Machine Learning Cross Validation Training

machine-learning-cross-validation-training

Summary

Orchestrate k-fold cross-validation model training across parallel GPU-bound compute nodes using SLURM job arrays and containerized environments. This skill ensures reproducible, distributed training of deep learning models on large datasets by partitioning data into k disjoint folds, training k independent model instances in parallel, and aggregating fold-level predictions.

When to use

You have a labeled dataset (e.g., mass spectra with molecular structures, SIRIUS 6 fingerprint annotations) that you wish to train a supervised deep learning model on, and you need to estimate generalization performance and reduce variance from a single train–test split. The dataset is large enough (e.g., SIRIUS 6 dataset with thousands of spectra) that serial k-fold training would be prohibitively slow, and you have access to a SLURM-managed HPC cluster with per-node GPUs.

When NOT to use

• Your dataset is very small (n < 100 samples) such that 10-fold stratification produces folds too small to train a deep model reliably; consider fewer folds or a single train–test split.
• You already have a pre-trained model and wish only to evaluate it on held-out test data; this skill is for model training, not inference.
• Your training script does not support or has not been refactored to accept a fold index as input; serial re-runs of the full script on different data subsets are needed first.
• You do not have access to a SLURM cluster or multiple GPUs; single-machine k-fold training is simpler and does not require this orchestration skill.

Inputs

• labeled training dataset (e.g., MGF file with spectra and SIRIUS 6 annotations, stored in DATA_LOC)
• modular training script accepting fold index (e.g., train.py with --fold argument)
• SLURM batch script template (e.g., run_singularity.sh with job-array directives)
• Singularity container image or Docker registry URI (e.g., docker://stravsm/msnovelist6)
• configuration file (.env) specifying DATA_LOC, CODE_LOC, SIF_LOC, RESULTS_LOC paths

Outputs

• k fold-specific trained model checkpoints (stored in RESULTS_LOC/fold_0, ..., fold_9)
• per-fold validation metrics and predictions (e.g., CSV files with scores, CSV/pickle with structure predictions)
• aggregated cross-validation performance report (mean and variance of fold-level metrics)
• SLURM job logs (stdout/stderr from each array task, useful for debugging convergence or GPU allocation)

How to apply

Structure your training codebase into a modular script (e.g., train.py) that accepts a fold index as a command-line argument and trains one model on 9/10 of your data while holding out the corresponding 1/10 fold for validation. Create a SLURM batch script that submits a job array with sbatch --array=0-9 to launch 10 parallel jobs, each assigned one GPU via SLURM resource requests (e.g., --gres=gpu:1). Containerize your training environment (Python, deep learning framework, dependencies) in a Singularity image built from a Docker registry (e.g., docker://stravsm/msnovelist6); build the image on a compute node (not the login node) and cache it to a scratch filesystem to avoid repeated rebuilds. In your job script (e.g., run_train.sh), set environment variables pointing to data, code, Singularity image, and results directories, then invoke singularity exec to run train.py inside the container for the fold index specified by $SLURM_ARRAY_TASK_ID. Each fold trains independently on GPU, writing model weights and validation metrics to a fold-specific results directory. After all 10 jobs complete, concatenate or aggregate the per-fold predictions and metrics to compute overall cross-validation performance (e.g., mean validation loss, F1 score across folds).

Related tools

• SLURM (job scheduler and resource manager; submits job arrays (--array=0-9) to distribute k fold-training jobs across compute nodes, each with one GPU allocation)
• Singularity (container engine; encapsulates training environment (Python, PyTorch/TensorFlow, dependencies); built from Docker image on compute node and invoked via singularity exec to run train.py in isolated environment)
• Python (programming language for training script (train.py); accepts fold index, loads fold-specific data subset, trains model on GPU, writes results to fold-specific directory)
• MSNovelist (example downstream application; trains de novo molecular structure prediction model using 10-fold cross-validation on SIRIUS 6 mass spectrometry dataset) — https://github.com/meowcat/MSNovelist

Examples

sbatch --array=0-9 run_singularity.sh

Evaluation signals

• All k job-array tasks complete successfully (check SLURM log: sacct -j JOBID shows all array indices 0–9 with EXIT_CODE 0).
• Each fold produces the expected output files in its results subdirectory (fold-specific model checkpoint, validation metrics CSV, predictions pickle); verify file count and schema consistency across folds.
• Aggregated cross-validation metric (e.g., mean validation loss, mean F1 score) lies within expected range relative to single train–test baseline; significant degradation or unexpected variance may indicate data leakage or fold-stratification failure.
• Per-fold training curves (loss vs. epoch) are smooth and convergent; noisy or divergent curves in individual folds suggest GPU allocation, data loading, or container environment issues.
• Wall-clock time for all 10 folds is approximately 1/10 of serial k-fold time (assuming full GPU utilization); significant deviation suggests job queueing delays or GPU contention.

Limitations

• Singularity image build must be run on a compute node, not the login node; building on the login node can cause environment conflicts or permission issues.
• Git repository checkout must occur on the login node before submitting jobs; checking out on compute nodes within the job may fail or introduce race conditions across parallel tasks.
• 10-fold cross-validation assumes your dataset is large enough and balanced enough that each fold contains representative samples; highly imbalanced or small datasets may require stratified k-fold or alternative splitting strategies.
• MSNovelist in this repository is retrained on SIRIUS 6 data and cannot be used with other fingerprint systems unless re-trained; the original MSNovelist relied on an old SIRIUS backend that is no longer running.
• Parallel GPU jobs share the same storage backend (scratch filesystem); writing from 10 concurrent jobs to RESULTS_LOC can cause contention or write conflicts if fold-specific subdirectories are not carefully isolated.

Evidence

• [other] The training workflow uses SLURM job-array submission with sbatch --array=0-9 to launch 10 parallel jobs, each assigned a single GPU.: "uses SLURM job-array submission with sbatch --array=0-9 to launch 10 parallel jobs, each assigned a single GPU"
• [other] The Singularity image is built on the compute node from the docker://stravsm/msnovelist6 registry, and each array index runs train.py to train one fold of the cross-validation model.: "Singularity image is built on the compute node from the docker://stravsm/msnovelist6 registry, and each array index runs train.py to train one fold"
• [other] On a job node, set SCRATCH_PATH environment variable and build the MSNovelist Singularity image from the stravsm/msnovelist6 Docker image, caching to $SCRATCH_PATH/singularity_cache.: "On a job node, set SCRATCH_PATH environment variable and build the MSNovelist Singularity image from the stravsm/msnovelist6 Docker image, caching to $SCRATCH_PATH"
• [other] Create a SLURM batch script (run_singularity.sh) that calls run_train.sh for each array element, with one GPU allocated per job.: "Create a SLURM batch script (run_singularity.sh) that calls run_train.sh for each array element, with one GPU allocated per job"
• [other] Each job invokes train.sh inside the Singularity container, which runs train.py to train one cross-validation fold of the model on the downloaded SIRIUS 6 dataset.: "Each job invokes train.sh inside the Singularity container, which runs train.py to train one cross-validation fold of the model"
• [methods] Build singularity image on job node. Freaks out on login node: SCRATCH_PATH=/cluster/scratch/$(id -un): "Build singularity image on job node. Freaks out on login node"
• [methods] Checkout git repo on login node. Seemingly doesn't work on job node.: "Checkout git repo on login node. Seemingly doesn't work on job node"