如何解决Run.get_context给出相同的运行ID 选项1:在运行内创建子运行选项2从控制平面创建行程选项3 Hyperdrive建议使用IMHO方法
我正在通过脚本文件提交培训。以下是train.py
脚本的内容。由于Run.get_context()
返回相同的运行ID,Azure ML将所有这些都视为一次运行(而不是如下所示的每个alpha值运行)。
train.py
from azureml.opendatasets import Diabetes
from azureml.core import Run
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.externals import joblib
import math
import os
import logging
# Load dataset
dataset = Diabetes.get_tabular_dataset()
print(dataset.take(1))
df = dataset.to_pandas_dataframe()
df.describe()
# Split X (independent variables) & Y (target variable)
x_df = df.dropna() # Remove rows that have missing values
y_df = x_df.pop("Y") # Y is the label/target variable
x_train,x_test,y_train,y_test = train_test_split(x_df,y_df,test_size=0.2,random_state=66)
print('Original dataset size:',df.size)
print("Size after dropping 'na':",x_df.size)
print("Training split size: ",x_train.size)
print("Test split size: ",x_test.size)
# Training
alphas = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0] # Define hyperparameters
# Create and log interactive runs
output_dir = os.path.join(os.getcwd(),'outputs')
for hyperparam_alpha in alphas:
# Get the experiment run context
run = Run.get_context()
print("Started run: ",run.id)
run.log("train_split_size",x_train.size)
run.log("test_split_size",x_train.size)
run.log("alpha_value",hyperparam_alpha)
# Train
print("Train ...")
model = Ridge(hyperparam_alpha)
model.fit(X = x_train,y = y_train)
# Predict
print("Predict ...")
y_pred = model.predict(X = x_test)
# Calculate & log error
rmse = math.sqrt(mean_squared_error(y_true = y_test,y_pred = y_pred))
run.log("rmse",rmse)
print("rmse",rmse)
# Serialize the model to local directory
if not os.path.isdir(output_dir):
os.makedirs(output_dir,exist_ok=True)
print("Save model ...")
model_name = "model_alpha_" + str(hyperparam_alpha) + ".pkl" # Pickle file
file_path = os.path.join(output_dir,model_name)
joblib.dump(value = model,filename = file_path)
# Upload the model
run.upload_file(name = model_name,path_or_stream = file_path)
# Complete the run
run.complete()
创作代码(即控制平面)
import os
from azureml.core import Workspace,Experiment,RunConfiguration,ScriptRunConfig,VERSION,Run
ws = Workspace.from_config()
exp = Experiment(workspace = ws,name = "diabetes-local-script-file")
# Create new run config obj
run_local_config = RunConfiguration()
# This means that when we run locally,all dependencies are already provided.
run_local_config.environment.python.user_managed_dependencies = True
# Create new script config
script_run_cfg = ScriptRunConfig(
source_directory = os.path.join(os.getcwd(),'code'),script = 'train.py',run_config = run_local_config)
run = exp.submit(script_run_cfg)
run.wait_for_completion(show_output=True)
解决方法
简短答案
选项1:在运行内创建子运行
run = Run.get_context()
将您当前正在运行的运行对象分配给run
。因此,在超参数搜索的每次迭代中,您都将登录到同一运行。要解决此问题,您需要为每个超参数值创建子(或子)运行。您可以使用run.child_run()
进行此操作。以下是实现此目标的模板。
run = Run.get_context()
for hyperparam_alpha in alphas:
# Get the experiment run context
run_child = run.child_run()
print("Started run: ",run_child.id)
run_child.log("train_split_size",x_train.size)
在diabetes-local-script-file
实验页面上,如果您单击“包含子运行”页面,则可以看到运行9
是父运行,运行10-19
是子运行。 “运行9”详细信息页面上还有一个“儿童跑步”标签。
长答案
我强烈建议抽象化超参数搜索,使其远离数据平面(即train.py
)并进入控制平面(即“创作代码”)。随着训练时间的增加,这变得特别有价值,并且您可以使用Azure ML的Hyperdrive
任意地并行化,也可以更智能地选择Hyperparameters。
选项2从控制平面创建行程
从代码中删除循环,添加如下代码(full data and control here)
import argparse
from pprint import pprint
parser = argparse.ArgumentParser()
parser.add_argument('--alpha',type=float,default=0.5)
args = parser.parse_args()
print("all args:")
pprint(vars(args))
# use the variable like this
model = Ridge(args.alpha)
下面的是如何使用脚本参数提交单次运行。要提交多个运行,只需在控制平面中使用循环即可。
alphas = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0] # Define hyperparameters
list_rcs = [ScriptRunConfig(
source_directory = os.path.join(os.getcwd(),'code'),script = 'train.py',arguments=['--alpha',a],run_config = run_local_config) for a in alphas]
list_runs = [exp.submit(rc) for rc in list_rcs]
选项3 Hyperdrive(建议使用IMHO方法)
通过这种方式,您可以将超参数源外包给Hyperdrive
。用户界面还将根据您的需要准确报告结果,并且通过API,您可以轻松下载最佳模型。请注意,您不能再在本地使用此功能,而必须使用AMLCompute
,但对我来说这是一个值得权衡的选择。This is a great overview。以下摘录(full code here)
param_sampling = GridParameterSampling( {
"alpha": choice(0.1,1.0)
}
)
estimator = Estimator(
source_directory = os.path.join(os.getcwd(),entry_script = 'train.py',compute_target=cpu_cluster,environment_definition=Environment.get(workspace=ws,name="AzureML-Tutorial")
)
hyperdrive_run_config = HyperDriveConfig(estimator=estimator,hyperparameter_sampling=param_sampling,policy=None,primary_metric_name="rmse",primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,max_total_runs=10,max_concurrent_runs=4)
run = exp.submit(hyperdrive_run_config)
run.wait_for_completion(show_output=True)
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