Release of v1.0 of BioNeMo Modular Co-Design (MoCo)#575
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nvdreidenbach merged 12 commits intoNVIDIA:mainfrom Jan 15, 2025
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Release of v1.0 of BioNeMo Modular Co-Design (MoCo)#575nvdreidenbach merged 12 commits intoNVIDIA:mainfrom
nvdreidenbach merged 12 commits intoNVIDIA:mainfrom
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Signed-off-by: Danny <dreidenbach@nvidia.com>
Signed-off-by: Danny <dreidenbach@nvidia.com>
Signed-off-by: Danny <dreidenbach@nvidia.com>
Signed-off-by: Danny <dreidenbach@nvidia.com>
Signed-off-by: Danny <dreidenbach@nvidia.com>
Signed-off-by: Danny <dreidenbach@nvidia.com>
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Signed-off-by: Peter St. John <pstjohn@nvidia.com>
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Release of v1.0 of BioNeMo Modular Co-Design (MoCo)
Introduces modular interpolants for various popular generative model
frameworks including continuous and discrete diffusion and flow
matching.
---
## Summary
Introduces MoCo.
## Details
See documentation.md for details.
## Usage
pip install bionemo-moco
```python
from bionemo.moco.interpolants import ContinuousFlowMatcher
from bionemo.moco.distributions.time import UniformTimeDistribution
from bionemo.moco.distributions.prior import GaussianPrior
uniform_time = UniformTimeDistribution()
moon_prior = GaussianPrior()
sigma = 0.1
cfm = ContinuousFlowMatcher(time_distribution=uniform_time,
prior_distribution=moon_prior,
sigma=sigma,
prediction_type="velocity")
```
see examples directory for notebook tutorials
## Testing
Unit tests for all key functions.
Tests for these changes can be run via:
```shell
pytest -v tests
```
---------
Signed-off-by: Danny <dreidenbach@nvidia.com>
Signed-off-by: Peter St. John <pstjohn@nvidia.com>
Co-authored-by: Peter St. John <pstjohn@nvidia.com>
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Release of v1.0 of BioNeMo Modular Co-Design (MoCo)
Introduces modular interpolants for various popular generative model frameworks including continuous and discrete diffusion and flow matching.
Summary
Introduces MoCo.
Details
See documentation.md for details.
Usage
pip install bionemo-moco
see examples directory for notebook tutorials
Testing
Unit tests for all key functions.
Tests for these changes can be run via: