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returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["api-rest","automatic-differentiation","containers","differentiable-programming","remote-procedure-calls","scientific-machine-learning"],"created_at":"2025-09-09T00:13:58.163Z","updated_at":"2026-03-09T14:01:55.866Z","avatar_url":"https://github.com/pasteurlabs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://github.com/pasteurlabs/tesseract-core/blob/main/docs/static/logo-dark.png\" width=\"128\" align=\"right\"\u003e\n  \u003cimg alt=\"\" src=\"https://github.com/pasteurlabs/tesseract-core/blob/main/docs/static/logo-light.png\" width=\"128\" align=\"right\"\u003e\n\u003c/picture\u003e\n\n### Tesseract Core\n\nUniversal, autodiff-native software components for [Simulation Intelligence](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/misc/faq.html#what-is-simulation-intelligence) 📦\n\n[Read the docs](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/) |\n[Report an issue](https://github.com/pasteurlabs/tesseract-core/issues) |\n[Community forum](https://si-tesseract.discourse.group/) |\n[Contribute](https://github.com/pasteurlabs/tesseract-core/blob/main/CONTRIBUTING.md)\n\n---\n\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.08385/status.svg)](https://doi.org/10.21105/joss.08385)\n[![SciPy](https://img.shields.io/badge/SciPy-2025-blue)](https://proceedings.scipy.org/articles/kvfm5762)\n\n## The problem\n\nReal-world scientific workflows span multiple tools, languages, and computing environments. You might have a mesh generator in C++, a solver in Julia, and post-processing in Python. Getting these to work together is painful. Getting gradients to flow through them for optimization is nearly impossible.\n\nExisting autodiff frameworks work great within a single codebase, but fall short when your pipeline crosses framework boundaries or includes legacy/commercial tools.\n\n## The solution\n\nTesseract packages scientific software into **self-contained, portable components** that:\n\n- **Run anywhere** — Local machines, cloud, HPC clusters. Same container, same results.\n- **Expose clean interfaces** — CLI, REST API, and Python SDK. No more deciphering undocumented scripts.\n- **Propagate gradients** — Each component can expose derivatives, enabling end-to-end optimization across heterogeneous pipelines.\n- **Self-document** — Schemas, types, and API docs are generated automatically.\n\n## Who is this for?\n\n- **Researchers** interfacing with (differentiable) simulators or probabilistic models, or who need to combine tools from different ecosystems\n- **R\u0026D engineers** packaging research code for use by others, without spending weeks on DevOps\n- **Platform engineers** deploying scientific workloads at scale with consistent interfaces and dependency isolation\n\n## Example: Shape optimization across tools\n\nThe [rocket fin optimization case study](https://si-tesseract.discourse.group/t/parametric-shape-optimization-of-rocket-fins-with-ansys-spaceclaim-pyansys-and-tesseract/109) combines three Tesseracts:\n\n```\n[SpaceClaim geometry] → [Mesh + SDF] → [PyMAPDL FEA solver]\n         ↑                                      |\n         └──────── gradients flow back ─────────┘\n```\n\nEach component uses a different differentiation strategy (analytic adjoints, finite differences, JAX autodiff), yet they compose into a single optimizable pipeline.\n\n## Quick start\n\n\u003e [!NOTE]\n\u003e Requires [Docker](https://docs.docker.com/engine/install/) and Python 3.10+.\n\n```bash\n$ pip install tesseract-core\n\n# Clone and build an example\n$ git clone https://github.com/pasteurlabs/tesseract-core\n$ tesseract build tesseract-core/examples/vectoradd\n\n# Run it\n$ tesseract run vectoradd apply '{\"inputs\": {\"a\": [1, 2], \"b\": [3, 4]}}'\n# → {\"result\": [4.0, 6.0], ...}\n\n# Compute the Jacobian\n$ tesseract run vectoradd jacobian '{\"inputs\": {\"a\": [1, 2], \"b\": [3, 4]}, \"jac_inputs\": [\"a\"], \"jac_outputs\": [\"result\"]}'\n\n# See auto-generated API docs\n$ tesseract apidoc vectoradd\n```\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://github.com/pasteurlabs/tesseract-core/blob/main/docs/img/apidoc-screenshot.png\" width=\"600\"\u003e\n\u003c/p\u003e\n\n## Core features\n\n- **Containerized** — Docker-based packaging ensures reproducibility and dependency isolation\n- **Multi-interface** — CLI, REST API, and Python SDK for the same component\n- **Differentiable** — First-class support for Jacobians, JVPs, and VJPs across component and network boundaries\n- **Schema-validated** — Pydantic models define explicit input/output contracts\n- **Language-agnostic** — Wrap Python, Julia, C++, [Fortran](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/examples/building-blocks/fortran.html), or any executable behind a thin Python API\n\n## Ecosystem\n\n- **[tesseract-core](https://github.com/pasteurlabs/tesseract-core)** — CLI, Python API, and runtime (this repo)\n- **[Tesseract-JAX](https://github.com/pasteurlabs/tesseract-jax)** — Embed Tesseracts as JAX primitives into end-to-end differentiable JAX programs\n- **[Tesseract-Streamlit](https://github.com/pasteurlabs/tesseract-streamlit)** — Auto-generate interactive web apps from Tesseracts\n\n## Learn more\n\n- [Documentation](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/)\n- [Creating your first Tesseract](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/creating-tesseracts/create.html)\n- [Differentiable programming guide](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/introduction/differentiable-programming.html)\n- [Design patterns](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/creating-tesseracts/design-patterns.html)\n- [Example gallery](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/examples/example_gallery.html)\n\n## License\n\nTesseract Core is licensed under the [Apache License 2.0](https://github.com/pasteurlabs/tesseract-core/blob/main/LICENSE) and is free to use, modify, and distribute (under the terms of the license).\n\nTesseract is a registered trademark of Pasteur Labs, Inc. and may not be used without permission.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpasteurlabs%2Ftesseract-core","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpasteurlabs%2Ftesseract-core","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpasteurlabs%2Ftesseract-core/lists"}