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Running Claude Code on an Air Cloud Container — From SSH Connection to AI-Assisted Coding

  • 4 days ago
  • 4 min read


Every GPU experiment starts the same way: before you write a single line of code, you're already fighting your environment. Matching CUDA versions, installing drivers, resolving package conflicts — two hours gone before anything actually runs.


Air Cloud solves this by letting you deploy a container with PyTorch and CUDA pre-configured, then connect via SSH immediately. No local setup required. Add Claude Code into the mix, and you can start writing, debugging, and running code with AI assistance the moment you connect.

This guide walks through the entire process, step by step.




Who This Is For


  • ML developers whose local machines can't keep up with GPU workloads

  • Claude Code users who want the same workflow on a remote server

  • Developers who want to connect to a remote GPU server via VSCode



Prerequisites





Step 1. Install the aircloud CLI


Air Cloud provides a dedicated CLI tool. Install it with a single command.

(This guide is based on macOS.)

pip install aircloud-cli

Once installed, register your API Key and server URL. You can find your API Key in the Air Cloud console under Settings.

aircloud config set api-base-url https://external.aieev.cloud:5007
aircloud config set api-key sk-여기에_발급받은_키를_입력하세요

*When issuing an API key, you must select access to the container you want to connect to.


Verify the setup:

aircloud whoami

You should see your organization ID, project ID, and user ID. If so, you're good to go.
You should see your organization ID, project ID, and user ID. If so, you're good to go.


Step 2. Generate an SSH Key and Register It

To connect via SSH, you'll need to generate a key pair locally and register the public key in Air Cloud.


Generate the Key Pair:

ssh-keygen -t ed25519 -C "your-email@example.com" -f ~/.ssh/aircloud_key -N ""

-f ~/.ssh/aircloud_key sets the key filename, and -N ""creates the key without a passphrase.


Copy the public key:

cat ~/.ssh/aircloud_key.pub

Copy the full string starting with ssh-ed25519 AAAA....



Register the public key in the Air Cloud console:



  1. Open the Air Cloud console

  2. Go to SSH Keys in the project sidebar

  3. Enter a recognizable name (e.g., macbook-pro)

  4. Paste the public key

  5. Select the endpoint to inject the key into and save


*Note: SSH keys are injected when the container starts. If you add a key to a running container, it will take effect on the next restart.



Step 3. Deploy a Container


In the Air Cloud console, deploy a new container. Select the Jupyter or Code environment template — SSH is enabled by default on these templates, so no additional configuration is needed.

Running Container
Running Container

After deployment, wait until the ACTIVE status shows True in the console. SSH connections will fail if the container is not active. If the status shows READY but ACTIVE is False, click the Start button to bring it up.


*Using a custom image? SSH works with custom images too, but your image must include openssh-server, host key generation, authorized_keys setup, and sshd running at boot.



Step 4. Connect via SSH


Find your Endpoint ID in the console, or retrieve it via CLI:

aircloud endpoints list

Then connect using the -i flag to specify your key file directly:

aircloud ssh <endpoint_id> -i ~/.ssh/aircloud_key

A successful connection looks like this:



Once the prompt changes to root@container-id:~#, you're inside the Air Cloud container. Verify the GPU is attached:

nvidia-smi
If you see GPU model and memory information, you're ready.
If you see GPU model and memory information, you're ready.

Step 5. Install and Run Claude Code

The Jupyter template includes Node.js out of the box. Install Claude Code directly:

npm install -g @anthropic-ai/claude-code

Set the API Key:

Remote servers don't have a browser for OAuth login. Set your Anthropic API Key as an environment variable instead:

export ANTHROPIC_API_KEY=sk-ant-여기에_앤트로픽_키를_입력하세요

To avoid setting this every session, add it to ~/.bashrc:

echo 'export ANTHROPIC_API_KEY=sk-ant-여기에_키를_입력하세요' >> ~/.bashrc
source ~/.bashrc

Launch Claude Code:

claude


Try It Out — Coding with Claude Code on a GPU Server


Screen showing Claude Code running and asking for the current server's GPU information.
Screen showing Claude Code running and asking for the current server's GPU information.

With Claude Code running, you can start working with AI directly on the GPU server. Here's a quick example to verify everything is working:


Write and run a GPU vs CPU benchmark:

Write a PyTorch script that compares matrix multiplication speed on GPU vs CPU, then run it

Claude Code will write the script and execute it inside the container. The output will show how much faster the GPU handles the computation compared to CPU.




Status of tasks running inside the container after launching Claude Code.
Status of tasks running inside the container after launching Claude Code.


Bonus: Connect with VSCode Remote SSH


If you prefer a GUI environment, you can connect to the container directly from VSCode.


1. Install the Remote - SSH extension

Search for Remote - SSH in the VSCode Extensions marketplace and install it.


2. Open an SSH tunnel

In your local terminal, set up the tunnel with --tunnel-only:

aircloud ssh <endpoint_id> -i ~/.ssh/aircloud_key --tunnel-only

3. Connect from VSCode

Ctrl+Shift+P (macOS: Cmd+Shift+P) → Remote-SSH: Connect to Host → select the tunneled host.


VSCode will treat the container's filesystem like a local folder. Install the Claude Code VSCode extension inside the remote environment, and you'll have a full AI-assisted IDE running on your GPU server.




Wrapping Up


We've walked through the full workflow: deploying an Air Cloud container, connecting via SSH, and running Claude Code on the GPU server. The key advantage of this setup is that you get the same AI coding experience you're used to locally, while all the heavy computation runs on dedicated GPU hardware in the cloud.

If cost is a concern, Air Cloud starts at $0.71/hr for an RTX 4090. Spin it up when you need it, shut it down when you're done.

If you get stuck at any point, the resources below should help.



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➡️ Deploy a GPU container and connect now: https://ap-1.aieev.cloud:3007/get-started

➡️ Ask the engineering team directly: https://www.aieev.com/contact-tech

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