diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
index ab9ac7f32..9754a2888 100644
--- a/.github/workflows/tests.yaml
+++ b/.github/workflows/tests.yaml
@@ -53,7 +53,7 @@ jobs:
     - name: Running all tests
       run: |
         if [ "${{ matrix.python-version }}" == "3.8" ]; then
-          make run_all_tests WANDB_API_KEY=${{ secrets.WANDB_API_KEY }} NEPTUNE_API_TOKEN=${{ secrets.NEPTUNE_API_TOKEN }} DOWNLOAD_ZOO_IN_TESTS=yes
+          make run_all_tests WANDB_API_KEY=${{ secrets.WANDB_API_KEY }} NEPTUNE_API_TOKEN=${{ secrets.NEPTUNE_API_TOKEN }} DOWNLOAD_ZOO_IN_TESTS=yes TEST_CLOUD_LOGGERS=yes
         else
           make run_all_tests WANDB_API_KEY=${{ secrets.WANDB_API_KEY }} NEPTUNE_API_TOKEN=${{ secrets.NEPTUNE_API_TOKEN }}
         fi
diff --git a/README.md b/README.md
index b2a3bc338..2ca0bf479 100644
--- a/README.md
+++ b/README.md
@@ -392,7 +392,7 @@ from oml.miners.inbatch_all_tri import AllTripletsMiner
 from oml.models import ViTExtractor
 from oml.samplers.balance import BalanceSampler
 from oml.utils.download_mock_dataset import download_mock_dataset
-from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
+from oml.lightning.pipelines.logging import NeptunePipelineLogger, TensorBoardPipelineLogger, WandBPipelineLogger
 
 dataset_root = "mock_dataset/"
 df_train, df_val = download_mock_dataset(dataset_root)
@@ -413,15 +413,15 @@ val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=4)
 metric_callback = MetricValCallback(metric=EmbeddingMetrics(extra_keys=[train_dataset.paths_key,]), log_images=True)
 
 # 1) Logging with Tensorboard
-logger = TensorBoardLogger(".")
+logger = TensorBoardPipelineLogger(".")
 
 # 2) Logging with Neptune
-# logger = NeptuneLogger(api_key="", project="", log_model_checkpoints=False)
+# logger = NeptunePipelineLogger(api_key="", project="", log_model_checkpoints=False)
 
 # 3) Logging with Weights and Biases
 # import os
 # os.environ["WANDB_API_KEY"] = ""
-# logger = WandbLogger(project="test_project", log_model=False)
+# logger = WandBPipelineLogger(project="test_project", log_model=False)
 
 # run
 pl_model = ExtractorModule(extractor, criterion, optimizer)
diff --git a/ci/requirements_optional.txt b/ci/requirements_optional.txt
index e2bcfb70d..d45c108f2 100644
--- a/ci/requirements_optional.txt
+++ b/ci/requirements_optional.txt
@@ -1,4 +1,4 @@
 grad-cam==1.4.6
 jupyter>=1.0.0
-neptune-client>=0.14.2, <1.0.0
+neptune>=1.0.0, <1.10.1
 wandb>=0.15.4
diff --git a/docs/readme/examples_source/extractor/train_val_pl.md b/docs/readme/examples_source/extractor/train_val_pl.md
index e9cef85e3..b4d379442 100644
--- a/docs/readme/examples_source/extractor/train_val_pl.md
+++ b/docs/readme/examples_source/extractor/train_val_pl.md
@@ -16,7 +16,7 @@ from oml.miners.inbatch_all_tri import AllTripletsMiner
 from oml.models import ViTExtractor
 from oml.samplers.balance import BalanceSampler
 from oml.utils.download_mock_dataset import download_mock_dataset
-from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
+from oml.lightning.pipelines.logging import NeptunePipelineLogger, TensorBoardPipelineLogger, WandBPipelineLogger
 
 dataset_root = "mock_dataset/"
 df_train, df_val = download_mock_dataset(dataset_root)
@@ -37,15 +37,15 @@ val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=4)
 metric_callback = MetricValCallback(metric=EmbeddingMetrics(extra_keys=[train_dataset.paths_key,]), log_images=True)
 
 # 1) Logging with Tensorboard
-logger = TensorBoardLogger(".")
+logger = TensorBoardPipelineLogger(".")
 
 # 2) Logging with Neptune
-# logger = NeptuneLogger(api_key="", project="", log_model_checkpoints=False)
+# logger = NeptunePipelineLogger(api_key="", project="", log_model_checkpoints=False)
 
 # 3) Logging with Weights and Biases
 # import os
 # os.environ["WANDB_API_KEY"] = ""
-# logger = WandbLogger(project="test_project", log_model=False)
+# logger = WandBPipelineLogger(project="test_project", log_model=False)
 
 # run
 pl_model = ExtractorModule(extractor, criterion, optimizer)
diff --git a/oml/interfaces/loggers.py b/oml/interfaces/loggers.py
new file mode 100644
index 000000000..d3f9d8d3e
--- /dev/null
+++ b/oml/interfaces/loggers.py
@@ -0,0 +1,18 @@
+from abc import abstractmethod
+
+from matplotlib import pyplot as plt
+from pytorch_lightning.loggers import Logger as LightningLogger
+
+from oml.const import TCfg
+
+
+class IFigureLogger:
+    @abstractmethod
+    def log_figure(self, fig: plt.Figure, title: str, idx: int) -> None:
+        raise NotImplementedError()
+
+
+class IPipelineLogger(LightningLogger, IFigureLogger):
+    @abstractmethod
+    def log_pipeline_info(self, cfg: TCfg) -> None:
+        raise NotImplementedError()
diff --git a/oml/lightning/callbacks/metric.py b/oml/lightning/callbacks/metric.py
index 882a19d36..40aad10b0 100644
--- a/oml/lightning/callbacks/metric.py
+++ b/oml/lightning/callbacks/metric.py
@@ -1,19 +1,18 @@
+import warnings
 from math import ceil
 from typing import Any, Optional
 
 import matplotlib.pyplot as plt
-import numpy as np
 import pytorch_lightning as pl
 from pytorch_lightning import Callback
-from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
 from pytorch_lightning.utilities.types import STEP_OUTPUT
 from torch.utils.data import DataLoader
 
 from oml.const import LOG_IMAGE_FOLDER
 from oml.ddp.patching import check_loaders_is_patched, patch_dataloader_to_ddp
+from oml.interfaces.loggers import IFigureLogger
 from oml.interfaces.metrics import IBasicMetric, IMetricDDP, IMetricVisualisable
 from oml.lightning.modules.ddp import ModuleDDP
-from oml.utils.images.images import figure_to_nparray
 from oml.utils.misc import flatten_dict
 
 
@@ -102,23 +101,16 @@ def _log_images(self, pl_module: pl.LightningModule) -> None:
         if not isinstance(self.metric, IMetricVisualisable):
             return
 
+        if not isinstance(pl_module.logger, IFigureLogger):
+            warnings.warn(
+                f"Unexpected logger {pl_module.logger}. Figures have not been saved. "
+                f"Please, use a child of {IFigureLogger}."
+            )
+            return
+
         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)
-                pl_module.logger.log_image(images=[fig_img], key=metric_log_str)
-            elif isinstance(pl_module.logger, TensorBoardLogger):
-                fig_img = figure_to_nparray(fig)
-                pl_module.logger.experiment.add_image(
-                    log_str, np.transpose(fig_img, (2, 0, 1)), pl_module.current_epoch
-                )
-            else:
-                raise ValueError(f"Logging with {type(pl_module.logger)} is not supported yet.")
-
+            pl_module.logger.log_figure(fig=fig, title=log_str, idx=pl_module.current_epoch)
             plt.close(fig=fig)
 
     def on_validation_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
diff --git a/oml/lightning/pipelines/logging.py b/oml/lightning/pipelines/logging.py
new file mode 100644
index 000000000..b73f447ba
--- /dev/null
+++ b/oml/lightning/pipelines/logging.py
@@ -0,0 +1,101 @@
+import warnings
+from pathlib import Path
+from typing import Any, Dict, List
+
+import matplotlib.pyplot as plt
+import numpy as np
+from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
+
+from oml.const import OML_PATH, TCfg
+from oml.interfaces.loggers import IPipelineLogger
+from oml.registry.transforms import save_transforms_as_files
+from oml.utils.images.images import figure_to_nparray
+from oml.utils.misc import dictconfig_to_dict, flatten_dict
+
+
+def prepare_config_to_logging(cfg: TCfg) -> Dict[str, Any]:
+    cwd = Path.cwd().name
+    flattened_dict = flatten_dict({**dictconfig_to_dict(cfg), **{"dir": cwd}}, sep="|")
+    return flattened_dict
+
+
+def prepare_tags(cfg: TCfg) -> List[str]:
+    cwd = Path.cwd().name
+    tags = list(cfg.get("tags", [])) + [cfg.get("postfix", "")] + [cwd]
+    tags = list(filter(lambda x: len(x) > 0, tags))
+    return tags
+
+
+class NeptunePipelineLogger(NeptuneLogger, IPipelineLogger):
+    def log_pipeline_info(self, cfg: TCfg) -> None:
+        warnings.warn(
+            "Unfortunately, in the case of using Neptune, you may experience that long experiments are "
+            "stacked and not responding. It's not an issue on OML's side, so, we cannot fix it."
+        )
+        self.log_hyperparams(prepare_config_to_logging(cfg))
+
+        tags = prepare_tags(cfg)
+        self.run["sys/tags"].add(tags)
+
+        # log transforms as files
+        for key, transforms_file in save_transforms_as_files(cfg):
+            self.run[key].upload(transforms_file)
+
+        # log source code
+        source_files = list(map(lambda x: str(x), OML_PATH.glob("**/*.py"))) + list(
+            map(lambda x: str(x), OML_PATH.glob("**/*.yaml"))
+        )
+        self.run["code"].upload_files(source_files)
+
+        # log dataframe
+        self.run["dataset"].upload(str(Path(cfg["dataset_root"]) / cfg["dataframe_name"]))
+
+    def log_figure(self, fig: plt.Figure, title: str, idx: int) -> None:
+        from neptune.types import File  # this is the optional dependency
+
+        self.experiment[title].log(File.as_image(fig))
+
+
+class WandBPipelineLogger(WandbLogger, IPipelineLogger):
+    def log_pipeline_info(self, cfg: TCfg) -> None:
+        # this is the optional dependency
+        import wandb
+
+        self.log_hyperparams(prepare_config_to_logging(cfg))
+
+        tags = prepare_tags(cfg)
+        self.experiment.tags = tags
+
+        # log transforms as files
+        keys_files = save_transforms_as_files(cfg)
+        if keys_files:
+            transforms = wandb.Artifact("transforms", type="transforms")
+            for _, transforms_file in keys_files:
+                transforms.add_file(transforms_file)
+            self.experiment.log_artifact(transforms)
+
+        # log source code
+        code = wandb.Artifact("source_code", type="code")
+        code.add_dir(OML_PATH, name="oml")
+        self.experiment.log_artifact(code)
+
+        # log dataset
+        dataset = wandb.Artifact("dataset", type="dataset")
+        dataset.add_file(str(Path(cfg["dataset_root"]) / cfg["dataframe_name"]))
+        self.experiment.log_artifact(dataset)
+
+    def log_figure(self, fig: plt.Figure, title: str, idx: int) -> None:
+        fig_img = figure_to_nparray(fig)
+        self.log_image(images=[fig_img], key=title)
+
+
+class TensorBoardPipelineLogger(TensorBoardLogger, IPipelineLogger):
+    def log_pipeline_info(self, cfg: TCfg) -> None:
+        pass
+
+    def log_figure(self, fig: plt.Figure, title: str, idx: int) -> None:
+        fig_img = figure_to_nparray(fig)
+        self.experiment.add_image(title, np.transpose(fig_img, (2, 0, 1)), idx)
+
+
+__all__ = ["IPipelineLogger", "TensorBoardPipelineLogger", "WandBPipelineLogger", "NeptunePipelineLogger"]
diff --git a/oml/lightning/pipelines/parser.py b/oml/lightning/pipelines/parser.py
index 2a8516961..213e98a95 100644
--- a/oml/lightning/pipelines/parser.py
+++ b/oml/lightning/pipelines/parser.py
@@ -1,21 +1,19 @@
-import warnings
 from pathlib import Path
 from typing import Any, Dict, Optional
 
 import torch
 from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
-from pytorch_lightning.loggers.logger import Logger
 from pytorch_lightning.strategies import DDPStrategy
 
-from oml.const import OML_PATH, TCfg
+from oml.const import TCfg
 from oml.datasets.base import DatasetWithLabels
+from oml.interfaces.loggers import IPipelineLogger
 from oml.interfaces.samplers import IBatchSampler
+from oml.lightning.pipelines.logging import TensorBoardPipelineLogger
 from oml.registry.loggers import get_logger_by_cfg
 from oml.registry.samplers import SAMPLERS_CATEGORIES_BASED, get_sampler_by_cfg
 from oml.registry.schedulers import get_scheduler_by_cfg
-from oml.registry.transforms import save_transforms_as_files
-from oml.utils.misc import dictconfig_to_dict, flatten_dict
+from oml.utils.misc import dictconfig_to_dict
 
 
 def parse_engine_params_from_config(cfg: TCfg) -> Dict[str, Any]:
@@ -59,82 +57,9 @@ def check_is_config_for_ddp(cfg: TCfg) -> bool:
     return bool(cfg["strategy"])
 
 
-def parse_logger_from_config(cfg: TCfg) -> Logger:
-    logger = TensorBoardLogger(".") if cfg.get("logger", None) is None else get_logger_by_cfg(cfg["logger"])
-    return logger
-
-
-def initialize_logging(cfg: TCfg) -> Logger:
-    logger = parse_logger_from_config(cfg)
-    cwd = Path.cwd().name
-
-    dict_to_log = flatten_dict({**dictconfig_to_dict(cfg), **{"dir": cwd}}, sep="|")
-
-    tags = list(cfg.get("tags", [])) + [cfg.get("postfix", "")] + [cwd]
-
-    if isinstance(logger, NeptuneLogger):
-        warnings.warn(
-            "Unfortunately, in the case of using Neptune, you may experience that long experiments are"
-            "stacked and not responding. It's not an issue on OML's side, so, we cannot fix it."
-        )
-        logger.log_hyperparams(dict_to_log)
-        upload_files_to_neptune_cloud(logger, cfg)
-        logger.run["sys/tags"].add(tags)
-
-    elif isinstance(logger, WandbLogger):
-        logger.log_hyperparams(dict_to_log)
-        upload_files_to_wandb_cloud(logger, cfg)
-        logger.experiment.tags = list(filter(lambda x: len(x) > 0, tags))  # it fails in the case of empty tag
-
-    elif isinstance(logger, TensorBoardLogger):
-        pass
-
-    else:
-        raise ValueError(f"Unexpected logger {type(logger)}")
-
-    return logger
-
-
-def upload_files_to_neptune_cloud(logger: NeptuneLogger, cfg: TCfg) -> None:
-    assert isinstance(logger, NeptuneLogger)
-
-    # log transforms as files
-    for key, transforms_file in save_transforms_as_files(cfg):
-        logger.run[key].upload(transforms_file)
-
-    # log source code
-    source_files = list(map(lambda x: str(x), OML_PATH.glob("**/*.py"))) + list(
-        map(lambda x: str(x), OML_PATH.glob("**/*.yaml"))
-    )
-    logger.run["code"].upload_files(source_files)
-
-    # log dataset
-    logger.run["dataset"].upload(str(Path(cfg["dataset_root"]) / cfg["dataframe_name"]))
-
-
-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
-    keys_files = save_transforms_as_files(cfg)
-    if keys_files:
-        transforms = wandb.Artifact("transforms", type="transforms")
-        for _, transforms_file in keys_files:
-            transforms.add_file(transforms_file)
-        logger.experiment.log_artifact(transforms)
-
-    # log source code
-    code = wandb.Artifact("source_code", type="code")
-    code.add_dir(OML_PATH, name="oml")
-    logger.experiment.log_artifact(code)
-
-    # log dataset
-    dataset = wandb.Artifact("dataset", type="dataset")
-    dataset.add_file(str(Path(cfg["dataset_root"]) / cfg["dataframe_name"]))
-    logger.experiment.log_artifact(dataset)
+def parse_logger_from_config(cfg: TCfg) -> IPipelineLogger:
+    logger = TensorBoardPipelineLogger(".") if cfg.get("logger", None) is None else get_logger_by_cfg(cfg["logger"])
+    return logger  # type: ignore
 
 
 def parse_scheduler_from_config(cfg: TCfg, optimizer: torch.optim.Optimizer) -> Dict[str, Any]:
@@ -182,9 +107,6 @@ def parse_ckpt_callback_from_config(cfg: TCfg) -> ModelCheckpoint:
 __all__ = [
     "parse_engine_params_from_config",
     "check_is_config_for_ddp",
-    "initialize_logging",
-    "upload_files_to_neptune_cloud",
-    "upload_files_to_wandb_cloud",
     "parse_scheduler_from_config",
     "parse_sampler_from_config",
     "parse_ckpt_callback_from_config",
diff --git a/oml/lightning/pipelines/train.py b/oml/lightning/pipelines/train.py
index edf802171..b59d8878b 100644
--- a/oml/lightning/pipelines/train.py
+++ b/oml/lightning/pipelines/train.py
@@ -11,9 +11,9 @@
 from oml.lightning.modules.extractor import ExtractorModule, ExtractorModuleDDP
 from oml.lightning.pipelines.parser import (
     check_is_config_for_ddp,
-    initialize_logging,
     parse_ckpt_callback_from_config,
     parse_engine_params_from_config,
+    parse_logger_from_config,
     parse_sampler_from_config,
     parse_scheduler_from_config,
 )
@@ -70,7 +70,9 @@ def extractor_training_pipeline(cfg: TCfg) -> None:
 
     cfg = dictconfig_to_dict(cfg)
     pprint(cfg)
-    logger = initialize_logging(cfg)
+
+    logger = parse_logger_from_config(cfg)
+    logger.log_pipeline_info(cfg)
 
     trainer_engine_params = parse_engine_params_from_config(cfg)
     is_ddp = check_is_config_for_ddp(trainer_engine_params)
diff --git a/oml/lightning/pipelines/train_postprocessor.py b/oml/lightning/pipelines/train_postprocessor.py
index 8d5e50511..97485328e 100644
--- a/oml/lightning/pipelines/train_postprocessor.py
+++ b/oml/lightning/pipelines/train_postprocessor.py
@@ -21,9 +21,9 @@
 )
 from oml.lightning.pipelines.parser import (
     check_is_config_for_ddp,
-    initialize_logging,
     parse_ckpt_callback_from_config,
     parse_engine_params_from_config,
+    parse_logger_from_config,
     parse_sampler_from_config,
     parse_scheduler_from_config,
 )
@@ -118,7 +118,9 @@ def postprocessor_training_pipeline(cfg: DictConfig) -> None:
 
     cfg = dictconfig_to_dict(cfg)
     pprint(cfg)
-    logger = initialize_logging(cfg)
+
+    logger = parse_logger_from_config(cfg)
+    logger.log_pipeline_info(cfg)
 
     trainer_engine_params = parse_engine_params_from_config(cfg)
     is_ddp = check_is_config_for_ddp(trainer_engine_params)
diff --git a/oml/registry/loggers.py b/oml/registry/loggers.py
index ca4f1e545..b5758c2b7 100644
--- a/oml/registry/loggers.py
+++ b/oml/registry/loggers.py
@@ -1,13 +1,20 @@
 import os
-from typing import Any, Dict, Union
-
-from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger, WandbLogger
-from pytorch_lightning.loggers.logger import Logger
+from typing import Any, Dict
 
 from oml.const import TCfg
+from oml.interfaces.loggers import IPipelineLogger
+from oml.lightning.pipelines.logging import (
+    NeptunePipelineLogger,
+    TensorBoardPipelineLogger,
+    WandBPipelineLogger,
+)
 from oml.utils.misc import dictconfig_to_dict
 
-LOGGERS_REGISTRY = {"wandb": WandbLogger, "neptune": NeptuneLogger, "tensorboard": TensorBoardLogger}
+LOGGERS_REGISTRY = {
+    "wandb": WandBPipelineLogger,
+    "neptune": NeptunePipelineLogger,
+    "tensorboard": TensorBoardPipelineLogger,
+}
 
 CLOUD_TOKEN_NAMES = {"wandb": "WANDB_API_KEY", "neptune": "NEPTUNE_API_TOKEN"}
 TOKEN_ERROR_MESSAGE = (
@@ -17,15 +24,15 @@
 )
 
 
-def get_logger(name: str, **kwargs: Dict[str, Any]) -> Logger:
+def get_logger(name: str, **kwargs: Dict[str, Any]) -> IPipelineLogger:
     if (name in CLOUD_TOKEN_NAMES) and (CLOUD_TOKEN_NAMES[name] not in os.environ):
         token_name = CLOUD_TOKEN_NAMES[name]
         raise ValueError(TOKEN_ERROR_MESSAGE.format(name.upper(), token_name, token_name))
 
-    return LOGGERS_REGISTRY[name](**kwargs)
+    return LOGGERS_REGISTRY[name](**kwargs)  # type: ignore
 
 
-def get_logger_by_cfg(cfg: TCfg) -> Union[bool, Logger]:
+def get_logger_by_cfg(cfg: TCfg) -> IPipelineLogger:
     cfg = dictconfig_to_dict(cfg)
     logger = get_logger(cfg["name"], **cfg["args"])
     return logger
diff --git a/oml/utils/misc.py b/oml/utils/misc.py
index 3842b7981..70e1d75be 100644
--- a/oml/utils/misc.py
+++ b/oml/utils/misc.py
@@ -7,7 +7,7 @@
 import torch
 from omegaconf import DictConfig, OmegaConf
 
-from oml.const import DOTENV_PATH, TCfg
+from oml.const import TCfg
 
 
 def find_value_ids(it: Iterable[Any], value: Any) -> List[int]:
@@ -68,12 +68,6 @@ def flatten_dict(
     return dict(items)
 
 
-def load_dotenv() -> None:
-    import dotenv  # this is the optional dependency
-
-    dotenv.load_dotenv(DOTENV_PATH)
-
-
 def dictconfig_to_dict(cfg: TCfg) -> Dict[str, Any]:
     if isinstance(cfg, DictConfig):
         cfg = OmegaConf.to_container(cfg, resolve=True)
@@ -181,7 +175,6 @@ def find_first_occurrences(x: List[Hashable]) -> List[int]:
     "set_global_seed",
     "one_hot",
     "flatten_dict",
-    "load_dotenv",
     "dictconfig_to_dict",
     "smart_sample",
     "clip_max",
diff --git a/tests/test_oml/test_registry/test_registry.py b/tests/test_oml/test_registry/test_registry.py
index 1b8d11a78..6654d5722 100644
--- a/tests/test_oml/test_registry/test_registry.py
+++ b/tests/test_oml/test_registry/test_registry.py
@@ -2,13 +2,14 @@
 from pathlib import Path
 from typing import Any, Dict
 
+import dotenv
 import pytest
 import yaml
 from omegaconf import OmegaConf
 from torch import nn
 from torch.optim import Optimizer
 
-from oml.const import CONFIGS_PATH, TCfg
+from oml.const import CONFIGS_PATH, DOTENV_PATH, TCfg
 from oml.registry.loggers import LOGGERS_REGISTRY, get_logger
 from oml.registry.losses import LOSSES_REGISTRY, get_criterion
 from oml.registry.miners import MINERS_REGISTRY, get_miner
@@ -33,7 +34,7 @@
     get_transforms_for_pretrained,
     save_transforms_as_files,
 )
-from oml.utils.misc import dictconfig_to_dict, load_dotenv
+from oml.utils.misc import dictconfig_to_dict
 
 
 def get_sampler_kwargs_runtime() -> Any:
@@ -64,7 +65,7 @@ def get_opt() -> Optimizer:
     ],
 )
 def test_registry(folder_name, registry, factory_fun, runtime_args) -> None:
-    load_dotenv()  # we need to load tokens for cloud loggers (Neptune, W & B)
+    dotenv.load_dotenv(DOTENV_PATH)  # we need to load tokens for cloud loggers (Neptune, W & B)
 
     for obj_name in registry.keys():
         cfg = dictconfig_to_dict(OmegaConf.load(CONFIGS_PATH / folder_name / f"{obj_name}.yaml"))
diff --git a/tests/test_runs/test_pipelines/configs/train_with_bboxes.yaml b/tests/test_runs/test_pipelines/configs/train_with_bboxes.yaml
index 6b149766c..8833ac2b7 100644
--- a/tests/test_runs/test_pipelines/configs/train_with_bboxes.yaml
+++ b/tests/test_runs/test_pipelines/configs/train_with_bboxes.yaml
@@ -17,7 +17,7 @@ transforms_train:
     im_size: 32
 
 transforms_val:
-  name: norm_resize_torch
+  name: norm_resize_albu
   args:
     im_size: 48
 
@@ -68,6 +68,11 @@ metric_args:
 
 log_images: True
 
+logger:
+  name: wandb
+  args:
+    project: "test_project"
+
 metric_for_checkpointing: OVERALL/cmc/5
 
 max_epochs: 2
diff --git a/tests/test_runs/test_pipelines/configs/train_with_categories.yaml b/tests/test_runs/test_pipelines/configs/train_with_categories.yaml
index 0fad02382..5342e47f8 100644
--- a/tests/test_runs/test_pipelines/configs/train_with_categories.yaml
+++ b/tests/test_runs/test_pipelines/configs/train_with_categories.yaml
@@ -12,7 +12,7 @@ num_workers: 0
 cache_size: 0
 
 transforms_train:
-  name: augs_albu
+  name: augs_torch
   args:
     im_size: 64
 
diff --git a/tests/test_runs/test_pipelines/configs/train_with_sequence.yaml b/tests/test_runs/test_pipelines/configs/train_with_sequence.yaml
index e34be89a4..12a60c03f 100644
--- a/tests/test_runs/test_pipelines/configs/train_with_sequence.yaml
+++ b/tests/test_runs/test_pipelines/configs/train_with_sequence.yaml
@@ -12,7 +12,7 @@ num_workers: 0
 cache_size: 10
 
 transforms_train:
-  name: augs_torch
+  name: augs_albu
   args:
     im_size: 64
 
@@ -76,9 +76,9 @@ max_epochs: 2
 valid_period: 1
 
 logger:
-  name: tensorboard
+  name: neptune
   args:
-    save_dir: "."
+    project: "oml-team/test"
 
 
 lightning_trainer_extra_args:
diff --git a/tests/test_runs/test_pipelines/test_pipelines.py b/tests/test_runs/test_pipelines/test_pipelines.py
index f3fe76a1b..5093564e7 100644
--- a/tests/test_runs/test_pipelines/test_pipelines.py
+++ b/tests/test_runs/test_pipelines/test_pipelines.py
@@ -1,19 +1,23 @@
+import os
 import shutil
 import subprocess
 import warnings
 from pathlib import Path
 from typing import List, Tuple
 
+import dotenv
 import pytest
 import torch
 import yaml  # type: ignore
 
-from oml.const import PROJECT_ROOT
+from oml.const import DOTENV_PATH, PROJECT_ROOT
 
 warnings.filterwarnings("ignore")
 
 SCRIPTS_PATH = PROJECT_ROOT / "tests/test_runs/test_pipelines/"
 
+dotenv.load_dotenv(DOTENV_PATH)  # we need to load tokens for cloud loggers (Neptune, W & B)
+
 
 def accelerator_devices_pairs() -> List[Tuple[str, int]]:
     pairs = [("cpu", 1), ("cpu", 2)]
@@ -53,12 +57,16 @@ def test_train_and_validate(accelerator: str, devices: int) -> None:
 
 
 @pytest.mark.long
+@pytest.mark.needs_optional_dependency
+@pytest.mark.skipif(os.getenv("TEST_CLOUD_LOGGERS") != "yes", reason="To have more control.")
 @pytest.mark.parametrize("accelerator, devices", accelerator_devices_pairs())
 def test_train_with_bboxes(accelerator: str, devices: int) -> None:
     run("train_with_bboxes.py", accelerator, devices)
 
 
 @pytest.mark.long
+@pytest.mark.needs_optional_dependency
+@pytest.mark.skipif(os.getenv("TEST_CLOUD_LOGGERS") != "yes", reason="To have more control.")
 @pytest.mark.parametrize("accelerator, devices", accelerator_devices_pairs())
 def test_train_with_sequence(accelerator: str, devices: int) -> None:
     run("train_with_sequence.py", accelerator, devices)