# Experiment Manager built using Rust
[](https://crates.io/crates/expman)
[](https://pypi.org/project/expman-rs/)
[](https://github.com/lokeshmohanty/expman-rs)
[](https://opensource.org/licenses/MIT)
[](https://lokeshmohanty.github.io/expman-rs)
High-performance experiment manager written in Rust, with a Python wrapper for non-blocking logging, a live web dashboard, and a friendly CLI.
## Features
- **Non-blocking Python logging**: `log_vector()` is a ~100ns channel send — never blocks your training loop
- **Live dashboard**: SSE-powered real-time metric streaming, run comparison charts, artifact browser
- **Scalar metric filtering**: Toggle which metric columns appear in the runs table with one click
- **Single binary**: CLI + web server in one `exp` binary — no Python runtime needed for the server
- **Efficient storage**: Batched Arrow/Parquet writes, not per-step read-concat-write
- **Nix dev environment**: Reproducible with `nix develop`
## Screenshots
<p align="center">
<img src="https://github.com/lokeshmohanty/expman-rs/raw/main/assets/dashboard-interactive.png" width="400" />
<img src="https://github.com/lokeshmohanty/expman-rs/raw/main/assets/dashboard-metrics.png" width="400" />
</p>
<p align="center">
<img src="https://github.com/lokeshmohanty/expman-rs/raw/main/assets/dashboard-artifacts.png" width="400" />
<img src="https://github.com/lokeshmohanty/expman-rs/raw/main/assets/dashboard-interactive-notebook.png" width="400" />
</p>
## Installation
### From Cargo
```bash
cargo install expman
```
### From PYPI
```bash
pip install expman-rs
```
### Alternatively: Download from GitHub Releases
- **Direct Download**: Download the pre-built standalone `exp` binaries or Python wheels directly from our [GitHub Releases](https://github.com/lokeshmohanty/expman-rs/releases).
## Quick Start
### Python
**Option A: Global Singleton (Easiest)**
```python
import expman as exp
exp.init("resnet_cifar10")
exp.log_params({"lr": 0.001})
exp.log_vector({"loss": 0.5}, step=0)
# Auto-closes on script exit
```
**Option B: Context Manager (Recommended for scope control)**
```python
from expman import Experiment
with Experiment("resnet_cifar10") as exp:
exp.log_vector({"loss": 0.5}, step=0)
```
## For Rust
Basic usage:
```rust
use expman::{ExperimentConfig, LoggingEngine, RunStatus};
fn main() -> anyhow::Result<()> {
let config = ExperimentConfig::new("my_rust_exp", "./experiments");
let engine = LoggingEngine::new(config)?;
engine.log_vector([("loss".to_string(), 0.5.into())].into(), Some(0));
engine.close(RunStatus::Finished);
Ok(())
}
```
### Dashboard
```bash
exp serve ./experiments
# Open http://localhost:8000
```
### CLI
```bash
exp list ./experiments # list all experiments
exp list ./experiments -e resnet # list runs for an experiment
exp inspect ./experiments/resnet/runs/20240101_120000
exp clean resnet --keep 5 --force # delete old runs
exp export ./experiments/resnet/runs/20240101_120000 --format csv
```
## Development
Please see [CONTRIBUTING.md](CONTRIBUTING.md) for detailed instructions on setting up your local environment, building the Python bindings, and important git configuration notes.
```bash
nix develop # enter dev shell
just test # run all tests
just dev-py # build Python extension (uv pip install -e .)
just serve ./experiments # start dashboard
just watch # watch mode for tests
just build-docs # build and open documentation
```
## Documentation
For detailed usage, refer to the source code modules in `src/`:
- [`cli`](src/cli/) - Command-line interface definitions and references.
- [`core`](src/core/) - Core high-performance async Rust logging engine.
- [`wrappers/python`](src/wrappers/python/) - Python extension Rust bindings.
- [`api`](src/api/) - Axum web server and SSE live streaming API.
- [`app`](src/app/) - Leptos frontend web application.
- [`python-package`](wrappers/python/) - Python package code and tests.
### Dashboard Features
- **Live Metrics**: Real-time SSE streaming of experiment metrics and logs.
- **Live Jupyter Notebooks**: Instantly spawn a live Jupyter instance natively bound to any run's execution environment directly from the UI, with auto-generated analytics boilerplate (Polars).
- **Scalar Filter**: Toggle individual metric columns in the Runs table via chip buttons — no page reload.
- **Deep Inspection**: View detailed run configurations, metadata, and artifacts.
- **Artifact Browser**: Preview `parquet`, `csv`, and other files directly in the browser.
- **Comparison View**: Overlay multiple runs on a shared timeline for analysis.
- **Server-side filtering**: Pass `?metrics=loss,acc` to `/api/experiments/:exp/runs` to limit which scalars are returned.
## Examples
Practical code samples are provided in the [examples/](examples/) directory. The Python example demonstrates logging metrics, alongside generating and storing rich media artifacts (audio, video, plots) directly natively.
- **Python**: [examples/python/basic_training.py](examples/python/basic_training.py)
- **Rust**: [examples/rust/logging.rs](examples/rust/logging.rs)
To run the Python examples, ensure you have built the extension first with `just dev-py` and installed the dev dependencies (`uv pip install -e ".[dev]"`).
To run the Rust example, use:
```bash
cargo run --example logging --features cli
```
## Experiments Layout
```
experiments/
my_experiment/
experiment.yaml # display name, description
20240101_120000/ # run directory
metrics.parquet # all logged metrics (Arrow/Parquet)
config.yaml # logged params/hyperparameters
run.yaml # run metadata (status, duration, timestamps)
run.log # text log
artifacts/ # user-saved files
```