* gnu/packages/machine-learning.scm (python-ax-platform): New variable.
---
gnu/packages/machine-learning.scm | 56 +++++++++++++++++++++++++++++++
1 file changed, 56 insertions(+)
Toggle diff (69 lines)
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm
index 1e34e5fd5e..384c980478 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -4154,6 +4154,62 @@ (define-public python-botorch
"BoTorch is a library for Bayesian Optimization built on PyTorch.")
(license license:expat)))
+(define-public python-ax-platform
+ (package
+ (name "python-ax-platform")
+ (version "0.3.2")
+ (source (origin
+ (method url-fetch)
+ (uri (pypi-uri "ax-platform" version))
+ (sha256
+ (base32
+ "1spyp8wjf0jyh7yf002sl4lc1rwbnzdki9ql9a3nzsyyx602flj8"))))
+ (build-system pyproject-build-system)
+ (arguments
+ ;; Ignore tests for tensorboard and torchx, which we don't have.
+ (list #:test-flags
+ #~(list "--ignore" "ax/metrics/tests/test_tensorboard.py"
+ "--ignore" "ax/runners/tests/test_torchx.py"
+ ;; This test tries to download online data.
+ "-k" "not test_torchvision_encode_decode")
+ ;; Ax checks for typeguard==2.13.3 which is the version we have,
+ ;; but the version report in typeguard is fault and displays 0.0.0.
+ #:phases #~(modify-phases %standard-phases
+ (delete 'sanity-check))))
+ (propagated-inputs (list python-botorch
+ python-ipywidgets
+ python-jinja2
+ python-pandas
+ python-plotly
+ python-scikit-learn
+ python-scipy
+ python-typeguard))
+ (native-inputs (list jupyter
+ python-beautifulsoup4
+ python-black
+ python-flake8
+ python-hypothesis
+ python-jinja2
+ python-jupyter-client
+ python-mypy
+ python-nbconvert
+ python-pyfakefs
+ python-pyre-extensions
+ python-pytest
+ python-pytest-cov
+ python-sqlalchemy
+ python-torchvision
+ python-yappi))
+ (home-page "https://ax.dev/")
+ (synopsis "Adaptive Experimentation Platform")
+ (description
+ "Ax is an accessible, general-purpose platform for
+understanding, managing, deploying, and automating adaptive experiments.
+Adaptive experimentation is the machine-learning guided process of
+iteratively exploring a (possibly infinite) parameter space in order to
+identify optimal configurations in a resource-efficient manner.")
+ (license license:expat)))
+
(define-public vosk-api
(let* ((openfst openfst-for-vosk)
(kaldi kaldi-for-vosk))
--
2.34.1