VESSL AI Documentation

read_sweep

vessl.read_sweep(
   sweep_name: str, **kwargs
)

Read sweep in the default organization/project. If you want to override the default organization/project, then pass organization_name or project_name as **kwargs. Args

sweep_name (str) : Sweep name.

Example

vessl.read_sweep(
    sweep_name="pitch-lord",
)

list_sweeps

vessl.list_sweeps(
   **kwargs
)

List sweeps in the default organization/project. If you want to override the default organization/project, then pass organization_name or project_name as **kwargs. Example

vessl.list_sweeps()

create_sweep

vessl.create_sweep(
   name: str, algorithm: str, parameters: List[SweepParameter], cluster_name: str,
   command: str, objective: SweepObjective = None, max_experiment_count: int = None,
   parallel_experiment_count: int = None, max_failed_experiment_count: int = None,
   resource_spec_name: str = None, processor_type: str = None, cpu_limit: float = None,
   memory_limit: str = None, gpu_type: str = 'Any', gpu_limit: int = None,
   image_url: str = None, *, early_stopping_name: str = None,
   early_stopping_settings: List[Tuple[str, str]] = None, message: str = None,
   hyperparameters: List[Tuple[str, str]] = None, dataset_mounts: List[str] = None,
   git_ref_mounts: List[str] = None, git_diff_mount: str = None,
   archive_file_mount: str = None, object_storage_mount: str = None,
   root_volume_size: str = None, working_dir: str = None,
   output_dir: str = MOUNT_PATH_OUTPUT, **kwargs
)

Create sweep in the default organization/project. If you want to override the default organization/project, then pass organization_name or project_name as **kwargs. Pass use_git_diff=True if you want to run experiment with uncommitted changes and pass use_git_diff_untracked=True if you want to run untracked changes(only valid if use_git_diff is set). Args

name (str) : Name
objective (Optional[vessl.SweepObjective]) : A sweep objective including goal, metric, and type.
max_experiment_count (Optional[int]) : The maximum number of experiments to run. Required unless grid search.
parallel_experiment_count (Optional[int]) : The number of experiments to run in parallel. Default: 1.
max_failed_experiment_count (Optional[int]) : The maximum number of experiments to allow to fail. Default: 1.
algorithm (str) : Parameter suggestion algorithm. grid, random, or bayesian.
parameters (List[vessl.SweepParameter]) : A list of parameters to search.
- SweepParameter
  - name(str): The names of hyperparameters to search.
  - type(str): int, double, categorical.
  - range(SweepParameterRange): Search range.
    - list(List[str]): A list of values to try. If list is given, min, max and step will be ignored.
    - min(str): The minimum value of the search range (inclusive).
    - max(str): The maximum value of the search range (inclusive).
    - step(Optional[str]): If provided, the values are limited to min + n*step.
cluster_name (str) : Cluster name(must be specified before other options).
command (str) : Start command to execute in experiment container.
resource_spec_name (str) : Resource type to run an experiment (for managed cluster only). Defaults to None.
cpu_limit (float) : Number of vCPUs (for custom cluster only). Defaults to None.
memory_limit (str) : Memory limit (for custom cluster only). Defaults to None. Example: “100Gi”, “500Mi”
gpu_type (str) : GPU type(name) (for custom cluster only). Defaults to “Any”. processor_type(str) cpu or gpu (for custom cluster only). Defaults to None. Example
gpu_limit (int) : Number of GPU cores (for custom cluster only). Defaults to None.
image_url (str) : Kernel docker image URL. Defaults to None.
early_stopping_name (str) : Early stopping algorithm name. Defaults to None.
early_stopping_settings (List[Tuple[str, str]]) : Early stopping algorithm settings. Defaults to None.
message (str) : Message. Defaults to None.
hyperparameters (List[str]) : A list of fixed hyperparameters. Defaults to None.
dataset_mounts (List[str]) : A list of dataset mounts. Defaults to None.
git_ref_mounts (List[str]) : A list of git repository mounts. Defaults to None.
git_diff_mount (str) : Git diff mounts. Defaults to None.
archive_file_mount (str) : Local archive file mounts. Defaults to None.
object_storage_mount (str) : Object storage mounts. Defaults to None.
root_volume_size (str) : Root volume size. Defaults to None.
working_dir (str) : Working directory path. Defaults to None.
output_dir (str) : Output directory path. Defaults to “/output/”.

Example

sweep_objective = vessl.SweepObjective(
    type="maximize",
    goal="0.99",
    metric="val_accuracy",
)

parameters = [
    vessl.SweepParameter(
        name="optimizer",
        type="categorical",
        range=vessl.SweepParameterRange(
            list=["adam", "sgd", "adadelta"]
        )
    ),
    vessl.SweepParameter(
        name="batch_size",
        type="int",
        range=vessl.SweepParameterRange(
            max="256",
            min="64",
            step="8",
        )
    )
]

# Custom Cluster
vessl.create_sweep(
    name="example-sweep-name",
    objective=sweep_objective,
    max_experiment_count=4,
    parallel_experiment_count=2,
    max_failed_experiment_count=2,
    algorithm="random",
    parameters=parameters,
    dataset_mounts=["/input:mnist"],
    cluster_name="dgx-cluster",
    processor_type="gpu",
    gpu_type="NVIDIA-A100-SXM4-80GB",
    gpu_limit=2,
    cpu_limit=30,
    memory_limit="100Gi",
    kernel_image_url="public.ecr.aws/vessl/kernels:py36.full-cpu",
    start_command="pip install requirements.txt && python main.py",
)

# VESSL-Managed Cluster
vessl.create_sweep(
    name="example-sweep-name",
    objective=sweep_objective,
    max_experiment_count=4,
    parallel_experiment_count=2,
    max_failed_experiment_count=2,
    algorithm="random",
    parameters=parameters,
    dataset_mounts=["/input:mnist"],
    cluster_name="aws-apne2",
    kernel_resource_spec_name="v1.cpu-4.mem-13",
    kernel_image_url="public.ecr.aws/vessl/kernels:py36.full-cpu",
    start_command="pip install requirements.txt && python main.py",
)

terminate_sweep

vessl.terminate_sweep(
   sweep_name: str, **kwargs
)

Terminate sweep in the default organization/project. If you want to override the default organization/project, then pass organization_name or project_name as **kwargs. Args

sweep_name (str) : Sweep name.

Example

vessl.terminate_sweep(
    sweep_name="pitch-lord",
)

list_sweep_logs

vessl.list_sweep_logs(
   sweep_name: str, tail: int = 200, **kwargs
)

List sweep logs in the default organization/project. If you want to override the default organization/project, then pass organization_name or project_name as **kwargs. Args

sweep_name (str) : Sweep name.
tail (int) : The number of lines to display from the end. Display all if -1. Defaults to 200.

Example

vessl.list_sweep_logs(
    sweep_name="pitch-lord",
)

get_best_sweep_experiment

vessl.get_best_sweep_experiment(
   sweep_name: str, **kwargs
)

Read sweep and return the best experiment info in the default organization/project. If you want to override the default organization/project, then pass organization_name or project_name as **kwargs. Args

sweep_name (str) : Sweep name.

Example

vessl.get_best_sweep_experiment(
    sweep_name="pitch-lord",
)

YAML Reference

CLI Reference

SDK Reference

Serverless Inference API Reference

Sweep

read_sweep

list_sweeps

create_sweep

terminate_sweep

list_sweep_logs

get_best_sweep_experiment

​read_sweep

​list_sweeps

​create_sweep

​terminate_sweep

​list_sweep_logs

​get_best_sweep_experiment

read_sweep

list_sweeps

create_sweep

terminate_sweep

list_sweep_logs

get_best_sweep_experiment