Update the README file.

README.md

@@ -4,65 +4,65 @@ To build:
 
 * Clone the _PyTorch_ repo as per [instructions](https://github.com/pytorch/pytorch#from-source).
 
-  ```
+  ```Shell
   git clone --recursive https://github.com/pytorch/pytorch
   cd pytorch/
   ```
 
 * Clone the _PyTorch/XLA_ repo:
 
-  ```
+  ```Shell
   git clone --recursive https://github.com/pytorch/xla.git
   ```
 
 * If a file named xla/.torch_commit_id exists, use its content to checkout the PyTorch commit ID:
 
-  ```
+  ```Shell
   git checkout $(cat xla/.torch_commit_id)
   ```
 
 * Apply PyTorch patches:
 
-  ```
+  ```Shell
   xla/scripts/apply_patches.sh
   ```
 
 * Install the Lark parser used for automatic code generation:
 
-  ```
+  ```Shell
   pip install lark-parser
   ```
 
 * Currently _PyTorch_ does not build with _GCC_ 6.x, 7.x, and 8.x (various kind of ICEs). _CLANG_ 7.x is known to be working, so install that in your VM:
 
-  ```
+  ```Shell
   sudo apt-get install clang-7 clang++-7
   export CC=clang-7 CXX=clang++-7
   ```
 
   You may need to add the following line to your _/etc/apt/sources.list_ file:
 
-  ```
+  ```Shell
   deb http://deb.debian.org/debian/ testing main
   ```
 
   And run the following command before trying again to install _CLANG_:
 
-  ```
+  ```Shell
   sudo apt-get update
   ```
 
 * Build _PyTorch_ from source following the regular [instructions](https://github.com/pytorch/pytorch#from-source).
 
-  ```
+  ```Shell
   python setup.py install
   ```
 
-* Install Bazel following the [instructions](https://docs.bazel.build/versions/master/install.html)
+* Install Bazel following the [instructions](https://docs.bazel.build/versions/master/install.html). You should be installing version >= 0.24.1.
 
 * Build the _PyTorch/XLA_ source:
 
-  ```
+  ```Shell
   cd xla/
   python setup.py install
   ```
@@ -71,7 +71,7 @@ To run the tests, follow __one__ of the options below:
 
 * Run on local CPU using the XRT client:
 
-  ```
+  ```Shell
   export XRT_DEVICE_MAP="CPU:0;/job:localservice/replica:0/task:0/device:XLA_CPU:0"
   export XRT_WORKERS="localservice:0;grpc://localhost:40934"
   ```
@@ -82,7 +82,7 @@ To run the tests, follow __one__ of the options below:
 
   - Set the XRT_TPU_CONFIG environment variable:
 
-    ```
+    ```Shell
     export XRT_TPU_CONFIG="tpu_worker;0;<IP of the TPU node>:8470"
     ```
 
@@ -94,7 +94,30 @@ seem to hang at startup, verify that the IP of your TPU node is still the same o
 the one you have configured.
 
 
-Then run `python test/test_operations.py`. Some of the tests are currently skipped.
+Then run `test/run_tests.sh` and `test/cpp/run_tests.sh` to verify the setup is working.
 
 
 [![CircleCI](https://circleci.com/gh/pytorch/xla.svg?style=svg)](https://circleci.com/gh/pytorch/xla)
+
+
+
+# How To Install Pre Built PyTorch TPU Wheels
+
+It is recommended to use Conda environments to isolate _PyTorch/TPU_ packages from the others.
+To install Anaconda follow the [instructions](https://docs.anaconda.com/anaconda/install/).
+Then create an environment dedicated to _PyTorch/TPU_ and activate it (activation should happen every time you want to work in such environment):
+
+```Shell
+conda create --name pytorch_tpu --clone base
+source activate pytorch_tpu
+```
+
+Install the _gsutil_ package to allow access to _GCS_ (Google Cloud Storage) following the [instructions](https://cloud.google.com/storage/docs/gsutil_install).
+
+Then run:
+
+```Shell
+scripts/update_torch_wheels.sh
+```
+
+The same script can be run again when you want to update the _PyTorch/TPU_ wheels.