diff --git a/pl-hydra/configs/test.yaml b/pl-hydra/configs/test.yaml
deleted file mode 100644
index 1e10d5c..0000000
--- a/pl-hydra/configs/test.yaml
+++ /dev/null
@@ -1,32 +0,0 @@
-# @package _global_
-
-# specify here default evaluation configuration
-defaults:
-  - _self_
-  - datamodule: mnist.yaml # choose the datamodule for evaluation
-  - model: mnist.yaml
-  - callbacks: null
-  - logger: null
-  - trainer: default.yaml
-  - log_dir: evaluation.yaml
-
-  - experiment: null
-
-  # enable color logging
-  - override hydra/hydra_logging: colorlog
-  - override hydra/job_logging: colorlog
-
-original_work_dir: ${hydra:runtime.cwd}
-
-data_dir: ${original_work_dir}/data/
-
-print_config: True
-
-ignore_warnings: True
-
-seed: null
-
-name: "default"
-
-# passing checkpoint path is necessary
-ckpt_path: ???
diff --git a/pl-hydra/configs/train.yaml b/pl-hydra/configs/train.yaml
index d9f03d7..114bcf9 100644
--- a/pl-hydra/configs/train.yaml
+++ b/pl-hydra/configs/train.yaml
@@ -44,7 +44,7 @@ defaults:
 
 # default name for the experiment, determines logging folder path
 # (you can overwrite this name in experiment configs)
-name: "resnet"
+name: "test"
 
 # path to original working directory
 # hydra hijacks working directory by changing it to the new log directory
diff --git a/pl-hydra/configs/trainer/ddp.yaml b/pl-hydra/configs/trainer/ddp.yaml
index 8a11249..bf10a88 100644
--- a/pl-hydra/configs/trainer/ddp.yaml
+++ b/pl-hydra/configs/trainer/ddp.yaml
@@ -1,6 +1,6 @@
 defaults:
   - default.yaml
 
-gpus: 4
+gpus: 2
 strategy: ddp
 sync_batchnorm: True
diff --git a/pl-hydra/src/testing_pipeline.py b/pl-hydra/src/testing_pipeline.py
deleted file mode 100644
index abd030a..0000000
--- a/pl-hydra/src/testing_pipeline.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import os
-from typing import List
-
-import hydra
-from omegaconf import DictConfig
-from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
-from pytorch_lightning.loggers import LightningLoggerBase
-
-from src import utils
-
-log = utils.get_logger(__name__)
-
-
-def test(config: DictConfig) -> None:
-    """Contains minimal example of the testing pipeline. Evaluates given checkpoint on a testset.
-
-    Args:
-        config (DictConfig): Configuration composed by Hydra.
-
-    Returns:
-        None
-    """
-
-    # Set seed for random number generators in pytorch, numpy and python.random
-    if config.get("seed"):
-        seed_everything(config.seed, workers=True)
-
-    # Convert relative ckpt path to absolute path if necessary
-    if not os.path.isabs(config.ckpt_path):
-        config.ckpt_path = os.path.join(hydra.utils.get_original_cwd(), config.ckpt_path)
-
-    # Init lightning datamodule
-    log.info(f"Instantiating datamodule <{config.datamodule._target_}>")
-    datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule)
-
-    # Init lightning model
-    log.info(f"Instantiating model <{config.model._target_}>")
-    model: LightningModule = hydra.utils.instantiate(config.model)
-
-    # Init lightning loggers
-    logger: List[LightningLoggerBase] = []
-    if "logger" in config:
-        for _, lg_conf in config.logger.items():
-            if "_target_" in lg_conf:
-                log.info(f"Instantiating logger <{lg_conf._target_}>")
-                logger.append(hydra.utils.instantiate(lg_conf))
-
-    # Init lightning trainer
-    log.info(f"Instantiating trainer <{config.trainer._target_}>")
-    trainer: Trainer = hydra.utils.instantiate(config.trainer, logger=logger)
-
-    # Log hyperparameters
-    if trainer.logger:
-        trainer.logger.log_hyperparams({"ckpt_path": config.ckpt_path})
-
-    log.info("Starting testing!")
-    trainer.test(model=model, datamodule=datamodule, ckpt_path=config.ckpt_path)
diff --git a/pl-hydra/test.py b/pl-hydra/test.py
deleted file mode 100644
index ee02d04..0000000
--- a/pl-hydra/test.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import dotenv
-import hydra
-from omegaconf import DictConfig
-
-# load environment variables from `.env` file if it exists
-# recursively searches for `.env` in all folders starting from work dir
-dotenv.load_dotenv(override=True)
-
-
-@hydra.main(config_path="configs/", config_name="test.yaml")
-def main(config: DictConfig):
-
-    # Imports can be nested inside @hydra.main to optimize tab completion
-    # https://github.com/facebookresearch/hydra/issues/934
-    from src import utils
-    from src.testing_pipeline import test
-
-    # Applies optional utilities
-    utils.extras(config)
-
-    # Evaluate model
-    return test(config)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/pl-hydra/tests/__init__.py b/pl-hydra/tests/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pl-hydra/tests/helpers/__init__.py b/pl-hydra/tests/helpers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pl-hydra/tests/helpers/module_available.py b/pl-hydra/tests/helpers/module_available.py
deleted file mode 100644
index d3137f3..0000000
--- a/pl-hydra/tests/helpers/module_available.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import platform
-from importlib.util import find_spec
-
-"""
-Adapted from:
-    https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/utilities/imports.py
-"""
-
-
-def _module_available(module_path: str) -> bool:
-    """Check if a path is available in your environment.
-
-    >>> _module_available('os')
-    True
-    >>> _module_available('bla.bla')
-    False
-    """
-    try:
-        return find_spec(module_path) is not None
-    except ModuleNotFoundError:
-        # Python 3.7+
-        return False
-
-
-_IS_WINDOWS = platform.system() == "Windows"
-_DEEPSPEED_AVAILABLE = not _IS_WINDOWS and _module_available("deepspeed")
-_FAIRSCALE_AVAILABLE = not _IS_WINDOWS and _module_available("fairscale.nn")
-_RPC_AVAILABLE = not _IS_WINDOWS and _module_available("torch.distributed.rpc")
diff --git a/pl-hydra/tests/helpers/run_command.py b/pl-hydra/tests/helpers/run_command.py
deleted file mode 100644
index 1670988..0000000
--- a/pl-hydra/tests/helpers/run_command.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from typing import List
-
-import pytest
-import sh
-
-
-def run_command(command: List[str]):
-    """Default method for executing shell commands with pytest."""
-    msg = None
-    try:
-        sh.python(command)
-    except sh.ErrorReturnCode as e:
-        msg = e.stderr.decode()
-    if msg:
-        pytest.fail(msg=msg)
diff --git a/pl-hydra/tests/helpers/runif.py b/pl-hydra/tests/helpers/runif.py
deleted file mode 100644
index 36d73e1..0000000
--- a/pl-hydra/tests/helpers/runif.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import sys
-from typing import Optional
-
-import pytest
-import torch
-from packaging.version import Version
-from pkg_resources import get_distribution
-
-"""
-Adapted from:
-    https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tests/helpers/runif.py
-"""
-
-from tests.helpers.module_available import (
-    _DEEPSPEED_AVAILABLE,
-    _FAIRSCALE_AVAILABLE,
-    _IS_WINDOWS,
-    _RPC_AVAILABLE,
-)
-
-
-class RunIf:
-    """RunIf wrapper for conditional skipping of tests.
-
-    Fully compatible with `@pytest.mark`.
-
-    Example:
-
-        @RunIf(min_torch="1.8")
-        @pytest.mark.parametrize("arg1", [1.0, 2.0])
-        def test_wrapper(arg1):
-            assert arg1 > 0
-    """
-
-    def __new__(
-        self,
-        min_gpus: int = 0,
-        min_torch: Optional[str] = None,
-        max_torch: Optional[str] = None,
-        min_python: Optional[str] = None,
-        skip_windows: bool = False,
-        rpc: bool = False,
-        fairscale: bool = False,
-        deepspeed: bool = False,
-        **kwargs,
-    ):
-        """
-        Args:
-            min_gpus: min number of gpus required to run test
-            min_torch: minimum pytorch version to run test
-            max_torch: maximum pytorch version to run test
-            min_python: minimum python version required to run test
-            skip_windows: skip test for Windows platform
-            rpc: requires Remote Procedure Call (RPC)
-            fairscale: if `fairscale` module is required to run the test
-            deepspeed: if `deepspeed` module is required to run the test
-            kwargs: native pytest.mark.skipif keyword arguments
-        """
-        conditions = []
-        reasons = []
-
-        if min_gpus:
-            conditions.append(torch.cuda.device_count() < min_gpus)
-            reasons.append(f"GPUs>={min_gpus}")
-
-        if min_torch:
-            torch_version = get_distribution("torch").version
-            conditions.append(Version(torch_version) < Version(min_torch))
-            reasons.append(f"torch>={min_torch}")
-
-        if max_torch:
-            torch_version = get_distribution("torch").version
-            conditions.append(Version(torch_version) >= Version(max_torch))
-            reasons.append(f"torch<{max_torch}")
-
-        if min_python:
-            py_version = (
-                f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
-            )
-            conditions.append(Version(py_version) < Version(min_python))
-            reasons.append(f"python>={min_python}")
-
-        if skip_windows:
-            conditions.append(_IS_WINDOWS)
-            reasons.append("does not run on Windows")
-
-        if rpc:
-            conditions.append(not _RPC_AVAILABLE)
-            reasons.append("RPC")
-
-        if fairscale:
-            conditions.append(not _FAIRSCALE_AVAILABLE)
-            reasons.append("Fairscale")
-
-        if deepspeed:
-            conditions.append(not _DEEPSPEED_AVAILABLE)
-            reasons.append("Deepspeed")
-
-        reasons = [rs for cond, rs in zip(conditions, reasons) if cond]
-        return pytest.mark.skipif(
-            condition=any(conditions),
-            reason=f"Requires: [{' + '.join(reasons)}]",
-            **kwargs,
-        )
diff --git a/pl-hydra/tests/shell/__init__.py b/pl-hydra/tests/shell/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pl-hydra/tests/shell/test_basic_commands.py b/pl-hydra/tests/shell/test_basic_commands.py
deleted file mode 100644
index f708ede..0000000
--- a/pl-hydra/tests/shell/test_basic_commands.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import pytest
-
-from tests.helpers.run_command import run_command
-from tests.helpers.runif import RunIf
-
-"""
-A couple of sanity checks to make sure the model doesn't crash with different running options.
-"""
-
-
-def test_fast_dev_run():
-    """Test running for 1 train, val and test batch."""
-    command = ["train.py", "++trainer.fast_dev_run=true"]
-    run_command(command)
-
-
-@pytest.mark.slow
-def test_cpu():
-    """Test running 1 epoch on CPU."""
-    command = ["train.py", "++trainer.max_epochs=1", "++trainer.gpus=0"]
-    run_command(command)
-
-
-# use RunIf to skip execution of some tests, e.g. when no gpus are available
-@RunIf(min_gpus=1)
-@pytest.mark.slow
-def test_gpu():
-    """Test running 1 epoch on GPU."""
-    command = [
-        "train.py",
-        "++trainer.max_epochs=1",
-        "++trainer.gpus=1",
-    ]
-    run_command(command)
-
-
-@RunIf(min_gpus=1)
-@pytest.mark.slow
-def test_mixed_precision():
-    """Test running 1 epoch with pytorch native automatic mixed precision (AMP)."""
-    command = [
-        "train.py",
-        "++trainer.max_epochs=1",
-        "++trainer.gpus=1",
-        "++trainer.precision=16",
-    ]
-    run_command(command)
-
-
-@pytest.mark.slow
-def test_double_validation_loop():
-    """Test running 1 epoch with validation loop twice per epoch."""
-    command = [
-        "train.py",
-        "++trainer.max_epochs=1",
-        "++trainer.val_check_interval=0.5",
-    ]
-    run_command(command)
diff --git a/pl-hydra/tests/shell/test_debug_configs.py b/pl-hydra/tests/shell/test_debug_configs.py
deleted file mode 100644
index a73dda8..0000000
--- a/pl-hydra/tests/shell/test_debug_configs.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import pytest
-
-from tests.helpers.run_command import run_command
-
-
-@pytest.mark.slow
-def test_debug_default():
-    command = ["train.py", "debug=default"]
-    run_command(command)
-
-
-def test_debug_limit_batches():
-    command = ["train.py", "debug=limit_batches"]
-    run_command(command)
-
-
-def test_debug_overfit():
-    command = ["train.py", "debug=overfit"]
-    run_command(command)
-
-
-@pytest.mark.slow
-def test_debug_profiler():
-    command = ["train.py", "debug=profiler"]
-    run_command(command)
-
-
-def test_debug_step():
-    command = ["train.py", "debug=step"]
-    run_command(command)
-
-
-def test_debug_test_only():
-    command = ["train.py", "debug=test_only"]
-    run_command(command)
diff --git a/pl-hydra/tests/shell/test_sweeps.py b/pl-hydra/tests/shell/test_sweeps.py
deleted file mode 100644
index 10a298d..0000000
--- a/pl-hydra/tests/shell/test_sweeps.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import pytest
-
-from tests.helpers.run_command import run_command
-
-"""
-A couple of tests executing hydra sweeps.
-
-Use the following command to skip slow tests:
-    pytest -k "not slow"
-"""
-
-
-@pytest.mark.slow
-def test_experiments():
-    """Test running all available experiment configs for 1 epoch."""
-    command = ["train.py", "-m", "experiment=glob(*)", "++trainer.max_epochs=1"]
-    run_command(command)
-
-
-@pytest.mark.slow
-def test_default_sweep():
-    """Test default Hydra sweeper."""
-    command = [
-        "train.py",
-        "-m",
-        "datamodule.batch_size=64,128",
-        "model.lr=0.01,0.02",
-        "trainer=default",
-        "++trainer.fast_dev_run=true",
-    ]
-    run_command(command)
-
-
-@pytest.mark.slow
-def test_optuna_sweep():
-    """Test Optuna sweeper."""
-    command = [
-        "train.py",
-        "-m",
-        "hparams_search=mnist_optuna",
-        "trainer=default",
-        "++trainer.fast_dev_run=true",
-    ]
-    run_command(command)
diff --git a/pl-hydra/tests/unit/__init__.py b/pl-hydra/tests/unit/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/pl-hydra/tests/unit/test_mnist_datamodule.py b/pl-hydra/tests/unit/test_mnist_datamodule.py
deleted file mode 100644
index 91e6182..0000000
--- a/pl-hydra/tests/unit/test_mnist_datamodule.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-import pytest
-import torch
-
-from src.datamodules.mnist_datamodule import MNISTDataModule
-
-
-@pytest.mark.parametrize("batch_size", [32, 128])
-def test_mnist_datamodule(batch_size):
-    datamodule = MNISTDataModule(batch_size=batch_size)
-    datamodule.prepare_data()
-
-    assert not datamodule.data_train and not datamodule.data_val and not datamodule.data_test
-
-    assert os.path.exists(os.path.join("data", "MNIST"))
-    assert os.path.exists(os.path.join("data", "MNIST", "raw"))
-
-    datamodule.setup()
-
-    assert datamodule.data_train and datamodule.data_val and datamodule.data_test
-    assert (
-        len(datamodule.data_train) + len(datamodule.data_val) + len(datamodule.data_test) == 70_000
-    )
-
-    assert datamodule.train_dataloader()
-    assert datamodule.val_dataloader()
-    assert datamodule.test_dataloader()
-
-    batch = next(iter(datamodule.train_dataloader()))
-    x, y = batch
-
-    assert len(x) == batch_size
-    assert len(y) == batch_size
-    assert x.dtype == torch.float32
-    assert y.dtype == torch.int64
diff --git a/pl-hydra/train.py b/pl-hydra/train.py
index 05b78f3..22b258e 100644
--- a/pl-hydra/train.py
+++ b/pl-hydra/train.py
@@ -6,8 +6,8 @@
 import hydra
 from omegaconf import DictConfig
 import os
-# Set the visible GPUs (curent machine has 16 GPUS [0-15])
-os.environ["CUDA_VISIBLE_DEVICES"]="11"
+# # Set the visible GPUs (curent machine has 16 GPUS [0-15])
+# os.environ["CUDA_VISIBLE_DEVICES"]="11"
 
 # load environment variables from `.env` file if it exists
 # recursively searches for `.env` in all folders starting from work dir
diff --git a/train_multi.py b/train_multi.py
index 47809bc..1be1d30 100644
--- a/train_multi.py
+++ b/train_multi.py
@@ -27,9 +27,9 @@
 # load helper functions 
 from utils.helper import set_random_seed, print_info, evaluate
 
-# Set the visible GPUs, in case of multi-GPU device, otherwise comment it
-# you can use `nvidia-smi` in terminal to see the available GPUS
-os.environ["CUDA_VISIBLE_DEVICES"]="13,14"
+# # Set the visible GPUs, in case of multi-GPU device, otherwise comment it
+# # you can use `nvidia-smi` in terminal to see the available GPUS
+# os.environ["CUDA_VISIBLE_DEVICES"]="13,14"
 
 
 ######################################################################
@@ -42,6 +42,7 @@
                                      
 # Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)
 DATASET_PATH = "../data"
+os.makedirs(CHECKPOINT_PATH,exist_ok=True)
 
 # Path to the folder where the models will be saved
 CHECKPOINT_PATH = "../saved_models/multi/"
diff --git a/train_pl.py b/train_pl.py
index 862bdc7..f8c3e3d 100644
--- a/train_pl.py
+++ b/train_pl.py
@@ -40,9 +40,9 @@
 from utils.helper import set_random_seed, print_info, evaluate
 from utils.plotter import plot_cm, plot_preds
 
-# Set the visible GPUs, in case of multi-GPU device, otherwise comment it
-# you can use `nvidia-smi` in terminal to see the available GPUS
-os.environ["CUDA_VISIBLE_DEVICES"]="13,14,15,16"
+# # Set the visible GPUs, in case of multi-GPU device, otherwise comment it
+# # you can use `nvidia-smi` in terminal to see the available GPUS
+# os.environ["CUDA_VISIBLE_DEVICES"]="13,14,15,16"
 
 ######################################################################
 # Set the Global values
@@ -55,6 +55,7 @@
 # Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)
 DATASET_PATH = "../data"
 LOG_PATH = "./logs"
+os.makedirs(CHECKPOINT_PATH,exist_ok=True)
 
 # Path to the folder where the models will be saved
 CHECKPOINT_PATH = "../saved_models/pl/"
diff --git a/train_simple.py b/train_simple.py
index 87cd2df..ac54e78 100644
--- a/train_simple.py
+++ b/train_simple.py
@@ -25,9 +25,9 @@
 # load helper functions 
 from utils.helper import set_random_seed, print_info, evaluate
 
-# Set the visible GPUs, in case of multi-GPU device, otherwise comment it
-# you can use `nvidia-smi` in terminal to see the available GPUS
-os.environ["CUDA_VISIBLE_DEVICES"]="12"
+# # Set the visible GPUs, in case of multi-GPU device, otherwise comment it
+# # you can use `nvidia-smi` in terminal to see the available GPUS
+# os.environ["CUDA_VISIBLE_DEVICES"]="12"
 
 
 ######################################################################
@@ -40,7 +40,7 @@
                                      
 # Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)
 DATASET_PATH = "../data"
-
+os.makedirs(DATASET_PATH,exist_ok=True)
 # Path to the folder where the models will be saved
 CHECKPOINT_PATH = "../saved_models/simple/"
 os.makedirs(CHECKPOINT_PATH,exist_ok=True)