Format according to markdownlint

README.md

@@ -20,7 +20,8 @@
 
 1. Create new environment, e.g. via `conda` or `virtualenv`:
 
-   Python minimum requirement >= 3.7 
+   Python minimum requirement >= 3.7
+
    ```bash
     sudo apt-get install python3-venv
     python3 -m venv env
@@ -61,31 +62,35 @@
 
 ### Docker
 
-Docker is the easiest way to enable PyTorch/JAX GPU support on Linux since only the [NVIDIA® GPU driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver) is required on the host machine (the NVIDIA® CUDA® Toolkit does not need to be installed). 
+Docker is the easiest way to enable PyTorch/JAX GPU support on Linux since only the [NVIDIA® GPU driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver) is required on the host machine (the NVIDIA® CUDA® Toolkit does not need to be installed).
 
 #### Docker requirements
 
--  Install [Docker](https://docs.docker.com/get-docker/) on your local host machine.
+- Install [Docker](https://docs.docker.com/get-docker/) on your local host machine.
 
 - For GPU support on Linux, [install NVIDIA Docker support](https://github.com/NVIDIA/nvidia-docker).
-   - Take note of your Docker version with docker -v. Versions earlier than 19.03 require nvidia-docker2 and the --runtime=nvidia flag. On versions including and after 19.03, you will use the nvidia-container-toolkit package and the --gpus all flag. Both options are documented on the page linked above.
+  - Take note of your Docker version with docker -v. Versions earlier than 19.03 require nvidia-docker2 and the --runtime=nvidia flag. On versions including and after 19.03, you will use the nvidia-container-toolkit package and the --gpus all flag. Both options are documented on the page linked above.
 
 #### Setup
 
 1. Clone this repository:
+
    ```bash
    git clone https://github.com/mlcommons/algorithmic-efficiency.git
    ```
 
 2. Build Docker
+
    ```bash
    cd algorithmic-efficiency/ && sudo docker build -t algorithmic-efficiency .
    ```
 
 3. Run Docker
+
    ```bash
    sudo docker run --gpus all -it --rm -v $PWD:/home/ubuntu/algorithmic-efficiency --ipc=host algorithmic-efficiency
    ```
+
    Currently docker method installs both PyTorch and JAX
 
    </details>