Docker Integration

If it runs in Docker, we can optimize it

HyperOptimizer is Docker-native. Package your code in a container, and we run hundreds of trials with different hyperparameter values. No SDK, no lock-in, no infrastructure to manage.

Get early access Setup guide

Why Docker?

The universal interface for optimization

Docker is the simplest contract: we give you CLI args, you give us stdout metrics. Everything else is yours.

Any language, any framework

Python, Rust, C++, Go: if it compiles and runs in a container, HyperOptimizer can optimize it. No SDK or client library required.

Minimal integration effort

Two changes to your code: parse --hpo-* CLI arguments and print metrics to stdout. That's the entire contract between your code and our platform.

Complete isolation

Each trial runs in its own container. We never access your filesystem, source code, or data. We only read the metric lines you print to stdout.

Parallel execution

We run multiple containers simultaneously (5 by default). The optimizer uses completed results to intelligently choose the next parameter sets.

How it works

Your role vs. ours

You

Build the Docker image

Run it for each trial with different parameter values

Configure parameters & ranges in the dashboard

Choose the next parameter set (Bayesian optimization)

Parse --hpo-* CLI args in your code

Inject those args at container start

Print metrics to stdout

Collect metrics, rank results, show the dashboard

Example

Two changes to your code

1. Parse CLI args

args = parse_args()
lookback = args.hpo_lookback_window
multiplier = args.hpo_atr_multiplier

result = run_backtest(
    lookback=lookback,
    multiplier=multiplier,
)

2. Print metrics

import json

metrics = {
    "sharpe": result.sharpe,
    "max_drawdown": result.max_dd,
}

for k, v in metrics.items():
    print(f"hpo.metrics.{k}={json.dumps(v)}")

More integrations