Include dependencies needed for the new sequence embedding models

[julenmendieta]

Greptile Summary

This PR extends the python-3.12.10-torch-cuda run environment with three pinned sequence-embedding libraries — ablang2==0.2.1 (antibody language model), sceptr==1.2.0 (TCR model), and rdkit==2026.3.3 (cheminformatics toolkit for PeptideCLM-2) — along with a matching changeset entry.

• python-3.12.10-torch-cuda/config.json: Three new entries appended to the dependencies array; the existing pins (transformers, polars-lts-cpu, numpy, pyarrow, torch) are untouched.
• .changeset/torch-cuda-ablang2-sceptr.md: Minor-version changeset documenting the additions and explicitly noting pip check was run to confirm no conflicts.

Confidence Score: 4/5

Adding three pinned libraries to the torch-cuda environment is a low-risk, additive change; the existing pins are untouched and the author notes pip check passed.

The change is purely additive — three new version-pinned packages in one environment's dependency list. The transitive dependencies (einops, rotary-embedding-torch, libtcrlm, tidytcells, pandas, blosum, Pillow) are not themselves pinned in config.json, so future pip resolves could pull in different transitive versions. The changeset documents the rationale clearly and claims pip check is clean, but that validation is not reproducible from the diff alone.

The only non-trivial file is python-3.12.10-torch-cuda/config.json. Transitive dependencies of the three new packages are not pinned, which could cause unexpected resolution changes in the future.

Important Files Changed

Filename	Overview
python-3.12.10-torch-cuda/config.json	Adds three new pinned dependencies (ablang2==0.2.1, sceptr==1.2.0, rdkit==2026.3.3) to the torch-cuda run environment; all are version-pinned and claimed conflict-free per pip check.
.changeset/torch-cuda-ablang2-sceptr.md	New changeset file documenting the minor version bump for the torch-cuda environment with clear description of added packages and their transitive dependencies.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    subgraph env["python-3.12.10-torch-cuda environment"]
        direction TB
        existing["Existing pins\ntransformers==4.53.2\npolars-lts-cpu==1.33.1\nnumpy==2.2.6\npyarrow==21.0.0\ntorch==2.7.0+cu126 (linux-x64)\ntorch==2.7.0 (macosx-aarch64)"]
        new["New pins (this PR)\nablang2==0.2.1\nsceptr==1.2.0\nrdkit==2026.3.3"]
    end

    ablang2["ablang2==0.2.1\n(Antibody LM)"] -->|pulls| einops["einops"]
    ablang2 -->|pulls| rotary["rotary-embedding-torch"]

    sceptr["sceptr==1.2.0\n(TCR model)"] -->|pulls| libtcrlm["libtcrlm"]
    sceptr -->|pulls| tidytcells["tidytcells"]
    sceptr -->|pulls| pandas["pandas"]
    sceptr -->|pulls| blosum["blosum"]

    rdkit["rdkit==2026.3.3\n(Cheminformatics)"] -->|pulls| Pillow["Pillow"]

    new --> ablang2
    new --> sceptr
    new --> rdkit

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%%{init: {'theme': 'base', 'themeVariables': {"darkMode": true, "background": "#0d1117", "primaryColor": "#21262d", "primaryTextColor": "#e6edf3", "primaryBorderColor": "#8b949e", "lineColor": "#8b949e", "textColor": "#e6edf3", "edgeLabelBackground": "#161b22", "actorBkg": "#21262d", "actorBorder": "#8b949e", "actorTextColor": "#e6edf3", "actorLineColor": "#8b949e", "signalColor": "#8b949e", "signalTextColor": "#e6edf3", "noteBkgColor": "#373320", "noteBorderColor": "#d4a72c", "noteTextColor": "#f0e6c0", "labelBoxBkgColor": "#21262d", "labelBoxBorderColor": "#8b949e", "labelTextColor": "#e6edf3", "loopTextColor": "#e6edf3", "activationBkgColor": "#30363d", "activationBorderColor": "#8b949e"}}}%%
flowchart TD
    subgraph env["python-3.12.10-torch-cuda environment"]
        direction TB
        existing["Existing pins\ntransformers==4.53.2\npolars-lts-cpu==1.33.1\nnumpy==2.2.6\npyarrow==21.0.0\ntorch==2.7.0+cu126 (linux-x64)\ntorch==2.7.0 (macosx-aarch64)"]
        new["New pins (this PR)\nablang2==0.2.1\nsceptr==1.2.0\nrdkit==2026.3.3"]
    end

    ablang2["ablang2==0.2.1\n(Antibody LM)"] -->|pulls| einops["einops"]
    ablang2 -->|pulls| rotary["rotary-embedding-torch"]

    sceptr["sceptr==1.2.0\n(TCR model)"] -->|pulls| libtcrlm["libtcrlm"]
    sceptr -->|pulls| tidytcells["tidytcells"]
    sceptr -->|pulls| pandas["pandas"]
    sceptr -->|pulls| blosum["blosum"]

    rdkit["rdkit==2026.3.3\n(Cheminformatics)"] -->|pulls| Pillow["Pillow"]

    new --> ablang2
    new --> sceptr
    new --> rdkit

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Prompt To Fix All With AI

Fix the following 1 code review issue. Work through them one at a time, proposing concise fixes.

---

### Issue 1 of 1
python-3.12.10-torch-cuda/config.json:13-15
**Transitive dependencies not pinned**

`ablang2`, `sceptr`, and `rdkit` each pull in several transitive packages (`einops`, `rotary-embedding-torch`, `libtcrlm`, `tidytcells`, `pandas`, `blosum`, `Pillow`) that are not pinned here. A future `pip` resolve could silently upgrade any of them and introduce an ABI or API mismatch with the pinned `numpy==2.2.6` or `torch==2.7.0` in this same environment. Consider adding explicit pins for the key transitives — especially `pandas` (which has historically broken APIs across minor versions) — either directly in this array or via a `constraints.txt` / lock file.

_{Reviews (1): Last reviewed commit: "Include dependencies needed for the new ..." | Re-trigger Greptile}

Greptile also left 1 inline comment on this PR.

[gemini-code-assist]

Code Review

This pull request adds the ablang2, sceptr, and rdkit libraries to the torch-cuda run environment. The reviewer identified that the specified version for rdkit (2026.3.3) does not exist on PyPI and is likely a typo for 2023.3.3 or 2024.3.3 in both the configuration and the changeset files.

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[greptile-apps]

Transitive dependencies not pinned

ablang2, sceptr, and rdkit each pull in several transitive packages (einops, rotary-embedding-torch, libtcrlm, tidytcells, pandas, blosum, Pillow) that are not pinned here. A future pip resolve could silently upgrade any of them and introduce an ABI or API mismatch with the pinned numpy==2.2.6 or torch==2.7.0 in this same environment. Consider adding explicit pins for the key transitives — especially pandas (which has historically broken APIs across minor versions) — either directly in this array or via a constraints.txt / lock file.

Prompt To Fix With AI

This is a comment left during a code review.
Path: python-3.12.10-torch-cuda/config.json
Line: 13-15

Comment:
**Transitive dependencies not pinned**

`ablang2`, `sceptr`, and `rdkit` each pull in several transitive packages (`einops`, `rotary-embedding-torch`, `libtcrlm`, `tidytcells`, `pandas`, `blosum`, `Pillow`) that are not pinned here. A future `pip` resolve could silently upgrade any of them and introduce an ABI or API mismatch with the pinned `numpy==2.2.6` or `torch==2.7.0` in this same environment. Consider adding explicit pins for the key transitives — especially `pandas` (which has historically broken APIs across minor versions) — either directly in this array or via a `constraints.txt` / lock file.

How can I resolve this? If you propose a fix, please make it concise.

Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!