Added instructions for how to install the nightly package. (#3)

* Added instructions for how to install the nightly package.

BUG=http://b/340367182

* update workflow to test the nightly.

.github/workflows/nightly_pip_test.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
-    
+
     runs-on: ubuntu-latest
     steps:
       - uses: actions/setup-python@v4
@@ -26,7 +26,7 @@ jobs:
           pip install -r https://raw.githubusercontent.com/google-ai-edge/ai-edge-torch/main/requirements.txt
       - name: Install ai-edge-torch
         run: |
-          pip install ai-edge-torch
+          pip install ai-edge-torch-nightly
       - name: Import ai-edge-torch
         run: |
           python -c "import ai_edge_torch"

README.md

@@ -101,7 +101,7 @@ pip install ai-edge-torch==0.1.1
 Alternately, the nightly version can be installed with:
 ```bash
 pip install -r https://raw.githubusercontent.com/google-ai-edge/ai-edge-torch/main/requirements.txt
-pip install ai-edge-torch
+pip install ai-edge-torch-nightly
 ```
 
 * The list of versioned releases can be seen [here](https://github.com/google-ai-edge/ai-edge-torch/releases).

setup.py

@@ -26,7 +26,9 @@
 then be run with TensorFlow Lite and MediaPipe.  This enables applications for
 Android, iOS and IOT that can run models completely on-device.
 
-More details are in the project's [GitHub repository](https://github.com/google-ai-edge/ai-edge-torch).
+[Install steps](https://github.com/google-ai-edge/ai-edge-torch#installation)
+and additional details are in the AI Edge Torch
+[GitHub repository](https://github.com/google-ai-edge/ai-edge-torch).
 """.lstrip()
 
 setup(