Cannot log artifact in R on Windows 10

Willingness to contribute

No. I cannot contribute a bug fix at this time.

System information

• Have I written custom code (as opposed to using a stock example script provided in MLflow): Yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
• MLflow installed from (source or binary): binary
• MLflow version: 1.17.0
• Python version: 3.8.10
• R version: 4.1.0
• mlflow R package version: 1.17.0
• Exact command to reproduce in R:

library(mlflow)
mlflow_log_param("foo", 42)
mlflow_log_metric("mse", 0.87)
mlflow_set_tag("training-data-cutoff", "Dec 2017")
writeLines("Blahdieblah", "output.txt")
mlflow_log_artifact("output.txt")
mlflow_end_run()

Describe the problem

I’ve installed MLFlow and the mlflow package in R. Logging parameters and metrics works fine, but when I try to log an artifact, I get an error:

Error in rethrow_call(c_processx_exec, command, c(command, args), pty,  : 
  Command 'C:/Users/XXXXXX/.conda/envs/r-mlflow-1.17.0/mlflow' not found @win/processx.c:982 (processx_exec)

Code to reproduce issue

library(mlflow)
writeLines("Blahdieblah", "output.txt")
mlflow_log_artifact("output.txt")
mlflow_end_run()

Other info / logs

When I log an artifact in Python, everything works fine. So I guess the problem is in R.

I have the problem when I try to connect to a local file store, as well as a dbfs store on Microsoft Azure.

Stack trace:

 1. mlflow:::mlflow_log_artifact("output.txt")
 2. mlflow:::mlflow_cli("artifacts", command, local_param, path,  ...
 3. withr:::with_envvar(env, { ...
 4. base:::force(code)
 5. processx:::run(mlflow_bin, args = unlist(args), echo = echo,  ...
 6. process$new(command, args, echo_cmd = echo_cmd, wd = wd, windows_verbatim_args = win ...
 7. processx:::initialize(...)
 8. processx:::process_initialize(self, private, command, args, stdin,  ...
 9. rethrow_call(c_processx_exec, command, c(command, args), pty,  ...

 x Command 'C:/Users/XXXXXX/.conda/envs/r-mlflow-1.17.0/mlflow' not found @win/processx.c:982 (processx_exec) 

What component(s), interfaces, languages, and integrations does this bug affect?

Components

• 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/projects: MLproject format, project running backends
• area/scoring: Local serving, model deployment tools, spark UDFs
• area/server-infra: MLflow server, JavaScript dev server
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, JavaScript, plotting
• 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

Language

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

Integrations

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

[lorenzwalthert]

Seems you are right in your analysis. I am afraid #3766 won’t get merged (soon) because it got too convoluted (with other changes), which means the build never passed and has accumulated too many merge conflicts. I think it would be a good thing to narrow the scope down and just fix the executable path in a small PR…

[mkspwr]

Thanks for getting to this point…but, after resolving this error…I am now stuck at this :

Error in wait_for(function() mlflow_rest(“experiments”, “list”, client = client), : Operation failed after waiting for 10 seconds