Alternatives to Weights & Biases for nanoGPT | Generated by AI

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Question: What are good alternatives to Weights & Biases for experiment tracking, and can they be used with nanoGPT training?

Answer

Yes, several solid alternatives exist depending on your priorities. For nanoGPT training specifically, your best bets are MLflow (most flexible) or Aim (lighter, better UI), with a hybrid option using both.

Quick Comparison

MLflow leads in flexibility and self-hosted deployments with strong model registry capabilities and minimal vendor lock-in, while Aim provides lightweight tracking with superior UI/UX for run comparison and visualization. Primary competitors include Neptune.ai, Comet ML, ClearML, and Aim, offering similar experiment tracking functionalities.

Open-source, self-hosted, zero cloud dependency. MLflow Tracking logs data to a local mlruns directory by default, with ability to configure remote databases and cloud storage.

Install:

pip install mlflow

Integration with nanoGPT — modify training loop:

import mlflow
import torch
from model import GPT  # from nanoGPT

mlflow.set_experiment("nanoGPT-baseline")

with mlflow.start_run(run_name="run-1"):
    # Log hyperparameters
    mlflow.log_param("n_layer", 6)
    mlflow.log_param("n_head", 6)
    mlflow.log_param("n_embd", 384)
    mlflow.log_param("learning_rate", 3e-4)
    mlflow.log_param("batch_size", 64)

    model = GPT(config)
    optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)

    for step in range(max_iters):
        logits, loss = model(X, Y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if step % 100 == 0:
            mlflow.log_metric("train_loss", loss.item(), step=step)

    # Save checkpoint
    torch.save(model.state_dict(), "model.pt")
    mlflow.log_artifact("model.pt")

View experiments:

mlflow ui
# Open http://localhost:5000

Focused on training tracking, with superior UI scalability — can handle thousands of metrics smoothly, outperforming MLflow UI when exploring hundreds of runs. Install:

pip install aim

nanoGPT integration:

from aim import Run

run = Run()
run["hparams"] = {
    "n_layer": 6,
    "n_head": 6,
    "n_embd": 384,
    "learning_rate": 3e-4,
}

for step in range(max_iters):
    logits, loss = model(X, Y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if step % 100 == 0:
        run.track(loss.item(), name="train_loss", step=step)

View dashboard:

aim up
# Open http://localhost:43800

Option 3: MLflow + Aim Hybrid (Best UI + Full Control)

Log with MLflow, visualize with Aim’s superior UI. Install aimlflow:

pip install aimlflow

Train with MLflow as shown above, then convert:

aimlflow sync -m /path/to/mlruns
aim up

This gives you MLflow’s full artifact/model registry plus Aim’s beautiful comparison interface — zero code changes.

Option 4: Minimal DIY (If You Want Full Autonomy)

Just log locally to JSON:

import json
from pathlib import Path

log_dir = Path("logs")
log_dir.mkdir(exist_ok=True)

metrics = {"train_loss": [], "step": []}

for step in range(max_iters):
    loss = ...
    metrics["train_loss"].append(loss.item())
    metrics["step"].append(step)

with open(log_dir / "metrics.json", "w") as f:
    json.dump(metrics, f)

Then analyze in Jupyter or plot with matplotlib. Ultra-simple, zero dependencies.

My Take for Your Workflow

Given you’re on M2 + RTX 4070 workstation with 62GB RAM:

  1. Start with MLflow — local-first, works everywhere, logging is 5 lines, UI is browser-based
  2. If you find MLflow UI slow (unlikely for nanoGPT scale), add aimlflow layer for Aim’s UI
  3. Skip cloud entirely — you don’t need W&B’s collab features for solo training

The mlruns/ folder stays local, versioned, reproducible. No cloud sync delays, no rate limits, no costs.

References:

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