semvec/pypi-packages huggingface dataset:
jupyter-kernel-agent 1.2.2
An agent to bring cluster kernel to local
Requires Python: >=3.8

# Jupyter Kernel Agent

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**Jupyter Kernel Agent** lets you run Jupyter code remotely by forwarding code to an HTTP endpoint and streaming results back in Jupyter’s native format.  
Connect your Jupyter environment to clusters, GPU servers, or any backend that implements the Kernel Agent API.

---

## Features

- 📡 Remote code execution over HTTP
- 🔑 Bearer token authentication
- 🖥️ Multiple backends (one kernel per endpoint)
- 🔄 Built-in magics for session management:
  - `%reset` — clear remote namespace / start new session
  - `%shutdown` — shut down remote session
  - `%welcome` — show kernel info
  - `%reconnect` — reconnect after disconnect
- 🛑 Disconnection handling: hold (default) or exit

---

## Installation

```bash
pip install jupyter-kernel-agent
```

This provides two CLI tools:
- `agent-server` — run the FastAPI backend for code execution
- `kernel_agent` — manage Jupyter kernelspecs (install/list/delete)

---

## Running the Server

Start the server with an API key:

```bash
agent-server --host 0.0.0.0 --port 8000 --api_key SECRET123
```

Test with curl:

```bash
curl -sX POST http://127.0.0.1:8000/kernel \
  -H "Authorization: Bearer SECRET123" \
  -H "Content-Type: application/json" \
  -d '{"action":"execute","session_id":"s1","code":"1+1","execution_count":1}'
```

Expected response:

```json
{"ok":true,"outputs":[{"type":"execute_result","data":{"text/plain":"2"},"execution_count":1,"metadata":{}}]}
```

---

## Installing a Jupyter Kernel

Install a kernel that connects to your server:

```bash
kernel_agent install \
  --endpoint http://127.0.0.1:8000/kernel \
  --name local \
  --api_key SECRET123 --user
```

List installed kernels:

```bash
kernel_agent list
```

You should see your new kernel listed and available in Jupyter.

---

## System-wide Installation

To install for all users (e.g., JupyterHub):

```bash
sudo kernel_agent install \
  --endpoint http://127.0.0.1:8000/kernel \
  --name cluster \
  --api_key SECRET123 \
  --sys-prefix
```

Or specify a custom prefix:

```bash
sudo kernel_agent install \
  --endpoint http://127.0.0.1:8000/kernel \
  --name cluster \
  --api_key SECRET123 \
  --prefix /usr/local
```

Verify with:

```bash
jupyter kernelspec list
```

---

## Usage in Notebooks

After installation, select your kernel (e.g., `local` or `cluster`) in Jupyter and run code as usual:

```python
1+1
!uname -a
%reset
```

Outputs will be returned from the remote server.

---

## License

Apache-2.0