bayesian-dpddm 1.0.4

Implements Bayesian D-PDDM for Post-Deployment Deterioration Monitoring of ML models.

Bayesian D-PDDM

Bayesian implementation of the D-PDDM algorithm for post-deployment deterioration monitoring. Bayesian D-PDDM is a Bayesian approximation to the D-PDDM algorithm which provably monitors model deterioration at deployment time. Bayesian D-PDDM:

• Flags deteriorating shifts in the unsupervised deployment data distribution
• Resists flagging non-deteriorating shifts, unlike classical OOD detection leveraging distances and/or metrics between data distributions.

Installation and Requirements

This implementation requires python>=3.11.

The easiest way to install bayesian_dpddm is with pip:

pip install bayesian_dpddm

You can also install by cloning the GitHub repo:

# Clone the repo
git clone https://github.com/teivng/bayesian_dpddm.git

# Navigate into repo directory 
cd bayesian_dpddm

# Install the required dependencies
pip install .

Sweeping Instructions

All experiments are running from the root directory of the repo. We use hydra-core as an argparse on steroids, in tandem with wandb for sweeping. For a sweeping configuration experiments/my_sweep.yaml, run:

wandb sweep experiments/my_sweep.yaml

for which wandb responds with:

wandb: Creating sweep from: experiments/my_sweep.yaml
wandb: Creating sweep with ID: <my_sweep_id>
wandb: View sweep at: https://wandb.ai/<my_wandb_team>/<my_project>/sweeps/<my_sweep_id>

Sweeping locally

Run sweep agent with: wandb agent <my_wandb_team>/<my_project>/<my_sweep_id>.

Sweeping with slurm

sbatch files format pre-configured for the Vaughan cluster. Edit the templates at will.

We execute a script to replace the wandb agent ... line in our .slrm files:

./experiments/replace_wandb_agent.sh "wandb agent <my_wandb_team>/<my_project>/<my_sweep_id>"

Finally, spam jobs on the cluster and maximize your allocation per qos:

./experiments/sbatch_all.sh

Edit this script per your allocation.

Usage and Tutorials

Coming soon.

Citation

Coming soon.