> For the complete documentation index, see [llms.txt](https://docs.everesteer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.everesteer.ai/getting-started/quickstart-tournament.md).

# Quickstart · tournament

{% stepper %}
{% step %}

### Get your API key

From your account page, click "Generate API key." Keep it in your environment, never in code.

```bash
export EIQ_API_KEY="ekq_live_8c4a92..."
```

{% endstep %}

{% step %}

### Install the SDK

One pip install. Python 3.10+. Optional extra for Modal compute.

```bash
pip install everestapi[compute]
```

{% endstep %}

{% step %}

### Pull the training data

A single client call. Obfuscated features, rank-normalised targets. This is the Tenzing dataset. Time is `exped_id`.

```python
from everestapi import EverestAPI

client = EverestAPI(api_key="YOUR_API_KEY")
train = client.download_dataset(split="train")
```

{% endstep %}

{% step %}

### Submit your first prediction

Train any model. Predict on the live split. Submit. Himalayas rounds open Tuesday to Saturday around 11:35 UTC and close the same day at 21:00 UTC.

```python
live = client.download_dataset(split="live")
preds = my_model.predict(live)
client.submit_futures_predictions(model_id="my-first", predictions=preds_dict)
```

{% endstep %}
{% endstepper %}

**Next:** stake on your prediction.


---

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