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MOHB: Multiobjective Hyperband for Hyperparameter Optimization

Getting started

https://github.com/ayushi-3536/mo_hyperband.git
cd mo_hyperband/
pip install -r requirements.txt

Tutorials/Example notebooks

To run PyTorch example: (note additional requirements)

PYTHONPATH=$PWD python examples/01_mo_pytorch_mnist_hpo.py \
     --min_budget 1 --max_budget 3 --verbose --runtime 60

MOHB Hyperparameters

The MO Hyperband components: MO_HB uses scalarization algorithms adapted from MOASHA to sort function values for multiple objectives

  • promotion algorithm: Allows 'Random Weight', 'Parego', 'Golovin'.

The Hyperband components:

  • min_budget: Needs to be specified for every MOHB instantiation and is used in determining the budget spacing for the problem at hand.
  • max_budget: Needs to be specified for every MOHB instantiation. Represents the full-budget evaluation or the actual black-box setting.
  • eta: (default=3) Sets the aggressiveness of Hyperband's aggressive early stopping by retaining 1/eta configurations every round

Running MOHB in a parallel setting

MOHB has been designed to interface a Dask client. MOHB can either create a Dask client during instantiation and close/kill the client during garbage colleciton. Or a client can be passed as an argument during instantiation.

  • Setting n_workers during instantiation
    If set to 1 (default) then the entire process is a sequential run without invoking Dask.
    If set to >1 then a Dask Client is initialized with as many workers as n_workers.
    This parameter is ignored if client is not None.
  • Setting client during instantiation
    When None (default), the a Dask client is created using n_workers specified.
    Else, any custom configured Dask Client can be created and passed as the client argument to MOHB.

Using GPUs in a parallel run

Certain target function evaluations (especially for Deep Learning) requires computations to be carried out on GPUs. The GPU devices are often ordered by device ID and if not configured, all spawned worker processes access these devices in the same order and can either run out of memory, or not exhibit parallelism.

For n_workers>1 and when running on a single node (or local), the single_node_with_gpus can be passed to the run() call to MOHB. Setting it to False (default) has no effect on the default setup of the machine. Setting it to True will reorder the GPU device IDs dynamically by setting the environment variable CUDA_VISIBLE_DEVICES for each worker process executing a target function evaluation. The re-ordering is done in a manner that the first priority device is the one with the least number of active jobs assigned to it by that MOHB run.

To run the PyTorch MNIST example on a single node using 2 workers:

PYTHONPATH=$PWD python examples/01_mo_pytorch_mnist_hpo.py --min_budget 1 --max_budget 3 \
  --verbose --runtime 60 --n_workers 2 --single_node_with_gpus

Multi-node runs

Multi-node parallelism is often contingent on the cluster setup to be deployed on. Dask provides useful frameworks to interface various cluster designs. As long as the client passed to MOHB during instantiation is of type dask.distributed.Client, MOHB can interact with this client and distribute its optimisation process in a parallel manner.

For instance, Dask-CLI can be used to create a dask-scheduler which can dump its connection details to a file on a cluster node accessible to all processes. Multiple dask-worker can then be created to interface the dask-scheduler by connecting to the details read from the file dumped. Each dask-worker can be triggered on any remote machine. Each worker can be configured as required, including mapping to specific GPU devices.

Some helper scripts can be found here, that can be used as reference to run MOHB in a multi-node manner on clusters managed by SLURM. (not expected to work off-the-shelf)

To run the PyTorch MNIST example on a multi-node setup using 4 workers:

bash utils/run_dask_setup.sh -f dask_dump/scheduler.json -e env_name -n 4
sleep 5
PYTHONPATH=$PWD python examples/01_mo_pytorch_mnist_hpo.py --min_budget 1 --max_budget 3 \
  --verbose --runtime 60 --scheduler_file dask_dump/scheduler.json