HyperSched

An experimental scheduler for accelerated hyperparameter tuning.

People: Richard Liaw, Romil Bhardwaj, Lisa Dunlap, Yitian Zou, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

For questions, open an issue or email rliaw [at] berkeley.edu

Please open an issue if you run into errors running the code!

Overview

HyperSched a dynamic application-level resource scheduler to track, identify, and preferentially allocate resources to the best performing trials to maximize accuracy by the deadline.

HyperSched is implemented as a TrialScheduler of Ray Tune.

Terminology:

Trial: One training run of a (randomly sampled) hyperparameter configuration

Experiment: A collection of trials.

Results:

HyperSched will allocate resources to the top performing trial

HyperSched can perform better than ASHA under time pressure.

Quick Start

This code has been tested with PyTorch 1.13 and Ray 0.7.6.

It is suggested that you install this on a cluster (and not your laptop). You can easily spin up a Ray cluster using the Ray cluster Launcher.

Install with:

pip install ray==0.7.6
git clone https://github.com/ucbrise/hypersched && cd hypersched
pip install -e .

Then, you can run CIFAR with a 1800 second deadline, as below:

python scripts/evaluate_dynamic_asha.py \
    --num-atoms=8 \
    --num-jobs=100 \
    --seed=1 \
    --sched hyper \
    --result-file="some-test.log" \
    --max-t=200 \
    --global-deadline=1800 \
    --trainable-id pytorch \
    --model-string resnet18 \
    --data cifar

See scripts for more usage examples.

Example Ray cluster configurations are provided in scripts/cluster_cfg.

Advanced Usage

Configuring HyperSched

# trainable.metric = "mean_accuracy"
sched = HyperSched(
    num_atoms,
    scaling_dict=get_scaling(
        args.trainable_id, args.model_string, args.data
    ),  # optional model for scaling
    deadline=args.global_deadline,
    resource_policy="UNIFORM",
    time_attr=multijob_config["time_attr"],
    mode="max",
    metric=trainable.metric,
    grace_period=config["min_allocation"],
    max_t= config["max_allocation"],
)

summary = Summary(trainable.metric)

analysis = tune.run(
  trainable,
  name=f"{uuid.uuid4().hex[:8]}",
  num_samples=args.num_jobs,
  config=config,
  verbose=1,
  local_dir=args.result_path
  if args.result_path and os.path.exists(args.result_path)
  else None,
  global_checkpoint_period=600,  # avoid checkpointing completely.
  scheduler=sched,
  resources_per_trial=trainable.to_resources(1)._asdict(),  # initial resources
  trial_executor=ResourceExecutor(
      deadline_s=args.global_deadline, hooks=[summary]
  )
)

Viewing Results

The hypersched.tune.Summary object will log both a text file and also a CSV for "experiment-level" statistics.

HyperSched Imagenet Training on AWS

1. Create an EBS volume with ImageNet (https://github.com/pytorch/examples/tree/master/imagenet)
2. Set the EBS volume for all nodes of your cluster. For example, as seen in scripts/imagenet.yaml;

head_node:
    InstanceType: p3.16xlarge
    ImageId: ami-0d96d570269578cd7
    BlockDeviceMappings:
      - DeviceName: "/dev/sdm"
        Ebs:
          VolumeType: "io1"
          Iops: 10000
          DeleteOnTermination: True
          VolumeSize: 250
          SnapshotId: "snap-01838dca0cbffad5c"

3. Launch the cluster. If you modify the yaml, you can then launch a cluster using ray up scripts/imagenet.yaml. Beware, this will cost some money. If you use the YAML, cluster will then setup a Ray cluster among the nodes launched.

4. Run the following command:

python ~/sosp2019/scripts/evaluate_dynamic_asha.py \
    --redis-address="localhost:6379" \
    --num-atoms=16 \
    --num-jobs=200 \
    --seed=0 \
    --sched hyper \
    --result-file="~/MY_LOG_FILE.log" \
    --max-t=500 \
    --global-deadline=7200 \
    --trainable-id pytorch \
    --model-string resnet50 \
    --data imagenet \

You can use the autoscaler to launch the experiment.

ray exec [CLUSTER.YAML] "<your python command here>"

Note: You may see that for imagenet, HyperSched does not isolate trials effectively (2 trials running by deadline). This is because we set the following parameters:

    if args.data == "imagenet":
        worker_config = {}
        worker_config.update(
            data_loader_pin=True,
            data_loader_workers=4,
            max_train_steps=100,
            max_val_steps=20,
            decay=True,
        )
        config.update(worker_config=worker_config)

This indicates that for the ImageNet experiment, 1 "Trainable iteration" is defined as 100 SGD updates. HyperSched depends on the ASHA adaptive allocation to terminate trials, and a particular setup of ImageNet will not trigger the ASHA termination. Feel free to push a patch for this (or raise an issue if you want me to fix it :).

TODOs

• Move PyTorch Trainable onto ray.experimental.sgd

Talks

Slides presented at SOCC

Cite

The proper citation for this work is:

@inproceedings{Liaw:2019:HDR:3357223.3362719,
 author = {Liaw, Richard and Bhardwaj, Romil and Dunlap, Lisa and Zou, Yitian and Gonzalez, Joseph E. and Stoica, Ion and Tumanov, Alexey},
 title = {HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline},
 booktitle = {Proceedings of the ACM Symposium on Cloud Computing},
 series = {SoCC '19},
 year = {2019},
 isbn = {978-1-4503-6973-2},
 location = {Santa Cruz, CA, USA},
 pages = {61--73},
 numpages = {13},
 url = {http://doi.acm.org/10.1145/3357223.3362719},
 doi = {10.1145/3357223.3362719},
 acmid = {3362719},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Distributed Machine Learning, Hyperparameter Optimization, Machine Learning Scheduling},
}