我嘗試以這種方式讀取指標： data, info = mlflow.get_run(run_id) print(data[1].metrics) # example of output: {'loss': 0.01}但它只得到最后一個值。是否可以手動讀取特定指標的所有步驟？
4 回答
慕沐林林
TA貢獻2016條經驗 獲得超9個贊
我遇到了同樣的問題，并且能夠使用mlflow.tracking.MlflowClient（）.get_metric_history來獲取指標的所有值。這將返回您使用 記錄的每個值。mlflow.log_metric(key, value)
快速示例（未經測試）
import mlflow
trackingDir = 'file:///....'
registryDir = 'file:///...'
runID = 'my run id'
metricKey = 'loss'
client = mlflow.tracking.MlflowClient(
tracking_uri=trackingDir,
registry_uri=registryDir,
)
metrics = client.get_metric_history(runID, metricKey)
從文檔
get_metric_history（run_id，鍵）[源] 返回與給定衡量指標記錄的所有值相對應的衡量指標對象列表。
參數 run_id – 運行的唯一標識符
鍵 – 運行中的指標名稱
返回 mlflow.entities 的列表。指標實體（如果已記錄），否則為空列表
from mlflow.tracking import MlflowClient
def print_metric_info(history):
for m in history:
print("name: {}".format(m.key))
print("value: {}".format(m.value))
print("step: {}".format(m.step))
print("timestamp: {}".format(m.timestamp))
print("--")
# Create a run under the default experiment (whose id is "0"). Since this is low-level
# CRUD operation, the method will create a run. To end the run, you'll have
# to explicitly end it.
client = MlflowClient()
experiment_id = "0"
run = client.create_run(experiment_id)
print("run_id:{}".format(run.info.run_id))
print("--")
# Log couple of metrics, update their initial value, and fetch each
# logged metrics' history.
for k, v in [("m1", 1.5), ("m2", 2.5)]:
client.log_metric(run.info.run_id, k, v, step=0)
client.log_metric(run.info.run_id, k, v + 1, step=1)
print_metric_info(client.get_metric_history(run.info.run_id, k))
client.set_terminated(run.info.run_id)
心有法竹
TA貢獻1866條經驗 獲得超5個贊
是的，您可以使用 來獲取實驗和運行信息。它將 MLflow 數據結構作為字典返回，您可以對其進行迭代以提取 listcomp 中所需的內容。下面是一個示例：MlffowClient APIs
def print_experiment_details(experiment_id, run_id):
"""
Method to print experiment run info and a specific run details
:param experiment_id: MLflow experiment ID
:param run_id: MLflow run ID within an experiment
:return: none
"""
print("Finished MLflow Run with run_id {} and experiment_id {}".format(run_id, experiment_id))
# Use MlflowClient API to list experiments and run info
client = MlflowClient()
print("=" * 80)
# Get a list of all experiments
print("List of all Experiments")
print("=" * 80)
[print(pprint.pprint(dict(exp), indent=4))
for exp in client.list_experiments()]
print("=" * 80)
print(f"List Run info for run_id={run_id}")
print(pprint.pprint(dict(mlflow.get_run(run_id))))
此輸出：
Running local model registry=sqlite:///mlruns.db
Finished MLflow Run with run_id 3f3b827dd6814649a2f84ebae09b26c6 and experiment_id 0
================================================================================
List of all Experiments
================================================================================
{ 'artifact_location': './mlruns/0',
'experiment_id': '0',
'lifecycle_stage': 'active',
'name': 'ODSC_TUTORIALS',
'tags': { 'mlflow.note.content': 'This is experiment for getting started '
'with MLflow ...'}}
None
================================================================================
List Run info for run_id=3f3b827dd6814649a2f84ebae09b26c6
{'data': <RunData: metrics={'metric_1': 0.9236238251076615,
'metric_2': 1.6732389715754346,
'metric_3': 2.249979396736294}, params={'n_estimators': '3', 'random_state': '42'}, tags={'mlflow.log-model.history': '[{"run_id": "3f3b827dd6814649a2f84ebae09b26c6", '
'"artifact_path": "sklearn-model", '
'"utc_time_created": "2020-03-18 '
'22:25:33.083332", "flavors": {"python_function": '
'{"loader_module": "mlflow.sklearn", '
'"python_version": "3.7.5", "data": "model.pkl", '
'"env": "conda.yaml"}, "sklearn": '
'{"pickled_model": "model.pkl", '
'"sklearn_version": "0.22.2.post1", '
'"serialization_format": "cloudpickle"}}}]',
'mlflow.note.content': 'This Run is for getting started with MLflow ...',
'mlflow.runName': 'LOCAL_REGISTRY',
'mlflow.source.git.commit': '0a3c6a3739deab77631318eca7fb9690b6dbad66',
'mlflow.source.name': '/Users/julesdamji/gits/tutorials/mlflow/labs/00_get_started.py',
'mlflow.source.type': 'LOCAL',
'mlflow.user': 'julesdamji'}>,
'info': <RunInfo: artifact_uri='./mlruns/0/3f3b827dd6814649a2f84ebae09b26c6/artifacts', end_time=1584570333841, experiment_id='0', lifecycle_stage='active', run_id='3f3b827dd6814649a2f84ebae09b26c6', run_uuid='3f3b827dd6814649a2f84ebae09b26c6', start_time=1584570332914, status='FINISHED', user_id='julesdamji'>}
您可以在此處獲取完整代碼
希望有所幫助。
至尊寶的傳說
TA貢獻1789條經驗 獲得超10個贊
使用MLflow客戶端（），您可以使用以下方法輕松獲取所有或選定的參數和指標：MlflowClientget_run(id).data
# create an instance of the MLflowClient,
# connected to the tracking_server_url
mlflow_client = mlflow.tracking.MlflowClient(
tracking_uri=tracking_server_url)
# list all experiment at this Tracking server
# mlflow_client.list_experiments()
# extract params/metrics data for run `test_run_id` in a single dict
run_data_dict = mlflow_client.get_run(test_run_id).data.to_dictionary()
# list all params and metrics for this run (test_run_id)
# pprint(run_data_dict)
print(run_data_dict['params']['algo'])
print(run_data_dict['metrics']['RMSE'])
鴻蒙傳說
TA貢獻1865條經驗 獲得超7個贊
我殘酷地解決了這個問題：我閱讀了特定[metric_name]的原始文件，其中包含特定的[run_id]。
path = f'./mlruns/0/[run_id]/metrics/[metric_name]'
with open(path) as f:
content = f.readlines()
metrics_for_step = [float(x.split(' ')[1]) for x in content]
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