[BUG] Cannot use databricks connection with R

Issues Policy acknowledgement

• I have read and agree to submit bug reports in accordance with the issues policy

Willingness to contribute

Yes. I would be willing to contribute a fix for this bug with guidance from the MLflow community.

MLflow version

mlflow, version 1.27.0

System information

• Window
• Python version: Python 3.10.0
• R version: R version 4.1.2 (2021-11-01)
• mlflow R package version: 2.0.1

Describe the problem

I am attempting to track and log parameters within DataBricks using mlflow. I can see the runs that I am creating quite clearly:

However there is an issue whereby I cannot actually log anything to this run. This is due to the way the package sets the active run ID - or rather doesn’t set it - when there is a client provided. See here. The result of this is that whenever you try to log something, it will not work as there is a check for the active run ID that takes place here. Ultimately this is because mlflow:::mlflow_get_active_run_id() is NULL because it is never set in mlflow::mlflow_start_run() when there is a client_id provided.

Tracking information

No response

Code to reproduce issue

library(mlflow)
client <- mlflow::mlflow_client(tracking_uri = "databricks")
experiment <- "1709256526326232"
run <- mlflow::mlflow_start_run(experiment_id = "1709256526326232", client = client)

mlflow:::mlflow_get_active_run_id()
# NULL

# Try to log a parameter
with(run, {
  mlflow::mlflow_log_param(
    key = "test",
    value = 1,
    client = client
  )
})
# Error: `with()` should only be used with `mlflow_start_run()`.

# Try to use `mlflow::mlflow_start_run()`:
with(
  mlflow::mlflow_start_run(
    experiment_id = "1709256526326232",
    client = mlflow::mlflow_client(tracking_uri = "databricks")
  ), {
  mlflow::mlflow_log_param(
    key = "test",
    value = 1,
    client = client
  )
})
# Error: `with()` should only be used with `mlflow_start_run()`.

Stack trace

3: stop("`with()` should only be used with `mlflow_start_run()`.",
       call. = FALSE)
2: with.mlflow_run(mlflow::mlflow_start_run(experiment_id = "1709256526326232", 
       client = mlflow::mlflow_client(tracking_uri = "databricks")),
       {
           mlflow::mlflow_log_param(key = "test", value = 1, client = client)   
       })
1: with(mlflow::mlflow_start_run(experiment_id = "1709256526326232",
       client = mlflow::mlflow_client(tracking_uri = "databricks")),
       {
           mlflow::mlflow_log_param(key = "test", value = 1, client = client)
       })

Other info / logs

No response

What component(s) does this bug affect?

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

What interface(s) does this bug affect?

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow’s components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

What language(s) does this bug affect?

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

What integration(s) does this bug affect?

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

[nathaneastwood]

Good question. Seems I had this set many months ago when I was first working with this code locally. Now we have DataBricks set up I have come back to it and so continued to use it which is where I found the issue. I think we can close this now, thanks for your (very) quick help!

[harupy]

Where did you get this code?

client <- mlflow::mlflow_client(tracking_uri = "databricks")
experiment <- "1709256526326232"
run <- mlflow::mlflow_start_run(experiment_id = "1709256526326232", client = client)