⚡️ Speed up function _get_python_env_file by 190%

[codeflash-ai]

📄 190% (1.90x) speedup for _get_python_env_file in mlflow/utils/virtualenv.py

⏱️ Runtime : 306 microseconds → 106 microseconds (best of 128 runs)

📝 Explanation and details

The optimization replaces an inefficient for loop iteration over model_config.flavors.items() with a direct dictionary lookup using .get().

Key changes:

• Instead of iterating through all flavors and checking if flavor == mlflow.pyfunc.FLAVOR_NAME, the code now directly accesses model_config.flavors.get(mlflow.pyfunc.FLAVOR_NAME)
• This eliminates the need to iterate through potentially many flavors just to find the pyfunc flavor

Why this optimization works:
The original code had O(n) complexity where n is the number of flavors, as it needed to check every flavor name against FLAVOR_NAME. The optimized version has O(1) complexity since dictionary lookups are constant time operations in Python.

Performance impact:
The line profiler shows the original loop (for flavor, config in model_config.flavors.items()) consumed 41.8% of total runtime, while the optimized direct lookup consumes only 15.6%. This is particularly effective for large flavor dictionaries - the test cases show dramatic improvements:

• Large flavors dict with pyfunc last: 1411% faster (50.1μs → 3.31μs)
• Large flavors dict without pyfunc: 1378% faster (50.5μs → 3.42μs)

Test case benefits:
The optimization performs consistently well across all scenarios, with 15-26% speedups for typical cases and massive improvements (1300%+) for large dictionaries. This suggests the function may be called frequently in MLflow workflows where models have many flavors, making this a valuable optimization for real-world usage.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 31 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import mlflow

imports

import pytest # used for our unit tests
from mlflow.utils.environment import _PYTHON_ENV_FILE_NAME
from mlflow.utils.virtualenv import _get_python_env_file

Helper classes for tests

class DummyModelConfig:
"""A dummy model config object with a 'flavors' attribute for testing."""
def init(self, flavors):
self.flavors = flavors

Basic Test Cases

def test_returns_virtualenv_path_when_env_dict_present():
# Test that the function returns the virtualenv path when present in env dict
virtualenv_path = "some/path/virtualenv.yaml"
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.VIRTUALENV: virtualenv_path,
mlflow.pyfunc.EnvType.CONDA: "some/path/conda.yaml"
}
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 3.84μs -> 3.04μs (26.4% faster)

def test_returns_default_when_env_not_dict():
# Test that the function returns the default env file when env is not a dict
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: "some/path/conda.yaml"
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 3.79μs -> 3.13μs (20.8% faster)

def test_returns_default_when_env_missing():
# Test that the function returns the default env file when ENV key is missing
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
"other_key": "value"
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 3.88μs -> 3.35μs (15.6% faster)

def test_returns_default_when_pyfunc_flavor_missing():
# Test that the function returns the default env file when pyfunc flavor is missing
flavors = {
"other_flavor": {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.VIRTUALENV: "some/path/virtualenv.yaml"
}
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 3.49μs -> 2.79μs (25.0% faster)

Edge Test Cases

def test_env_dict_missing_virtualenv_key():
# Test that the function raises KeyError if VIRTUALENV key is missing in env dict
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.CONDA: "some/path/conda.yaml"
}
}
}
config = DummyModelConfig(flavors)
with pytest.raises(KeyError):
_get_python_env_file(config) # 4.42μs -> 3.68μs (20.0% faster)

def test_env_dict_virtualenv_key_is_none():
# Test that the function returns None if VIRTUALENV value is None
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.VIRTUALENV: None
}
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 3.87μs -> 3.18μs (21.6% faster)

def test_flavors_is_empty():
# Test that the function returns default env file when flavors is empty
flavors = {}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 3.12μs -> 3.04μs (2.66% faster)

def test_env_is_empty_dict():
# Test that the function raises KeyError if env dict is empty
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: {}
}
}
config = DummyModelConfig(flavors)
with pytest.raises(KeyError):
_get_python_env_file(config) # 4.21μs -> 3.81μs (10.4% faster)

def test_flavors_is_none():
# Test that the function raises AttributeError when flavors is None
config = DummyModelConfig(None)
with pytest.raises(AttributeError):
_get_python_env_file(config) # 3.69μs -> 3.75μs (1.41% slower)

def test_env_is_none():
# Test that the function returns default env file when env is None
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: None
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 4.13μs -> 3.28μs (26.1% faster)

def test_flavors_not_dict():
# Test that the function raises AttributeError when flavors is not a dict
config = DummyModelConfig(["not", "a", "dict"])
with pytest.raises(AttributeError):
_get_python_env_file(config) # 3.77μs -> 3.73μs (0.911% faster)

Large Scale Test Cases

def test_large_flavors_dict_with_pyfunc_last():
# Test with a large flavors dict where pyfunc flavor is last
flavors = {f"flavor_{i}": {"some_key": "some_value"} for i in range(999)}
virtualenv_path = "large/path/virtualenv.yaml"
flavors[mlflow.pyfunc.FLAVOR_NAME] = {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.VIRTUALENV: virtualenv_path,
mlflow.pyfunc.EnvType.CONDA: "large/path/conda.yaml"
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 50.1μs -> 3.31μs (1411% faster)

def test_large_flavors_dict_without_pyfunc():
# Test with a large flavors dict without pyfunc flavor
flavors = {f"flavor_{i}": {"some_key": "some_value"} for i in range(1000)}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 50.5μs -> 3.42μs (1378% faster)

def test_large_env_dict_with_many_keys():
# Test with a large env dict with many keys, including virtualenv
env_dict = {f"envtype_{i}": f"path_{i}.yaml" for i in range(999)}
virtualenv_path = "many/path/virtualenv.yaml"
env_dict[mlflow.pyfunc.EnvType.VIRTUALENV] = virtualenv_path
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: env_dict
}
}
config = DummyModelConfig(flavors)
codeflash_output = _get_python_env_file(config); result = codeflash_output # 3.97μs -> 3.22μs (23.2% faster)

def test_large_env_dict_missing_virtualenv_key():
# Test with a large env dict missing the virtualenv key
env_dict = {f"envtype_{i}": f"path_{i}.yaml" for i in range(1000)}
flavors = {
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: env_dict
}
}
config = DummyModelConfig(flavors)
with pytest.raises(KeyError):
_get_python_env_file(config) # 4.54μs -> 3.95μs (15.0% faster)

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

#------------------------------------------------
import mlflow

imports

import pytest
from mlflow.utils.environment import _PYTHON_ENV_FILE_NAME
from mlflow.utils.virtualenv import _get_python_env_file

Helper class to mimic the model_config object

class ModelConfig:
def init(self, flavors):
self.flavors = flavors

Basic Test Cases

def test_returns_env_file_when_pyfunc_flavor_with_virtualenv_dict():
# Scenario: pyfunc flavor present, ENV is a dict with VIRTUALENV key
expected_path = "envs/virtualenv.yaml"
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.VIRTUALENV: expected_path,
mlflow.pyfunc.EnvType.CONDA: "envs/conda.yaml"
}
}
})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.24μs -> 3.08μs (5.17% faster)

def test_returns_default_when_pyfunc_flavor_missing():
# Scenario: pyfunc flavor not present
model_config = ModelConfig({
"other_flavor": {}
})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.83μs -> 3.16μs (21.4% faster)

def test_returns_default_when_env_not_dict():
# Scenario: pyfunc flavor present, ENV is not a dict
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: "some_string"
}
})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.73μs -> 3.04μs (22.7% faster)

def test_returns_default_when_env_missing():
# Scenario: pyfunc flavor present, ENV key missing
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
"not_env": "value"
}
})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 4.02μs -> 3.43μs (17.1% faster)

Edge Test Cases

def test_returns_default_when_env_dict_missing_virtualenv_key():
# Scenario: pyfunc flavor present, ENV is dict, but VIRTUALENV key missing
env_dict = {
mlflow.pyfunc.EnvType.CONDA: "envs/conda.yaml"
}
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: env_dict
}
})
# Should raise KeyError because code expects EnvType.VIRTUALENV to exist
with pytest.raises(KeyError):
_get_python_env_file(model_config) # 4.30μs -> 3.68μs (16.9% faster)

def test_returns_default_when_flavors_is_empty():
# Scenario: flavors dict is empty
model_config = ModelConfig({})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.38μs -> 3.15μs (7.24% faster)

def test_returns_default_when_flavors_is_none():
# Scenario: flavors attribute is None
class ModelConfigNoneFlavors:
flavors = None
model_config = ModelConfigNoneFlavors()
with pytest.raises(AttributeError):
_get_python_env_file(model_config) # 3.86μs -> 3.81μs (1.47% faster)

def test_returns_default_when_flavors_is_not_a_dict():
# Scenario: flavors attribute is not a dict (e.g., a list)
class ModelConfigListFlavors:
flavors = []
model_config = ModelConfigListFlavors()
with pytest.raises(AttributeError):
_get_python_env_file(model_config) # 4.05μs -> 3.84μs (5.39% faster)

def test_returns_default_when_env_is_empty_dict():
# Scenario: pyfunc flavor present, ENV is an empty dict
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: {}
}
})
with pytest.raises(KeyError):
_get_python_env_file(model_config) # 4.39μs -> 3.93μs (11.8% faster)

def test_returns_default_when_env_is_none():
# Scenario: pyfunc flavor present, ENV is None
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: None
}
})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.88μs -> 3.19μs (21.9% faster)

def test_returns_default_when_env_is_int():
# Scenario: pyfunc flavor present, ENV is an integer
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: 42
}
})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.75μs -> 3.25μs (15.6% faster)

Large Scale Test Cases

def test_large_number_of_flavors_with_pyfunc_last():
# Scenario: flavors dict has many flavors, pyfunc flavor is last
flavors = {f"flavor_{i}": {} for i in range(999)}
expected_path = "envs/virtualenv_large.yaml"
flavors[mlflow.pyfunc.FLAVOR_NAME] = {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.VIRTUALENV: expected_path
}
}
model_config = ModelConfig(flavors)
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 50.6μs -> 3.41μs (1383% faster)

def test_large_env_dict_with_virtualenv_key():
# Scenario: ENV dict has many keys, including VIRTUALENV
env_dict = {f"envtype_{i}": f"path_{i}.yaml" for i in range(999)}
expected_path = "envs/virtualenv_large.yaml"
env_dict[mlflow.pyfunc.EnvType.VIRTUALENV] = expected_path
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: env_dict
}
})
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.86μs -> 3.27μs (18.3% faster)

def test_large_number_of_flavors_without_pyfunc():
# Scenario: flavors dict has many flavors, none are pyfunc
flavors = {f"flavor_{i}": {} for i in range(1000)}
model_config = ModelConfig(flavors)
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 49.1μs -> 3.42μs (1334% faster)

def test_large_number_of_flavors_with_pyfunc_first():
# Scenario: flavors dict has many flavors, pyfunc flavor is first
flavors = {mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: {
mlflow.pyfunc.EnvType.VIRTUALENV: "envs/virtualenv_first.yaml"
}
}}
for i in range(1, 1000):
flavors[f"flavor_{i}"] = {}
model_config = ModelConfig(flavors)
codeflash_output = _get_python_env_file(model_config); result = codeflash_output # 3.91μs -> 3.34μs (17.2% faster)

def test_large_env_dict_missing_virtualenv_key():
# Scenario: ENV dict has many keys, but missing VIRTUALENV
env_dict = {f"envtype_{i}": f"path_{i}.yaml" for i in range(1000)}
model_config = ModelConfig({
mlflow.pyfunc.FLAVOR_NAME: {
mlflow.pyfunc.ENV: env_dict
}
})
with pytest.raises(KeyError):
_get_python_env_file(model_config) # 4.45μs -> 3.85μs (15.4% faster)

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-_get_python_env_file-mhurgrmy and push.