remove M1-M3 instructions

README.md

@@ -22,8 +22,6 @@ Check out our new tutorial series that walks through AMPL's end-to-end modeling
 ## Table of contents
 - [Install](#install)
    - [Quick Install](#installation-quick-summary)
-   - [Install with M1 - M3 Chips](#install-with-m1---m3-chips)
-      - [Quick Install for M1 - M3 Chips](#installation-quick-summary-for-m1---m3-chips)
    - [Jupyter kernel](#create-jupyter-notebook-kernel-optional)
    - [Docker](#install-with-docker)
    - [Uninstall](#uninstall)
@@ -44,6 +42,8 @@ Check out our new tutorial series that walks through AMPL's end-to-end modeling
 ## Install
 AMPL 1.6 supports Python 3.9 CPU or CUDA-enabled machines using CUDA 11.8 on Linux. All other systems are experimental. For a quick install summary, see [here](#install-summary). We do not support other CUDA versions because there are multiple ML package dependency conflicts that can occur. For more information you can look at [DeepChem](https://deepchem.readthedocs.io/en/latest/get_started/installation.html), [TensorFlow](https://www.tensorflow.org/install/pip), [PyTorch](https://pytorch.org/get-started/locally/), [DGL](https://www.dgl.ai/pages/start.html).
 
+For installation on Apple Silicon M Chips, please see the Docker container instructions.
+
 ### Create pip environment
 
 #### 1. Create a virtual env with Python 3.9 
@@ -152,107 +152,6 @@ cd ..
 pip install -e .                        
 ```
 ---
-## Install with M1 - M3 chips
-AMPL is built on Linux machines but the instructions below have been tested successfully on several M-chip Macs. This local installation requires a few different steps compared to a Linux installation, including using a Linux emulator ([OrbStack](https://orbstack.dev) is recommended), installing Linux packages and using conda instead of venv to manage the Python version. If you have run into errors or found an even easier method for M chips, please [let us know](https://github.com/ATOMScience-org/AMPL/issues)!
-
-### Install Orbstack
-- Download and install Orbstack
-  - https://orbstack.dev/download
-- Create a Linux machine
-  - Distribution select: Ubuntu
-  - Version select: 24.04 LTS (Noble Numbat)
-  - CPU type: Apple
-
-### Installing Miniconda3
-- Open Linux terminal in Orbstack
-- Install wget
-
-```bash
-sudo apt update
-sudo apt install wget
-```
-- Install Miniconda3
-
-```bash
-cd ~                         # install miniconda in your Ubuntu home directory
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh
-chmod +x Miniconda3-latest-Linux-aarch64.sh
-./Miniconda3-latest-Linux-aarch64.sh
-source .bashrc               # reload the shell with conda activated
-```
-
-### Create conda environment with Miniconda3
-
-#### 1. Create a conda env with Python 3.9.12 and activate it
-Your conda env will need 2.8Gb space.
-
-```bash
-conda create -n atomsci-env python=3.9.12
-conda activate atomsci-env
-```
-
-#### 4. Clone AMPL repository
-```bash
-sudo apt install git                                    # install git to Linux terminal
-mkdir repos                                             # optional - install repos in a new directory
-cd repos                                                # optional
-git clone https://github.com/ATOMScience-org/AMPL.git   # clones AMPL repository
-```
-
-#### 5. Install pip requirements
-Depending on system performance, creating the environment can take some time.
-
-- Install these packages onto your Ubuntu machine so that you don't receive h5py wheel build error or gcc error
-```bash
-sudo apt install pkg-config libhdf5-dev libxrender1 gcc
-```
-- Use the MChip installation requirements file:
-```bash
-pip install pip --upgrade
-cd AMPL/pip
-pip install -r mchip_requirements.txt
-```
-
-### Install AMPL
-Run the following to build the AMPL modules. This is required.
-> ***Note:*** *Should be in the AMPL directory*
-```bash
-# return to AMPL parent directory
-cd ..
-./build.sh
-pip install -e .
-```
-
----
-## Installation Quick Summary for M1 - M3 chips
-> ***Note:*** *Should run these commands on Ubuntu terminal through Orbstack*
-```bash
-sudo apt update
-sudo apt install wget                                   # install wget to Linux terminal
-
-cd ~                                                    # install miniconda in Linux home directory
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh
-chmod +x Miniconda3-latest-Linux-aarch64.sh
-./Miniconda3-latest-Linux-aarch64.sh
-source .bashrc                                          # reload the shell with conda activated
-
-conda create -n atomsci-env python=3.9.12               # create environment with Python 3.9.12
-conda activate atomsci-env                              # activate atomsci-env environment 
-
-sudo apt install git                                    # install git to Linux terminal
-git clone https://github.com/ATOMScience-org/AMPL.git   # clones AMPL repository
-
-cd AMPL/pip    
-sudo apt install pkg-config lixrender1 libhdf5-dev gcc  # fix h5py and gcc install errors
-
-pip install pip --upgrade
-pip install -r mchip_requirements.txt                     # install mchip_requirements.txt
-
-cd ..                                                   # return to AMPL parent directory
-./build.sh                            
-pip install -e .                        
-```
----
 ## Create jupyter notebook kernel (optional)
 To run AMPL from Jupyter Notebook. To setup a new kernel, first activate your environment and then run the following command: