Theano-PyMC
Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
Decision gist · record as of 2026-08-14
No, unless you are maintaining existing code that already depends on it. The package is abandoned with no updates since 2021-01-22. High installation friction, lack of maintenance, and no security updates make it unsuitable for new development. For new projects, use an actively maintained alternative.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NumPy and compiled dependencies; GPU support limited to float32; no active maintenance or compatibility updates for modern Python/CUDA versions.
- Installation has high friction due to compiled dependencies.
- The package is abandoned and has not been maintained since 2021, making it unsuitable for new projects or long-term use.
License · maintenance · safety
BSD (permissive) — BSD permissive license allows commercial and private use with minimal restrictions, though the abandoned status means no ongoing legal or security updates.
last release 2021-01-22 (2030 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 179,435 downloads/mo, #10,171 on PyPI
Alternatives
Verify before relying
pip install Theano-PyMC
import theano
import theano.tensor as T
x = T.dscalar('x')
y = T.exp(x)
f = theano.function([x], y)- Whether the package remains compatible with current NumPy versions and Python 3.9+
- Current state of GPU support and CUDA compatibility given the 2021-01-22 last release
- Whether the claimed 140x GPU speedup still holds on contemporary hardware
What it is and what it does
Theano-PyMC is a compiler for symbolic mathematical expressions that translates NumPy-like code into optimized CPU or GPU implementations. It was designed to handle large-scale numerical computations with automatic differentiation, making it useful for machine learning and scientific research. The package generates C code dynamically and includes built-in optimizations for numerical stability.
However, Theano-PyMC is no longer maintained. The project was abandoned, with the latest release on 2021-01-22. While it remains functional for existing code, it will not receive bug fixes, security patches, or compatibility updates for newer Python versions, NumPy releases, or CUDA toolkits. For new projects, actively maintained alternatives are recommended.
Use it for
- Running legacy scientific computing code that was built on Theano before the project ended
- Educational study of symbolic differentiation and compiler design for numerical computation
- Reproducing published research results that explicitly depend on Theano-PyMC
- GPU-accelerated mathematical expression evaluation in existing production systems with frozen dependencies
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless you are maintaining existing code that already depends on it.
The package is abandoned with no updates since 2021-01-22. High installation friction, lack of maintenance, and no security updates make it unsuitable for new development. For new projects, use an actively maintained alternative.
Install
theano-pymc on PyPI
Before you install
Installation has high friction due to compiled dependencies. The package is abandoned and has not been maintained since 2021, making it unsuitable for new projects or long-term use.
Requires NumPy and compiled dependencies; GPU support limited to float32; no active maintenance or compatibility updates for modern Python/CUDA versions.
License in practice
BSD permissive license allows commercial and private use with minimal restrictions, though the abandoned status means no ongoing legal or security updates.
Quickstart
pip install Theano-PyMC
import theano
import theano.tensor as T
x = T.dscalar('x')
y = T.exp(x)
f = theano.function([x], y)Verify before relying
- Whether the package remains compatible with current NumPy versions and Python 3.9+
- Current state of GPU support and CUDA compatibility given the 2021-01-22 last release
- Whether the claimed 140x GPU speedup still holds on contemporary hardware
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,030 days since the last release |
| First released | |
| Downloads | 179,435 / month, #10,171 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 6 - MatureIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: MathematicsTopic :: Software Development :: Code GeneratorsTopic :: Software Development :: Compilers |
Evidence: Theano-PyMC-1.1.2.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “symbolic math compilation”
- Theano-PyMCTheano-PyMC is an optimizing compiler for mathematical expressions on…
- pytensorPyTensor is a Python library for defining, optimizing, and evaluating…
- TheanoTheano is a Python library for defining, optimizing, and evaluating…
Give your agent the search over MCP, or paste the wish link into any chat.
More Mathematics packages
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
kiwisolver is a Python binding to a fast C++ implementation of the Cassowary constraint solver, enabling you to solve systems of linear constraints and inequalities.
Install it if you need to solve constraint systems; skip it if you only need simple linear algebra.
SymPy is a Python library for symbolic mathematics, performing algebraic manipulation, calculus, equation solving, and mathematical expression simplification without numerical approximation.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
PyTorch provides GPU-accelerated tensor computation and automatic differentiation for building and training deep neural networks in Python.
onnxruntime loads and executes Open Neural Network Exchange (ONNX) models with a focus on inference performance across CPUs and accelerators.
Install it if you have ONNX models to run in production or development.
See also numexpr · pytensor · Theano · casadi · nvidia-nvvm · nvidia-cusolver-cu11 · onemkl-sycl-sparse · mkl · onemkl-sycl-blas · symengine