PyCharm
From Install to Remote Dev

Navigate to jetbrains.com/student and click "Apply now".

Click the confirmation link. JetBrains will create a JetBrains Account linked to your .edu address.

Choose the Professional edition. There is also a JetBrains Toolbox App (recommended) that manages all JetBrains IDEs and updates them automatically.

On first launch, choose "Log in to JetBrains Account" and enter the account you created with your .edu email. The license activates instantly.

On the welcome screen, click "New Project". Choose a folder, select "Python" as project type, and pick "New environment using Virtualenv" — PyCharm will create a virtual environment automatically.

PyCharm will look for Python installations on your system. If you don't see Python 3.10+, install it from python.org first, then re-open PyCharm.

Right-click the project root in the Project panel → New → Python File. Name it main.py. Type print("Hello, PyCharm!") and press ⌃R (Mac) or Shift+F10 (Windows) to run it.

Choose the directory where your project files will live.

Then set Environment to Select existing and Type to Python.

PyCharm creates the project using your Anaconda Python, giving you immediate access to NumPy, Pandas, TensorFlow, and all other Anaconda packages.

Enter the two lines below in the editor, then right-click anywhere in the editor and select Run 'python_eval'.

Copyimport numpy as np
print('hello world')

PyCharm opens the notebook in its built-in Jupyter editor. A new .ipynb file appears in your project.

The cell executes and hello world appears immediately below it with a green checkmark and a timing badge.

Mac: ⌘, → Project → Python Interpreter. Windows: Ctrl+Alt+S → Project → Python Interpreter.

Select Virtualenv Environment → New. PyCharm suggests a path like project-folder/.venv. Choose the base Python version (e.g., Python 3.11).

You'll see the interpreter change in the status bar (bottom right). Future package installs go into this venv only.

You see a list of installed packages. Click the + button.

PyCharm runs pip install numpy in your venv. You can see the output in the panel below.

In the Variables panel, click "+" → New Watch". Type any expression like total * 2 or len(scores). It updates automatically at every step.

Press ⌥F8 (Mac) or Alt+F8 (Win) while paused. Type any Python expression — it runs in the current scope. Great for testing a fix without restarting the whole program.

Or open an existing repo: File → Open → select the folder. PyCharm detects .git/ automatically.

Click + and paste your GitHub URL. Name it origin.

Copyhttps://github.com/your-username/your-repo.git

A panel shows all changed files. Check the ones you want to stage, write a commit message, and press "Commit" or "Commit and Push".

Click any changed file in the panel to see a side-by-side diff right there. Red = removed, green = added. You can even edit directly in the diff view.

A dialog shows the commits about to be pushed and the target branch. Confirm and click Push.

Enter a name (e.g., PycharmGitHub), choose Private, and click Create repository. You can make it public later — private repos are visible only to people you share them with.

Enter your GitHub username and password if prompted. All your repositories will appear in the list.

PyCharm clones the (empty) repository and opens it as a new project. There will be no files yet.

Enter the line below. Notice the filename appears in green — this means it is a new file not yet tracked by Git.

Copyprint('Hello World')

Type a description like Add file EvalGithub.py in the message box at the bottom of the Commit panel. Click Commit and Push….

Click + to add a new SSH connection. Fill in:

PyCharm will try to SSH into the server. You should see a green "Successfully connected" message. If not, check the host, port, and key path.

Select the SSH configuration you just created. PyCharm will connect and browse the remote Python installations.

Navigate to the Python path on the remote server, e.g. /usr/bin/python3 or the path to a conda env:

Copy# System Python
/usr/bin/python3

# Conda environment on remote
/home/username/miniconda3/envs/myenv/bin/python

# Module-loaded Python on HPC
# First SSH in and run: module load python/3.11 && which python

PyCharm asks where to copy your files on the remote server, e.g. /home/username/projects/my_project. This is where your code will live on the remote.

In the New Target: SSH dialog, select Existing for SSH Connection and choose your AWS configuration from the dropdown. Click Next, then Next again.

Click Browse on the Sync folders field and select your local project directory. Click Browse on the Remote path field and navigate to the directory on the AWS instance where your project lives (e.g., /home/ubuntu/PythonProject1). Click OK and OK.

The remote interpreter appears in the list (e.g., Remote Python 3.12.3 (sftp://ubuntu@…)). Click on it to select it.

Giving it a clear name makes it easy to identify when switching between local and remote.

Wait for the update to finish (visible in the status bar at the bottom). Once complete, you can run python_eval.py — it now executes on your AWS instance, not your local computer.

Click the interpreter name in the status bar (bottom right of the PyCharm window). A popup lists all configured interpreters — select AWS to run remotely, or your local Anaconda interpreter to run locally.

Enter a name for the server (e.g., AWS) and click OK.

Fill in the SSH connection details for your AWS instance:

Click Test Connection to verify. Click the pencil icon above the left column and rename the connection to AWS. Click OK.

Set the Root path to the base directory on your AWS instance (e.g., /home/ubuntu/projects). Click OK to save the deployment server configuration.

Add a mapping that tells PyCharm which local folder corresponds to which remote folder:

You can have multiple mappings — for example, your main code folder maps to your home directory, but a large data folder maps to a scratch/data partition on the server.

Add any paths you do NOT want to upload to the server:

Copy.venv/           # local venv — remote has its own
__pycache__/
.git/
*.local.py       # local-only config files
.DS_Store