Custom Components

This is a guide on how to build a simple app and custom component spec and launch it via two different schedulers.

See the Quickstart Guide for installation and basic usage.

Hello World

Lets start off with writing a simple “Hello World” python app. This is just a normal python program and can contain anything you’d like.

Note

This example uses Jupyter Notebook %%writefile to create local files for example purposes. Under normal usage you would have these as standalone files.

%%writefile my_app.py

import sys
import argparse

def main(user: str) -> None:
    print(f"Hello, {user}!")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Hello world app"
    )
    parser.add_argument(
        "--user",
        type=str,
        help="the person to greet",
        required=True,
    )
    args = parser.parse_args(sys.argv[1:])

    main(args.user)

Writing my_app.py

Now that we have an app we can write the component file for it. This function allows us to reuse and share our app in a user friendly way.

We can use this component from the torchx cli or programmatically as part of a pipeline.

%%writefile my_component.py

import torchx.specs as specs

def greet(user: str, image: str = "my_app:latest") -> specs.AppDef:
    return specs.AppDef(
        name="hello_world",
        roles=[
            specs.Role(
                name="greeter",
                image=image,
                entrypoint="python",
                args=[
                    "-m", "my_app",
                    "--user", user,
                ],
            )
        ],
    )

Writing my_component.py

We can execute our component via torchx run. The local_cwd scheduler executes the component relative to the current directory.

%%sh
torchx run --scheduler local_cwd my_component.py:greet --user "your name"

torchx 2023-10-19 01:30:40 INFO     Tracker configurations: {}
torchx 2023-10-19 01:30:40 INFO     Log directory not set in scheduler cfg. Creating a temporary log dir that will be deleted on exit. To preserve log directory set the `log_dir` cfg option
torchx 2023-10-19 01:30:40 INFO     Log directory is: /tmp/torchx_vhrx5xwc
torchx 2023-10-19 01:30:40 INFO     Waiting for the app to finish...
greeter/0 Hello, your name!
torchx 2023-10-19 01:30:41 INFO     Job finished: SUCCEEDED

local_cwd://torchx/hello_world-z7smwf9lmt2fbc

If we want to run in other environments, we can build a Docker container so we can run our component in Docker enabled environments such as Kubernetes or via the local Docker scheduler.

Note

This requires Docker installed and won’t work in environments such as Google Colab. If you have not done so already follow the install instructions on: https://docs.docker.com/get-docker/

%%writefile Dockerfile.custom

FROM ghcr.io/pytorch/torchx:0.1.0rc1

ADD my_app.py .

Writing Dockerfile.custom

Once we have the Dockerfile created we can create our docker image.

%%sh
docker build -t my_app:latest -f Dockerfile.custom .

#0 building with "default" instance using docker driver

#1 [internal] load build definition from Dockerfile.custom
#1 transferring dockerfile: 99B done
#1 DONE 0.0s

#2 [internal] load .dockerignore
#2 transferring context: 2B done
#2 DONE 0.0s

#3 [internal] load metadata for ghcr.io/pytorch/torchx:0.1.0rc1
#3 DONE 0.3s

#4 [internal] load build context
#4 transferring context: 425B done
#4 DONE 0.0s

#5 [1/2] FROM ghcr.io/pytorch/torchx:0.1.0rc1@sha256:a738949601d82e7f100fa1efeb8dde0c35ce44c66726cf38596f96d78dcd7ad3
#5 resolve ghcr.io/pytorch/torchx:0.1.0rc1@sha256:a738949601d82e7f100fa1efeb8dde0c35ce44c66726cf38596f96d78dcd7ad3 done
#5 sha256:3dbec59e804974689ff0739216fb012d3e1cd6694632cd3a85b74b572266ec5c 7.21kB / 7.21kB done
#5 sha256:d2e110be24e168b42c1a2ddbc4a476a217b73cccdba69cdcb212b812a88f5726 857B / 857B 0.1s done
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#5 DONE 80.9s

#6 [2/2] ADD my_app.py .
#6 DONE 0.0s

#7 exporting to image
#7 exporting layers
#7 exporting layers 2.3s done
#7 writing image sha256:f351ae7bd2e728237e94c22477ea2d8752623176326077ee87de63a0de063ccd done
#7 naming to docker.io/library/my_app:latest done
#7 DONE 2.3s

We can then launch it on the local scheduler.

%%sh
torchx run --scheduler local_docker my_component.py:greet --image "my_app:latest" --user "your name"

torchx 2023-10-19 01:32:05 INFO     Tracker configurations: {}
torchx 2023-10-19 01:32:05 INFO     Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2023-10-19 01:32:05 INFO     To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2023-10-19 01:32:05 INFO     Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2023-10-19 01:32:05 WARNING  failed to pull image my_app:latest, falling back to local: 404 Client Error for http+docker://localhost/v1.43/images/create?tag=latest&fromImage=my_app: Not Found ("pull access denied for my_app, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")
torchx 2023-10-19 01:32:05 INFO     Building workspace docker image (this may take a while)...
torchx 2023-10-19 01:32:10 INFO     Built new image `sha256:52cf53683303523cf5d23f8ab54090bb10cf77b32180860fad2ba007bea9b53d` based on original image `my_app:latest` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=greeter.
torchx 2023-10-19 01:32:10 INFO     Waiting for the app to finish...
greeter/0 Hello, your name!
torchx 2023-10-19 01:32:11 INFO     Job finished: SUCCEEDED

local_docker://torchx/hello_world-q29cbvlb70wx1c

If you have a Kubernetes cluster you can use the Kubernetes scheduler to launch this on the cluster instead.

$ docker push my_app:latest
$ torchx run --scheduler kubernetes my_component.py:greet --image "my_app:latest" --user "your name"

Builtins

TorchX also provides a number of builtin components with premade images. You can discover them via:

%%sh
torchx builtins

Found 11 builtin components:
  1. dist.ddp
  2. dist.spmd
  3. metrics.tensorboard
  4. serve.torchserve
  5. utils.binary
  6. utils.booth
  7. utils.copy
  8. utils.echo
  9. utils.python
 10. utils.sh
 11. utils.touch

You can use these either from the CLI, from a pipeline or programmatically like you would any other component.

%%sh
torchx run utils.echo --msg "Hello :)"

torchx 2023-10-19 01:32:13 INFO     Tracker configurations: {}
torchx 2023-10-19 01:32:13 INFO     Checking for changes in workspace `file:///home/runner/work/torchx/torchx/docs/source`...
torchx 2023-10-19 01:32:13 INFO     To disable workspaces pass: --workspace="" from CLI or workspace=None programmatically.
torchx 2023-10-19 01:32:13 INFO     Workspace `file:///home/runner/work/torchx/torchx/docs/source` resolved to filesystem path `/home/runner/work/torchx/torchx/docs/source`
torchx 2023-10-19 01:33:40 INFO     Building workspace docker image (this may take a while)...
torchx 2023-10-19 01:33:47 INFO     Built new image `sha256:416218d6f57e9536b0590e851ad5d642dfcda6391822a4d656856b415ae83c92` based on original image `ghcr.io/pytorch/torchx:0.6.0` and changes in workspace `file:///home/runner/work/torchx/torchx/docs/source` for role[0]=echo.
torchx 2023-10-19 01:33:47 INFO     Waiting for the app to finish...
torchx 2023-10-19 01:33:47 INFO     Job finished: SUCCEEDED
echo/0 Hello :)

local_docker://torchx/echo-swspz00mrk9tz