gs_brain: from data
to trained models.

git clone https://github.com/dominexmacedon-docs/greenServeFE-.git
cd /path/to/your/greenServeFE-build
make clean all
mkdir -p dist
cc -D_POSIX_C_SOURCE=200809L -std=c11 -O2 -Wall -Wextra -Wpedantic \
  -Wno-unused-parameter -fPIC -shared -I. $(pkg-config --cflags libcurl) \
  -o dist/gs_brain.so ai-modules/gs_brain.c \
  $(pkg-config --libs libcurl) -lm
export GREENSERVE_MODULE_PATH="$PWD/dist"

Run the GSVE script with the greenServeFE executable built by your checkout, for example ./greenServeFE test.gsve if that executable is present at the repository root. Some distributions install the runtime separately; use that runtime's actual path.

Example 1 — Train and evaluate logistic regression

This small, reproducible binary-classification example uses petal length and width from the public Iris dataset. Labels are simplified to setosa = 0 and versicolor = 1. The values below are embedded so this example does not depend on an unimplemented CSV reader.

import gs_brain;

// Four training examples per class from the Iris measurements.
define X_train = [
    [1.4, 0.2], [1.3, 0.2], [1.5, 0.2], [1.7, 0.4],
    [4.7, 1.4], [4.5, 1.5], [4.9, 1.5], [4.0, 1.3]
];
define y_train = [0, 0, 0, 0, 1, 1, 1, 1];

// Keep separate records for a simple held-out check.
define X_test = [[1.9, 0.4], [1.4, 0.3], [4.6, 1.3], [4.2, 1.5]];
define y_test = [0, 0, 1, 1];

define model = gs_brain.learn.train_logistic(
    X_train, y_train, 3000, 0.05, 0.001
);
define probabilities = gs_brain.learn.predict_proba(X_test, model);
define predictions = gs_brain.learn.predict_logistic(X_test, model);
define accuracy = gs_brain.learn.accuracy_score(y_test, predictions);
define report = gs_brain.learn.classification_report(y_test, predictions, 1);

print("Training loss:");
print(model.loss);
print("Held-out accuracy:");
print(accuracy);
print("Probabilities:");
print(probabilities);
print("Predictions:");
print(predictions);
print("Classification report:");
print(report);

if (accuracy == 1 && model.loss < 0.1) {
    print("PASS: Iris training and held-out smoke test");
} else {
    print("CHECK: inspect model output and training settings");
}

Example 2 — Test numeric and training components

The component test in the private development checkout is intended to exercise independent primitives before scaling an experiment: matrix multiplication and mean, gradient propagation, one-hot encoding, SGD updates and checkpoint save/load.

// Run the repository's component test with the same module path.
greenServeFE tests/gs_brain-components.gsve

The component test source and manual GitHub Actions workflow are available inside the private build-source checkout to authorized collaborators. Run the workflow and inspect its logs before treating the current checkout as validated. Public visitors can use the GSVE examples and public release information linked below.

Suggested corpus layout

data/
  raw/            # immutable source snapshots; access controlled
  manifests/      # source, license, timestamp, checksum, provenance
  interim/        # decoded records and validation reports
  processed/      # deduplicated, normalized, filtered records
  splits/
    train/
    validation/
    test/
  tokenizer/      # tokenizer config and versioned vocabulary
  reports/        # quality, duplication, language and leakage reports
artifacts/
  checkpoints/
  evaluation/
  model-card.md

Keep large raw corpora and credentials out of Git history. Use dataset hosting or object storage with access controls and versioned manifests; publish only data you are authorized to redistribute.

Language-model data preparation

1. Collect permitted text and store provenance separately from the text itself.
2. Decode UTF-8, normalize line endings, detect language and remove malformed or unwanted records.
3. Deduplicate documents and overlapping passages before creating data splits.
4. Train or select a tokenizer; freeze and version its configuration before large training runs.
5. Pack token sequences into fixed-length blocks, create next-token labels and apply correct masks for padding or ignored tokens.
6. Inspect examples manually, test the loader, and estimate token count, storage size and training steps.

Important: the current gs_brain module does not automatically perform all of these steps. Tokenization, scalable corpus loading, batching and sequence packing must be implemented and validated in the data pipeline before claiming end-to-end language-model training.

Practical scaling sequence

1. Prove correctness on CPU. Unit-test matrix operations, gradients, loss, optimizer updates, checkpoint round trips and deterministic data batches.
2. Profile before optimizing. Measure examples/tokens per second, peak host/device memory, input pipeline utilization and time spent in each operation.
3. Choose a supported backend. A GPU is useful only if the framework has working kernels and memory management for it. The current gs_brain C module should be treated as CPU-oriented unless the exact revision proves otherwise.
4. Scale one dimension at a time. Try larger batches or one accelerator, then multiple devices. Compare throughput and numerical behavior to the baseline.
5. Use the cluster scheduler. Ask the HPC administrator for approved job scripts, CUDA/ROCm or other accelerator modules, MPI/runtime versions, scratch-space rules and wall-time limits. Do not run long jobs on login nodes.
6. Test interruption and recovery. Deliberately restart a short job from a checkpoint before allocating expensive compute.

Example: Slurm job template for a future GPU-enabled trainer

This is an infrastructure template only. It will not make gs_brain GPU-capable by itself; replace the executable and arguments with the real, tested GPU-enabled training program for your cluster.

#!/bin/bash
#SBATCH --job-name=gs-brain-train
#SBATCH --output=logs/%x-%j.out
#SBATCH --error=logs/%x-%j.err
#SBATCH --time=02:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:1
#SBATCH --mem=32G

set -euo pipefail
mkdir -p logs artifacts/checkpoints
module list
nvidia-smi
# Replace with your real, validated GPU training command:
./trainer-gpu --config configs/experiment.yaml \
  --data data/processed --output artifacts/checkpoints

Cluster directives differ by site; some use --gpus, partitions, account/project IDs, containers or different accelerator resource syntax. Follow local documentation. Do not submit this template unchanged and assume it will train a model.

• Record source revision, compiler flags, OS, CPU/GPU model, driver and library versions.
• Keep train, validation and test datasets separate; never tune on the test set.
• Log the metric definition, evaluation data version and the number of evaluated examples.
• Store configuration and checkpoints atomically, and verify a fresh process can reload them.
• Report failures and limitations, not just the best run.