diff --git a/.github/workflows/legacy-checkpoints.yml b/.github/workflows/legacy-checkpoints.yml
new file mode 100644
index 0000000000000..36c4df760aaaf
--- /dev/null
+++ b/.github/workflows/legacy-checkpoints.yml
@@ -0,0 +1,40 @@
+name: Create Legacy Ckpts
+
+# https://help.github.com/en/actions/reference/events-that-trigger-workflows
+on:
+  workflow_dispatch:
+
+jobs:
+  create-legacy-ckpts:
+    runs-on: ubuntu-20.04
+    steps:
+    - uses: actions/checkout@v2
+
+    - uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+
+    - name: Install dependencies
+      run: |
+        pip install -r requirements.txt --find-links https://download.pytorch.org/whl/cpu/torch_stable.html --quiet
+        pip install awscli
+
+    - name: Configure AWS credentials
+      uses: aws-actions/configure-aws-credentials@v1
+      with:
+        aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+        aws-secret-access-key: ${{ secrets.AWS_SECRET_KEY_ID }}
+        aws-region: us-east-1
+
+    - name: Generate checkpoint
+      run: |
+        while IFS= read -r line; do
+            bash legacy/generate_checkpoints.sh $line
+        done <<< $(cat legacy/back-compatible-versions.txt)
+
+    - name: Push files to S3
+      working-directory: ./legacy
+      run: |
+        aws s3 sync legacy/checkpoints/ s3://pl-public-data/legacy/checkpoints/
+        zip -r checkpoints.zip checkpoints
+        aws s3 cp checkpoints.zip s3://pl-public-data/legacy/ --acl public-read
diff --git a/legacy/README.md b/legacy/README.md
index 5ffcecf971f3a..efbd18f7eede6 100644
--- a/legacy/README.md
+++ b/legacy/README.md
@@ -14,6 +14,6 @@ unzip -o checkpoints.zip
 To back populate collection with past version you can use following bash:
 
 ```bash
-bash generate_checkpoints.sh 1.0.2 1.0.3 1.0.4
+bash generate_checkpoints.sh "1.3.7" "1.3.8"
 zip -r checkpoints.zip checkpoints/
 ```
diff --git a/legacy/back-compatible-versions.txt b/legacy/back-compatible-versions.txt
new file mode 100644
index 0000000000000..d9141ba2d3d0b
--- /dev/null
+++ b/legacy/back-compatible-versions.txt
@@ -0,0 +1,39 @@
+1.0.0
+1.0.1
+1.0.2
+1.0.3
+1.0.4
+1.0.5
+1.0.6
+1.0.7
+1.0.8
+1.1.0
+1.1.1
+1.1.2
+1.1.3
+1.1.4
+1.1.5
+1.1.6
+1.1.7
+1.1.8
+1.2.0
+1.2.1
+1.2.2
+1.2.3
+1.2.4
+1.2.5
+1.2.6
+1.2.7
+1.2.8
+1.2.10
+1.3.0
+1.3.1
+1.3.2
+1.3.3
+1.3.4
+1.3.5
+1.3.6
+1.3.7
+1.3.8
+1.4.0
+1.4.1
diff --git a/legacy/generate_checkpoints.sh b/legacy/generate_checkpoints.sh
index 53e09e5135206..e5152e55f1da6 100644
--- a/legacy/generate_checkpoints.sh
+++ b/legacy/generate_checkpoints.sh
@@ -2,12 +2,14 @@
 # Sample call:
 #  bash generate_checkpoints.sh 1.0.2 1.0.3 1.0.4
 
+set -e
+
 LEGACY_PATH="$( cd "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
-FROZEN_MIN_PT_VERSION="1.4"
+FROZEN_MIN_PT_VERSION="1.6"
 
 echo $LEGACY_PATH
 # install some PT version here so it does not need to reinstalled for each env
-pip install virtualenv "torch==1.5" --quiet --no-cache-dir
+pip install virtualenv "torch==1.6" --quiet
 
 ENV_PATH="$LEGACY_PATH/vEnv"
 
@@ -23,14 +25,14 @@ do
   # activate and install PL version
   source "$ENV_PATH/bin/activate"
   # there are problem to load ckpt in older versions since they are saved the newer versions
-  pip install "pytorch_lightning==$ver" "torch==$FROZEN_MIN_PT_VERSION" --quiet --no-cache-dir
+  pip install "pytorch_lightning==$ver" "torch==$FROZEN_MIN_PT_VERSION" "torchmetrics" "scikit-learn" --quiet
 
   python --version
   pip --version
   pip list | grep torch
 
-  python "$LEGACY_PATH/zero_training.py"
-  cp "$LEGACY_PATH/zero_training.py" ${LEGACY_PATH}/checkpoints/${ver}
+  python "$LEGACY_PATH/simple_classif_training.py"
+  cp "$LEGACY_PATH/simple_classif_training.py" ${LEGACY_PATH}/checkpoints/${ver}
 
   mv ${LEGACY_PATH}/checkpoints/${ver}/lightning_logs/version_0/checkpoints/*.ckpt ${LEGACY_PATH}/checkpoints/${ver}/
   rm -rf ${LEGACY_PATH}/checkpoints/${ver}/lightning_logs
diff --git a/legacy/simple_classif_training.py b/legacy/simple_classif_training.py
new file mode 100644
index 0000000000000..5189d0c1819b6
--- /dev/null
+++ b/legacy/simple_classif_training.py
@@ -0,0 +1,170 @@
+# 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
+
+import torch
+import torch.nn.functional as F
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from torch import nn
+from torch.utils.data import DataLoader, Dataset
+from torchmetrics import Accuracy
+
+import pytorch_lightning as pl
+from pytorch_lightning import LightningDataModule, LightningModule, seed_everything
+from pytorch_lightning.callbacks import EarlyStopping
+
+PATH_LEGACY = os.path.dirname(__file__)
+
+
+class SklearnDataset(Dataset):
+    def __init__(self, x, y, x_type, y_type):
+        self.x = x
+        self.y = y
+        self._x_type = x_type
+        self._y_type = y_type
+
+    def __getitem__(self, idx):
+        return torch.tensor(self.x[idx], dtype=self._x_type), torch.tensor(self.y[idx], dtype=self._y_type)
+
+    def __len__(self):
+        return len(self.y)
+
+
+class SklearnDataModule(LightningDataModule):
+    def __init__(self, sklearn_dataset, x_type, y_type, batch_size: int = 128):
+        super().__init__()
+        self.batch_size = batch_size
+        self._x, self._y = sklearn_dataset
+        self._split_data()
+        self._x_type = x_type
+        self._y_type = y_type
+
+    def _split_data(self):
+        self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(
+            self._x, self._y, test_size=0.20, random_state=42
+        )
+        self.x_train, self.x_valid, self.y_train, self.y_valid = train_test_split(
+            self.x_train, self.y_train, test_size=0.40, random_state=42
+        )
+
+    def train_dataloader(self):
+        return DataLoader(
+            SklearnDataset(self.x_train, self.y_train, self._x_type, self._y_type),
+            shuffle=True,
+            batch_size=self.batch_size,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            SklearnDataset(self.x_valid, self.y_valid, self._x_type, self._y_type), batch_size=self.batch_size
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            SklearnDataset(self.x_test, self.y_test, self._x_type, self._y_type), batch_size=self.batch_size
+        )
+
+
+class ClassifDataModule(SklearnDataModule):
+    def __init__(self, num_features=24, length=6000, num_classes=3, batch_size=128):
+        data = make_classification(
+            n_samples=length,
+            n_features=num_features,
+            n_classes=num_classes,
+            n_clusters_per_class=2,
+            n_informative=int(num_features / num_classes),
+            random_state=42,
+        )
+        super().__init__(data, x_type=torch.float32, y_type=torch.long, batch_size=batch_size)
+
+
+class ClassificationModel(LightningModule):
+    def __init__(self, num_features=24, num_classes=3, lr=0.01):
+        super().__init__()
+        self.save_hyperparameters()
+
+        self.lr = lr
+        for i in range(3):
+            setattr(self, f"layer_{i}", nn.Linear(num_features, num_features))
+            setattr(self, f"layer_{i}a", torch.nn.ReLU())
+        setattr(self, "layer_end", nn.Linear(num_features, num_classes))
+
+        self.train_acc = Accuracy()
+        self.valid_acc = Accuracy()
+        self.test_acc = Accuracy()
+
+    def forward(self, x):
+        x = self.layer_0(x)
+        x = self.layer_0a(x)
+        x = self.layer_1(x)
+        x = self.layer_1a(x)
+        x = self.layer_2(x)
+        x = self.layer_2a(x)
+        x = self.layer_end(x)
+        logits = F.softmax(x, dim=1)
+        return logits
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
+        return [optimizer], []
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self.forward(x)
+        loss = F.cross_entropy(logits, y)
+        self.log("train_loss", loss, prog_bar=True)
+        self.log("train_acc", self.train_acc(logits, y), prog_bar=True)
+        return {"loss": loss}
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self.forward(x)
+        self.log("val_loss", F.cross_entropy(logits, y), prog_bar=False)
+        self.log("val_acc", self.valid_acc(logits, y), prog_bar=True)
+
+    def test_step(self, batch, batch_idx):
+        x, y = batch
+        logits = self.forward(x)
+        self.log("test_loss", F.cross_entropy(logits, y), prog_bar=False)
+        self.log("test_acc", self.test_acc(logits, y), prog_bar=True)
+
+
+def main_train(dir_path, max_epochs: int = 20):
+    seed_everything(42)
+    stopping = EarlyStopping(monitor="val_acc", mode="max", min_delta=0.005)
+    trainer = pl.Trainer(
+        default_root_dir=dir_path,
+        gpus=int(torch.cuda.is_available()),
+        precision=(16 if torch.cuda.is_available() else 32),
+        checkpoint_callback=True,
+        callbacks=[stopping],
+        min_epochs=3,
+        max_epochs=max_epochs,
+        accumulate_grad_batches=2,
+        deterministic=True,
+    )
+
+    dm = ClassifDataModule()
+    model = ClassificationModel()
+    trainer.fit(model, datamodule=dm)
+    res = trainer.test(model, datamodule=dm)
+    assert res[0]["test_loss"] <= 0.7
+    assert res[0]["test_acc"] >= 0.85
+    assert trainer.current_epoch < (max_epochs - 1)
+
+
+if __name__ == "__main__":
+    path_dir = os.path.join(PATH_LEGACY, "checkpoints", str(pl.__version__))
+    main_train(path_dir)
diff --git a/legacy/zero_training.py b/legacy/zero_training.py
deleted file mode 100644
index d1716b38962df..0000000000000
--- a/legacy/zero_training.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# 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
-
-import torch
-from torch.utils.data import Dataset
-
-import pytorch_lightning as pl
-
-PATH_LEGACY = os.path.dirname(__file__)
-
-
-class RandomDataset(Dataset):
-    def __init__(self, size, length: int = 100):
-        self.len = length
-        self.data = torch.randn(length, size)
-
-    def __getitem__(self, index):
-        return self.data[index]
-
-    def __len__(self):
-        return self.len
-
-
-class DummyModel(pl.LightningModule):
-    def __init__(self):
-        super().__init__()
-        self.layer = torch.nn.Linear(32, 2)
-
-    def forward(self, x):
-        return self.layer(x)
-
-    def _loss(self, batch, prediction):
-        # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
-        return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))
-
-    def _step(self, batch, batch_idx):
-        output = self.layer(batch)
-        loss = self._loss(batch, output)
-        # return {'loss': loss}  # used for PL<1.0
-        return loss  # used for PL >= 1.0
-
-    def training_step(self, batch, batch_idx):
-        return self._step(batch, batch_idx)
-
-    def validation_step(self, batch, batch_idx):
-        self._step(batch, batch_idx)
-
-    def test_step(self, batch, batch_idx):
-        self._step(batch, batch_idx)
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
-        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
-        return [optimizer], [lr_scheduler]
-
-    def train_dataloader(self):
-        return torch.utils.data.DataLoader(RandomDataset(32, 64))
-
-    def val_dataloader(self):
-        return torch.utils.data.DataLoader(RandomDataset(32, 64))
-
-    def test_dataloader(self):
-        return torch.utils.data.DataLoader(RandomDataset(32, 64))
-
-
-def main_train(dir_path, max_epochs: int = 5):
-
-    trainer = pl.Trainer(default_root_dir=dir_path, checkpoint_callback=True, max_epochs=max_epochs)
-
-    model = DummyModel()
-    trainer.fit(model)
-
-
-if __name__ == "__main__":
-    path_dir = os.path.join(PATH_LEGACY, "checkpoints", str(pl.__version__))
-    main_train(path_dir)
diff --git a/tests/checkpointing/test_legacy_checkpoints.py b/tests/checkpointing/test_legacy_checkpoints.py
index 8693965a52abc..040cd642556cf 100644
--- a/tests/checkpointing/test_legacy_checkpoints.py
+++ b/tests/checkpointing/test_legacy_checkpoints.py
@@ -14,84 +14,79 @@
 import glob
 import os
 import sys
+from unittest.mock import patch
 
 import pytest
+import torch
 
-from pytorch_lightning import Trainer
-from tests import _PATH_LEGACY
+import pytorch_lightning as pl
+from pytorch_lightning import Callback, Trainer
+from pytorch_lightning.callbacks import EarlyStopping
+from tests import _PATH_LEGACY, _PROJECT_ROOT
 
 LEGACY_CHECKPOINTS_PATH = os.path.join(_PATH_LEGACY, "checkpoints")
 CHECKPOINT_EXTENSION = ".ckpt"
+# load list of all back compatible versions
+with open(os.path.join(_PROJECT_ROOT, "legacy", "back-compatible-versions.txt")) as fp:
+    LEGACY_BACK_COMPATIBLE_PL_VERSIONS = [ln.strip() for ln in fp.readlines()]
 
 
-# todo: add more legacy checkpoints - for < v0.8
-@pytest.mark.parametrize(
-    "pl_version",
-    [
-        # "0.8.1",
-        "0.8.3",
-        "0.8.4",
-        # "0.8.5", # this version has problem with loading on PT<=1.4 as it seems to be archive
-        # "0.9.0", # this version has problem with loading on PT<=1.4 as it seems to be archive
-        "0.10.0",
-        "1.0.0",
-        "1.0.1",
-        "1.0.2",
-        "1.0.3",
-        "1.0.4",
-        "1.0.5",
-        "1.0.6",
-        "1.0.7",
-        "1.0.8",
-        "1.1.0",
-        "1.1.1",
-        "1.1.2",
-        "1.1.3",
-        "1.1.4",
-        "1.1.5",
-        "1.1.6",
-        "1.1.7",
-        "1.1.8",
-        "1.2.0",
-        "1.2.1",
-        "1.2.2",
-        "1.2.3",
-        "1.2.4",
-        "1.2.5",
-        "1.2.6",
-        "1.2.7",
-        "1.2.8",
-        "1.2.10",
-        "1.3.0",
-        "1.3.1",
-        "1.3.2",
-        "1.3.3",
-        "1.3.4",
-        "1.3.5",
-        "1.3.6",
-        "1.3.7",
-        "1.3.8",
-    ],
-)
-def test_resume_legacy_checkpoints(tmpdir, pl_version: str):
-    path_dir = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
+@pytest.mark.parametrize("pl_version", LEGACY_BACK_COMPATIBLE_PL_VERSIONS)
+def test_load_legacy_checkpoints(tmpdir, pl_version: str):
+    PATH_LEGACY = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
+    with patch("sys.path", [PATH_LEGACY] + sys.path):
+        from simple_classif_training import ClassifDataModule, ClassificationModel
+
+        path_ckpts = sorted(glob.glob(os.path.join(PATH_LEGACY, f"*{CHECKPOINT_EXTENSION}")))
+        assert path_ckpts, f'No checkpoints found in folder "{PATH_LEGACY}"'
+        path_ckpt = path_ckpts[-1]
+
+        model = ClassificationModel.load_from_checkpoint(path_ckpt)
+        trainer = Trainer(default_root_dir=str(tmpdir))
+        dm = ClassifDataModule()
+        res = trainer.test(model, datamodule=dm)
+        assert res[0]["test_loss"] <= 0.7
+        assert res[0]["test_acc"] >= 0.85
+        print(res)
 
-    # todo: make this as mock, so it is cleaner...
-    orig_sys_paths = list(sys.path)
-    sys.path.insert(0, path_dir)
-    from zero_training import DummyModel
 
-    path_ckpts = sorted(glob.glob(os.path.join(path_dir, f"*{CHECKPOINT_EXTENSION}")))
-    assert path_ckpts, 'No checkpoints found in folder "%s"' % path_dir
-    path_ckpt = path_ckpts[-1]
+class LimitNbEpochs(Callback):
+    def __init__(self, nb: int):
+        self.limit = nb
+        self._count = 0
 
-    model = DummyModel.load_from_checkpoint(path_ckpt)
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=6)
-    trainer.fit(model)
+    def on_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        self._count += 1
+        if self._count >= self.limit:
+            trainer.should_stop = True
+
+
+@pytest.mark.parametrize("pl_version", LEGACY_BACK_COMPATIBLE_PL_VERSIONS)
+def test_resume_legacy_checkpoints(tmpdir, pl_version: str):
+    PATH_LEGACY = os.path.join(LEGACY_CHECKPOINTS_PATH, pl_version)
+    with patch("sys.path", [PATH_LEGACY] + sys.path):
+        from simple_classif_training import ClassifDataModule, ClassificationModel
 
-    # todo
-    # model = DummyModel()
-    # trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, resume_from_checkpoint=path_ckpt)
-    # trainer.fit(model)
+        path_ckpts = sorted(glob.glob(os.path.join(PATH_LEGACY, f"*{CHECKPOINT_EXTENSION}")))
+        assert path_ckpts, f'No checkpoints found in folder "{PATH_LEGACY}"'
+        path_ckpt = path_ckpts[-1]
 
-    sys.path = orig_sys_paths
+        dm = ClassifDataModule()
+        model = ClassificationModel()
+        es = EarlyStopping(monitor="val_acc", mode="max", min_delta=0.005)
+        stop = LimitNbEpochs(1)
+        trainer = Trainer(
+            default_root_dir=str(tmpdir),
+            gpus=int(torch.cuda.is_available()),
+            precision=(16 if torch.cuda.is_available() else 32),
+            checkpoint_callback=True,
+            callbacks=[es, stop],
+            max_epochs=21,
+            accumulate_grad_batches=2,
+            deterministic=True,
+            resume_from_checkpoint=path_ckpt,
+        )
+        trainer.fit(model, datamodule=dm)
+        res = trainer.test(model, datamodule=dm)
+        assert res[0]["test_loss"] <= 0.7
+        assert res[0]["test_acc"] >= 0.85
diff --git a/tests/deprecated_api/test_remove_1-5.py b/tests/deprecated_api/test_remove_1-5.py
index 7ff4b98907d1d..3ea319a27a07a 100644
--- a/tests/deprecated_api/test_remove_1-5.py
+++ b/tests/deprecated_api/test_remove_1-5.py
@@ -14,7 +14,6 @@
 """Test deprecated functionality which will be removed in v1.5.0"""
 import os
 from typing import Any, Dict
-from unittest import mock
 
 import pytest
 import torch
diff --git a/tests/utilities/test_data.py b/tests/utilities/test_data.py
index 6f6de3ff16c81..6c5b5a8e33ffb 100644
--- a/tests/utilities/test_data.py
+++ b/tests/utilities/test_data.py
@@ -1,7 +1,6 @@
 import pytest
 import torch
 from torch.utils.data.dataloader import DataLoader
-from torch.utils.data.dataset import Dataset, IterableDataset
 
 from pytorch_lightning.utilities.data import extract_batch_size, get_len, has_iterable_dataset, has_len
 from tests.helpers.boring_model import RandomDataset, RandomIterableDataset