borzoi

# Borzoi — DNA → Functional Track Prediction ## Prerequisites | Requirement | Minimum | Recommended | | ----------- | ------- | ----------- | | Python | 3.10+ | 3.11 | | CUDA | 12.1+ | 12.4+ | | GPU VRAM | 16 GB | 24 GB+ | ## How to run ```python from borzoi_pytorch import Borzoi model = Borzoi.from_pretrained("johahi/borzoi-replicate-0").cuda().eval() # input: (batch, 4, 524288) one-hot DNA → output: (batch, tracks, 6144) bins ``` Borzoi consumes ~524 kb one-hot windows and emits binned predictions across 7,611 human tracks (the separate 2,608-track mouse head is off by default; enable via `enable_mouse_head=True` and select with `forward(..., is_human=False)`). For variant scoring, run ref/alt windows centred on the variant and compare per-track output. ## Output format `(B, T, L)` tensor — `T` tracks × `L` 32-bp bins. Track metadata (assay, biosample) is in `borzoi_pytorch.pytorch_borzoi_model.TRACKS_DF` (or `model.tracks_df` when using the `AnnotatedBorzoi` subclass) — the base `Borzoi` model has no `targets` attribute. ## Remote compute Needs ≥24 GB VRAM and either pre-cached HF weights or egress to `huggingface.co`. Read `compute_details({provider, mode:'read'})` for an environment with `borzoi-pytorch`, then: ```python c = host.compute.create(provider) job = c.submit_job( intent="Borzoi track prediction for 1 locus — 1×GPU, ~2 min", inputs=[{"src": "borzoi_run.py", "dst_filename": "borzoi_run.py"}], command="python3 borzoi_run.py", # env selection is host-specific — see compute_details for your provider outputs=["tracks.npz"], timeout_seconds=1800, ) print(job.job_id) # cell ends here — kernel never blocks on compute ``` Then call the `wait_for_notification` brain-tool. When the `compute_done` notification arrives, act on its payload: ```python save_artifacts(payload["featured_files"]) # paths under hpc/<job_id>/ ``` For the full result dict (`output_files`, `remote_workdir`, …), re-enter the kernel: `c.attach_job(job_id).result()` then `c.close()`. See the `remote-compute-ssh` / `remote-compute-modal` skill for the orchestration details. If the provider exposes a weight-cache mount, point `HF_HOME` at it inside `borzoi_run.py` (path is in `compute_details`). ## Troubleshooting | Symptom | Cause | Fix | | ------------------------------ | ------------------------ | ------------------------------------ | | `module has no __version__` | Package exposes no attr | Use `importlib.metadata.version("borzoi-pytorch")` | | Shape mismatch on input | Wrong window length | Pad/crop to 524288 bp (fixed; not exposed as a model attribute) | --- **Next**: combine track deltas with `evo2` likelihood deltas for a two-axis variant prioritisation.