# Keep data and checkpoints on a volume

> Upload a dataset once, read it from every run, save checkpoints that outlast the run, and download them.

This guide shows how to use a volume to keep files between runs. A volume is persistent storage that belongs to your org. Every run can read and write every volume at `/mnt/<volume>`.

Files a run writes anywhere else, including its working directory `/workspace`, are deleted when the run ends.

## Upload a dataset once

`div run` uploads your working directory on every run. Keep large data out of it and upload the data to a volume instead:

```bash
div storage put training-data ./data
```

`div storage put` creates the volume if it does not exist. The directory lands under its own name, so the files are at `/mnt/training-data/data/` in a run.

If your code is in a git repository, add the data directory to `.gitignore` so `div run` does not upload it. Outside a repository, move the data out of the directory you run from.

## Read it from a run

```python
import pandas as pd

df = pd.read_parquet("/mnt/training-data/data/train.parquet")
```

## Save checkpoints

Write checkpoints under `/mnt/<volume>`:

```python
trainer.save_model("/mnt/checkpoints/lora-v1/final")
```

The volume must exist when the run starts. A run cannot create one while it is running. To make sure it exists, do one of these:

- Create it first with `div storage put checkpoints <path>`.
- Name the path in the run's command. `div run` creates any volume that appears as `/mnt/<volume>` in the command before the run starts:

  ```bash
  div run -- 'mkdir -p /mnt/checkpoints/lora-v1 && python train.py'
  ```

If a run writes to a directory under `/mnt` that is not a volume, the files are lost when the run ends, and the end of the run's log says how much was lost.

## Download the results

```bash
div storage ls checkpoints lora-v1
div storage get checkpoints lora-v1/final
```

`div storage get` downloads a directory into a local directory of the same name. Downloads have no size limit.

## Large files

Each uploaded file can be at most 256 MB, and each file must finish uploading within 5 minutes. For larger datasets or base weights, download them inside a run and write them straight to a volume:

```bash
div run --gpu cpu --no-upload -- 'pip install huggingface_hub && huggingface-cli download Qwen/Qwen2.5-7B --local-dir /mnt/weights/qwen2.5-7b'
```

## Related

- [`div storage`](https://docs.divergentlabs.xyz/compute/cli/storage.md)
- [Runtime environment](https://docs.divergentlabs.xyz/compute/runtime.md#filesystem)