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Overview

../_images/components_diagram.jpg

Note

Diagram above for illustration purposes only. Not all boxes are currently available out-of-the-box.

This module contains a collection of builtin TorchX components. The directory structure is organized by component category. Components are simply templetized app specs. Think of them as a factory methods for different types of job definitions. The functions that return specs.AppDef in this module are what we refer to as components.

You can browse the library of components in the torchx.components module or on our docs page.

Using Builtins

Once you’ve found a builtin component, you can either:

  1. Run the component as a job

  2. Use the component in the context of a workflow (pipeline)

In both cases, the component will run as a job, with the difference being that the job will run as a standalone job directly on a scheduler or a “stage” in a workflow with upstream and/or downstream dependencies.

Note

Depending on the semantics o the component, the job may be single node or distributed. For instance, if the component has a single role where the role.num_replicas == 1, then the job is a single node job. If the component has multiple roles and/or if any of the role’s num_replicas > 1, then the job is a multi-node distributed job.

Not sure whether you should run the component as a job or as a pipeline stage? Use this rule of thumb:

  1. Just getting started? Familiarize yourself with the component by running it as a job

  2. Need job dependencies? Run the components as pipeline stages

  3. Don’t need job dependencies? Run the component as a job

Authoring

Since a component is simply a python function that returns an specs.AppDef, authoring your own component is as simple as writing a python function with the following rules:

  1. The component function must return an specs.AppDef and the return type must be specified

  2. All arguments of the component must be PEP 484 type annotated and the type must be one of
    1. Primitives: int, float, str, bool

    2. Optional primitives: Optional[int], Optional[float], Optional[str]

    3. Maps of primitives: Dict[Primitive_key, Primitive_value]

    4. Lists of primitives: List[Primitive_values]

    5. Optional collections: Optional[List], Optional[Dict]

    6. VAR_ARG: *arg (useful when passing through arguments to the entrypoint script)

  3. (optional) A docstring in google format (in particular see function_with_pep484_type_annotations). This docstring is purely informative in that torchx cli uses it to autogenerate an informative --help message, which is useful when sharing components with others. If the component does not have a docstring the --help option will still work, but the parameters will have a canned description (see below). Note that when running components programmatically via torchx.runner, the docstring is not picked up by torchx at all.

Below is an example component that launches DDP scripts, it is a simplified version of the torchx.components.dist.ddp() builtin.

import os
import torchx.specs as specs

def ddp(
    *script_args: str,
    image: str,
    script: str,
    host: str = "aws_p3.2xlarge",
    nnodes: int = 1,
    nproc_per_node: int = 1,
) -> specs.AppDef:
   return specs.AppDef(
       name=os.path.basename(script),
       roles=[
           spec.Role(
               name="trainer",
               image=image,
               resource=specs.named_resources[host],
               num_replicas=nnodes,
               entrypoint="python",
               args=[
                   "-m",
                   "torch.distributed.run",
                   "--rdzv_backend=etcd",
                   "--rdzv_endpoint=localhost:5900",
                   f"--nnodes={nnodes}",
                   f"--nprocs_per_node={nprocs_per_node}",
                   "-m",
                   script,
                   *script_args,
               ],
           ),
       ]
   )

Assuming the component above is saved in example.py, we can run --help on it as:

$ torchx ./example.py:ddp --help
usage: torchx run ...torchx_params... ddp  [-h] --image IMAGE --script SCRIPT [--host HOST]
                                          [--nnodes NNODES] [--nproc_per_node NPROC_PER_NODE]
                                          ...

AppDef: ddp. TIP: improve this help string by adding a docstring ...<omitted for brevity>...

positional arguments:
  script_args           (required)

optional arguments:
  -h, --help            show this help message and exit
  --image IMAGE         (required)
  --script SCRIPT       (required)
  --host HOST           (default: aws_p3.2xlarge)
  --nnodes NNODES       (default: 1)
  --nproc_per_node NPROC_PER_NODE
                        (default: 1)

If we include a docstring as such:

def ddp(...) -> specs.AppDef:
  """
  DDP Simplified.

  Args:
     image: name of the docker image containing the script + deps
     script: path of the script in the image
     script_args: arguments to the script
     host: machine type (one from named resources)
     nnodes: number of nodes to launch
     nproc_per_node: number of scripts to launch per node

  """

  # ... component body same as above ...
  pass

Then the --help message would reflect the function and parameter descriptions in the docstring as such:

usage: torchx run ...torchx_params... ddp  [-h] --image IMAGE --script SCRIPT [--host HOST]
                                          [--nnodes NNODES] [--nproc_per_node NPROC_PER_NODE]
                                          ...

App spec: DDP simplified.

positional arguments:
  script_args           arguments to the script

optional arguments:
  -h, --help            show this help message and exit
  --image IMAGE         name of the docker image containing the script + deps
  --script SCRIPT       path of the script in the image
  --host HOST           machine type (one from named resources)
  --nnodes NNODES       number of nodes to launch
  --nproc_per_node NPROC_PER_NODE
                        number of scripts to launch per node

Validating

To validate that you’ve defined your component correctly you can either:

  1. (easiest) Dryrun your component’s --help with the cli: torchx run --dryrun ~/component.py:train --help

  2. Use the component linter (see dist_test.py as an example)

Running as a Job

You can run a component as a job with the torchx cli or programmatically with the torchx.runner. Both are identical, in fact the cli uses the runner under the hood, so the choice is yours. The quickstart guide walks though the basics for you to get started.

Programmatic Run

To run builtins or your own component programmatically, simply invoke the component as a regular python function and pass it along to the torchx.runner. Below is an example of calling the utils.echo builtin:

from torchx.components.utils import echo
from torchx.runner import get_runner

get_runner().run(echo(msg="hello world"), scheduler="local_cwd")

CLI Run (Builtins)

When running components from the cli, you have to pass which component function to invoke. For builtin components this is of the form {component_module}.{component_fn}, where the {component_module} is the module path of the component relative to torchx.components and the {component_fn} is the component function within that module. So for torchx.components.utils.echo, we’d drop the torchx.components prefix and run it as

$ torchx run utils.echo --msg "hello world"

See CLI docs for more information.

CLI Run (Custom)

To run your custom component with the cli, you have to use a slightly different syntax of the form {component_path}:{component_fn}. Where {component_path} is the file path of your component’s python file, and {component_fn} is the name of the component function within that file. Assume your component is in /home/bob/component.py and the component function is called train(), you would run this as

# option 1. use absolute path
$ torchx run /home/bob/component.py:train --help

# option 2. let the shell do the expansion
$ torchx run ~/component.py:train --help

# option 3. same but after CWD to $HOME
$ cd ~/
$ torchx run ./component.py:train --help

# option 4. files can be relative to CWD
$ cd ~/
$ torchx run component.py:train --help

Note

builtins can be run this way as well given that you know the install directory of TorchX!

Passing Component Params from CLI

Since components are simply python functions, using them programmatically is straight forward. As seen above, when running components via the cli’s run subcommand the component parameters are passed as program arguments using the double-dash + param_name syntax (e.g --param1=1 or --param1 1). The cli autogenerates argparse parser based on the docstring of the component. Below is a summary on how to pass component parameters of various types, imagine the component is defined as:

# in comp.py
from typing import Dict, List
import torchx.specs as specs

def f(i: int, f: float, s: str, b: bool, l: List[str], d: Dict[str, str], *args) -> specs.AppDef:
   """
   Example component

   Args:
       i: int param
       f: float param
       s: string param
       b: bool param
       l: list param
       d: map param
       args: varargs param

   Returns: specs.AppDef
   """

   pass
  1. Help: torchx run comp.py:f --help

  2. Primitives (int, float, str): torchx run comp.py:f --i 1 --f 1.2 --s "bar"

  3. Bool: torchx run comp.py:f --b True (or --b False)

  4. Maps: torchx run comp.py:f --d k1=v1,k2=v2,k3=v3

  5. Lists: torchx run comp.py:f --l a,b,c

  6. VAR_ARG: *args are passed as positionals rather than arguments, hence they are specified at the end of the command. The -- delimiter is used to start the VAR_ARGS section. This is useful when the component and application have the same arguments or when passing through the --help arg. Below are a few examples: * *args=["arg1", "arg2", "arg3"]: torchx run comp.py:f --i 1 arg1 arg2 arg3 * *args=["--flag", "arg1"]: torchx run comp.py:f --i 1 --flag arg1 `` * ``*args=["--help"]: torchx run comp.py:f -- --help * *args=["--i", "2"]: torchx run comp.py:f --i 1 -- --i 2

Run in a Pipeline

The torchx.pipelines define adapters that convert a torchx component into the object that represents a pipeline “stage” in the target pipeline platform (see Pipelines for a list of supported pipeline orchestrators).

Additional Resources

See:

  1. Components defined in this module as expository examples

  2. Defining your own component quick start guide

  3. Component best practices guide

  4. App best practices guide

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