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[BUG] OSError: [Errno 30] Read-only file system: '/app'

See original GitHub issue

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Please fill in this bug report template to ensure a timely and thorough response.

Willingness to contribute

The MLflow Community encourages bug fix contributions. Would you or another member of your organization be willing to contribute a fix for this bug to the MLflow code base?

Yes. I can contribute a fix for this bug independently.
Yes. I would be willing to contribute a fix for this bug with guidance from the MLflow community.
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): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): MacOS 12.3.1
MLflow installed from (source or binary): binary
MLflow version (run mlflow --version): 1.12.1
Python version: 3.8
npm version, if running the dev UI:
Exact command to reproduce:

Describe the problem

When trying to log the mlflow pyfunc model to mlflow tracking server, it is giving an error as mentioned in title. Expected behaviour is that no error should come.

Code to reproduce issue

import mlflow
import os

#removed below params due to confidentiality

os.environ['MLFLOW_S3_ENDPOINT_URL'] = ""
os.environ['AWS_ACCESS_KEY_ID'] = ""
os.environ['AWS_SECRET_ACCESS_KEY'] = ""
mlflow.set_tracking_uri("")
mlflow.set_registry_uri("")

class AwesomeModel(mlflow.pyfunc.PythonModel):
    def load_context(self, context):
        pass
    def predict(self,context,inp_df):
        return 5

with mlflow.start_run() as run:
    mlflow.pyfunc.log_model(
                        python_model=AwesomeModel(),
                        artifact_path="ml-storage",
                        artifacts=None,
                        registered_model_name="ml_serving_demo_model")

Other info / logs

Traceback (most recent call last):
  File "prob.py", line 19, in <module>
    mlflow.pyfunc.log_model(
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/pyfunc/__init__.py", line 993, in log_model
    return Model.log(
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/models/model.py", line 173, in log
    mlflow.tracking.fluent.log_artifacts(local_path, artifact_path)
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/tracking/fluent.py", line 557, in log_artifacts
    MlflowClient().log_artifacts(run_id, local_dir, artifact_path)
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/tracking/client.py", line 911, in log_artifacts
    self._tracking_client.log_artifacts(run_id, local_dir, artifact_path)
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/tracking/_tracking_service/client.py", line 287, in log_artifacts
    self._get_artifact_repo(run_id).log_artifacts(local_dir, artifact_path)
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/store/artifact/local_artifact_repo.py", line 57, in log_artifacts
    mkdir(artifact_dir)
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/utils/file_utils.py", line 112, in mkdir
    raise e
  File "/opt/anaconda3/envs/env38/lib/python3.8/site-packages/mlflow/utils/file_utils.py", line 109, in mkdir
    os.makedirs(target)
  File "/opt/anaconda3/envs/env38/lib/python3.8/os.py", line 211, in makedirs
    makedirs(head, exist_ok=exist_ok)
  File "/opt/anaconda3/envs/env38/lib/python3.8/os.py", line 211, in makedirs
    makedirs(head, exist_ok=exist_ok)
  File "/opt/anaconda3/envs/env38/lib/python3.8/os.py", line 211, in makedirs
    makedirs(head, exist_ok=exist_ok)
  [Previous line repeated 1 more time]
  File "/opt/anaconda3/envs/env38/lib/python3.8/os.py", line 221, in makedirs
    mkdir(name, mode)
OSError: [Errno 30] Read-only file system: '/app'

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: MLflow Model server, model deployment tools, Spark UDFs
area/server-infra: MLflow Tracking server backend
area/tracking: Tracking Service, tracking client APIs, autologging

Interface

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

Language