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Merge pull request #622 from chronicgiardia/docs/colabfold-macos-install
docs: add LocalColabFold macOS Apple Silicon install runbook
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Safi
@@ -426,6 +426,14 @@ Token reduction scales with corpus size. 6 files fits in a context window anyway
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graphify sends file contents to your AI coding assistant's underlying model API for semantic extraction of docs, papers, and images — Anthropic (Claude Code), OpenAI (Codex), or whichever provider your platform uses. Code files are processed locally via tree-sitter AST — no file contents leave your machine for code. Video and audio files are transcribed locally with faster-whisper — audio never leaves your machine. No telemetry, usage tracking, or analytics of any kind. The only network calls are to your platform's model API during extraction, using your own API key.
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## Optional integrations
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Runbooks for setting up extra tooling alongside graphify. None of these are required.
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| Integration | Doc |
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| LocalColabFold (ColabFold + AlphaFold2) on macOS Apple Silicon | [`docs/colabfold-macos-install.md`](docs/colabfold-macos-install.md) |
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## Tech stack
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NetworkX + Leiden (graspologic) + tree-sitter + vis.js. Semantic extraction via Claude (Claude Code), GPT-4 (Codex), or whichever model your platform runs. Video transcription via faster-whisper + yt-dlp (optional, `pip install graphifyy[video]`). No Neo4j required, no server, runs entirely locally.
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@@ -0,0 +1,170 @@
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# Installing LocalColabFold on macOS Apple Silicon
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A reproducible runbook for installing
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[LocalColabFold](https://github.com/YoshitakaMo/localcolabfold)
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(ColabFold + AlphaFold2 weights) on Apple Silicon (arm64) Macs, so you can
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predict protein structures locally and feed the resulting PDBs / confidence
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scores into a graphify corpus.
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This document is **optional** and unrelated to graphify's pipeline. It is
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included as a known-good recipe for users who want a CPU-capable local
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ColabFold install alongside graphify on the same machine.
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## Why a separate runbook
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LocalColabFold's current top-level installer (`install_colabbatch_linux.sh`)
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hardcodes `Miniforge3-Linux-x86_64.sh` and later runs
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`pip install jax[cuda12]==0.5.3`. Both fail on Apple Silicon: there are no
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macOS CUDA wheels, and Apple GPUs are unreachable from JAX's CUDA backend.
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Upstream stopped shipping a current macOS installer after v1.5.5. The
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**v1.5.5 M1 installer** (bundled in the LocalColabFold repo at
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`v1.5.5_old_installers/install_colabbatch_M1mac.sh`) is the working Apple
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Silicon path — ColabFold 1.6.1, JAX 0.4.23 CPU build.
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## Verified configuration
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- Hardware: Apple Silicon (arm64)
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- OS: macOS (Darwin)
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- ColabFold installed: 1.6.1 (commit `de5ab5f`)
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- JAX: 0.4.23 (CPU build)
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- 62-residue smoke test (1 model, 1 recycle, single-sequence MSA): ~60 s wall
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## Prerequisites (Homebrew)
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The M1 installer fail-fasts on these. Install once before running:
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```bash
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brew tap brewsci/bio # custom tap for hh-suite
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brew install brewsci/bio/hh-suite \
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kalign \
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mmseqs2
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# verify
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for c in wget hhsearch kalign mmseqs; do
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printf "%-10s " "$c" && command -v "$c" || echo MISSING
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done
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```
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Roughly ~600 MB of brew downloads (gcc, open-mpi, hh-suite are the heavy
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items); 5–10 minutes on a modern internet link.
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## Install
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Clone LocalColabFold (or use an existing clone), then from the repo root:
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```bash
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git clone https://github.com/YoshitakaMo/localcolabfold.git
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cd localcolabfold
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bash v1.5.5_old_installers/install_colabbatch_M1mac.sh
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```
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What the installer does, in order:
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1. Downloads `Miniforge3-MacOSX-arm64.sh` and installs into
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`localcolabfold/conda/`
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2. `conda update -n base conda -y`
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3. Creates env at `localcolabfold/colabfold-conda` with
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`python=3.10 openmm==8.0.0 pdbfixer==1.9` from conda-forge
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4. `pip install "colabfold[alphafold] @ git+https://github.com/sokrypton/ColabFold"`
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5. `pip install jax==0.4.23 jaxlib==0.4.23` (CPU build)
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6. `pip install silence_tensorflow`
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7. Downloads `update_M1mac.sh`
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8. `python -m colabfold.download` — pulls AlphaFold2 weights into
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`~/Library/Caches/colabfold` (~5.3 GB total: 3.82 GB multimer_v3 +
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3.47 GB AlphaFold2-ptm)
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Total install time: ~10–30 min depending on network and AlphaFold weight
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download speed.
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## Add to PATH
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The installer's last step prints the line for your shell. Adapt the prefix
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to wherever you cloned LocalColabFold:
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```bash
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export PATH="$(pwd)/localcolabfold/colabfold-conda/bin:$PATH"
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```
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Add the line to `~/.zshrc` to persist across shell sessions.
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## Smoke test
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A minimal CPU-only inference exercising the JAX path (no MMseqs2 server,
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no templates, single model, single recycle). Use any short FASTA — the
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LocalColabFold repo ships `1BJP_1.fasta` (62 residues).
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```bash
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colabfold_batch \
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--num-models 1 --num-recycle 1 \
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--msa-mode single_sequence \
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--random-seed 0 \
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1BJP_1.fasta out_smoketest/
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```
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Expected output in `out_smoketest/`:
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- `*.pdb` — predicted structure
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- `*_scores_*.json` — per-residue confidence
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- `*_pae.png`, `*_plddt.png`, `*_coverage.png` — diagnostic plots
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- `1BJP_1.done.txt` — sentinel marker
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Sample log lines on success:
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```
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Running colabfold 1.6.1 (de5ab5f795ed95c70a7a9b6a9dc6bb5625016142)
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WARNING: no GPU detected, will be using CPU
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Query 1/1: 1BJP_1 (length 62)
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alphafold2_ptm_model_1_seed_000 recycle=0 pLDDT=56.7 pTM=0.363
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alphafold2_ptm_model_1_seed_000 took 18.1s (1 recycles)
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```
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The low pLDDT (~57) is **expected** — single-sequence MSA, 1 model, and
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1 recycle is the cheapest config that still exercises the JAX inference
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path. It is **not** representative of structure quality. Use the
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production config below for real predictions.
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## Production run (CPU-friendly defaults)
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LocalColabFold's bundled `run_colabfoldbatch_sample.sh` uses
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`--use-gpu-relax`, `--templates`, and `--amber` — all of which require
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either GPU or extra setup that doesn't apply on macOS. Use this
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CPU-friendly variant instead:
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```bash
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colabfold_batch \
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--num-recycle 3 \
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--num-models 5 \
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--model-order 1,2,3,4,5 \
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--random-seed 0 \
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YOUR_INPUT.fasta out_dir/
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# Dropped from the sample script:
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# --use-gpu-relax (no GPU)
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# --amber (slow on CPU; pdbfixer-only is fine for many use cases)
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# --templates (requires hhsearch + a PDB templates DB)
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```
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MSA defaults to MMseqs2 server-side queries — needs network, ~30–90 s
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roundtrip per sequence.
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Expect 5–15 min per ~100-residue monomer on this hardware. Multimers and
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longer chains scale much worse on CPU.
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## Honest caveats
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- **CPU-only.** No CUDA wheels for macOS exist; JAX has experimental
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Metal support but LocalColabFold's pinned `jax==0.4.23` is the CPU
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build. Inference can be 30–100× slower than a recent NVIDIA GPU.
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- **ColabFold version is 1.6.1**, pinned by the v1.5.5 installer track.
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Newer features in ColabFold `main` aren't available here. For latest
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features either use Google Colab or a Linux + NVIDIA box.
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- **Weights cache is ~5.3 GB** at `~/Library/Caches/colabfold`. Move with
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`XDG_CACHE_HOME` if disk is tight.
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- **Apple Silicon Metal** is not used by this JAX. There's no
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straightforward way to enable it without changing the pinned versions
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in the installer script.
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## Update
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```bash
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bash localcolabfold/update_M1mac.sh
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```
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## Uninstall / reset
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```bash
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# Remove the conda env + Miniforge (keeps weights cache)
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rm -rf localcolabfold/
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# Optionally drop weights cache
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rm -rf ~/Library/Caches/colabfold
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# Optionally remove brew prereqs
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brew uninstall brewsci/bio/hh-suite kalign mmseqs2
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brew untap brewsci/bio
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```
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