Workflow Management#

Workflow IDs#

Each workflow has a unique workflow_id. By default, when you call .run() or .run_async(), a random id is generated. It is recommended that you explicitly assign each workflow an id via .run(workflow_id="id").

If .run() is called with a previously created workflow id, the workflow will be resumed from the previous execution.

Workflow Status#

A workflow can be in one of several statuses:




The workflow is currently running in the cluster.


The workflow is queued and waiting to be executed.


This workflow failed with an application error. It can be resumed from the failed task.


This workflow failed with a system error. It can be resumed from the failed task.


The workflow was canceled. Its result is unavailable, and it cannot be resumed.


The workflow has been executed successfully.

Single workflow management APIs#

import ray
from ray import workflow

def task():
    return 3, workflow_id="workflow_id")

# Get the status of a workflow.
    status = workflow.get_status(workflow_id="workflow_id")
    assert status in {
except workflow.exceptions.WorkflowNotFoundError:
    print("Workflow doesn't exist.")

# Resume a workflow.
# return is an ObjectRef which is the result of this workflow

# Cancel a workflow.

# Delete the workflow.

Bulk workflow management APIs#

# List all running workflows.

# List RUNNING and CANCELED workflows.
print(workflow.list_all({"RUNNING", "CANCELED"}))

# List all workflows.

# Resume all resumable workflows. This won't include failed workflow

# To resume workflows including failed ones, use `include_failed=True`
[("workflow_id_1", "RUNNING"), ("workflow_id_2", "RUNNING")]
[("workflow_id_1", "RUNNING"), ("workflow_id_2", "CANCELED")]
[("workflow_id_1", "RUNNING"), ("workflow_id_2", "CANCELED")]
[("workflow_id_1", ObjectRef), ("workflow_id_2", ObjectRef)]
[("workflow_id_1", ObjectRef), ("workflow_id_3", ObjectRef)]

Recurring workflows#

Ray Workflows currently has no built-in job scheduler. You can however easily use any external job scheduler to interact with your Ray cluster (via job submission) to trigger workflow runs.

Storage Configuration#

Ray Workflows supports multiple types of storage backends out of the box, including:

  • Local file system: Data is stored locally. This option is only suitable for single node testing, as the data must be stored on a shared file system (such as NFS) for use with multi-node clusters. To use local storage, specify ray.init(storage="/path/to/storage_dir") or ray start --head --storage="/path/to/storage_dir".

  • S3: This is a popular choice for production environments, as it offers scalable and durable object storage. Enable S3 storage with ray.init(storage="s3://bucket/path") or ray start --head --storage="s3://bucket/path".

Ray utilizes pyarrow internally as the storage engine. For a full list of storage options supported by pyarrow, please refer to the documentation at Pyarrow.fs.FileSystem.


If you are having trouble using a storage option that is supported by pyarrow, make sure that you have the correct version of pyarrow installed. For example, GCS (Google Cloud Storage) filesystem is only supported in pyarrow >= 9.0.

If left unspecified, /tmp/ray/workflow_data will be used for temporary storage. This default setting will only work for single-node Ray clusters.

Concurrency Control#

Ray Workflows supports concurrency control. You can support the maximum running workflows and maximum pending workflows via workflow.init() before executing any workflow. workflow.init() again with a different configuration would raise an error except None is given.

For example, workflow.init(max_running_workflows=10, max_pending_workflows=50) means there will be at most 10 workflows running, and 50 workflows pending. And calling with different values on another driver will raise an exception. If they are set to be None, it’ll use the previous value set.

Submitting workflows when the number of pending workflows is at maximum would raise queue.Full("Workflow queue has been full"). Getting the output of a pending workflow would be blocked until the workflow finishes running later.

A pending workflow has the PENDING status. After the pending workflow gets interrupted (e.g., a cluster failure), it can be resumed. When resuming interrupted workflows that were running and pending with workflow.resume_all(), running workflows have higher priority than pending workflows (i.e. the pending workflows would still likely be pending).


Workflows does not guarantee that resumed workflows are run in the same order .