import mlflow
from mlflow.tracking import MlflowClient
from mlflow.entities import ViewType
if __name__ == "__main__":
def print_run_infos(run_infos):
for r in run_infos:
print("- run_id: {}, lifecycle_stage: {}".format(r.run_id, r.lifecycle_stage))
# Create two runs
with mlflow.start_run() as run1:
mlflow.log_metric("click_rate", 1.55)
with mlflow.start_run() as run2:
mlflow.log_metric("click_rate", 2.50)
# Delete the last run
client = MlflowClient()
client.delete_run(run2.info.run_id)
# Get all runs under the default experiment (whose id is 0)
print("Active runs:")
print_run_infos(mlflow.list_run_infos("0", run_view_type=ViewType.ACTIVE_ONLY))
print("Deleted runs:")
print_run_infos(mlflow.list_run_infos("0", run_view_type=ViewType.DELETED_ONLY))
print("All runs:")
print_run_infos(mlflow.list_run_infos("0", run_view_type=ViewType.ALL, order_by=["metric.click_rate DESC"]))
class MlflowManager(object):
_instance_lock = threading.Lock()
_init_flag = False
def __init__(self):
if self._init_flag is False:
tracking_uri = "file:./mlruns"
# tracking_uri = "mysql://*****:*****@10.117.61.106:3306/qdmlflow?charset=utf8"
artifact_location = None
print('[MlflowManager] init start: tracking_uri: %s' % tracking_uri)
self.artifact_location = artifact_location
self.tracking_uri = tracking_uri
self.client = MlflowClient(tracking_uri=self.tracking_uri)
mlflow.set_tracking_uri(uri=self.tracking_uri)
print('[MlflowManager] init end ...')
self._init_flag = True
return
# 设计成单例模式
def __new__(cls, *args, **kwargs):
print('[MlflowManager] __new__ 1')
if not hasattr(MlflowManager, "_instance"):
print('[MlflowManager] __new__ 2')
with MlflowManager._instance_lock:
print('[MlflowManager] __new__ 3')
if not hasattr(MlflowManager, "_instance"):
print('[MlflowManager] __new__ 4')
MlflowManager._instance = object.__new__(cls)
return MlflowManager._instance
# =========================================================================
def log_param(self, run_id, key, value):
self.client.log_param(run_id, key, value)
# 判断实验是否存在
def is_experiment_exist(self, experiment_name):
find_experiment = self.client.get_experiment_by_name(experiment_name)
if find_experiment:
return True
else:
return False
# 判断version_name是否存在
def is_version_name_exist(self, experiment_id, version_name, view='active_only'):
view_type = ViewType.from_string(view) if view else ViewType.ACTIVE_ONLY
runs = self.client.store.search_runs([experiment_id], None, view_type)
for run in runs:
tags = {k: v for k, v in run.data.tags.items()}
run_name = tags.get(mlflow_tags.MLFLOW_RUN_NAME, "")
if run_name == version_name:
return True
return False
# =======================================================
# 获取某个实验的某个主版本的所有子版本（孩子）
def get_minor_versions(self, experiment_id, version_id, view='active_only'):
view_type = ViewType.from_string(view) if view else ViewType.ACTIVE_ONLY
runs = self.client.store.search_runs([experiment_id], None, view_type)
minor_versions_list = []
for run in runs:
tags = {k: v for k, v in run.data.tags.items()}
parent_run_id = tags.get(mlflow_tags.MLFLOW_PARENT_RUN_ID, "")
if parent_run_id == version_id:
minor_versions_list.append(run)
return minor_versions_list
# 获取某个实验的某个主版本的子版本（孩子）个数
def get_minor_versions_count(self, experiment_id, version_id, view='active_only'):
minor_versions_list = self.get_minor_versions(experiment_id, version_id, view)
return len(minor_versions_list)
# 判断子版本是不是主版本的子版本（孩子）
def is_minor_version(self, experiment_id, version_id, minor_version_name, view='active_only'):
minor_versions_list = self.get_minor_versions(experiment_id, version_id, view)
for minor_version in minor_versions_list:
tags = {k: v for k, v in minor_version.data.tags.items()}
run_name = tags.get(mlflow_tags.MLFLOW_RUN_NAME, "")
if run_name == minor_version_name:
return True
return False
# 获取某个实验的某个主版本的某个子版本ID
def get_minor_version_id(self, experiment_id, version_id, minor_version_name, view='active_only'):
minor_versions_list = self.get_minor_versions(experiment_id, version_id, view)
for minor_version in minor_versions_list:
tags = {k: v for k, v in minor_version.data.tags.items()}
run_name = tags.get(mlflow_tags.MLFLOW_RUN_NAME, "")
if run_name == minor_version_name:
return minor_version.info.run_id
return None
# 自动生成一个子版本名称，格式如：1.1, 1.2, 2.3
def generate_minor_version_name(self, experiment_id, version_id, version_name):
count = self.get_minor_versions_count(experiment_id=experiment_id, version_id=version_id)
minor_version_name = version_name + "." + str(count + 1)
return minor_version_name
# 清理多余的子版本（按时间清理，保留运行时间最近的n个）
def clean_minor_versions(self, experiment_id, version_id, reserve_count=4):
try:
minor_versions = self.get_minor_versions(experiment_id=experiment_id, version_id=version_id)
if len(minor_versions) <= reserve_count:
return
# minor_versions按时间有序排列，只需要遍历后面的几个，直接删除即可
for i in range(reserve_count, len(minor_versions)):
print('[minor_version]', i, minor_versions[i].info.experiment_id, minor_versions[i].info.run_id,
minor_versions[i].info.start_time)
self.client.delete_run(run_id=minor_versions[i].info.run_id)
except Exception as e:
content = '[clean_minor_versions][error] {}'.format(e)
content = '[{}] {} {}'.format(experiment_id, version_id, content)
print(content)
# =======================================================
# 获取某个实验中，所有主版本
def get_major_versions(self, experiment_id, view='active_only'):
view_type = ViewType.from_string(view) if view else ViewType.ACTIVE_ONLY
runs = self.client.store.search_runs([experiment_id], None, view_type)
major_versions_list = []
for run in runs:
tags = {k: v for k, v in run.data.tags.items()}
parent_run_id = tags.get(mlflow_tags.MLFLOW_PARENT_RUN_ID, "")
if parent_run_id == "":
major_versions_list.append(run)
return major_versions_list
# 获取某个实验中，主版本个数
def get_major_versions_count(self, experiment_id, view='active_only'):
major_versions_list = self.get_major_versions(experiment_id, view)
return len(major_versions_list)
# 通过experiment_id和version_name查找主版本ID
# view可选值: 'active_only', 'deleted_only', 'all'
def get_major_version_id(self, experiment_id, version_name, view='active_only'):
major_versions_list = self.get_major_versions(experiment_id, view)
for major_version in major_versions_list:
tags = {k: v for k, v in major_version.data.tags.items()}
run_name = tags.get(mlflow_tags.MLFLOW_RUN_NAME, "")
if run_name == version_name:
return major_version.info.run_id
return None
# 自动生成一个主版本名称，格式如：1, 2, 3
def generate_major_version_name(self, experiment_id):
count = self.get_major_versions_count(experiment_id=experiment_id)
major_version_name = str(count + 1)
return major_version_name
# =======================================================
def get_versions(self, experiment_id, view='active_only'):
view_type = ViewType.from_string(view) if view else ViewType.ACTIVE_ONLY
runs = self.client.store.search_runs([experiment_id], None, view_type)
return runs
# 通过experiment_id和version_name查找版本ID，不区分主版本或子版本
def get_version_id(self, experiment_id, version_name, view='active_only'):
runs = self.get_versions(experiment_id, view)
for run in runs:
tags = {k: v for k, v in run.data.tags.items()}
run_name = tags.get(mlflow_tags.MLFLOW_RUN_NAME, "")
if run_name == version_name:
return run.info.run_id
return None
# 获取最优的版本
def get_best_version(self, experiment_name, metrics='rmse'):
experiment = self.client.get_experiment_by_name(experiment_name)
if experiment:
experiment_id = experiment.experiment_id
best_version_name = None
best_version_id = None
best_value = 0.0
version_list = self.get_versions(experiment_id)
print(len(version_list))
for version in version_list:
if metrics in version.data.metrics.keys():
print(version.data.metrics[metrics])
if version.data.metrics[metrics] > best_value:
best_value = version.data.metrics[metrics]
tags = {k: v for k, v in version.data.tags.items()}
best_version_name = tags.get(mlflow_tags.MLFLOW_RUN_NAME, "")
best_version_id = version.info.run_id
return best_version_name, best_version_id
else:
print('[get_best_version][error] experiment not found: %s' % experiment_name)
return None, None
# =======================================================
# 创建一个实验
def create_experiment(self, experiment_name):
if not self.is_experiment_exist(experiment_name):
experiment_id = self.client.create_experiment(name=experiment_name, artifact_location=None)
print('[create_experiment][success] experiment_name: %s; experiment_id: %s' %
(experiment_name, experiment_id))
return True
else:
print('[create_experiment][error] experiment_name: %s is exist' % experiment_name)
return False
def get_run(self, run_id='27f2872ffe3144b59200350a83ac11a5'):
run = self.client.get_run(run_id)
print(run)
print(run.data.params)
# =================================================================
# 运行一个新的主版本，新增一条运行记录
def create_and_run_major_version(self, experiment_name, func, args):
experiment = self.client.get_experiment_by_name(experiment_name)
# 判断实验是否存在
if experiment:
experiment_id = experiment.experiment_id
# 自动生成一个主版本名称
major_version_name = self.generate_major_version_name(experiment_id)
run = mlflow.start_run(experiment_id=experiment_id, run_name=major_version_name)
if run:
print('[run_major_version_new] start experiment: %s major_version: %s' %
(experiment_name, major_version_name))
# 运行函数
func(experiment_name=experiment_name, experiment_id=experiment_id,
version_name=major_version_name, version_id=run.info.run_id, args=args)
mlflow.end_run()
else:
print('[run_major_version_new][error] experiment not found: %s' % experiment_name)
# 运行一个新的子版本，新增一条运行记录
def create_and_run_minor_version(self, experiment_name, version_name, func, args):
experiment = self.client.get_experiment_by_name(experiment_name)
# 判断实验是否存在
if experiment:
experiment_id = experiment.experiment_id
version_id = self.get_major_version_id(experiment_id=experiment_id, version_name=version_name)
# 主版本是否存在
if version_id:
with mlflow.start_run(experiment_id=experiment_id, run_id=version_id):
# 自动生成一个子版本名称
minor_version_name = self.generate_minor_version_name(experiment_id, version_id, version_name)
run = mlflow.start_run(experiment_id=experiment_id, run_name=minor_version_name, nested=True)
if run:
print(
'[run_minor_version_new] start experiment: %s major_version: %s minor_version_name: %s' %
(experiment_name, version_name, minor_version_name))
# 运行函数
func(experiment_name=experiment_name, experiment_id=experiment_id,
version_name=minor_version_name, version_id=run.info.run_id, args=args)
mlflow.end_run()
else:
print('[run_minor_version_new][error] major_version not found: %s' % version_name)
else:
print('[run_minor_version_new][error] experiment not found: %s' % experiment_name)
import warnings
from mlflow.tracking import MlflowClient
if __name__ == '__main__':
warnings.filterwarnings("ignore")
# Create a run under the default experiment (whose ID is "0").
client = MlflowClient()
expriment_id = "0"
run = client.create_run(expriment_id)
run_id = run.info.run_id
print("run_id: {}; lifecycle_stage: {}".format(run_id,
run.info.lifecycle_stage))
print("--")
client.delete_run(run_id)
del_run = client.get_run(run_id)
print("run_id: {}; lifecycle_stage: {}".format(
run_id, del_run.info.lifecycle_stage))
k: v
for k, v in r.data.tags.items() if not k.startswith("mlflow.")
}
print("tags: {}".format(tags))
# Create an experiment and log two runs with metrics and tags under it
experiment_id = mlflow.create_experiment("Social NLP Experiments")
with mlflow.start_run(experiment_id=experiment_id) as run:
mlflow.log_metric("m", 1.55)
mlflow.set_tag("s.release", "1.1.0-RC")
with mlflow.start_run(experiment_id=experiment_id):
mlflow.log_metric("m", 2.50)
mlflow.set_tag("s.release", "1.2.0-GA")
# Search all runs under experiment id and order by descending value of the metric 'm'
client = MlflowClient()
runs = client.search_runs(experiment_id, order_by=["metrics.m DESC"])
print_run_info(runs)
print("--")
# Delete the first run
client.delete_run(run_id=run.info.run_id)
# Search only deleted runs under the experiment_id and use a case insensitive pattern
# in the filter_string for the tag.
filter_string = "tags.s.release ILIKE '%rc%'"
runs = client.search_runs(experiment_id,
run_view_type=ViewType.DELETED_ONLY,
filter_string=filter_string)
print_run_info(runs)
