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https://github.com/TUM-DAML/seml

SEML: Slurm Experiment Management Library
https://github.com/TUM-DAML/seml

experiment-manager experiment-tracking hyperparameter-optimization orchestration slurm slurm-workload-manager utility

Last synced: 27 days ago
JSON representation

SEML: Slurm Experiment Management Library

Host: GitHub
URL: https://github.com/TUM-DAML/seml
Owner: TUM-DAML
License: other
Created: 2019-10-13T22:33:46.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2024-10-11T13:05:34.000Z (2 months ago)
Last Synced: 2024-10-30T10:55:03.308Z (about 1 month ago)
Topics: experiment-manager, experiment-tracking, hyperparameter-optimization, orchestration, slurm, slurm-workload-manager, utility
Language: Python
Homepage:
Size: 1.64 MB
Stars: 168
Watchers: 4
Forks: 30
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-institutional-hpc - SEML - SEML: Slurm Experiment Management Library. Integration of the sacred experiment manager and SLURM. (SLURM wrappers and libraries)

README

![Github Actions](https://github.com/TUM-DAML/seml/workflows/Test/badge.svg)

# `SEML`: Slurm Experiment Management Library
**`SEML`** is the missing link between the open-source workload scheduling system `Slurm`, the experiment management tool `sacred`, and a `MongoDB` experiment database. It is lightweight, hackable, written in pure Python, and scales to thousands of experiments.

Keeping track of computational experiments can be annoying and failure to do so can lead to lost results, duplicate running of the same experiments, and lots of headaches.
While workload scheduling systems such as [`Slurm`](https://slurm.schedmd.com/overview.html) make it easy to run many experiments in parallel on a cluster, it can be hard to keep track of which parameter configurations are running, failed, or completed.
[`sacred`](https://github.com/IDSIA/sacred) is a great tool to collect and manage experiments and their results, especially when used with a [`MongoDB`](https://www.mongodb.com/). However, it is lacking integration with workload schedulers.

**`SEML`** enables you to
* very easily define hyperparameter search spaces using YAML files,
* run these hyperparameter configurations on a compute cluster using `Slurm`,
* and to track the experimental results using `sacred` and `MongoDB`.

In addition, **`SEML`** offers many more features to make your life easier, such as
* automatically saving and loading your source code for reproducibility,
* easy debugging on Slurm or locally,
* automatically checking your experiment configurations,
* extending Slurm with local workers,
* and keeping track of resource usage (experiment runtime, RAM, etc.).

## Get started
### New projects
The fastest way to get started with `SEML` is via [`uv`](https://docs.astral.sh/uv/):
1. Install `uv`:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Setup a new project
```bash
# uvx will execute `SEML` in a temporary virtual environment
# and run it to setup your new project.
uvx seml project init my_new_project
```
3. Setup a virtual environment
```bash
cd my_new_project
uv sync
```
4. Activate your virtual environment
```bash
source .venv/bin/activate
```
5. Configure `SEML`:
```bash
seml configure
```

When executing `SEML` make sure to always use the `seml` command from your project's virtual environment and only use `uvx seml` for high-level commands that do not affect experiments (like setting up new projects).

### Existing projects
If you want to include `SEML` into existing projects, you can install it via:
```bash
pip install seml
```
Then configure your MongoDB via:
```bash
seml configure
```

### SSH Port Forwarding
If your MongoDB is only accessible via an SSH port forward, **`SEML`** allows you to directly configure this as well if you install the `ssh_forward` dependencies via:
```bash
pip install seml[ssh_forward]
```
It remains to configure the SSH settings:
```bash
seml configure --ssh_forward
```

### Development
For development, we recommend [`uv`](https://docs.astral.sh/uv/) which you can install via
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
Setup the right environment use and activate it:
```bash
uv sync --locked
source .venv/bin/activate
```
Alternatively, you can install the repository in any Python environment via:
```bash
pip install -e .[dev]
```

#### Pre-commit hooks
Make sure to install the pre-commit hooks via
```bash
pre-commit install
```

## Documentation
Documentation is available in our [docs.md](docs.md) or via the CLI:
```python
seml --help
```

## Example
See our simple [example](examples) to get familiar with how **`SEML`** works.

## CLI completion
SEML supports command line completion. To install this feature run:
```bash
seml --install-completion {shell}
```

If you are using the zsh shell, you might have to append `compinit -D` to the `~/.zshrc` file (see this [issue](https://github.com/tiangolo/typer/issues/180#issuecomment-812620805)).

## Slurm version

SEML should work with Slurm 18.08 and above out of the box. Version 17.11 and earlier do not have a SIGNALING job state, which you have to remove from the SLURM_STATES defined in SEML's settings (`seml/settings.py`). Earlier versions have not been tested and might have other issues.

## Contact
Contact us at [email protected], [email protected], or [email protected] for any questions.

## Cite
When you use SEML in your own work, please cite the software along the lines of the following bibtex:

```
@software{seml_2023,
author = {Z{\"u}gner, Daniel and Gasteiger, Johannes and Gao, Nicholas and Dominik Fuchsgruber},
title = {{SEML: Slurm Experiment Management Library}},
url = {https://github.com/TUM-DAML/seml},
version = {0.4.0},
year = {2023}
}
```