Releases: cornellius-gp/gpytorch
Releases · cornellius-gp/gpytorch
Updates to variational inference, experimental Pyro integration, minor bug fixes
Pre-release
Variational inference has been refactored
- Easier to experiment with different variational approximations
- Massive performance improvement for SV-DKL
Experimental Pyro integration for variational inference
- See the example Pyro notebooks
Lots of tiny bug fixes
(Too many to name, but everything should be better 😬)
0.1.0.rc2
Beta rough cut (0.1.0.rc1)
Beta release
GPyTorch is now available on pip! pip install gpytorch
.
Important! This release requires the preview build of PyTorch (>= 1.0). You should either build from source or install pytorch-nightly. See the PyTorch docs for specific installation instructions.
If you were previously using GPyTorch, see the migration guide to help you move over.
What's new
- Batch mode: it is possible to train multiple GPs simultaneously
- Improved multitask models
Breaking changes
gpytorch.random_variables
have been replaced bygpytorch.distributions
. These build upon PyTorch distributions.gpytorch.random_variables.GaussianRandomVariable
->gpytorch.distributions.MultivariateNormal
.gpytorch.random_variables.MultitaskGaussianRandomVariable
->gpytorch.distributions.MultitaskMultivariateNormal
.
Utilities
gpytorch.utils.scale_to_bounds
is nowgpytorch.utils.grid.scale_to_bounds
Kernels
GridInterpolationKernel
,GridKernel
,InducingPointKernel
- the attributebase_kernel_module
has becomebase_kernel
(for consistency)AdditiveGridInterpolationKernel
no longer exists. Now use `AdditiveStructureKernel(GridInterpolationKernel(...))MultiplicativeGridInterpolationKernel no longer exists. Now use
ProductStructureKernel(GridInterpolationKernel(...))`.
Attributes (n_*
-> num_*
)
- IndexKernel: n_tasks -> num_tasks
- LCMKernel: n_tasks -> num_tasks
- MultitaskKernel: n_tasks -> num_tasks
- MultitaskGaussianLikelihood: n_tasks -> num_tasks
- SoftmaxLikelihood: n_features -> num_features
- MultitaskMean: n_tasks -> num_tasks
- VariationalMarginalLogLikelihood: n_data -> num_data
- SpectralMixtureKernel: n_dimensions -> ard_num_dims, n_mixtures -> num_mixtures
Old GPyTorch (Alpha)
Alpha release
We strongly encourage you to check out our beta release for lots of improvements!
However, if you still need an old version, or need to use PyTorch 0.4, you can install this release.