Devel - #232
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fix: not rendering poscar
Add concept-level and deepmd-skill support for distilling a DPA-4c student model from a fine-tuned DPA-4 teacher: - mlff concept SKILL: rewrite the distillation procedure into explicit Phase A (teacher NPT MD) -> Phase B (teacher inference labeling) -> Phase C (student training + two-level evaluation), with strict phase ordering gates and seed/training-set provenance checks. - deepmd SKILL + supported models reference: document DPA-4c CLI usage (--pt-expt, never --finetune due to OOM for sel=[999999]), the --skip-neighbor-stat requirement, and the recommended Bohrium image (dpa4-mlip-340e01f9) on a 5090 GPU. - deepmd_prepare.py: fix epoch-key alias handling so exactly one epoch key remains in the training dict (avoids dargs strict-schema failure). - references/dpa4c_distill_input.json: verified input template from a completed 1M-step DPA-4c distillation run.
The run_python/run_bash/run_python_file/run_skill_script tools called proc.kill() inside their TimeoutError/CancelledError handlers without guarding against an already-reaped process. Under uvloop this raises ProcessLookupError, which masks the original CancelledError and surfaces a full traceback (observed during step cancellation while a long bash job is running; see the run_bash traceback in api-server.log). Extract _terminate_subprocess(): it only kills when the process is still alive, tolerates the race that leaves no PID, and never raises, so the caller's original exception propagates correctly. Timeout output is unchanged.
fix(web): chat scroll and stop status handling
Agent graph enhancement
feat: add plugin for bohr cli job
fix: harden subprocess cleanup to avoid masking CancelledError
Concept skill split (qchempku2017): - Extract the fine-tuning and distillation procedures from the main concept SKILL.md into references/fine-tuning.md and references/distillation.md. SKILL.md now stays a concise overview that points to the two reference files (365 -> 97 lines). Wording & correctness fixes per review: - Rename "Phase" -> "Stage" throughout (avoid confusion with crystalline phases). - Distillation: strengthen the seed-vs-training-set rule to explicitly forbid relabeling existing DFT structures with the teacher. - Distillation Stage Zero gate: "DO NOT use pretrained models"; clarify that multi-task-capable models (e.g. DPA-3) must be single-task fine-tuned on the target system to qualify as a teacher. - Unify the distillation frame multiplier to ~100x (was inconsistent 20x/100x). - Stage B: rephrase labeling as "inferencing with the teacher model to obtain energy, forces and virial". - Clarify the configuration-diversity check (NPT lattice-vector fluctuation on the same supercell), and classify systems whose initial structures span multiple distinct cell types as complex. - Use ~1000 epochs (num_epochs keyword) instead of training steps. deepmd skill: - Remove the duplicated DPA-4c distillation block from SKILL.md; keep a short pointer to supported_deepmd_models.md. Keep the --init-model flag; make the --finetune prohibition wording stricter. - supported_deepmd_models.md: rename the DPA-4c section heading.
…pochs) - distillation.md: 1000 epochs -> 50 epochs. - dpa4c_distill_input.json: replace numb_steps=1000000 with num_epochs=50, consistent with the num_epochs keyword recommended since deepmd 3.2.0.
feat(skills): add DPA-4c distillation workflow
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