Hi all. I want to deploy a dagster on my local machine. I have a flow of 3 tasks: a -> b -> c. I didn't find a good tutorial for this. In version 0.13.16 I used @job, @graph and @op. Is there a better way to do this? Also, I want to be able to start this flow programmatically like this:

client = DagsterGraphQLClient(localhost, port_number=3000)
    new_run_id: str = client.submit_job_execution(
        "my_job",
        run_config={"resources": {"values": {"config": config}}},
        tags={'dagster/priority': 4, "tag1": "value1", "tag2": "value2"}
    )

When I run a job with DagsterGraphQLClient I get this error:

ExperimentalWarning: Class `DagsterGraphQLClient` is experimental. It may break in future versions, even between dot releases. To mute warnings for experimental functionality, invoke warnings.filterwarnings("ignore", category=dagster.ExperimentalWarning) or use one of the other methods described at <https://docs.python.org/3/library/warnings.html#describing-warning-filters>.
  client = DagsterGraphQLClient(hostname="localhost", port_number=3000)

How can I submit a job from another computer in a different way? Or is there a better way to execute the job? Thanks in advance

The op/graph/job paradigm hasn't changed a whole lot since 0.13.16, so it seem that for a simple task flow of a -> b -> c you'd be fine just implementing it with those features. With regards to the graphql client usage - that's just a warning, not an error. Honestly I just ignore that warning. It does feel like the experimental flag should be removed if the DagsterGraphQLClient is the recommended way to submit jobs to a Dagster instance though

Thank you. I saw that now: 1. For running dagit and dagster-daemon need just run

dagster dev

. 2. Loading definitions(jobs, graphs, sensors) in

___init___.py

instead of

repository.py

3. Don't need

workspace.yaml

Are there any other changes that might prevent it from working?