Skip to content

Commit

Permalink
Version 0.1.3 (#228)
Browse files Browse the repository at this point in the history
Summary:
Add changelog and bump version to 0.1.3.

Pull Request resolved: #228

Test Plan: ran local tests

Reviewed By: sdaulton

Differential Revision: D16754050

Pulled By: Balandat

fbshipit-source-id: 1bc7811b5e15146fe9b4cd88e031fc49211b35ad
  • Loading branch information
Balandat authored and facebook-github-bot committed Aug 10, 2019
1 parent a107ff9 commit 836eecc
Show file tree
Hide file tree
Showing 6 changed files with 36 additions and 5 deletions.
2 changes: 1 addition & 1 deletion .conda/meta.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ requirements:
- python>=3.6
run:
- pytorch>=1.2
- gpytorch>=0.3.4
- gpytorch>=0.3.5
- scipy

test:
Expand Down
31 changes: 31 additions & 0 deletions CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,37 @@
The release log for BoTorch.


## [0.1.3] - Aug 9, 2019

Compatibility & maintenance release

#### Compatibility
* Updates to support breaking changes in PyTorch to boolean masks and tensor
comparisons (#224).
* Require PyTorch >=1.2 (#225).
* Require GPyTorch >=0.3.5 (itself a compatibility release).

#### New Features
* Add `FixedFeatureAcquisitionFunction` wrapper that simplifies optimizing
acquisition functions over a subset of input features (#219).
* Add `ScalarizedObjective` for scalarizing posteriors (#210).
* Change default optimization behavior to use L-BFGS-B by for box constraints
(#207).

#### Bug fixes
* Add validation to candidate generation (#213), making sure constraints are
strictly satisfied (rater than just up to numerical accuracy of the optimizer).

#### Minor changes
* Introduce `AcquisitionObjective` base class (#220).
* Add propagate_grads context manager, replacing the `propagate_grads` kwarg in
model `posterior()` calls (#221)
* Add `batch_initial_conditions` argument to `joint_optimize()` for
warm-starting the optimization (ec3365a37ed02319e0d2bb9bea03aee89b7d9caa).
* Add `return_best_only` argument to `joint_optimize()` (#216). Useful for
implementing advanced warm-starting procedures.


## [0.1.2] - July 9, 2019

Maintenance release
Expand Down
2 changes: 1 addition & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@ Optimization simply use Ax.
**Installation Requirements**
- Python >= 3.6
- PyTorch >= 1.2
- gpytorch >= 0.3.4
- gpytorch >= 0.3.5
- scipy


Expand Down
2 changes: 1 addition & 1 deletion botorch/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
from .utils import manual_seed


__version__ = "0.1.2"
__version__ = "0.1.3"


__all__ = [
Expand Down
2 changes: 1 addition & 1 deletion docs/getting_started.md
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ Before jumping the gun, we recommend you start with the high-level

- Python >= 3.6
- PyTorch >= 1.2
- gpytorch >= 0.3.4
- gpytorch >= 0.3.5
- scipy

BoTorch is easily installed via
Expand Down
2 changes: 1 addition & 1 deletion setup.py
Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@
long_description=long_description,
long_description_content_type="text/markdown",
python_requires=">=3.6",
install_requires=["torch>=1.2", "gpytorch>=0.3.4", "scipy"],
install_requires=["torch>=1.2", "gpytorch>=0.3.5", "scipy"],
packages=find_packages(),
extras_require={
"dev": DEV_REQUIRES,
Expand Down

0 comments on commit 836eecc

Please sign in to comment.