Your best model from last month?
You probably can't reproduce it.
And when someone asks what changed… you don't have an answer.
One command. A complete record of what actually happened. Works with any training script — no code changes, no framework.
Let your agents roar.
Most ML teams can’t tell you what’s in their own model.
Not because they’re careless — loggers require constant manual work to maintain lineage. Lineage becomes ongoing technical debt. It’s never the highest priority. So in the end you’re guessing what’s changed.
Last month's best model. Nobody can reproduce it.
Two engineers changed the config. Neither remembers what.
A checkpoint in S3. No one knows what code produced it.
"We have datasets that I couldn't trace exactly how they were put together."
— ML Engineer, leading AI company
These aren't edge cases.
They're last quarter.
An H100 job finishes. Best result in weeks. The engineer who launched it had local changes that never got committed. The model is unreproducible. The $12,000 is gone.
48 hours tracking down which params.yaml produced the weights in s3://bucket/model_v2_FIXED_final/. Nobody knows. You try to reconstruct it from memory.
Your lead researcher accepted another offer. The only person who knows how the SOTA model was actually configured is walking out the door. That knowledge leaves with her.
None of these required an MLOps team to prevent. Just accurate lineage.
One command.
No instrumentation.
Lineage captured at runtime.
No code changes. No frameworks. No loggers. No infrastructure lock-in.
Just roar run before your existing command.
A runtime observer records what actually happened — not what a logger remembered to log. If it ran, roar saw it.
- env & deps
- data & S3 objects
- config & args
- git SHA & diff
- GPU / CUDA state
- model artifacts
Capture what ran. Store the record. Control what runs next.
Three components give you a complete record of how your models are actually built — and what gets to run next. Use any piece on its own.
If it ran, roar saw it.
A CLI that captures lineage at runtime — data, code, environment, artifacts. No code changes. No loggers. No frameworks.
Read about roar →Every model has a recipe.
A content-addressable registry of every artifact and job. Resolve any artifact's hash back to the code, data, and environment that made it.
Read about GLaaS →Approve before compute.
Training requests as pull requests. Nothing runs until someone — or a policy — says go.
Why TReqs →Outcomes, not dashboards.
Questions you couldn't answer before, now answered on demand.
complete record
Every run captured automatically — a queryable record of what actually happened, not a convention or a log file.
run it again exactly
Re-run any past run with the same env, data, and config. Not "close enough."
what changed
Diff any two runs: code, data, hyperparams, hardware.
artifact trace
Every checkpoint points back to the exact run that produced it.
cost attribution
Know which experiment cost what. Know who spent what.
approve before compute
Nothing runs until someone — or a policy — says go. Runs are reviewed with context before they start.
Decide what gets to run — and why — before the money moves.
Right now, anyone — or any agent — can burn $1,000 on a run. And you find out after.
TReqs turns that into a training request before it runs.
- file a request: config + data + compute budget
- team or policy reviews it before it starts
- roar captures what actually ran
- the request resolves with a real artifact
- Without TReqs: runs happen. You investigate later.
- With TReqs: runs are reviewed with context before they start.
Pull requests for training runs. Control plane for ML compute.
Free for individuals. Paid when you're a team.
Anyone who only needs to read results, browse lineage, or track costs is a free reader seat — including finance and leadership. Paid seats are for people who run or approve.
Individual use. The CLI and hosted public lineage.
- roar CLI, unlimited runs
- hosted GLaaS · public lineage
- 1 writer seat · 1-year retention
- 5 projects · BYO node
Shared lineage, unlimited readers, TReqs coordination.
- everything in Free
- private lineage · unlimited retention
- full TReqs approvals & policies
- unlimited reader seats (free)
- Slack · API & cost attribution
When procurement, security, or scale call.
- everything in Team
- SSO, SAML, audit logs
- fully customizable policies
- dedicated support & SLA
Also a Starter tier at $19/seat for 1–3 ML engineers · see full pricing →
Try it on your last training job.
Thirty seconds to install. No account needed. No code changes.