diff --git a/pl_examples/repro.py b/pl_examples/repro.py
deleted file mode 100644
index 22f1650852a06..0000000000000
--- a/pl_examples/repro.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-import unittest.mock
-
-import torch
-from torch.utils.data import DataLoader, Dataset
-
-from pytorch_lightning import LightningModule, Trainer
-
-
-class RandomDataset(Dataset):
-    def __init__(self, size, length):
-        self.len = length
-        self.data = torch.randn(length, size)
-
-    def __getitem__(self, index):
-        return self.data[index]
-
-    def __len__(self):
-        return self.len
-
-
-class BoringModel(LightningModule):
-    def __init__(self):
-        super().__init__()
-        self.layer = torch.nn.Linear(32, 2)
-
-    def forward(self, x):
-        return self.layer(x)
-
-    def training_step(self, batch, batch_idx):
-        print(batch.sum())
-        loss = self(batch).sum()
-        self.log("train_loss", loss)
-        return {"loss": loss}
-
-    def validation_step(self, batch, batch_idx):
-        loss = self(batch).sum()
-        self.log("valid_loss", loss)
-
-    def test_step(self, batch, batch_idx):
-        loss = self(batch).sum()
-        self.log("test_loss", loss)
-
-    def on_train_epoch_end(self):
-        print("epoch ended")
-
-    def configure_optimizers(self):
-        return torch.optim.SGD(self.layer.parameters(), lr=0.1)
-
-    def on_load_checkpoint(self, checkpoint):
-        pass
-
-
-@unittest.mock.patch.dict(os.environ, {"PL_FAULT_TOLERANT_TRAINING": "1"})
-def run():
-    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
-
-    model = BoringModel()
-    trainer = Trainer(
-        default_root_dir=os.getcwd(),
-        limit_train_batches=3,
-        limit_val_batches=0,
-        num_sanity_val_steps=0,
-        max_epochs=1,
-        weights_summary=None,
-    )
-    trainer.fit(model, train_dataloader=train_data)
-
-    trainer.save_checkpoint("lightning_logs/auto.pt")
-
-    trainer = Trainer(
-        default_root_dir=os.getcwd(),
-        limit_train_batches=3,
-        limit_val_batches=0,
-        num_sanity_val_steps=0,
-        max_epochs=3,
-        weights_summary=None,
-        resume_from_checkpoint="lightning_logs/auto.pt",
-    )
-    trainer.fit(model, train_dataloader=train_data)
-
-
-if __name__ == "__main__":
-    run()