| --- |
| license: other |
| license_name: mixed-upstream-oss |
| license_link: https://github.com/ai4nucleome/GLMap |
| pretty_name: GLMap scoring containers |
| tags: |
| - genomics |
| - dna |
| - genomic-language-model |
| - apptainer |
| - singularity |
| - container |
| - bioinformatics |
| - reproducibility |
| viewer: false |
| --- |
| |
| # 🧬 🗺️ GLMap scoring containers |
|
|
| Prebuilt **Apptainer / Singularity** images that carry the GPU runtime |
| environments for **scoring all 123 genomic language models (gLMs)** profiled in |
| [**GLMap**](https://github.com/ai4nucleome/GLMap) — *Profiling genomic language |
| models as individuals in a population*. |
|
|
| The 123 models span runtime stacks that are **mutually incompatible** (Python |
| 3.8–3.12, PyTorch 1.13–2.9, CUDA 11.7–12.4) and can never share one interpreter. |
| These four images package every environment so you can recompute the |
| likelihood responses **without setting up a single conda env**. |
|
|
| > Only need the **analysis** (precomputed scores, figures/tables)? You don't |
| > need these images at all — `pip install -e .` on the |
| > [GLMap repo](https://github.com/ai4nucleome/GLMap) gives a torch-free stack. |
|
|
| ## What's here |
|
|
| Each image is **self-contained** (the shared CUDA 12.8 base is already inside; |
| download only the group(s) for the models you want to score): |
|
|
| | Image | Size | Envs | Model families | |
| |---|---|---|---| |
| | `bio-default.sif` | 17 GB | base / dnabert2 / megadna | NT, GENA-LM, ModernBERT, GROVER, Mistral-DNA, NTv3, … (most); DNABERT-2 / DNABERT-S; megaDNA | |
| | `bio-cu118.sif` | 20 GB | caduceus / gf / hyena-dna | Caduceus; GenomeOcean; HyenaDNA | |
| | `bio-cu121.sif` | 15 GB | PlantCAD | PlantCAD2 | |
| | `bio-evo.sif` | 24 GB | evo / evo2 | Evo-1 / Evo-1.5; Evo-2 (7B) | |
|
|
| Each image holds its envs as isolated micromamba environments and dispatches to |
| the right one per model via the `GLMAP_ENV` variable. |
|
|
| ## Download |
|
|
| ```bash |
| # one image |
| hf download Tim419/GLMap-containers bio-default.sif --repo-type dataset --local-dir . |
| |
| # or all four |
| hf download Tim419/GLMap-containers --repo-type dataset --local-dir . |
| ``` |
|
|
| ## Run |
|
|
| Bind your [GLMap checkout](https://github.com/ai4nucleome/GLMap) at `/work` |
| (code, panel, audit, model weights) and pick the env with `GLMAP_ENV`: |
|
|
| ```bash |
| GLMAP_ENV=caduceus apptainer run --nv \ |
| --bind "$PWD":/work --pwd /work bio-cu118.sif \ |
| scripts/score/scoring_worker.py --from-audit \ |
| --hf-ids kuleshov-group/caduceus-ph_seqlen-131k_d_model-256_n_layer-16 |
| ``` |
|
|
| Or run the **full 123-model sweep** straight through the images: |
|
|
| ```bash |
| python scripts/score/run_scoring_sweep.py \ |
| --backend container --image-dir <dir with the .sif files> --hf-cache "$HF_HOME" |
| ``` |
|
|
| - `--nv` exposes the host GPU. |
| - On compute nodes **without user namespaces**, use `singularity run --nv` |
| (the same `.sif`) — the GLMap sweep takes `--container-runtime singularity`. |
| - **HyenaDNA / megaDNA** also need their loader code on the bound checkout: it |
| is in the GLMap repo after `bash models/setup_external_models.sh` (HyenaDNA's |
| is vendored; megaDNA's weight auto-downloads from the HF Hub). |
|
|
| See [`container/README.md`](https://github.com/ai4nucleome/GLMap/blob/main/container/README.md) |
| and [`models/env_routing.md`](https://github.com/ai4nucleome/GLMap/blob/main/models/env_routing.md) |
| for the full model → image/env routing. |
|
|
| ## License |
|
|
| These images bundle many third-party open-source runtimes (PyTorch, |
| Transformers, mamba-ssm, flash-attn, evo2, …) and each model family's loader |
| code — **each remains under its own upstream license**. The GLMap glue code is |
| Apache-2.0. Individual model **weights** are downloaded separately and follow |
| their own licenses. Consult each upstream project before redistribution or |
| commercial use. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{hou2026glmap, |
| title = {Profiling genomic language models as individuals in a population}, |
| author = {Hou, Yusen and Long, Weicai and Su, Houcheng and Feng, Junning and Zhang, Yanlin}, |
| journal = {In submission}, |
| year = {2026} |
| } |
| ``` |
|
|
| Project: <https://github.com/ai4nucleome/GLMap> · Panel dataset: |
| [`Tim419/GLMap-panels`](https://huggingface.co/datasets/Tim419/GLMap-panels) |
|
|
