reference/sdk/sweep.md

---
title: Sweep
version: EN
---

### read_sweep
```python
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**
```python
vessl.read_sweep(
    sweep_name="pitch-lord",
)
```

----

## list_sweeps
```python
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**
```python
vessl.list_sweeps()
```

----

## create_sweep
```python
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**
```python
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
```python
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**
```python
vessl.terminate_sweep(
    sweep_name="pitch-lord",
)
```

----

## list_sweep_logs
```python
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**
```python
vessl.list_sweep_logs(
    sweep_name="pitch-lord",
)
```

----

## get_best_sweep_experiment
```python
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**
```python
vessl.get_best_sweep_experiment(
    sweep_name="pitch-lord",
)
```