Integrating TaskVine with Merlin

 Graduate student, Barry Sly-Delgado, completed a summer internship onsite at Lawrence Livermore National Laboratory where he worked on integrating TaskVine with Merlin, an executor for machine learning workflows. Barry worked as a member of the WEAVE team under Brian Gunnarson and Charles Doutriaux. 

Previously, Merlin used Celery to distribute tasks across a compute cluster. With TaskVine's addition, utilization of in-cluster resources (bandwidth, disk) is available for workflow execution. Existing Merlin specifiacations can use TaskVine as a task scheduler with little change to the specification itself.

Merlin works with TaskVine by utilizing the Vine Stem, a DAG manager that borrows the concepts of groups and chains to create workflows from Celery. With this, the Vine Stem sends tasks (Merlin Steps) to the TaskVine manager for execution. Execution of these tasks eventually create a directory hierarchy that previous Merlin workflows already do. In addition to the workflow specification, a Merlin Specification contains specifications for starting workers, which are submitted via a batch system (HTCondor, UGE,Slurm)

                                 Architecture of Merlin with TaskVine

 

 

             Sample Merlin Specification Block With TaskVine as Task Server

 

 The TaskVine task server option will be included in an upcoming release of Merlin. We would be happy to find more use cases so please check it out once released!

 

 

Introducing Shepherd: Simplifying Integration of Service Workflows into Task-Based Workflows

We are pleased to announce the release of Shepherd, an open-source tool designed to streamline the integration of service workflows into task-based workflows. Shepherd enables users to manage applications that require dynamic dependencies and state monitoring, ensuring that each component in a workflow starts only after its dependencies have reached specified states.

What Is Shepherd?

Shepherd is a local workflow manager and service orchestrator that launches and monitors applications according to a user-defined configuration. It bridges the gap between persistent services and traditional task-based workflows by treating services as first-class tasks. This approach allows for seamless integration of services that run indefinitely or until explicitly stopped, alongside actions that run to completion.

By analyzing log outputs and file contents, Shepherd tracks the internal states of tasks without the need to modify their original code. This capability is particularly useful when integrating persistent services into workflows where tasks may depend on the internal states of these services rather than their completion.

Key Features

• Services as Tasks: Treats both actions (tasks that run to completion) and services (persistent tasks that require explicit termination) as tasks within a workflow, allowing integration into task-based workflow managers.
• Dynamic Dependency Management: Initiates tasks based on the internal states of other tasks, enabling complex state-based dependencies. Supports both "any" and "all" dependency modes for flexible configurations.
• State Monitoring: Continuously monitors the internal state of each task by analyzing standard output and specified files, updating states in real-time based on user-defined patterns.
• Graceful Startup and Shutdown: Ensures tasks start only when their dependencies are met and provides controlled shutdown mechanisms to maintain system stability and prevent data loss.
• Logging and Visualization: Generates comprehensive logs and state transition data, aiding in performance analysis and debugging.
• Integration with Larger Workflows: By encapsulating service workflows into single tasks, Shepherd enables integration with larger distributed workflow managers, enhancing workflow flexibility and reliability.

Documentation and Source Code

For detailed documentation, examples, and source code, please visit the Shepherd GitHub Repository.

TaskVine at ParslFest 2024

On September 26-27 members of the CCL team attended ParslFest 2024 in Chicago, Illinois to speak about TaskVine and connect with our ongoing collaborators at the Parsl Project.

Grad student Colin Thomas delivered a talk titled "Parsl/TaskVine: Interactions between DAG Manager and Workflow Executor." 

 The talk highlighted recent developments in the implementation of TaskVine Temporary Files within the Parsl/TaskVine Executor. Temporary files allow users to express intermediate data in their workflows. Intermediate data are items produced by tasks, and consumed by tasks. With the use of TaskVine Temporary Files, this intermediate data will remain in the cluster for the duration of a workflow. Keeping this data in the cluster can benefit a workflow that produces intermediate files of considerable quantity or size.

 In addition to adding temporary files to the Parsl/TaskVine executor, the talk presented ongoing work on the development of intermediate data-based "task-grouping" in TaskVine and extending it to the TaskVine executor. The concept of grouping related tasks may sound familiar to Pegasus users or other pre-compiled DAG workflow systems. Currently task-grouping is focused on scheduling sequences of tasks which contain intermediate dependencies. The desired outcome is for TaskVine to interpret pieces of the DAG to identify series of sequentially-dependent tasks in order to schedule them on the same worker, such that intermediate data does not need to travel to another host. Extending this capability to Parsl brought about an interesting challenge which questioned the relationship between DAG manager and executor. 

The problem is that in the typical workflow executor scheme, such as with Parsl or Dask, the executor will receive tasks from the DAG manager only when they are ready to run. In order for TaskVine to identify sequentially dependent tasks it needs to receive tasks which are future dependencies of those that are ready to run. 

The solution to this came about as a special implementation of a Data Staging Provider in Parsl, which is the mechanism for determining whether data dependencies are met. When the TaskVine staging provider encounters temporary files, it will lie to the Parsl DFK (Data Flow Kernel) in saying the file exists. Therefore Parsl will send all temporary-dependent tasks to TaskVine without waiting for the files to actually materialize. This offers TaskVine relevant pieces of the DAG to inspect and identify dependent sequences.

The impact of task grouping was evaluated on a benchmark application. Each benchmark run consists of 20 task sequences of variable length. Each task in a sequence produces and consumes an intermediate file of variable size. The effect on running time is shown while adjusting these parameters. Grouping tasks was shown to benefit the performance across a variety of sequence lengths, and when intermediate data size exceeds 500MB. 

In addition to the positive benchmark results, this talk promoted discussion about this non-traditional interaction between DAG manager and executor. The DAG manager has insight into future tasks and their dependencies.  The executor possesses knowledge about data locality. Combining this information to make better scheduling decisions proved to have some utility, yet it challenged the typical scheme of communication between Parsl and the TaskVine executor.