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SKILL.md
TorchDrug
Use TorchDrug as a modular PyTorch graph-learning stack:
load a datasets.* dataset,
choose a models.* representation model,
wrap it in a tasks.* objective,
train and evaluate it with core.Engine.
The current official documentation and latest release are both 0.2.1. Treat
newer Python or PyTorch combinations as unverified rather than silently assuming
compatibility.
Start with the version guard
Before generating or debugging code, inspect the environment:
Apple Silicon: PyTorch 1.13 or later, CPU only; no MPS support
If the project uses Python 3.11+ or PyTorch 2.1+, create a compatible environment
or explicitly test a source build. Do not present such combinations as supported.
Installation
Prefer a dedicated Python 3.10 environment and pin the TorchDrug release:
Install torch-scatter and torch-cluster wheels matched to the exact PyTorch
and CUDA pair, following the
official installation page. For a
CPU-only PyTorch 2.0 environment, one reproducible wheel combination is:
Do not copy a CUDA wheel URL between environments. Match the PyTorch version,
CUDA build, Python ABI, and platform. On Apple Silicon, the official docs require
building and from source; pin reviewed source
revisions and expect CPU execution.
torch-scatter
torch-cluster
Canonical property-prediction workflow
Use the documented ClinTox → GIN → PropertyPrediction → Engine pattern:
Add gpus=[0] only when a supported CUDA device is available. Omit gpus for
CPU execution.
For binary classification, task.predict(batch) returns logits; apply
torch.sigmoid when probabilities are needed. In 0.2.1, normalized regression
predictions are returned on the original target scale, which is a breaking change
from older releases.
Choose the official workflow
Molecular property prediction
Dataset: datasets.ClinTox, BBBP, Tox21, QM9, or another documented
molecule dataset.
Model: start with models.GIN; use edge_input_dim when the selected feature
configuration supplies edge features.
Follow the 0.2.1 API. The official docs are not a rolling latest-version
site.
Prefer documented feature names. Use atom_feature, bond_feature,
residue_feature, and mol_feature; node_feature, edge_feature, and
graph_feature are deprecated aliases in relevant dataset constructors.
Let Engine preprocess tasks. If composing pre-trained tasks without
constructing their solvers, call each task's preprocess() manually.
Keep paired splits synchronized. For retrosynthesis, reset the same random
seed before splitting reaction and synthon datasets.
Use TorchDrug collation. Use data.graph_collate or core.Engine;
generic PyTorch collation does not know how to pack TorchDrug graphs.
Separate model, task, and engine arguments. A common source of invented
code is passing task options to a model or passing raw models where a composed
task is required.
Validate generated chemistry. Treat model outputs as candidates, not as
experimentally valid or synthesizable compounds.
Troubleshooting
Installation or import failure
Check Python, PyTorch, torch-scatter, and torch-cluster as one compatibility
set. Most failures are binary-wheel mismatches, unsupported Python versions, or
attempts to use MPS.
Feature dimension mismatch
Build model dimensions from the loaded dataset:
dataset.node_feature_dim
dataset.edge_feature_dim
dataset.num_bond_type
dataset.num_entity and dataset.num_relation for knowledge graphs
Do not hard-code dimensions copied from a different feature configuration.
Device mismatch
Pass gpus=[0] to core.Engine for supported CUDA execution. For manual
prediction, collate first and move the entire nested batch with utils.cuda.
Checkpoint mismatch
Recreate the same model and feature configuration. For pretraining-to-fine-tuning
transfer, load the checkpoint's "model" state with strict=False; for a complete
solver, use solver.save() and solver.load().
