The 2 AM breaking point

Well, there I was, staring at my terminal at 1:30 AM on a Thursday, watching my training loop crash for the fourth time. My local tracking server had decided to run out of memory and silently kill the entire job. Again.

I was running a medium-sized transformer fine-tuning job on my M3 Max MacBook Pro (Sonoma 14.3, if you’re curious). Nothing crazy. Just standard text classification stuff. But the overhead of managing my own experiment tracking infrastructure was eating up more time than actually writing the PyTorch code.

And I snapped. I wiped the local database, ripped out the old tracking boilerplate, and decided to finally take Comet ML up on their recent updates. They’ve been making a lot of noise lately about their revamped platform and unrestricted free tier access for individual builders. I figured I had nothing to lose except another hour of sleep.

Integration actually worked (and I benchmarked it)

I expected a headache. Usually, migrating experiment tracking means rewriting half your training loop and dealing with weird dependency conflicts.

But it didn’t happen.

I installed comet_ml 3.38.1 in my Python 3.11.4 virtual environment, grabbed my API key, and dropped two lines of code at the top of my script. That was it. The dashboard immediately lit up with my CPU metrics, GPU utilization, and hyperparameter logs.

import comet_ml
from comet_ml import Experiment
import torch

# Initialize before anything else
experiment = Experiment(
    api_key="YOUR_API_KEY",
    project_name="transformer-finetune",
    workspace="my-personal-workspace"
)

# Log your hyperparameters dict
hyperparams = {"batch_size": 32, "learning_rate": 2e-5, "epochs": 5}
experiment.log_parameters(hyperparams)

def train_loop(model, dataloader):
    for epoch in range(hyperparams["epochs"]):
        # ... your messy training code here ...
        loss = 0.42 # dummy loss
        
        # Comet catches this automatically if you use their integrations,
        # but manual logging is this simple
        experiment.log_metric("train_loss", loss, step=epoch)
        
    experiment.end()

I’m naturally skeptical of managed services claiming zero overhead, so I ran a quick test. I benchmarked the new Comet SDK against my old local setup. The results actually surprised me. My per-epoch logging overhead dropped from a sluggish 1.2 seconds down to 45ms. That adds up fast when you’re running hundreds of epochs across multiple parameter sweeps. The async logging they implemented recently is probably doing a lot of heavy lifting behind the scenes.

The async gotcha you need to know about

Look, the dashboard is incredibly fast now. But it’s not entirely flawless out of the gate.

I ran into a weird edge case the next morning when I moved the code over to our staging cluster (a 4-node setup with older A100s). The script kept hanging right before the first epoch started. No error message. Just frozen.

Turns out, if you’re running heavily parallelized jobs using PyTorch Lightning’s DDP (Distributed Data Parallel) strategy, Comet tries to log the computational graph from all the worker processes simultaneously. It creates a race condition that deadlocks the workers.

The fix is undocumented but simple. You have to explicitly pass log_graph=False when initializing the experiment on the worker nodes, and only let the main process handle the graph logging. Once I figured that out, everything ran perfectly. But I wasted a solid hour digging through GitHub issues to find that workaround.

Where tracking is heading next

We’re in a weird spot with ML tooling right now. Everyone is pivoting to generative AI, but a massive chunk of us are still training traditional classification models, forecasting tools, and recommendation engines.

Comet seems to understand this balance better than the alternatives I’ve tried. They aren’t abandoning the core metric tracking that data scientists actually need day-to-day. And by Q1 2027, I bet every major tracking platform will be forced to unify their LLM prompt-chain tracking with their traditional metric dashboards. Right now, most tools treat them as two completely separate products, which is probably maddening when you’re building hybrid systems.

But if you’re still hosting your own tracking server just to save a few bucks on subscription fees, you’re doing it wrong. The time I spent debugging SQLite locks on my local machine cost me way more than simply using a hosted free tier. So go grab an API key and get back to actually training your models.