Decades of machine learning research at your fingertips.
- Deterministic Blocks
- Reproducible baselines for a variety of tasks
- Integrated Hyperparameter Optimizer (Orion)
- Experiment Tracking
- Model Zoo
- Pretrained Models
- Multi GPU training
- Automatic Checkpointing
- Mixed precision Available
Run any baselines in a few lines of code
$ pip install olympus
$ export OLYMPUS_DATA_PATH=/fast
$ olympus --devices 0 classification --batch-size 32 --epochs 10 --dataset mnist --model resnet18
{
"train_accuracy": 0.6458333333333334,
"train_loss": 2.109870990117391,
"elapsed_time": 9,
"sample_count": 960,
"epoch": 9,
"adversary_accuracy": 0.3020833333333333,
"adversary_loss": 2.234758218129476,
"adversary_distortion": 0.2575291295846303,
"validation_accuracy": 0.5986421725239617,
"validation_loss": 2.108673614815782
}
{
"temperature.gpu": 34.083333333333336,
"utilization.gpu": 10.333333333333334,
"utilization.memory": 0.0,
"memory.total": 32480.0,
"memory.free": 31672.833333333332,
"memory.used": 807.1666666666666
}
Writing a full pipeline has never been easier, even when optimizing over hyper parameters !
.. literalinclude:: ../examples/hpo_simple.py :language: python :linenos:
pip install git+git://github.com/mila-iqia/olympus.git
sudo apt-get install swig
# pip install pyrex
pip install fanova