Adding temporary README.md with instructions that will need to be altered slighty

Auto-SKLearn_AutoML/README.md

+ # Auto-sklearn AutoML Installation instructions
+
+***Note, below was an installation written for another package Brandon wrote 
+for another project. This will be updated to reflect Auto-Sklearn at a later time.
+Currently, we are still working on getting our whole team to have the ability to
+download this package. Once directions are solidified this will be updated.
+For the mean time, below will have a pretty similar set up. To avoid a failed
+installation on Windows Machienes we are using the HPCC. We  will have our own
+seperate enviroment.yaml file to ensure that this does not effect future Windows
+users of main directory.***
+
+[`PyTorch`](https://pytorch.org/) is a popular software/package for 
+tensor computation and construction of Neural Networks. It is a pacakge for python, 
+as well as C++. The software is also
+[open source](https://github.com/pytorch/pytorch) and has a rich community 
+that has a plethora of tutorials on how to use many of the features. One of 
+`PyTorch`'s strengths is that it can utilize GPUs to perform calculations. 
+This allows for significant speed up in training and computation. One thing 
+that is interesting is the code is "not a Python binding into a monolithic 
+C++ framework. It is built to be deeply integrated into Python." `PyTorch` 
+also has many libraries such as [`torchaudio`](https://pytorch.org/audio/stable/index.html) 
+for audio, [`torchtext`](https://pytorch.org/text/stable/index.html) for text, 
+[`torchvision`](https://pytorch.org/vision/stable/index.html) for computer vision, 
+[`TorchElastic`](https://pytorch.org/elastic/0.2.1/index.html) for running on 
+changing environments, [`TorchServe`](https://pytorch.org/serve/) for serving 
+`PyTorch` models. This package is great for projects that require large amounts
+of computation through neural networks, such as neural networks that take in images. 
+
+## ANACONDA SET UP:
+
+In order to run this example easily, it is suggested that Anaconda 3 with python 
+version 3.6 or higer is used. With using Anaconda, since many python pacakges
+are already installed and conda is pre-installed, the setup will be less intensive.
+
+If you already have Anaconda 3 with python version 3.6 or higher, you can skip
+to Virtual Enviroment Setup.
+
+Below are instructions on how to install Anaconda 3 with python version 3.8 on
+the HPCC. We will also set up a new virtual enviroment with the packages we 
+will need to install to get our example working. Please note this would probably
+work best if you can keep this README.md open and also have access to a terminal
+in another window.
+
+### INSTALL Anaconda 3 w/ Python 3.8:
+
+The following was created on 3/22/2021. It is possbile at the time you are reading
+this the Anaconda version has updated. If this is the case, visit Anconda's website
+and use the link to the newest Anconda version in place of the link below. Also
+make sure to change the bash command to run the appropiate script.
+
+1. In your home directory (~), or wherever you would like,
+you will want to download the installation script from Anaconda's website. 
+This can easily be done with this command.
+
+```bash
+curl -O https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
+```
+
+2. Next we need to run the installation script. To do this makes sure you are
+in the same directory as the file we just downloaded. Then run the following.
+
+```bash
+bash Anaconda3-2020.11-Linux-x86_64.sh
+```
+
+3. The installation process will ask you a few things
+
+4. Read through the License agreement by pressing/holding `ENTER`
+
+5. Type "yes" to accept the terms
+
+6. You will then be asked where you would like to install anaconda 3. 
+Press "Enter" to save it in `~/anaconda3`, or if you have another location 
+you would like to save it, you can enter this here. But note this guide 
+assumes this is installed in the home directory. You may neeed to modify 
+directions later based on where you save anaconda3.
+
+7. Once finished installing, it will ask if you would like the installer to 
+initalize Anaconda3. For using this as a module, you are going to want 
+to say "No". However, if you want Anaconda3 always avalible to you anytime, 
+you can say "yes" to this.
+
+8. You can now delete or move the `Anaconda3-2020.11-Linux-x86.sh` file to 
+wherever you would like.
+
+9. Now you can load in Anaconda 3 with the following command.
+
+```bash
+module load Anaconda/3
+```
+
+10. You can test that the installation worked properly if the follwing command 
+returns `conda 4.9.2` or something equivalent.
+
+```bash
+conda --version
+```
+
+### Virtual Enviroment Setup: 
+
+From the last section, make sure you Anaconda 3 is active. If you followed the
+process above this was done with 
+
+```bash
+module load Anaconda/3
+conda --version
+```
+
+If a version number was given you are good to go.
+
+There are two routes to go from here. Either you can load the enviroment included
+or you can create a new enviroment
+
+**Load the Enviroment from yml**
+
+Loading the enviroment allows you to use the same exact enviroment I used to 
+run the code
+
+1. In the terminal navigate to the same folder this README.md is in 
+(`/pytorch_classifier`).
+
+2. You can create the enviroment from the `pytorch_classifier.yml` file with
+```bash
+conda env create -f pytorch_classifier.yml
+```
+
+3. To activate the enviroment we can use
+```bash
+conda activate pytorch_classifier
+```
+
+**Create new Enviroment**
+
+Setting up a new virtual enviroment will help keep your base Anaconda enviroment 
+clean so that way you can have seperate enviroment where we can install our 
+packages and not have to worry about them conflicting in the future with other
+packages.
+
+1. In the terminal navigate to the same folder this README.md is in 
+(`/pytorch_classifier`).
+
+2. Now we can create our new enviroment with this command.
+```bash
+conda create -n pytorch_classifier pytorch torchvision requests cudatoolkit=10.2 -c pytorch
+```
+
+3. We are now able to activate our enviroment with 
+```bash
+conda activate pytorch_classifier
+```
+
+**Notes**
+
+Note that when you are done and want to get out of this enviroment you can use
+```bash
+conda deactivate
+```
+
+Also if in the future you would like to delete the enviroment you can use
+```bash
+conda env remove -n pytorch_classifier
+```
+
+## Module Setup
+
+In order to take full advantage of this example, it is suggested to use a
+developer node with GPU access (`dev-intel16-k80`,
+`dev-amd20-v100` ---- Note that `dev-intel14-k20` does not work with this
+example).
+
+If Anaconda 3 is installed with the appropiate package and enviroment from above.
+We can load Anaconda 3 with `pytorch_classifier` enviroment as
+```bash
+module load Anaconda/3
+conda activate pytorch_classifier
+```
+
+Next, we need to load the appropiate modules for use of CUDA
+```bash
+module load GCC/8.3.0
+module load CUDA/10.2.89
+```
+
+## Running the Code
+
+The code comes from [this tutorial](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py)
+I simply just combined it into one script and implemented GPU usage.
+
+To run the code is simple. I have included a makefile to streamline the process.
+
+In order to start the program simply run
+```
+make
+```
+
+This should autodetect if you are on a node with GPU and run the appropiate
+version of the code.
+
+The code will train a deep neural network with 10000 images.
+The network will then be tested with 10000 images.
+The results of the classification are then printed at the end.
+
+The program will produce the following:
+ * `/data` --  folder containing data for training and testing model
+ * `training.png` -- Sample grid of training images
+ * `testing.png` -- Sample grid of testing images 
+ * `cifar_net.pth` -- Trained Neural Network 
+
+These files can easily be deleted with this command
+```
+make clean
+```
+
+If you would like to run this as a HPCC job, you can use the command
+```
+sbatch classifier.sb
+```
+
+This concludes this README, below is references
+
+# References
+
+Code:  
+https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py  
+
+Conda Enviroments:  
+https://towardsdatascience.com/a-guide-to-conda-environments-bc6180fc533#e814
+https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#removing-an-environment