Pin pip in conda.yaml #5437
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Pin pip in conda.yaml #5437
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area/models
Signed-off-by: dbczumar <corey.zumar@databricks.com>
dbczumar
commented
Mar 1, 2022
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| env = yaml.safe_load(_conda_header) | ||
| env["dependencies"] = ["python={}".format(PYTHON_VERSION)] | ||
| if conda_deps is not None: |
There was a problem hiding this comment.
It's never None and would be unexpected behavior if it is.
Signed-off-by: dbczumar <corey.zumar@databricks.com>
harupy
approved these changes
Mar 2, 2022
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Signed-off-by: dbczumar corey.zumar@databricks.com
What changes are proposed in this pull request?
Pin pip in conda.yaml. Pip has been known to make backwards-incompatible changes to resolution logic, rendering certain environments unresolvable. This problem recently occurred when Python 3.6 reached EOL and recent versions of pip failed to resolve certain dependencies in Python 3.6 environments.
How is this patch tested?
Does this PR change the documentation?
ci/circleci: build_doccheck. If it's successful, proceed to thenext step, otherwise fix it.
Detailson the right to open the job page of CircleCI.Artifactstab.docs/build/html/index.html.Release Notes
When saving an MLflow Model, the
conda.yamlenvironment files now records the version ofpipthat is currently installed.Is this a user-facing 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/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