`ovo.cli.scheduler_cli`¶

Module Contents¶

`run`	Run OVO job synchronously and save output to directory (shorhand for submit –sync)
`submit`	Submit a job through OVO scheduler
`jupyter`	Run JupyterLab server in a selected environment as a job using OVO scheduler
`format_help`
`status`	Get job status
`cancel`	Cancel job execution
`download`	Download job output files into a directory
`worker`	Start queue and worker server to process jobs of a scheduler specified with queue submission arg in OVO config

ovo.cli.scheduler_cli.run(pipeline_name: str = typer.Argument(default=None, help='Pipeline to submit'), output: str = typer.Argument(default=None, help='Output directory'), scheduler_key: str = typer.Option(None, '--scheduler', help='Scheduler key'), link: bool = typer.Option(default=None, help='Do not copy output files, create a symlink instead'), ctx: typer.Context = typer.Option(None), help: bool | None = typer.Option(None, '--help', '-h', help='Show pipeline-specific help'))¶: Run OVO job synchronously and save output to directory (shorhand for submit –sync)

ovo.cli.scheduler_cli.submit(pipeline_name: str = typer.Argument(default=None, help='Pipeline to submit'), output: str = typer.Option(None, '--output', help='Custom output directory (requires --sync)'), scheduler_key: str = typer.Option(None, '--scheduler', help='Scheduler key'), sync: bool = typer.Option(default=None, help='Run nextflow process synchronously, waiting for completion'), link: bool = typer.Option(default=None, help='Do not copy output files, create a symlink instead (when --output is used)'), ctx: typer.Context = typer.Option(None), help: bool | None = typer.Option(None, '--help', '-h', help='Show pipeline-specific help'))¶: Submit a job through OVO scheduler

ovo.cli.scheduler_cli.jupyter(dirs: list[str] = typer.Argument(help='Directory to run from. Will be mounted inside the container.'), env: str = typer.Option(default=None, help='Name of conda environment or container to run in, for example rfdiffusion. Runs in current environment if not specified.'), scheduler_key: str = typer.Option(None, '--scheduler', help='Scheduler key'), ip: str = typer.Option(default='0.0.0.0', help='Host to bind Jupyter server to'), port: int = typer.Option(default=8888, help='Port to bind Jupyter server to'), run_parameters: str = typer.Option(default='', help='Additional commandline parameters to pass to Jupyter server'), timeout: int = typer.Option(default=3600, help='Timeout in seconds to wait for Jupyter server to start'))¶: Run JupyterLab server in a selected environment as a job using OVO scheduler

ovo.cli.scheduler_cli.status(job_id: str = typer.Argument(help='Job ID'), scheduler_key: str = typer.Option(None, '--scheduler', help='Scheduler key'))¶: Get job status

ovo.cli.scheduler_cli.cancel(job_id: str = typer.Argument(help='Job ID'), scheduler_key: str = typer.Option(None, '--scheduler', help='Scheduler key'))¶: Cancel job execution

ovo.cli.scheduler_cli.download(job_id: str = typer.Argument(help='Job ID'), output: str = typer.Argument(help='Output directory'), scheduler_key: str = typer.Option(None, '--scheduler', help='Scheduler key'))¶: Download job output files into a directory

ovo.cli.scheduler_cli.worker(num_workers: int = typer.Option(1, '-n', '--workers', help='Number of worker threads'), connect: bool = typer.Option(False, '--connect', help='Connect to existing queue server instead of starting a new one. Enables running workers with different ENV vars such as CUDA_VISIBLE_DEVICES.'), scheduler_key: str = typer.Option(None, '--scheduler', help='Scheduler key to use'), host: str = typer.Option(None, '--host', help='Run server on custom host (override config)'), port: int = typer.Option(None, '--port', help='Run server on custom port (override config)'))¶: Start queue and worker server to process jobs of a scheduler specified with queue submission arg in OVO config