Training loss not convergence
🐛 Bug
Running the example code below,  at my pc , the loss not change:
Epoch 29:  43%|████████████████████████▋                                 | 800/1875 [00:04<00:05, 189.48it/s, loss=1.483, v_num=87]
as u see, even at epoch29, the loss is around 1.5.
code:
`import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision import transforms
import pytorch_lightning as pl
class MNISTModel(pl.LightningModule):
def __init__(self):
    super(MNISTModel, self).__init__()
    self.l1 = torch.nn.Linear(28 * 28, 10)

def forward(self, x):
    return torch.relu(self.l1(x.view(x.size(0), -1)))

def training_step(self, batch, batch_nb):
    x, y = batch
    loss = F.cross_entropy(self(x), y)
    tensorboard_logs = {'train_loss': loss}
    return {'loss': loss, 'log': tensorboard_logs}

def configure_optimizers(self):
    return torch.optim.Adam(self.parameters(), lr=0.02)

train_loader = DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
mnist_model = MNISTModel()
trainer = pl.Trainer(gpus=1, progress_bar_refresh_rate=20)
trainer.fit(mnist_model, train_loader)  `
Please reproduce using [the BoringModel and post here]
Here comes the strange thing, with same code, I running at the colab,
(https://colab.research.google.com/drive/1rPmzscbE4tCo7k8pj7FQJxck3JcM-Zx8?usp=sharing)
Got this
Epoch 1: 77% 1440/1875 [00:06<00:01, 230.34it/s, loss=0.669, v_num=0]
Even at epoch 1, I got loss = 0.669!
To Reproduce
Colab:https://colab.research.google.com/drive/1rPmzscbE4tCo7k8pj7FQJxck3JcM-Zx8?usp=sharing
my local pc maybe you cannot Reach
Expected behavior
Colab and my pc get Same behavior , in other words,  my pc loss should convergence.
Environment

PyTorch Version (e.g., 1.0): 1.6.0
OS (e.g., Linux): Linux
How you installed PyTorch (conda, pip, source):   pip
Python version: 3.7.4
CUDA/cuDNN version: 10.1
GPU models and configuration:   Gforce1060 (1080ti also get loss around 1.5 all the time.)
Any other relevant information: