7 +68,7 @@ def test_from_csv(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_test_valid(tmpdir):
     csv_path = csv_data(tmpdir)
     dm = TextClassificationData.from_csv(
@@ -89,7 +90,7 @@ def test_test_valid(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_json(tmpdir):
     json_path = json_data(tmpdir)
     dm = TextClassificationData.from_json("sentence", "lab", backbone=TEST_BACKBONE, train_file=json_path, batch_size=1)
@@ -105,7 +106,7 @@ def test_text_module_not_found_error():
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 @pytest.mark.parametrize(
     "cls, kwargs",
     [
diff --git a/tests/text/classification/test_model.py b/tests/text/classification/test_model.py
index b628086657..18863b9637 100644
--- a/tests/text/classification/test_model.py
+++ b/tests/text/classification/test_model.py
@@ -12,13 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+from unittest import mock
 
 import pytest
 import torch
 
 from flash import Trainer
-from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text import TextClassifier
+from flash.text.classification.data import TextClassificationPostprocess, TextClassificationPreprocess
+from tests.helpers.utils import _SERVE_TESTING, _TEXT_TESTING
 
 # ======== Mock functions ========
 
@@ -41,7 +43,7 @@ def __len__(self) -> int:
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_init_train(tmpdir):
     model = TextClassifier(2, TEST_BACKBONE)
     train_dl = torch.utils.data.DataLoader(DummyDataset())
@@ -49,7 +51,7 @@ def test_init_train(tmpdir):
     trainer.fit(model, train_dl)
 
 
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_jit(tmpdir):
     sample_input = {"input_ids": torch.randint(1000, size=(1, 100))}
     path = os.path.join(tmpdir, "test.pt")
@@ -66,3 +68,14 @@ def test_jit(tmpdir):
     out = model(sample_input)["logits"]
     assert isinstance(out, torch.Tensor)
     assert out.shape == torch.Size([1, 2])
+
+
+@pytest.mark.skipif(not _SERVE_TESTING, reason="serve libraries aren't installed.")
+@mock.patch("flash._IS_TESTING", True)
+def test_serve():
+    model = TextClassifier(2, TEST_BACKBONE)
+    # TODO: Currently only servable once a preprocess and postprocess have been attached
+    model._preprocess = TextClassificationPreprocess(backbone=TEST_BACKBONE)
+    model._postprocess = TextClassificationPostprocess()
+    model.eval()
+    model.serve()
diff --git a/tests/text/seq2seq/core/test_data.py b/tests/text/seq2seq/core/test_data.py
index bf63bb86fb..4f2144aa90 100644
--- a/tests/text/seq2seq/core/test_data.py
+++ b/tests/text/seq2seq/core/test_data.py
@@ -12,12 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
-from pathlib import Path
 
 import pytest
 
 from flash.core.utilities.imports import _TEXT_AVAILABLE
-from flash.text import TextClassificationData
 from flash.text.seq2seq.core.data import (
     Seq2SeqBackboneState,
     Seq2SeqCSVDataSource,
@@ -27,13 +25,14 @@
     Seq2SeqPostprocess,
     Seq2SeqSentencesDataSource,
 )
+from tests.helpers.utils import _TEXT_TESTING
 
 if _TEXT_AVAILABLE:
     from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 @pytest.mark.parametrize(
     "cls, kwargs",
     [
diff --git a/tests/text/seq2seq/summarization/test_data.py b/tests/text/seq2seq/summarization/test_data.py
index 5abc9afe11..2ab09f3636 100644
--- a/tests/text/seq2seq/summarization/test_data.py
+++ b/tests/text/seq2seq/summarization/test_data.py
@@ -16,8 +16,8 @@
 
 import pytest
 
-from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text import SummarizationData
+from tests.helpers.utils import _TEXT_TESTING
 
 TEST_BACKBONE = "sshleifer/tiny-mbart"  # super small model for testing
 
@@ -47,7 +47,7 @@ def json_data(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_csv(tmpdir):
     csv_path = csv_data(tmpdir)
     dm = SummarizationData.from_csv("input", "target", backbone=TEST_BACKBONE, train_file=csv_path, batch_size=1)
@@ -57,7 +57,7 @@ def test_from_csv(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_files(tmpdir):
     csv_path = csv_data(tmpdir)
     dm = SummarizationData.from_csv(
@@ -78,7 +78,7 @@ def test_from_files(tmpdir):
     assert "input_ids" in batch
 
 
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_postprocess_tokenizer(tmpdir):
     """Tests that the tokenizer property in ``SummarizationPostprocess`` resolves correctly when a different backbone is
     used.
@@ -99,7 +99,7 @@ def test_postprocess_tokenizer(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_json(tmpdir):
     json_path = json_data(tmpdir)
     dm = SummarizationData.from_json("input", "target", backbone=TEST_BACKBONE, train_file=json_path, batch_size=1)
diff --git a/tests/text/seq2seq/summarization/test_metric.py b/tests/text/seq2seq/summarization/test_metric.py
index 7b829333a3..9f17397b02 100644
--- a/tests/text/seq2seq/summarization/test_metric.py
+++ b/tests/text/seq2seq/summarization/test_metric.py
@@ -14,11 +14,11 @@
 import pytest
 import torch
 
-from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text.seq2seq.summarization.metric import RougeMetric
+from tests.helpers.utils import _TEXT_TESTING
 
 
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_rouge():
     preds = "My name is John".split()
     target = "Is your name John".split()
diff --git a/tests/text/seq2seq/summarization/test_model.py b/tests/text/seq2seq/summarization/test_model.py
index 7e380e331e..69b030e402 100644
--- a/tests/text/seq2seq/summarization/test_model.py
+++ b/tests/text/seq2seq/summarization/test_model.py
@@ -17,8 +17,8 @@
 import torch
 
 from flash import Trainer
-from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text import SummarizationTask
+from tests.helpers.utils import _TEXT_TESTING
 
 # ======== Mock functions ========
 
@@ -41,7 +41,7 @@ def __len__(self) -> int:
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_init_train(tmpdir):
     model = SummarizationTask(TEST_BACKBONE)
     train_dl = torch.utils.data.DataLoader(DummyDataset())
@@ -49,7 +49,7 @@ def test_init_train(tmpdir):
     trainer.fit(model, train_dl)
 
 
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_jit(tmpdir):
     sample_input = {
         "input_ids": torch.randint(1000, size=(1, 32)),
diff --git a/tests/text/seq2seq/translation/test_data.py b/tests/text/seq2seq/translation/test_data.py
index 64e613d6a6..244cb27d4a 100644
--- a/tests/text/seq2seq/translation/test_data.py
+++ b/tests/text/seq2seq/translation/test_data.py
@@ -16,8 +16,8 @@
 
 import pytest
 
-from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text import TranslationData
+from tests.helpers.utils import _TEXT_TESTING
 
 TEST_BACKBONE = "sshleifer/tiny-mbart"  # super small model for testing
 
@@ -47,7 +47,7 @@ def json_data(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_csv(tmpdir):
     csv_path = csv_data(tmpdir)
     dm = TranslationData.from_csv("input", "target", backbone=TEST_BACKBONE, train_file=csv_path, batch_size=1)
@@ -57,7 +57,7 @@ def test_from_csv(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_files(tmpdir):
     csv_path = csv_data(tmpdir)
     dm = TranslationData.from_csv(
@@ -79,7 +79,7 @@ def test_from_files(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_from_json(tmpdir):
     json_path = json_data(tmpdir)
     dm = TranslationData.from_json("input", "target", backbone=TEST_BACKBONE, train_file=json_path, batch_size=1)
diff --git a/tests/text/seq2seq/translation/test_model.py b/tests/text/seq2seq/translation/test_model.py
index 808d0a0ada..dd0ac0979a 100644
--- a/tests/text/seq2seq/translation/test_model.py
+++ b/tests/text/seq2seq/translation/test_model.py
@@ -17,8 +17,8 @@
 import torch
 
 from flash import Trainer
-from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text import TranslationTask
+from tests.helpers.utils import _TEXT_TESTING
 
 # ======== Mock functions ========
 
@@ -41,7 +41,7 @@ def __len__(self) -> int:
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_init_train(tmpdir):
     model = TranslationTask(TEST_BACKBONE)
     train_dl = torch.utils.data.DataLoader(DummyDataset())
@@ -49,7 +49,7 @@ def test_init_train(tmpdir):
     trainer.fit(model, train_dl)
 
 
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_jit(tmpdir):
     sample_input = {
         "input_ids": torch.randint(128, size=(1, 4)),
diff --git a/tests/text/test_data_model_integration.py b/tests/text/test_data_model_integration.py
index f6c5137252..9f65b02639 100644
--- a/tests/text/test_data_model_integration.py
+++ b/tests/text/test_data_model_integration.py
@@ -17,8 +17,8 @@
 import pytest
 from pytorch_lightning import Trainer
 
-from flash.core.utilities.imports import _TEXT_AVAILABLE
 from flash.text import TextClassificationData, TextClassifier
+from tests.helpers.utils import _TEXT_TESTING
 
 TEST_BACKBONE = "prajjwal1/bert-tiny"  # super small model for testing
 
@@ -36,7 +36,7 @@ def csv_data(tmpdir):
 
 
 @pytest.mark.skipif(os.name == "nt", reason="Huggingface timing out on Windows")
-@pytest.mark.skipif(not _TEXT_AVAILABLE, reason="text libraries aren't installed.")
+@pytest.mark.skipif(not _TEXT_TESTING, reason="text libraries aren't installed.")
 def test_classification(tmpdir):
 
     csv_path = csv_data(tmpdir)
diff --git a/tests/video/classification/test_model.py b/tests/video/classification/test_model.py
index fcb1bc68bd..a15c0d0b52 100644
--- a/tests/video/classification/test_model.py
+++ b/tests/video/classification/test_model.py
@@ -22,6 +22,7 @@
 
 import flash
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _VIDEO_AVAILABLE
+from tests.helpers.utils import _VIDEO_TESTING
 
 if _FIFTYONE_AVAILABLE:
     import fiftyone as fo
@@ -123,7 +124,7 @@ def mock_encoded_video_dataset_folder(tmpdir):
             yield str(tmp_dir), video_duration
 
 
-@pytest.mark.skipif(not _VIDEO_AVAILABLE, reason="PyTorchVideo isn't installed.")
+@pytest.mark.skipif(not _VIDEO_TESTING, reason="PyTorchVideo isn't installed.")
 def test_video_classifier_finetune(tmpdir):
 
     with mock_encoded_video_dataset_file() as (
@@ -189,7 +190,7 @@ def test_video_classifier_finetune(tmpdir):
         trainer.finetune(model, datamodule=datamodule)
 
 
-@pytest.mark.skipif(not _VIDEO_AVAILABLE, reason="PyTorchVideo isn't installed.")
+@pytest.mark.skipif(not _VIDEO_TESTING, reason="PyTorchVideo isn't installed.")
 @pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone isn't installed.")
 def test_video_classifier_finetune_fiftyone(tmpdir):
 
@@ -259,7 +260,7 @@ def test_video_classifier_finetune_fiftyone(tmpdir):
         trainer.finetune(model, datamodule=datamodule)
 
 
-@pytest.mark.skipif(not _VIDEO_AVAILABLE, reason="PyTorchVideo isn't installed.")
+@pytest.mark.skipif(not _VIDEO_TESTING, reason="PyTorchVideo isn't installed.")
 def test_jit(tmpdir):
     sample_input = torch.rand(1, 3, 32, 256, 256)
     path = os.path.join(tmpdir, "test.pt")