Fix batch scoring pipeline artifact writing on Databricks #6766
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jerrylian-db
commented
Sep 12, 2022
mlflow/pipelines/steps/predict.py
Outdated
| ".mlflow", | ||
| _get_execution_directory_basename(self.pipeline_root), | ||
| _SCORED_OUTPUT_FOLDER_NAME, | ||
| ) |
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In Databricks, it seems like the spark writes parquet files as a folder. This makes it difficult for the get_artifact logic to work because when the artifact path has a .parquet file extension, it reads the folder as if it were a file. Thus, I've updated the scored_data artifact writing and reading logic to an artifact path that does not have a .parquet file extension.
jinzhang21
reviewed
Sep 13, 2022
mlflow/pipelines/steps/predict.py
Outdated
Comment on lines
135
to
151
| if databricks_utils.is_in_databricks_runtime(): | ||
| dbfs_path = os.path.join( | ||
| ".mlflow", | ||
| _get_execution_directory_basename(self.pipeline_root), | ||
| _SCORED_OUTPUT_FOLDER_NAME, | ||
| ) | ||
| shutil.rmtree("/dbfs/" + dbfs_path, ignore_errors=True) | ||
| scored_sdf.coalesce(1).write.format("parquet").save(dbfs_path) | ||
| _logger.info("Moving artifact from DBFS to driver disk") | ||
| shutil.copytree( | ||
| "/dbfs/" + dbfs_path, os.path.join(output_directory, _SCORED_OUTPUT_FOLDER_NAME) | ||
| ) | ||
| shutil.rmtree("/dbfs/" + dbfs_path) | ||
| else: | ||
| scored_sdf.coalesce(1).write.format("parquet").save( | ||
| os.path.join(output_directory, _SCORED_OUTPUT_FILE_NAME) | ||
| ) |
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Can we abstract this logic into a util function e.g. in https://github.com/mlflow/mlflow/blob/master/mlflow/utils/file_utils.py? I can easily see it's used in other places in the future.
sunishsheth2009
approved these changes
Sep 13, 2022
jinzhang21
approved these changes
Sep 14, 2022
nnethery
pushed a commit
to nnethery/mlflow
that referenced
this pull request
Feb 1, 2024
* wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> * wip Signed-off-by: Jerry Liang <jerry.liang@databricks.com> Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
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Related Issues/PRs
#xxx
What changes are proposed in this pull request?
This change fixes the artifact writing and reading logic for the batch scoring predict step.
How is this patch tested?
I cloned the example MLP regression template repo. Updated the requirements.txt file to point to my fix branch. Ran the entire Databricks notebook in the example. Then, I added notebook cells and ran the ingest_scoring and predict steps. Both of which succeeded. Finally, I loaded the scored dataset artifact using the
get_artifactAPI and it succeeded.See screenshots:
Does this PR change the documentation?
Detailslink on thePreview docscheck.Release Notes
Is this a user-facing change?
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesarea/examples: Example codearea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/pipelines: Pipelines, Pipeline APIs, Pipeline configs, Pipeline Templatesarea/projects: MLproject format, project running backendsarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows: Windows supportLanguage
language/r: R APIs and clientslanguage/java: Java APIs and clientslanguage/new: Proposals for new client languagesIntegrations
integrations/azure: Azure and Azure ML integrationsintegrations/sagemaker: SageMaker integrationsintegrations/databricks: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notes