From 5e71c880965c8ba90ff3d4ff525d3ad0333448ed Mon Sep 17 00:00:00 2001
From: Jirka Borovec <Borda@users.noreply.github.com>
Date: Wed, 23 Dec 2020 21:29:00 +0100
Subject: [PATCH] releasing feature as nightly (#5233)

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
(cherry picked from commit c479351a938240fbda6774a404494ee399ff361a)
---
 .github/workflows/nightly.yml |  4 +++-
 README.md                     | 28 +++++++++++++++++++++-------
 2 files changed, 24 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
index 92fd99c402..eb3e55268b 100644
--- a/.github/workflows/nightly.yml
+++ b/.github/workflows/nightly.yml
@@ -13,7 +13,10 @@ jobs:
     runs-on: ubuntu-20.04
 
     steps:
+    # does nightly releases from feature branch
     - uses: actions/checkout@v2
+      with:
+        ref: release/1.2-dev
     - uses: actions/setup-python@v2
       with:
         python-version: 3.7
@@ -29,7 +32,6 @@ jobs:
         ls -lh dist/
 
     - name: Delay releasing
-      if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
       uses: juliangruber/sleep-action@v1
       with:
         time: 5m
diff --git a/README.md b/README.md
index 84d9571395..c10f404c3d 100644
--- a/README.md
+++ b/README.md
@@ -101,7 +101,7 @@ Lightning can automatically export to ONNX or TorchScript for those cases.
 
 ## How To Use
 
-#### Step 0: Install
+### Step 0: Install
 
 Simple installation from PyPI
 ```bash
@@ -114,12 +114,26 @@ From Conda
 conda install pytorch-lightning -c conda-forge
 ```
 
-Install bleeding-edge (no guarantees)   
+#### Install bleeding-edge - future 1.2
+
+the actual status of 1.2 [nightly] is following:
+
+![CI base testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20base%20testing/badge.svg?branch=release%2F1.2-dev&event=push)
+![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.2-dev&event=push)
+![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.2-dev&event=push)
+![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.2-dev&event=push)
+![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.2-dev&event=push)
+
+Install future release from the source (no guarantees)   
 ```bash
-pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@master --upgrade
+pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.2-dev --upgrade
+```
+or nightly from testing PyPI
+```bash
+pip install -iU https://test.pypi.org/simple/ pytorch-lightning
 ```
 
-#### Step 0: Add these imports
+### Step 1: Add these imports
 
 ```python
 import os
@@ -132,7 +146,7 @@ from torchvision import transforms
 import pytorch_lightning as pl
 ```
 
-#### Step 1: Define a LightningModule (nn.Module subclass)
+### Step 2: Define a LightningModule (nn.Module subclass)
 A LightningModule defines a full *system* (ie: a GAN, autoencoder, BERT or a simple Image Classifier).
 
 ```python
@@ -163,9 +177,9 @@ class LitAutoEncoder(pl.LightningModule):
         return optimizer
 ```
 
-###### Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.
+**Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.**
 
-#### Step 2: Train!
+### Step 3: Train!
 
 ```python
 dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())