Model Training Infrastructure and Experimentation Questions

WITH per_feature AS (
  SELECT fd.run_b,
         SUM(fd.dist * f.weight) AS feature_score
  FROM feature_distance fd
  JOIN feature_weights f ON fd.feature = f.feature
  WHERE fd.run_a = :failing_run_id
  GROUP BY fd.run_b
),
label_scores AS (
  SELECT run_b, js_divergence(:failing_run_label_hist, label_hist) AS label_score
  FROM label_histograms
  WHERE run_b != :failing_run_id
)
SELECT p.run_b,
       p.feature_score,
       l.label_score,
       (p.feature_score + :w_label * l.label_score + abs(r.row_count - :fail_row_count)/:norm_rowcount) AS total_score
FROM per_feature p
JOIN label_scores l USING(run_b)
JOIN runs r ON r.run_id = p.run_b
ORDER BY total_score ASC
LIMIT 50;

POST /api/v1/tenants/{t}/models
{
  "name":"fraud-detector",
  "version":"v1.2",
  "artifact_uri":"s3://bucket/..",
  "schema":"...",
  "metadata":{...}
}

POST /api/v1/tenants/{t}/deployments
{
  "model_ref":"fraud-detector:v1.2",
  "env":"prod",
  "strategy":"canary",
  "strategy_params":{"steps":[{"traffic":5,"duration":"10m"},{"traffic":50,"duration":"30m"}]},
  "metrics":[{"name":"latency_p95","type":"decrease","threshold":0.05},{"name":"auroc","type":"increase","threshold":0.01}],
  "auto_rollback": true
}

POST /api/v1/tenants/{t}/experiments
{
  "name":"feat-X-ab-test",
  "variants":[{"model":"m:v1","traffic":50},{"model":"m:v2","traffic":50}],
  "duration":"7d",
  "metrics":["conversion","precision"],
  "assignment_rule":"user_id_hash%100"
}

POST /api/v1/tenants/{t}/models/{name}:{version}/promote
{ "from":"staging","to":"prod","run_tests":true }

import torch, random, numpy as np

def save_checkpoint(path, model, optimizer, scheduler, scaler, epoch, global_step, grad_accum,
                    dataloader_rng_state=None):
    ckpt = {
        "model": model.state_dict(),
        "optimizer": optimizer.state_dict(),
        "scheduler": scheduler.state_dict() if scheduler is not None else None,
        "scaler": scaler.state_dict() if scaler is not None else None,
        "epoch": epoch,
        "global_step": global_step,
        "grad_accum": grad_accum,
        # RNG states for deterministic resume
        "rng_python": random.getstate(),
        "rng_numpy": np.random.get_state(),
        "rng_torch_cpu": torch.get_rng_state(),
        "rng_torch_cuda": torch.cuda.get_rng_state_all() if torch.cuda.is_available() else None,
        "dataloader_rng": dataloader_rng_state  # optional: e.g., sampler epoch/position
    }
    torch.save(ckpt, path)

def load_checkpoint(path, model, optimizer, scheduler, scaler):
    ckpt = torch.load(path, map_location="cpu")
    model.load_state_dict(ckpt["model"])
    optimizer.load_state_dict(ckpt["optimizer"])
    if scheduler is not None and ckpt["scheduler"] is not None:
        scheduler.load_state_dict(ckpt["scheduler"])
    if scaler is not None and ckpt.get("scaler") is not None:
        scaler.load_state_dict(ckpt["scaler"])

    # restore RNGs
    random.setstate(ckpt["rng_python"])
    np.random.set_state(ckpt["rng_numpy"])
    torch.set_rng_state(ckpt["rng_torch_cpu"])
    if torch.cuda.is_available() and ckpt["rng_torch_cuda"] is not None:
        torch.cuda.set_rng_state_all(ckpt["rng_torch_cuda"])

    return ckpt["epoch"], ckpt["global_step"], ckpt["grad_accum"], ckpt.get("dataloader_rng")

# Training loop with gradient accumulation
def train(train_loader, model, optimizer, scheduler=None, scaler=None,
          grad_accum_steps=4, start_epoch=0, start_step=0, device="cuda"):
    model.to(device)
    global_step = start_step
    for epoch in range(start_epoch, num_epochs):
        # if using DistributedSampler: sampler.set_epoch(epoch) for deterministic order
        if hasattr(train_loader.sampler, "set_epoch"):
            train_loader.sampler.set_epoch(epoch)

        optimizer.zero_grad()
        for step, batch in enumerate(train_loader):
            # Optionally skip already-processed batches when resuming mid-epoch:
            if epoch == start_epoch and step < (start_step % len(train_loader)):
                continue

            inputs, targets = batch
            inputs, targets = inputs.to(device), targets.to(device)

            with torch.cuda.amp.autocast(enabled=(scaler is not None)):
                outputs = model(inputs)
                loss = loss_fn(outputs, targets) / grad_accum_steps

            if scaler is not None:
                scaler.scale(loss).backward()
            else:
                loss.backward()

            # accumulate gradients; step every grad_accum_steps
            if (global_step + 1) % grad_accum_steps == 0:
                if scaler is not None:
                    scaler.step(optimizer)
                    scaler.update()
                else:
                    optimizer.step()
                if scheduler is not None:
                    scheduler.step()
                optimizer.zero_grad()

                # save checkpoint periodically
                if global_step % save_every_steps == 0:
                    save_checkpoint(checkpoint_path, model, optimizer, scheduler, scaler,
                                    epoch, global_step, grad_accum_steps)
            global_step += 1