Made some of dependencies optional

Made some of dependencies optional

.github/workflows/tests.yaml

@@ -48,6 +48,7 @@ jobs:
       run: |
         python3 -m pip install --upgrade pip
         python3 -m pip install -r ci/requirements_tests.txt
+        python3 -m pip install -r ci/requirements_optional.txt
         python3 -m pip install -e .
     - name: Running all tests
       run: |

Makefile

@@ -65,7 +65,7 @@ run_all_tests: download_mock_dataset wandb_login
 
 .PHONY: run_short_tests
 run_short_tests: download_mock_dataset
-	export PYTORCH_ENABLE_MPS_FALLBACK=1; pytest --disable-warnings -sv -m "not long" tests
+	export PYTORCH_ENABLE_MPS_FALLBACK=1; pytest --disable-warnings -sv -m "not long and not needs_optional_dependency" tests
 	pytest --disable-warnings --doctest-modules --doctest-continue-on-failure -sv oml
 
 .PHONY: test_converters

README.md

@@ -455,7 +455,7 @@ features_queries = inference_on_images(extractor, paths=queries, transform=trans
 features_galleries = inference_on_images(extractor, paths=galleries, transform=transform, **args)
 
 # Now we can explicitly build pairwise matrix of distances or save you RAM via using kNN
-use_knn = True
+use_knn = False
 top_k = 3
 
 if use_knn:

ci/Dockerfile

@@ -29,7 +29,9 @@ RUN pip install Cython==0.29.28
 ### Python libs
 COPY ci/requirements.txt requirements.txt
 COPY ci/requirements_tests.txt requirements_tests.txt
+COPY ci/requirements_optional.txt requirements_optional.txt
 RUN pip install --no-cache-dir -r requirements.txt ${EXTRA_INDEX}
+RUN pip install --no-cache-dir -r requirements_optional.txt
 RUN pip install --no-cache-dir -r requirements_tests.txt
 
 WORKDIR /oml-lib

ci/requirements.txt

@@ -11,10 +11,4 @@ hydra-core==1.3.0
 python-dotenv>=0.17.0
 validators>=0.18.0
 gdown>=4.0
-
-# todo: make it optional
-grad-cam>=1.4.6
-jupyter>=1.0.0
 tensorboard
-neptune-client>=0.14.2, <1.0.0
-wandb>=0.15.4

ci/requirements_optional.txt

@@ -0,0 +1,4 @@
+grad-cam>=1.4.6
+jupyter>=1.0.0
+neptune-client>=0.14.2, <1.0.0
+wandb>=0.15.4

docs/readme/examples_source/extractor/retrieval_usage.md

@@ -26,7 +26,7 @@ features_queries = inference_on_images(extractor, paths=queries, transform=trans
 features_galleries = inference_on_images(extractor, paths=galleries, transform=transform, **args)
 
 # Now we can explicitly build pairwise matrix of distances or save you RAM via using kNN
-use_knn = True
+use_knn = False
 top_k = 3
 
 if use_knn:

oml/lightning/callbacks/metric.py

@@ -4,7 +4,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pytorch_lightning as pl
-from neptune.new.types import File
 from pytorch_lightning import Callback
 from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
 from pytorch_lightning.utilities.types import STEP_OUTPUT
@@ -106,6 +105,8 @@ def _log_images(self, pl_module: pl.LightningModule) -> None:
         for fig, metric_log_str in zip(*self.metric.visualize()):
             log_str = f"{LOG_IMAGE_FOLDER}/{metric_log_str}"
             if isinstance(pl_module.logger, NeptuneLogger):
+                from neptune.new.types import File  # this is the optional dependency
+
                 pl_module.logger.experiment[log_str].log(File.as_image(fig))
             elif isinstance(pl_module.logger, WandbLogger):
                 fig_img = figure_to_nparray(fig)

oml/lightning/pipelines/parser.py

@@ -3,7 +3,6 @@
 from typing import Any, Dict, Optional
 
 import torch
-import wandb
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
 from pytorch_lightning.loggers.logger import Logger
@@ -114,6 +113,9 @@ def upload_files_to_neptune_cloud(logger: NeptuneLogger, cfg: TCfg) -> None:
 
 
 def upload_files_to_wandb_cloud(logger: WandbLogger, cfg: TCfg) -> None:
+    # this is the optional dependency
+    import wandb
+
     assert isinstance(logger, WandbLogger)
 
     # log transforms as files

tests/test_imports.py

@@ -9,7 +9,7 @@
 
 from oml.const import PROJECT_ROOT
 
-LIBS_TO_IGNORE = ["torch_xla"]
+LIBS_TO_IGNORE = ["torch_xla", "pytorch_grad_cam", "wandb", "neptune", "IPython"]
 
 NEED_TO_TEST_NOTEBOOKS = True
 

tests/test_oml/test_models/test_visualisations.py

@@ -18,7 +18,7 @@ def get_numpy_image() -> np.ndarray:
     return np.random.randint(0, 256, size=IMAGE_SIZE, dtype=np.uint8)
 
 
-@pytest.mark.long
+@pytest.mark.needs_optional_dependency
 @pytest.mark.parametrize(
     "draw_function",
     [
@@ -44,7 +44,7 @@ def test_visualisation(draw_function: Any, image: Union[np.ndarray, Image.Image]
     assert image_modified.shape == IMAGE_SIZE
 
 
-@pytest.mark.long
+@pytest.mark.needs_optional_dependency
 @pytest.mark.parametrize(
     "draw_function",
     [

tests/test_oml/test_registry/test_registry.py

@@ -60,9 +60,7 @@ def get_opt() -> Optimizer:
         ("transforms", TRANSFORMS_REGISTRY, get_transforms, None),
         ("pairwise_model", PAIRWISE_MODELS_REGISTRY, get_pairwise_model, None),
         ("postprocessor", POSTPROCESSORS_REGISTRY, get_postprocessor, None),
-        # We marked these secret required tests as "long" to skip them on push requests from public
-        # forks to OML. Nevertheless, the tests will be executed if PR is merged with all the needed credentials.
-        pytest.param("logger", LOGGERS_REGISTRY, get_logger, None, marks=pytest.mark.long),
+        pytest.param("logger", LOGGERS_REGISTRY, get_logger, None, marks=pytest.mark.needs_optional_dependency),
     ],
 )
 def test_registry(folder_name, registry, factory_fun, runtime_args) -> None: