# How to Share a Comet ML Experiment With People Outside the Workspace

Canonical: https://commareports.com/share-comet-report
Published: 2026-09-14

> Comet panels and reports need a workspace seat. Export the figures and metrics into a page and share a URL your stakeholders can actually open.

# Share a Comet ML experiment

You ran forty variants. Comet logged all of them, and the panel comparing the
top five is genuinely good — the loss curves, the confusion matrices, the
parameter sweep.

The person who decides whether this model ships is a product director without a
Comet seat, and the comparison they need is between two of those forty runs.

## Tracking and communicating are different jobs

Comet is built for the first: log everything, query it later, never lose a run.
That is the right design for an experiment tracker and the wrong shape for a
decision document.

A stakeholder does not want a workspace. They want one page that says *we
should ship variant 31, here is why, here is what it costs*, with the figures
underneath. Forty runs in a comparison table is an invitation to ask about run
17, which nobody needs to discuss.

## Export the argument

Pull the figures and metrics from the experiment and assemble one page:

```python
exp = comet_ml.API().get_experiment("ws", "churn", exp_key)
metrics = {m["metricName"]: m["metricValue"] for m in exp.get_metrics_summary()}
# fetch logged figures, then render one HTML document and publish it
```

Structure it as an argument rather than a dump:

1. **The recommendation**, in one sentence.
2. **The comparison** — candidate against the model in production, on the
   metric that matters and the one you are worried about.
3. **The figures** — the two or three that carry the claim.
4. **The appendix** — hyperparameters, data version, run ids in Comet for
   anyone who wants to dig.

The Comet run links in the appendix are what keep this honest: an interested
reader can go from the claim to the raw run in one click, if they have a seat.

## Public projects are not the answer

Comet's public-project setting makes the whole project world-readable. For
published research that is fine; for a churn model trained on customer data it
is a disclosure decision that should not be made to solve a sharing problem.

A restricted report shows the people who need it exactly what they need — see
[security](/security).

## Questions land on the figure

The reply you want is not "looks good". It is "why does recall drop on the
held-out set?", asked on the chart that shows recall dropping — see
[commenting on HTML](/comment-on-html). That question, answered in place,
becomes the record of why the model shipped, which is the artifact you will
want when someone asks in six months.

## Worth knowing

- **Publish from the training job.** At the end of a run, build the page and
  update the same report id, so the link in the ticket always shows the current
  best candidate.
- **Static figures, not embedded dashboards.** A page that tries to embed
  live Comet panels is a page that will ask your reader to log in.
- **5 MB per report body.** Downscale plots; nobody needs a 4000px loss curve.

## Try it

Free — unlimited reports, commenters and revisions.

**[Read the API reference →](/docs/api)**

### Related

- [Share a ClearML experiment](/share-clearml-report) ·
  [Share a Weights & Biases report](/share-wandb-report)
- [Share an MLflow report](/share-mlflow-report) ·
  [Share a TensorBoard report](/share-tensorboard-report)
- [Comma for data scientists](/for/data-scientists) ·
  [Commenting on HTML](/comment-on-html)