Run.get_context() 给出相同的 运行 id
Run.get_context() gives the same run id
我正在通过脚本文件提交培训。以下是 train.py 脚本的内容。 Azure ML 将所有这些视为一个 运行（而不是下面编码的每个 alpha 值 运行），因为 Run.get_context() 返回相同的 运行 id。
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()
实验视图
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)
简答
选项 1：在 运行
内创建 child 运行 秒
run = Run.get_context() 将您当前所在 运行 的 运行 object 分配给 run。因此，在超参数搜索的每次迭代中，您都登录到相同的 运行。要解决这个问题，您需要为每个超参数值创建 child（或子）运行。您可以使用 run.child_run() 执行此操作。以下是实现此目的的模板。
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，您可以看到 运行 9 是 parent 运行 和 运行 的 10-19 是 child 运行，如果您单击“包含 child 运行”页面。 运行 9 详细信息页面上还有一个“Child 运行s”选项卡。
长答案
我强烈建议将超参数搜索从数据平面（即 train.py）抽象到控制平面（即“编写代码”）。随着训练时间的增加，这变得特别有价值，您可以任意并行化，也可以使用 Azure ML Hyperdrive.
更智能地选择超参数
选项 2 从控制平面创建 运行s
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)
下面是如何使用脚本参数提交单个 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（恕我直言，推荐方法）
通过这种方式，您可以将超参数源外包给 Hyperdrive。 UI 还将准确报告您想要的结果，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它，必须使用 AMLCompute，但对我来说这是值得的 trade-off.This is a great overview. Excerpt below (full code here)
param_sampling = GridParameterSampling( {
"alpha": choice(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)
}
)
estimator = Estimator(
source_directory = os.path.join(os.getcwd(), 'code'),
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)
我正在通过脚本文件提交培训。以下是 train.py 脚本的内容。 Azure ML 将所有这些视为一个 运行（而不是下面编码的每个 alpha 值 运行），因为 Run.get_context() 返回相同的 运行 id。
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()
实验视图
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)
简答
选项 1：在 运行
内创建 child 运行 秒run = Run.get_context() 将您当前所在 运行 的 运行 object 分配给 run。因此，在超参数搜索的每次迭代中，您都登录到相同的 运行。要解决这个问题，您需要为每个超参数值创建 child（或子）运行。您可以使用 run.child_run() 执行此操作。以下是实现此目的的模板。
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，您可以看到 运行 9 是 parent 运行 和 运行 的 10-19 是 child 运行，如果您单击“包含 child 运行”页面。 运行 9 详细信息页面上还有一个“Child 运行s”选项卡。
长答案
我强烈建议将超参数搜索从数据平面（即 train.py）抽象到控制平面（即“编写代码”）。随着训练时间的增加，这变得特别有价值，您可以任意并行化，也可以使用 Azure ML Hyperdrive.
选项 2 从控制平面创建 运行s
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)
下面是如何使用脚本参数提交单个 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（恕我直言，推荐方法）
通过这种方式，您可以将超参数源外包给 Hyperdrive。 UI 还将准确报告您想要的结果，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它，必须使用 AMLCompute，但对我来说这是值得的 trade-off.This is a great overview. Excerpt below (full code here)
param_sampling = GridParameterSampling( {
"alpha": choice(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0)
}
)
estimator = Estimator(
source_directory = os.path.join(os.getcwd(), 'code'),
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)
