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Utils

This contains TorchX utility components that are ready-to-use out of the box. These are components that simply execute well known binaries (e.g. cp) and are meant to be used as tutorial materials or glue operations between meaningful stages in a workflow.

torchx.components.utils.echo(msg: str = 'hello world', image: str = 'ghcr.io/pytorch/torchx:0.2.0', num_replicas: int = 1) AppDef[source]

Echos a message to stdout (calls echo)

Parameters:
  • msg – message to echo

  • image – image to use

  • num_replicas – number of replicas to run

torchx.components.utils.touch(file: str, image: str = 'ghcr.io/pytorch/torchx:0.2.0') AppDef[source]

Touches a file (calls touch)

Parameters:
  • file – file to create

  • image – the image to use

torchx.components.utils.sh(*args: str, image: str = 'ghcr.io/pytorch/torchx:0.2.0', num_replicas: int = 1, cpu: int = 1, gpu: int = 0, memMB: int = 1024, h: Optional[str] = None, env: Optional[Dict[str, str]] = None, max_retries: int = 0, mounts: Optional[List[str]] = None) AppDef[source]

Runs the provided command via sh. Currently sh does not support environment variable substitution.

Parameters:
  • args – bash arguments

  • image – image to use

  • num_replicas – number of replicas to run

  • cpu – number of cpus per replica

  • gpu – number of gpus per replica

  • memMB – cpu memory in MB per replica

  • h – a registered named resource (if specified takes precedence over cpu, gpu, memMB)

  • env – environment varibles to be passed to the run (e.g. ENV1=v1,ENV2=v2,ENV3=v3)

  • max_retries – the number of scheduler retries allowed

  • mounts – mounts to mount into the worker environment/container (ex. type=<bind/volume>,src=/host,dst=/job[,readonly]). See scheduler documentation for more info.

torchx.components.utils.copy(src: str, dst: str, image: str = 'ghcr.io/pytorch/torchx:0.2.0') AppDef[source]

copy copies the file from src to dst. src and dst can be any valid fsspec url.

This does not support recursive copies or directories.

Parameters:
  • src – the source fsspec file location

  • dst – the destination fsspec file location

  • image – the image that contains the copy app

torchx.components.utils.python(*args: str, m: Optional[str] = None, c: Optional[str] = None, script: Optional[str] = None, image: str = 'ghcr.io/pytorch/torchx:0.2.0', name: str = 'torchx_utils_python', cpu: int = 1, gpu: int = 0, memMB: int = 1024, h: Optional[str] = None, num_replicas: int = 1) AppDef[source]

Runs python with the specified module, command or script on the specified image and host. Use -- to separate component args and program args (e.g. torchx run utils.python --m foo.main -- --args to --main)

Note: (cpu, gpu, memMB) parameters are mutually exclusive with h (named resource) where

h takes precedence if specified for setting resource requirements. See registering named resources.

Parameters:
  • args – arguments passed to the program in sys.argv[1:] (ignored with –c)

  • m – run library module as a script

  • c – program passed as string (may error if scheduler has a length limit on args)

  • script – .py script to run

  • image – image to run on

  • name – name of the job

  • cpu – number of cpus per replica

  • gpu – number of gpus per replica

  • memMB – cpu memory in MB per replica

  • h – a registered named resource (if specified takes precedence over cpu, gpu, memMB)

  • num_replicas – number of copies to run (each on its own container)

Returns:

torchx.components.utils.booth(x1: float, x2: float, trial_idx: int = 0, tracker_base: str = '/tmp/torchx-util-booth', image: str = 'ghcr.io/pytorch/torchx:0.2.0') AppDef[source]

Evaluates the booth function, f(x1, x2) = (x1 + 2*x2 - 7)^2 + (2*x1 + x2 - 5)^2. Output result is accessible via FsspecResultTracker(outdir)[trial_idx]

Parameters:
  • x1 – x1

  • x2 – x2

  • trial_idx – ignore if not running hpo

  • tracker_base – URI of the tracker’s base output directory (e.g. s3://foo/bar)

  • image – the image that contains the booth app

torchx.components.utils.binary(*args: str, entrypoint: str, name: str = 'torchx_utils_binary', num_replicas: int = 1, cpu: int = 1, gpu: int = 0, memMB: int = 1024, h: Optional[str] = None) AppDef[source]

Test component

Parameters:
  • args – arguments passed to the program in sys.argv[1:] (ignored with –c)

  • name – name of the job

  • num_replicas – number of copies to run (each on its own container)

  • cpu – number of cpus per replica

  • gpu – number of gpus per replica

  • memMB – cpu memory in MB per replica

  • h – a registered named resource (if specified takes precedence over cpu, gpu, memMB)

Returns:

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