The fastest path to yourbest-performingmodel
Automate your training pipeline.
Scale your model development without sacrificing control or quality.
Join the waitlist for Revis Cloud:
Or try the CLI now:
pip install revisThe problem
Experimentation loops are repetitive and manual, eating up time and compute.
Manual toil
Repetitive setup and tuning steal focus from model innovation.
Idle compute
GPUs idle between runs while you manage the process.
Scattered results
Results scatter across notebooks, making it hard to build on what worked.
The solution
Point Revis at your training script, set a budget, and let it run. Wake up to a PR with your best model and a full history of what was tried.
Revis goes beyond hyperparameter optimization. It handles the full experimentation loop—architectures, training strategies, data augmentation, not just learning rates and batch sizes.
$ revis loop --budget 4h
Starting experiment loop...
[iter 1] lr=0.001, batch=32 → loss: 2.34
[iter 2] lr=0.003, batch=64 → loss: 1.87
[iter 3] lr=0.003, batch=128 → loss: 1.52
...
Done. Best: iter 14, loss 0.31
PR ready: github.com/you/repo/pull/42
What's next
The CLI is just the start. We're building a managed platform for teams:
Every experiment across your team builds shared context—Revis gets smarter for everyone.
Get notified, review results, and kick off new experiments without leaving Slack.
We handle orchestration, scheduling, and storage. You focus on the models.