Model and Datasets
==================

DeepCell models and training datasets are licensed under a 
[modified Apache license][license] for non-commercial academic use only.
An API key for accessing datasets and models can be obtained at <https://users.deepcell.org/login/>.

[license]: https://github.com/vanvalenlab/deepcell-auth/blob/main/ASSET_LICENSE

API Key Usage
-------------

The token that is issued by <https://users.deepcell.org> should be added as an
environment variable:

```bash
export DEEPCELL_ACCESS_TOKEN=<token-from-users.deepcell.org>
```

This line can be added to your shell configuration (e.g. ``.bashrc``, ``.zshrc``,
``.bash_profile``, etc.) to automatically grant access to DeepCell models/data
upon login.

(download_models)=
Models
------

The model can be downloaded for local use:

```python
>>> from deepcell_types.utils import download_model

>>> download_model()
```

A specific version can be requested:

```python
download_model(version="2026-06-15")
```

A listing of available pre-trained model versions is available from
`deepcell_types.utils.list_model_versions()`.

To fetch a baseline checkpoint instead of the main DeepCellTypes model:

```python
from deepcell_types.utils import download_baseline_checkpoint

# One of: "cellsighter", "maps", "xgboost"
download_baseline_checkpoint("maps")
```

All three baselines ship in a single compressed bundle, which holds each one's
weights plus any companion file needed at inference (`maps` ships `_stats.npz`;
`xgboost` ships `.remap.json`). The bundle is unpacked automatically and the
returned list gives the local path of every file for the baseline you asked
for.

Because the bundle is shared, the first call downloads all three baselines
(646 MB) regardless of which one you request; the other two are then served
from the local cache without a further download.

The Nimbus baseline is not served here: its pretrained weights are
distributed upstream, so install it with
`pip install nimbus-inference==0.0.5` on Python 3.11 (which fetches the weights
automatically) rather than `download_baseline_checkpoint`.

Training Data
-------------

```{warning}
The training dataset is over 1.3 TB - make sure you have space and sufficient
network bandwidth before attempting to download.
```

Similarly, training data can be downloaded for local use with:

```python
>>> from deepcell_types.utils import download_training_data
>>> download_training_data()
```