crested

library1.9.0pypypi✓ verified 85d ago

CREsted (Cis-Regulatory Element Sequence Training, Explanation, and Design) is a Python library for deep learning on regulatory genomics. It provides tools for training, interpreting, and designing genomic models, with support for popular architectures like Borzoi and DilatedCNN. Current version is 1.8.1, released on 2026-03-19. Requires Python 3.11 - 3.13. Released under MIT license.

ai-ml

pip install crested

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · CRESTED

crested

ai-mlpythonv1.9.0

Install

32.6s avg

Import

3793ms

Disk

864MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.5.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 32.6s · import 2.276s · 959MB

864MB installed

● package 864MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

crested

✓ import crested

Main import, top-level module.

crested.tl

✓ import crested.tl

Training and inference module. Triggers backend load.

crested.pp

✓ import crested.pp

Preprocessing module.

crested.pl

✓ import crested.pl

Plotting module, reorganized in v1.7.0.

crested.Genome

✓ from crested import Genome

✗ crested.Genome()

Genome class for sequence loading.

Minimal example to load a pretrained model, create test data, and run predictions.

import crested # Load a pretrained model from the repository model = crested.pp.load_model('BorzoiHuman') # Create a simple AnnData object with a genomic region data = crested.pp.example_data() # Make predictions crested.tl.predict(model, data) # Inspect predictions print(data.obs.head())

crested --version

Debug

Known issues

breakingIn v1.7.0, the `crested.pl` module was reorganized. Old function names like `crested.pl.contribution_scores` are deprecated in favor of `crested.pl.explain.contribution_scores`. Old names still work as aliases but will be removed in a future version.

fix

Update imports: use `crested.pl.explain.contribution_scores` instead of `crested.pl.contribution_scores`. See https://github.com/aertslab/CREsted/blob/main/docs/api/renaming.md

affects: >=1.7.0

deprecatedThe `Chrombpnet` architecture was renamed to `DilatedCNN` in v1.4.0. The old name still works but is deprecated.

fix

Use `DilatedCNN` instead of `Chrombpnet` when specifying model architecture.

affects: >=1.4.0

gotchaLazy imports: `crested` does not require torch or tensorflow for import of submodules `pp`, `io`, `datasets`, `pl`. However, `crested.tl` will raise an error if no backend is installed. This is intentional but can confuse first-time users.

fix

Install either torch or tensorflow before using `crested.tl`.

affects: >=1.6.0

gotchaPython version requirement: `crested` requires Python 3.11, 3.12, or 3.13. Installing on 3.10 or 3.14+ will fail.

fix

Use Python 3.11-3.13. Check with `python --version`.

affects: >=1.6.0

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'crested.tl'

`crested.tl` is only imported when accessed; it requires a backend (torch or tensorflow). This error occurs if you try to import it directly without having installed the backend.

fix

Install torch (`pip install torch`) or tensorflow (`pip install tensorflow`). Then use `import crested.tl`.

AttributeError: module 'crested.pl' has no attribute 'contribution_scores'

Since v1.7.0, the function was moved to `crested.pl.explain.contribution_scores`.

fix

Use `crested.pl.explain.contribution_scores` instead.

ValueError: This model requires a TensorFlow backend, but TensorFlow is not installed.

Some pretrained models (e.g., Borzoi) were trained with TensorFlow and require it for prediction.

fix

Install tensorflow: `pip install tensorflow`.

RuntimeError: Expected all tensors to be on the same device

Common when mixing CPU and GPU tensors during prediction. Often due to not moving input data to the model's device.

fix

Ensure input AnnData object's `.X` is on the same device as the model: `model.to(device)` and `data.X = data.X.to(device)`.

Upgrade

Version history

1.9.0latest on PyPI · released Jun 17, 2026

Audit

Dependencies

torchoptionalRequired for model training and prediction

tensorflowoptionalRequired for TensorFlow backend models

Agent activity

10 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/aertslab/crested ↗

packagehttps://pypi.org/project/crested/ ↗

docshttps://crested.readthedocs.io/ ↗