Configures and runs TabPFN training pipelines.
Install
mkdir -p .claude/skills/tabpfn-feature-encoder-runner && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13566" && unzip -o skill.zip -d .claude/skills/tabpfn-feature-encoder-runner && rm skill.zipInstalls to .claude/skills/tabpfn-feature-encoder-runner
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use when running, configuring, validating, or documenting the tabpfn-feature-encoder training repo, including conda setup, runner scripts, output artifacts, tests, and Git hygiene.Key capabilities
- →Run the `tabpfn-feature-encoder` training repository.
- →Configure TabPFN experiments using YAML files.
- →Validate output artifacts and Git hygiene.
- →Execute performance evaluations for source-task, CP, and open-data transfers.
- →Manage the conda environment for the feature encoder.
How it works
This skill manages the `tabpfn-feature-encoder` training workflow by running scripts for training, transfer evaluations, and plotting, while ensuring the correct conda environment setup and output artifact generation.
Inputs & outputs
When to use tabpfn-feature-encoder-runner
- →Running encoder training
- →Configuring TabPFN experiments
- →Executing performance evaluations
About this skill
TabPFN Feature Encoder Runner
Repo Basics
- Repo root:
tabpfn-feature-encoder. - Main 12-class source residual config:
configs/source_residual_mlp.yaml. - Particle GNN config:
configs/source_gnn.yaml. - Particle transformer config:
configs/source_transformer.yaml. - Full workflow launcher:
bash scripts/run_full_workflow.sh. - Dispatcher spelling:
bash scripts/run full workflow. - Launcher:
bash scripts/run_source_encoder.sh. - Source transfer rerun:
bash scripts/run_source_transfer.sh. - CP transfer rerun:
bash scripts/run_cp_transfer.sh. - Open-data transfer rerun:
bash scripts/run_gamgam_transfer.sh. - Context comparison plots:
bash scripts/plot_context_comparison.sh. - Test runner:
bash scripts/run_tests.sh. - Package CLI:
tabpfn-encoder-train train --config configs/source_residual_mlp.yaml. - Output dir is configured by
output_dir.
Environment
Use the existing conda env:
conda activate tabpfn
python -m pip install -e ".[train,atlas,plots]"
The runner falls back to conda run --no-capture-output -n tabpfn if the console script is not on PATH.
Runner Behavior
scripts/run_full_workflow.sh:
- Runs
configs/source_residual_mlp.yaml,configs/source_gnn.yaml, andconfigs/source_transformer.yamlby default. - For each config, trains the 12-class source encoder and then runs source-task, CP even/odd, and open-data transfer evaluations.
- Runs configs in parallel by default when multiple GPUs are visible, with one config per GPU.
- Streams per-config logs to the terminal and writes full logs to
runs/workflow_logs/<timestamp>/. - Set
TABPFN_WORKFLOW_STREAM_LOGS=0to disable live log streaming. - Select GPUs with
TABPFN_WORKFLOW_GPUS=0,1,2,3 bash scripts/run_full_workflow.sh. - Force sequential execution with
TABPFN_WORKFLOW_PARALLEL=0 bash scripts/run_full_workflow.sh. - Runs context comparison plotting at the end unless
TABPFN_WORKFLOW_PLOT=0is set. - Accepts optional config paths to restrict the workflow:
bash scripts/run_full_workflow.sh configs/source_residual_mlp.yaml.
scripts/run_source_encoder.sh:
- Sets
TABPFN_MODEL_CACHE_DIRto$SCRATCH/tabpfn_model_cacheunless already set. - Sets
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:Trueunless already set. - Reuses an existing checkpoint from
~/.cache/tabpfnwhen available. - Accepts an optional config path:
bash scripts/run_source_encoder.sh configs/other.yaml. - Runs the GNN with:
bash scripts/run_source_encoder.sh configs/source_gnn.yaml. - Runs the transformer with:
bash scripts/run_source_encoder.sh configs/source_transformer.yaml. - Reruns source-task transfer from a checkpoint with:
bash scripts/run_source_transfer.sh. - Reruns CP even/odd transfer from a checkpoint with:
bash scripts/run_cp_transfer.sh. - Reruns open-data transfer from a checkpoint with:
bash scripts/run_gamgam_transfer.sh. - Plots encoder comparison PDFs with:
bash scripts/plot_context_comparison.sh.
Model Layout
- Keep distinct encoder definitions in separate files under
src/tabpfn_feature_encoder/models/. - Current modules:
mlp.py,feature_gate.py,feature_mixer.py,gnn.py,transformer.py. - Keep model selection in
models/factory.py. - Keep PyTorch import helpers in
models/torch_utils.py. - Leave
models/encoders.pyas a compatibility re-export layer, not the place for new model logic.
Source Training Expectations
- Training uses frozen TabPFN support/query episodes; only encoder weights are optimized.
- For the 12-class source task, the trainer uses binary ECOC by default:
encoder.tabpfn_max_classes: 2,encoder.many_class_redundancy: 4. - Default source configs use
output_dim: 72,learning_rate: 0.0002,grad_clip_norm: 1.0, andvalidation_episodes: 8. - Source embeddings are detached before fitting the TabPFN support prompt by
default (
detach_support_gradients: true) so the encoder learns from query gradients without differentiating through prompt construction. - Validation is episodic and rotates through validation support/query contexts using the same 50/50 support/query split as training.
- Epoch logs should include
grad_norm_mean,grad_norm_max, andskipped_nonfinite_updates.
Validation Commands
Prefer these before finalizing code changes:
bash scripts/run_tests.sh
Pass pytest selectors through for focused checks, for example:
bash scripts/run_tests.sh tests/test_config.py
Clean generated Python/cache files before committing:
find . -type d -name __pycache__ -prune -exec rm -rf {} +
find src -maxdepth 2 -type d -name '*.egg-info' -prune -exec rm -rf {} +
Artifacts
Training saves:
metrics.jsontraining_summary.jsonepoch_metrics.csvencoder_classifier.pklrun_metadata.jsonsource_generalization/source_12_class_generalization_metrics.jsonsource_generalization/source_12_class_generalization_context_scan_metrics.csvsource_generalization/source_12_class_generalization_context_scan_roc_auc.pngsource_generalization/source_12_class_generalization_baseline_proba.npysource_generalization/source_12_class_generalization_frozen_encoder_proba.npycp_generalization/cp_even_odd_generalization_metrics.jsoncp_generalization/cp_even_odd_generalization_context_scan_metrics.csvcp_generalization/cp_even_odd_generalization_context_scan_roc_auc.pngcp_generalization/cp_even_odd_generalization_baseline_proba.npycp_generalization/cp_even_odd_generalization_frozen_encoder_proba.npyopen_data_generalization_metrics.jsonintransfer.output_diropen_data_generalization_context_scan_metrics.csvintransfer.output_diropen_data_generalization_context_scan_roc_auc.pngintransfer.output_diropen_data_generalization_baseline_proba.npyintransfer.output_diropen_data_generalization_frozen_encoder_proba.npyintransfer.output_dircontext_scan_comparison/*_roc_auc_comparison.pdfcontext_scan_comparison/*_accuracy_comparison.pdf
Terminal metrics print to three decimals; CSV/JSON keep full precision.
When not to use it
- →When the task is not related to running, configuring, validating, or documenting the `tabpfn-feature-encoder` training repo.
- →When the task involves model logic outside of `src/tabpfn_feature_encoder/models/`.
Prerequisites
Limitations
- →This skill is specific to the `tabpfn-feature-encoder` training repository.
- →It relies on predefined configuration files and runner scripts.
- →It requires a `tabpfn` conda environment to be active.
How it compares
This skill orchestrates the entire training and evaluation workflow for the feature encoder, including environment setup and artifact management, providing a standardized and reproducible process compared to manual execution.
Compared to similar skills
tabpfn-feature-encoder-runner side by side with the closest alternatives in the catalog.
Try saying
Example prompts that trigger this skill in your AI assistant.
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