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Fix spark jobs #10340

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merged 4 commits into from Nov 10, 2023
Merged

Fix spark jobs #10340

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73d16c0
Fix spark jobs
harupy Nov 9, 2023
02a7418
extra_requirements
harupy Nov 9, 2023
1d76155
Fix typo
harupy Nov 10, 2023
048566a
check if additional_pip_deps contains mlflow
harupy Nov 10, 2023
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9 changes: 6 additions & 3 deletions mlflow/utils/environment.py
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Expand Up @@ -214,9 +214,13 @@ def _mlflow_conda_env(
:return: ``None`` if ``path`` is specified. Otherwise, the a dictionary representation of the
Conda environment.
"""
pip_deps = ([f"mlflow=={VERSION}"] if install_mlflow else []) + (
additional_pip_deps if additional_pip_deps else []
additional_pip_deps = additional_pip_deps or []
mlflow_deps = (
[f"mlflow=={VERSION}"]
if install_mlflow and not _contains_mlflow_requirement(additional_pip_deps)
else []
)
pip_deps = mlflow_deps + additional_pip_deps
conda_deps = additional_conda_deps if additional_conda_deps else []
if pip_deps:
pip_version = _get_pip_version()
Expand Down Expand Up @@ -554,7 +558,6 @@ def _process_conda_env(conda_env):
# User-specified `conda_env` may contain requirements/constraints file references
pip_reqs = _get_pip_deps(conda_env)
pip_reqs, constraints = _parse_pip_requirements(pip_reqs)

if not _contains_mlflow_requirement(pip_reqs):
pip_reqs.insert(0, _generate_mlflow_version_pinning())

Expand Down
6 changes: 4 additions & 2 deletions tests/spark/test_spark_model_export.py
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Expand Up @@ -57,7 +57,7 @@
@pytest.fixture
def spark_custom_env(tmp_path):
conda_env = os.path.join(tmp_path, "conda_env.yml")
additional_pip_deps = ["pyspark", "pytest"]
additional_pip_deps = ["/opt/mlflow", "pyspark", "pytest"]
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smart fix :)

if Version(pyspark.__version__) <= Version("3.3.2"):
# Versions of PySpark <= 3.3.2 are incompatible with pandas >= 2
additional_pip_deps.append("pandas<2")
Expand Down Expand Up @@ -355,7 +355,9 @@ def test_model_deployment(spark_model_iris, model_path, spark_custom_env):
reason="The dev version of pyspark built from the source doesn't exist on PyPI or Anaconda",
)
def test_sagemaker_docker_model_scoring_with_default_conda_env(spark_model_iris, model_path):
mlflow.spark.save_model(spark_model_iris.model, path=model_path)
mlflow.spark.save_model(
spark_model_iris.model, path=model_path, extra_pip_requirements=["/opt/mlflow"]
)

scoring_response = score_model_in_sagemaker_docker_container(
model_uri=model_path,
Expand Down