# Paperspace Usage Information
## Preliminary
You should have been provided with Paperspace credits - these will allow you to spawn paid instances with better GPUs than what colab or paperspace offer on their free-tier. Additionally, unless you enable auto-shutdown, these instances will stay awake permanently, and not abort half-way through training.

## Instance Setup
Once you have signed up for Paperspace's gradient platform and applied your coupon:
1. Go to console.paperspace.com and create a under the "ICL Deep Learning 2023" team (selected at top left)
2. Create a notebook within that project. We recommend using the PyTorch 1.10 GPU runtime
3. Select a machine - you can use the free-tier CPU or GPU machines for most of the coursework questions, but when training your models we recommend using a paid P4000 instance. Choose your **auto-shutdown** period carefully, base on the network's training time, and whether you will need to go AFK
4. You can **directly connect the instance to your gitlab repository** by
a. Going to Advanced Options and pasting your gitlab link into the workspace field (```https://gitlab.doc.ic.ac.uk/<course>/<your_repo>.git```) **the .git is needed** - the easiest way to get this link is to copy the HTTPS clone link from gitlab.
b. Entering your doc username (e.g. **afs219** - not your email!) and password into the workspace username and password information
**Note** it seems there is a bug for passwords containing special characters (% & etc.) - the password field on paperspace will not properly pass these to the gitlab login, and you will get "Access denied". The current fix is to change your gitlab password.)

## Using the Paperspace Gradient Environment
Once your instance has spun-up, you will be greeted by a .ipynb similar to Google Colab. You can choose to work here, or press the Jupyter icon to the side () to open JupyterLab.

If you are going to work in the web, I would strongly recommend using JupyterLab over their notebook environment. However, the best development experience is provided by working with VSCode and connecting to the remote kernel.

## Connecting VSCode to Gradient
In order to make this work you will need the Jupyter extension installed on VSCode. You should also ```git clone``` your repo onto whichever machine you'll be working from.

Assuming you have opened the repository within VScode, and have the ```dl_cw_X.ipynb``` file open you can then follow the steps in https://docs.paperspace.com/gradient/notebooks/notebooks-remote-kernel/

<!--
1. We will first need to retrieve the jupyter kernel url and token from Paperspace. Boot an instance as described above, and then create a dummy ```notebook.ipynb```.
a. Open the JupyterLab environment on Paperspace, and take note of the url (e.g. ```https://ncu2a5zm3q.clg07azjl.paperspacegradient.com/lab```)
b. Run the following snippet within a notebook, to retrieve the token

    from notebook import notebookapp
    notebook_token = list(notebookapp.list_running_servers())[0]['token']
    print(notebook_token)

this should return an alphanumerical string (e.g. ```310bdadb353de5d42de0c70c69ec0dd1```)
2. Combine these two to form the link with the token:

3. Now press the Jupyter Server selection button at the bottom-right in VSCode ![](https://i.imgur.com/mXz6kKN.png) and paste in the url.

After reloading your VSCode window, it should work. Remember that you can run terminal commands through ipython directly by using ```!the_command```. For example, you can check that you are on the remote machine by running ```!pwd``` within the notebook - you should find you are in ```/notebooks``` by default. -->