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feat: bootstrap autolens_profiling scaffolding
Phase 0 of the autolens_profiling z_feature roadmap tracked at PyAutoPrompt/z_features/autolens_profiling.md. - README.md with vision/scope, JAX-gradient out-of-scope note, related repos, how-to-read guide, and roadmap. - LICENSE (MIT, matching sister PyAutoLabs repos). - .gitignore mirrored from autolens_workspace_developer + profiler/cache additions. - Folder skeleton: likelihood/, simulators/, searches/, results/ each with a placeholder README pointing at the phase that will populate it. Tracks PyAutoLens issue #513. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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‎.gitignore‎

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__pycache__/
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*.pyc
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output/
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root.log
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*.log
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.codex/
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# Generated artifacts — never check in
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image.fits
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path/
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output_path/
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# Profiler artifacts
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*.prof
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*.pstats
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# JAX / numba / matplotlib caches (when not redirected via env vars)
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.jax_cache/
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__jax_cache__/
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.numba_cache/
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.mplconfig/
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# Local virtualenvs
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.venv/
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venv/

‎LICENSE‎

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MIT License
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Copyright (c) 2018 Jammy2211
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# autolens_profiling
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Profiling and run-time tracking for [PyAutoLens](https://github.com/PyAutoLabs/PyAutoLens) likelihood functions, simulators, and samplers across CPU, laptop GPU, and HPC GPU.
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## Vision
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This repository is the single home for PyAutoLens performance measurement. It exists so that the run-times that matter for science — fitting a real lens, simulating an Euclid-resolution dataset, sampling a model with Nautilus — are visible, reproducible, and versioned across PyAutoLens releases.
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**What is profiled:**
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- **Likelihood functions** — imaging, interferometer, point-source, and datacube paths, across the MGE, pixelization, and Delaunay model compositions used in real science cases.
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- **Simulators** — run-time tracking for the imaging, interferometer, point-source, cluster, group, and multi-plane simulators.
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- **Searches / samplers** — sampler-level profiling, starting with Nautilus. Other samplers (Dynesty, Emcee, BlackJAX, NumPyro, NSS, LBFGS, PocoMC) follow in later sweeps.
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**Hardware tiers covered:**
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- CPU (single-machine, numpy backend).
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- Laptop GPU (consumer-class, JAX backend).
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- HPC GPU (A100 and similar, JAX backend).
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**Dataset framing:**
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Results are framed by **astronomy instrument** (HST, Euclid, JWST, …) rather than by raw pixel counts. Pixel counts are recorded too, but the headline numbers a reader sees first are the ones that map onto a real observing programme.
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## JAX gradients — currently out of scope
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Gradient profiling (`jax.grad` of the likelihood, autodiff-based optimisers) is **not yet** part of this repo. It is tracked in [`PyAutoLabs/autolens_workspace_developer/jax_profiling/gradient/`](https://github.com/PyAutoLabs/autolens_workspace_developer/tree/main/jax_profiling/gradient) and will fold into this repo in a future phase once the gradient story stabilises.
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## How to read this repo
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Each profiling script writes a **versioned artifact pair** under `results/`:
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```
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results/<section>/<subfolder>/<profile_name>_summary_v<YYYY>.<M>.<D>.<PATCH>.json
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results/<section>/<subfolder>/<profile_name>_summary_v<YYYY>.<M>.<D>.<PATCH>.png
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```
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The version string matches the PyAutoLens release that produced the numbers (e.g. `v2026.5.1.4`). Older versions are retained alongside newer ones, so trends across releases stay inspectable. The JSON carries structured timings; the PNG is the at-a-glance plot.
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Examples that already exist in the source-of-truth repo:
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- `imaging_summary_v2026.5.1.4.json` / `.png`
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- `point_source_summary_v2026.5.1.4.json`
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- `delaunay_sparse_cpu_likelihood_summary_hst_v2026.5.1.4.json`
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## Section index
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| Folder | Contents |
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|--------|----------|
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| [`likelihood/`](./likelihood/README.md) | Likelihood JIT profiling — imaging, interferometer, point-source, datacube. |
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| [`simulators/`](./simulators/README.md) | Run-time tracking for the PyAutoLens simulators. |
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| [`searches/`](./searches/README.md) | Sampler / search profiling, Nautilus first. |
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| [`results/`](./results/README.md) | Versioned JSON + PNG artifacts written by the above scripts. |
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## Roadmap
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This repo is being built in phases:
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| Phase | Title | Status |
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|-------|-------|--------|
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| 0 | Repo bootstrap (this commit) | ✓ shipped |
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| 1 | Mirror JIT likelihood profiling scripts + per-section READMEs | not yet started |
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| 2 | Mirror simulator profiling scripts + run-time tracking | not yet started |
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| 3 | Nautilus profiling, design for sampler expansion | not yet started |
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| 4 | Top-level + per-section README dashboard with instrument framing | not yet started |
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| 5 | GitHub Actions for lint + profile re-runs + README refresh | not yet started |
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The full multi-phase plan lives in the internal `PyAutoLabs/PyAutoPrompt/z_features/autolens_profiling.md` tracker (not publicly readable). The high-level shape is captured above.
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## Related repos
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- [`PyAutoLabs/PyAutoLens`](https://github.com/PyAutoLabs/PyAutoLens) — the library being profiled.
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- [`PyAutoLabs/autolens_workspace`](https://github.com/PyAutoLabs/autolens_workspace) — user-facing science scripts and tutorials.
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- [`PyAutoLabs/autolens_workspace_developer`](https://github.com/PyAutoLabs/autolens_workspace_developer) — the developer workspace; **source of truth during the migration**. Each phase mirrors the relevant subdirectories from here into this repo.
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- [`Jammy2211/autolens_colab_profiling`](https://github.com/Jammy2211/autolens_colab_profiling) — sibling repo, Colab-specific scope. Not yet migrated to PyAutoLabs.
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## Package vs scripts
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This repo is a **collection of standalone profiling scripts**, not an installable Python package. There is no `pyproject.toml`. Run scripts from the repo root.
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Scripts follow the JIT conventions documented in `autolens_workspace_developer/CLAUDE.md`:
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- Extract `.array` from autoarray types before crossing the `jax.jit` boundary (autoarray types are not JAX pytrees as inputs).
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- Pass `xp=jnp` through PyAutoLens / PyAutoGalaxy / PyAutoArray functions to select the JAX backend.
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## Community & support
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- **Slack** — [PyAutoLens workspace](https://join.slack.com/t/pyautolens/shared_invite/zt-2cufp4eyf-fXfgMxRGuvg~bMrI3uOAxg) for questions.
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- **Issues** — file profiling bugs and feature requests on this repo's [issue tracker](https://github.com/PyAutoLabs/autolens_profiling/issues).

‎likelihood/README.md‎

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# likelihood
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Likelihood JIT profiling scripts and per-section READMEs.
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Populated by **Phase 1** of the `autolens_profiling` roadmap. Will mirror `autolens_workspace_developer/jax_profiling/jit/{imaging,interferometer,point_source,datacube}/`, preserving the step-by-step profile narrative each script already prints.
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See the top-level [README](../README.md) for the full phase plan.

‎results/README.md‎

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# results
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Versioned profiling artifacts written by scripts under [`likelihood/`](../likelihood/README.md), [`simulators/`](../simulators/README.md), and [`searches/`](../searches/README.md). Layout mirrors the source folders.
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## Filename convention
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```
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<profile_name>_summary_v<YYYY>.<M>.<D>.<PATCH>.json
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<profile_name>_summary_v<YYYY>.<M>.<D>.<PATCH>.png
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```
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The version string is the PyAutoLens release that produced the numbers (e.g. `v2026.5.1.4`). Older versions are kept alongside newer ones so cross-release trends stay inspectable.
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Example:
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```
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results/likelihood/imaging/imaging_summary_v2026.5.1.4.json
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results/likelihood/imaging/imaging_summary_v2026.5.1.4.png
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results/likelihood/imaging/mge/delaunay_sparse_cpu_likelihood_summary_hst_v2026.5.1.4.json
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```
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Populated as Phases 1–3 land. See the top-level [README](../README.md) for the full phase plan.

‎searches/README.md‎

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# searches
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Sampler / search profiling — Nautilus first, with the folder layout designed so that other samplers (Dynesty, Emcee, BlackJAX, NumPyro, NSS, LBFGS, PocoMC) can slot in under their own prompts.
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Populated by **Phase 3** of the `autolens_profiling` roadmap. Will mirror `autolens_workspace_developer/searches_minimal/`.
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See the top-level [README](../README.md) for the full phase plan.

‎simulators/README.md‎

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# simulators
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Run-time tracking for the PyAutoLens simulators (imaging, interferometer, point-source, cluster, group, multi).
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Populated by **Phase 2** of the `autolens_profiling` roadmap. Will mirror `autolens_workspace_developer/jax_profiling/simulators/`. JIT support is partial across the simulator suite — placeholder scripts will land for cases where upstream JIT is not yet implemented, linking to the relevant tracking issues.
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See the top-level [README](../README.md) for the full phase plan.

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