Flyte enables production-grade orchestration for machine learning workflows and data processing created to accelerate local workflows to production.

Flyte

I might of course just be holding this wrong and fighting how flyte expects you to do stuff, but I'm kinda hoping there is some way to avoid duplicating the tracking of dependencies

Hmm you can pass that config to the client and it will work

I tried passing `--config` but it didn't seem to work, I'll try again in case I screwed something up while testing

`poetry run pyflyte --config=path/to/config-sandbox.yaml run --remote workflows/example.py wf --input=...` with
```admin:
  # For GRPC endpoints you might want to use dns:///flyte.myexample.com
  endpoint: localhost:30080
  insecure: true

# # This is not a needed configuration, only useful if you want to explore the data in sandbox. For non sandbox, please
# # do not use this configuration, instead prefer to use aws, gcs, azure sessions. Flytekit, should use fsspec to
# # auto select the right backend to pull data as long as the sessions are configured. For Sandbox, this is special, as
# # minio is s3 compatible and we ship with minio in sandbox.
storage:
  connection:
    endpoint: <http://localhost:30002>
    access-key: minio
    secret-key: miniostorage

images:
  default: localhost:30000/myimage:latest```
as the config results in the default image `<http://cr.flyte.org/flyteorg/flytekit:py3.11-1.13.9|cr.flyte.org/flyteorg/flytekit:py3.11-1.13.9>` being used

I think the coding may have a bug - AFK, will try later in the day once I get a chance

Thanks, and just to clarify, this is on Flytekit Version: 1.13.9, also no rush I need to head home soon anyway. I just wanted to ask since I couldn't make any sense of this. Most likely we'll just use `ImageSpec` for now just so we can get something in prod sooner, and later see of this concern was even worth following up on