MLflow vs DVC vs W&B: MNIST Training 3 Ways Compared

The Same Model, Three Different Tracking Nightmares

Here's something that should be simple: train a basic CNN on MNIST, log metrics, save the model. I ran this exact workflow through MLflow, DVC, and Weights & Biases to see which one actually gets out of your way.

The answer wasn't what I expected.

Most comparisons focus on feature matrices. "MLflow has a model registry!" "W&B has beautiful dashboards!" "DVC handles large files!" Sure. But what happens when you just want to track a training run at 11pm and not spend 45 minutes fighting configuration files?

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Setting Up the Baseline: One CNN, Three Trackers

Let me establish what we're working with. This is deliberately simple—a 2-conv-layer CNN that gets ~99% accuracy on MNIST in under 5 epochs. The goal isn't model performance. It's tracking overhead.

python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import time

class SimpleCNN(nn.Module):
    def __init__(self):

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*Continue reading the full article on [TildAlice](https://tildalice.io/mlflow-dvc-wandb-mnist-comparison/)*