> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trainy.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Create an account, generate an API key, and log your first experiment.

# Hello Pluto

## Prerequisites

* `git` - required for sending commit authorship and other metadata

## Launch Your First Experiement

Start logging your first experiment in 4 steps:

1. Create an account/organization at [https://pluto.trainy.ai](https://pluto.trainy.ai)
2. Install the Python SDK. In your Python environment, install the latest build
   ```text theme={null}
   pip install "pluto-ml-nightly[full]"
   ```
   or the release build
   ```text theme={null}
   pip install "pluto-ml[full]"
   ```
3. If you haven't already, request an API key through the terminal via:
   ```python theme={null}
   import pluto
   pluto.login()
   ```
   or login via the shell command
   ```text theme={null}
   pluto login <API_KEY>
   ```
4. Log your first experiment!
   ```python theme={null}
   # hello_pluto.py
   import pluto
   config = {'lr': 0.001, 'epochs': 1000}
   run = pluto.init(project="pluto", name="experiment", config=config)
   # insert custom model training code
   for i in range(config['epochs']):
       pluto.log({"val/loss": 0})
   run.finish()
   ```

The code will print out a url where you can view your current run. The web dashboard allows you to compare time series between runs. The output for the above script might be the following.

```text theme={null}
(pluto) ubuntu@ip-111-111-111-111:~$ python hello_pluto.py
pluto: 🚀 19:52:01 | Authentication: logged in as trainy
pluto: 🚀 19:52:01 | Interface: find live updates at https://pluto.trainy.ai/o/myorg/projects/my-project/mzHx3
pluto: 🚀 19:52:06 | Interface: find 1001 synced entries at https://pluto.trainy.ai/o/myorg/projects/my-project/mzHx3
```