๐ฎ A refreshing functional take on deep learning, compatible with your favorite libraries
The main new feature of Thinc v9 is the support for learning rate schedules that can take the training dynamics into account. For example, the new
plateau.v1
schedule scales the learning rate when no progress has been found after a given number of evaluation steps. Another visible change is thatAppleOps
is now part of Thinc, so it is not necessary anymore to installthinc-apple-ops
to use the AMX units on Apple Silicon.
plateau.v1
schedule (#842). This schedule scales the learning rate if training was found to be stagnant for a given period.thinc-apple-ops
is integrated into Thinc (#927). Starting with this version of Thinc, it is not necessary anymore to install thinc-apple-ops
.Schedule
class (#804).thinc.backends.linalg
has been removed (#742). The same functionality is provided by implementations in BLAS that are better tested and more performant.thinc.extra.search
has been removed (#743). The beam search functionality in this module was strongly coupled to the spaCy transition parser and has therefore moved to spaCy in v4.@adrianeboyd, @danieldk, @honnibal, @ines, @kadarakos, @shadeMe, @svlandeg
cupy.cublas
import (#921).@danieldk, @honnibal, @ines, @svlandeg
Add the ParametricAttention_v2 layer, which adds support for key transformations (#913).
@danieldk, @honnibal, @ines, @svlandeg
To improve loading times and reduce conflicts, MXNet and TensorFlow are no longer imported automatically (#890).
MXNet and TensorFlow support needs to be enabled explicitly. Previously, MXNet and TensorFlow were imported automatically if they were available in the current environment.
To enable MXNet:
from thinc.api import enable_mxnet
enable_mxnet()
To enable TensorFlow:
from thinc.api import enable_tensorflow
enable_tensorflow()
With spaCy CLI commands you can provide this custom code using -c code.py
. For training use spacy train -c code.py
and to package your code with your pipeline use spacy package -c code.py
.
Future deprecation warning: built-in MXNet and TensorFlow support will be removed in Thinc v9. If you need MXNet or TensorFlow support in the future, you can transition to using a custom copy of the current MXNetWrapper
or TensorFlowWrapper
in your package or project.
@adrianeboyd, @danieldk, @honnibal, @ines, @svlandeg
distutils
to setuptools
/sysconfig
(#888).@adrianeboyd, @Ankush-Chander, @danieldk, @honnibal, @ines, @svlandeg
pad
as a CUDA kernel (#860).unflatten
(#861).cupy
kernels (#870).@adrianeboyd, @danieldk, @honnibal, @ines, @shadeMe, @svlandeg
premap_ids.v1
layer for mapping from ints to ints (#815).Dockerfile
(#843, #844, #845).@adrianeboyd, @danieldk, @essenmitsosse, @honnibal, @ines, @kadarakos, @patjouk, @polm, @svlandeg