Kermt Pretrain Scratch
NVIDIAOfficialPublished by NVIDIA
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- Data and AI
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill orchestrates the end-to-end pretraining of a KERMT model from scratch. Unlike continuing an existing model, this process initializes the model randomly and builds a new vocabulary directly from the user-provided corpus. It is a computationally intensive workflow designed for domain-specific chemistry research where existing checkpoints are insufficient. The skill handles data validation, vocabulary generation, and the launch of distributed training within a containerized environment.
What it does
- Validates the user-provided SMILES corpus for pretraining compatibility.
- Generates a fresh vocabulary based on the provided dataset.
- Configures model architecture using default settings.
- Launches a detached distributed training process using the KERMT container.
- Provides a detailed run manifest for reproducibility and tracking.
- Offers monitoring guidance for long-running training sessions.
When to use it
- When you need to train a model on a custom chemistry domain not covered by released checkpoints.
- When you require a full end-to-end reproduction of a pretraining configuration.
- When you have sufficient compute resources for a multi-day training job.
Advantages
- Enables complete control over the model's training data and vocabulary.
- Supports multiple pretraining objectives including vocab, cmim, and hybrid modes.
- Ensures reproducibility through detailed manifest generation and command replay.
- Automates complex containerized orchestration for distributed training.
Usage examples
- “Pretrain a new KERMT model from scratch using my_corpus.csv with the hybrid objective.”
- The agent validates the CSV, builds a fresh vocabulary, estimates the required compute time, and launches the training process in a detached container.
- “Start a scratch pretraining run on my_data.csv and log to wandb.”
- The agent initiates the pretraining workflow, configuring the environment for Weights & Biases logging and providing the run directory for monitoring.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/kermt-pretrain-scratch/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip kermt-pretrain-scratch.zip -d .agents/skills
Documentation by NVIDIA
kermt-pretrain-scratch
Pretrain a brand-new KERMT model from scratch on a user-provided corpus. Useful
when you want to retrain a model on a custom chemistry domain rather than
extending one of the released checkpoints. Significantly more expensive than
kermt-continue-pretrain — no warm start, so the loss curves need to descend
from scratch over many epochs.
Skill and runtime paths
Set SKILL_DIR to the absolute path of this installed skill directory. Export
KERMT_REPO as the absolute path to the KERMT checkout used for model
execution. The bundled container helper mounts that checkout at
/workspace and this skill at /skill (read-only). Commands inside
the container use /skill/scripts/; defaults are bundled in config/.
Hardware requirements
Same as kermt-continue-pretrain:
GPUs: 1–N CUDA-capable. The runner auto-detects via
torch.cuda.device_count();--gpus 0,2overrides. Single-GPU fallback:--batch_size 32 --save_interval 500. Multi-GPU keeps defaults (--batch_size 256etc.). Note:--gpus Nuses torch.cuda indexing, which can differ fromnvidia-smi's display order on multi-GPU hosts (PCI bus vs. CUDA enumeration). To target a specific physical GPU, setCUDA_VISIBLE_DEVICESbefore invoking, or runpython -c "import torch; print([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])"to confirm which device you're picking.VRAM: the default
--batch-size 256is sized for A100-class hardware (80 GB VRAM). On smaller GPUs, downscale to avoid OOM:GPU class VRAM Suggested --batch-sizeL4, T4, V100 16 GB 16–24 GB 32–64 A100 40 GB, L40, A40 40–48 GB 128 A100 80 GB, H100, H200 80 GB 256 (default) These are rough starting points — pass
--batch-size Nto override.Disk: tens of GB for shards + vocab + checkpoints, scaled by epochs.
Wall time: this is the big difference. Pretraining from scratch on an 11M-mol corpus at 100 epochs typically takes days even on a multi-GPU box. The skill prints an estimate before launching; confirm with the user.
When to invoke
User wants to train a new model on a custom corpus (e.g. domain-specific chemistry that the released ckpts don't cover).
User wants to reproduce a pretrain config end-to-end without depending on a released ckpt.
For continuing an existing released ckpt, use kermt-continue-pretrain. For
adding a cMIM decoder to an encoder-only grover_base ckpt, use
kermt-add-cmim-pretrain.
Inputs
Required:
--csv <path>— the pretrain corpus CSV with asmilescolumn. Single file by convention; multi-file corpora deferred. Use--val-csvfor a separate validation set.--pretrain-target-mode {vocab|cmim|hybrid}— which pretrain objective to use. No default — must be set explicitly so the user makes an informed choice:vocab— original GROVER-style atom + bond vocab prediction (encoder-only output, lightweight).cmim— contrastive + SMILES reconstruction objective. Requires building a SMILES vocab from the corpus.hybrid— both vocab and contrastive objectives jointly (the state-of-the-art config from the KERMT manuscript).
Optional:
--val-csv <path>— separate validation CSV. Without it, prepare_data auto-splits the input by--val-frac 0.1(random shuffle with--seed).Training-hyperparameter overrides:
--epochs N/--batch-size N/--init-lr F/--max-lr F/--final-lr F/--warmup-epochs F/--weight-decay F/--dropout F/--save-interval N/--seed N. Anything not given is filled fromconfig/defaults_pretrain.json.--vocab-loss-weight F(hybrid only) /--latent-dim N/--contrastive-temperature F(cmim and hybrid only).--wandb-project NAME/--wandb-run-name NAME— optional Weights & Biases logging. When--wandb-projectis set, rank 0 logs train/val losses; the run name is honored only alongside a project. Off by default.--gpus 0,2— restrict to a GPU subset.
Workflow
Let $KERMT_REPO be the path to your kermt repo checkout.
Pre-flight: ensure container + system probe (same as
kermt-continue-pretrainstep 1). Refuse to proceed ifcheck_systemreports gaps.Compute run directory.
RUN_DIR=$KERMT_REPO/runs/pretrain-scratch_$(date -u +%Y-%m-%dT%H-%M-%SZ)Validate the corpus (no ckpt to validate, so this is the only input check):
"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> -- \ "python /skill/scripts/check_data.py --mode pretrain --csv /data/<basename>"Abort on
ok: false.Prepare the data — no vocab pass-through (we want fresh vocab from corpus):
"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \ "python /skill/scripts/prepare_data.py --mode pretrain \\ --csv /data/<basename> --out /runs/data \\ [--val-csv /data/<val-basename>] [--val-frac 0.1] [--seed 0]"Outputs land at
$RUN_DIR/data/prepare_data.jsonwithvocab_source: "built_fresh".Estimate runtime + warn loudly. This is critical for pretrain-from-scratch:
"Pretraining from scratch is days-scale even on multi-GPU; the released KERMT checkpoints were each trained on millions of molecules for hundreds of GPU-hours. If you mainly want to leverage existing knowledge for a downstream task, consider
kermt-continue-pretrainfrom a released ckpt instead, which converges in hours instead of days."Show the corpus size × epochs × GPU count → estimated wall time.
Ask for explicit confirmation unless
--yeswas given.
Launch the runner detached.
"$SKILL_DIR/scripts/kermt_container.sh" run_detached \\ --name kermt-pretrain-scratch-<ts> \\ --run-dir $RUN_DIR -- \\ "python /skill/scripts/run_pretrain_local.py \\ --from-scratch --pretrain-target-mode <vocab|cmim|hybrid> \\ --prepare-manifest /runs/data/prepare_data.json \\ --out /runs \\ [--epochs N --batch-size N ...]"Note: NO
--ckptflag (the runner refuses if both--from-scratchand--ckptare given). The runner uses thearchgroup fromconfig/defaults_pretrain.jsonto size the model.Report to the user. Always include all of the following — do not omit the TensorBoard line under output-length pressure:
Container name + id
$RUN_DIR/run.json(the manifest withworkflow: pretrain-scratch,from_scratch: true,vocab_check: null,archfrom defaults, fullcmd_replay)Log file:
$RUN_DIR/logs/pretrain_ddp.logTensorBoard:
$RUN_DIR/logs/tb(open withtensorboard --logdir $RUN_DIR/logs/tb)Suggest
kermt-monitor <RUN_DIR>for progress.
Hard rules
Never accept a
--ckptflag. From-scratch is exclusive with input ckpt — the runner enforces this; the skill should too.Never silently default
--pretrain-target-mode. This is a significant architectural choice (vocab = lightweight, hybrid = SOTA). Prompt the user if not given on the CLI.Strong warning before launching. From-scratch pretrain is the most expensive workflow. The user needs to know what they're committing to.
Common errors
--pretrain-target-mode is required when --from-scratch is set→ user forgot the mode flag. Prompt.--from-scratch is incompatible with --ckpt→ user provided both; ask which one they meant.defaults_pretrain.json has no arch group→ repo state issue (should never happen on a fresh clone); points the user at runningkermt-setupagain.
What's in the manifest after a from-scratch run
Same reproducibility fields as continue-pretrain (repo.commit, kermt_image,
cmd_replay, args_applied), plus:
workflow:"pretrain-scratch"from_scratch:trueinputs.ckpt:nullckpt_symlink:nullvocab_check:null(not verified — vocab built from corpus is authoritative for from-scratch)arch: the values pulled fromconfig/defaults_pretrain.json'sarchgroup (with any future CLI overrides applied).
Replayability
Same as continue-pretrain: cmd_replay is a copy-pasteable command. If
ok_to_replay: false, the kermt repo working tree was dirty at launch
time — check repo.commit and git checkout it first.
Files
- ATTRIBUTION.mdAdded by AutoGineer851 B
- BENCHMARK.md7.6 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md9.3 KiB
- config/defaults_pretrain.json2.6 KiB
- evals/evals.json5.6 KiB
- scripts/_utils.pyScript14.1 KiB
- scripts/check_checkpoint.pyScript20.1 KiB
- scripts/check_data.pyScript11.9 KiB
- scripts/kermt_container.shScript18.9 KiB
- scripts/prepare_data.pyScript36.5 KiB
- scripts/run_pretrain_local.pyScript34.8 KiB
- skill-card.md4.9 KiB
- skill.oms.sig6.1 KiB
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Requirements and compatibility
Written for: Claude Code, OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
The author says: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron.
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Attribution and licence
- Published by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/bionemo-kermt-pretrain-scratch · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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