replacing tf.Session() to tf.compat.v1.Session()
#4784
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* init Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * dummy tf update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: unknown <pariharlakshika@gmail.com>
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@lakshikaparihar Thanks for the contribution! The DCO check failed. Please sign off your commits by following the instructions here: https://github.com/mlflow/mlflow/runs/3532234049. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.rst#sign-your-work for more details. |
Signed-off-by: unknown <pariharlakshika@gmail.com>
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* Fix tensorflow dev cross test (mlflow#4780) * init Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * dummy tf update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: unknown <pariharlakshika@gmail.com> * changes in flower classifier train.py Signed-off-by: unknown <pariharlakshika@gmail.com> Co-authored-by: WeichenXu <weichen.xu@databricks.com> Signed-off-by: Anjali Samad <samad.anjali.14@gmail.com>
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What changes are proposed in this pull request?
In TF 2.0 you should use
tf.compat.v1.Session()instead oftf.Session(). Otherwise it was giving errorAttributeError: module 'tensorflow' has no attribute 'Session'How is this patch tested?
Unit Test
Release Notes
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