Skip to main content

Hyperparameter tuning

Project description

# autotune
Hyperparameter tuning on GPUs

[![Build Status](https://travis-ci.org/vzhong/autotune.svg?branch=master)](https://travis-ci.org/vzhong/autotune)

## Installation

```bash
pip install git+git://github.com/vzhong/autotune.git

# Or get it straight from PyPI

pip install autotune
```

## Usage

You can use the binary:

```bash
autotune -h
```

Or use it programmatically:

```python
from autotune.tuner import RandomSearch
from autotune.spec import Spec

config = Spec.load('myconf.json')
tuner = RandomSearch('myprog.bin', config)
tuner.tune(2, out='output')
```

where `myconf.json` looks something like:

```json
{
"foo": [-1, 1],
"bar": [2.0, 3.0]
}
```

This will run 2 commands `myprog.bin --foo $FOO --bar $BAR` where `$FOO` is an integer sampled between `-1` and `1` and `$BAR` is a float sampled between `2.0` and `3.0`.
You can pass in an optional parameter `name='nickname'`, which will add to the command `--nickname $HASH`, where `$HASH` is a hash of the specific parameters used for this command.
You can also pass in an optional parameter `gpu=True`, which will queue jobs onto aavailable GPUs.
The command then becomes `CUDA_VISIBLE_DEVICES=$GPU myprog.bin --foo $FOO --bar $BAR --gpu 0`, where `$GPU` is a free GPU (e.g. no memory usage).


Project details


Release history Release notifications

This version
History Node

0.0.3

History Node

0.0.2

History Node

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
autotune-0.0.3-py3-none-any.whl (6.4 kB) Copy SHA256 hash SHA256 Wheel py3 Jan 3, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page