使用 AutoTune 进行超参数优化
本教程演示如何使用 AutoTune 对模型进行超参数优化（以 Keras 模型的单机训练为例）。
准备训练脚本
在开始超参数优化实验之前，你需要提前准备训练脚本。你可以使用模型构建控制台中的 Notebook 编辑训练脚本。
创建 PVC
参照创建 PVC 教程创建名为 autotune-mnist-keras、大小为 1Gi 的 PVC。
创建 Notebook
从模型构建控制台进入 Notebook 列表，点击右上角的创建 Notebook。
创建 Notebook 时，在存储卷选择前面创建的 PVC 的名称 autotune-mnist-keras。
创建完成之后，点击打开进入 Notebook。
在 Notebook 中编辑训练脚本
以下是一个 Keras 训练脚本，在此基础上做简单的修改以应用在 AutoTune 实验中。
import argparse
import json
import logging
import os
import time
import tensorflow as tf
from tensorflow.keras import callbacks, datasets, layers, models, optimizers
parser = argparse.ArgumentParser(
description='Distributed training of Keras model for MNIST with '
'MultiWorkerMirroredStrategy.')
parser.add_argument('--log_dir',
type=str,
default=None,
help='Path of the TensorBoard log directory.')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='Disable CUDA training.')
parser.add_argument('--save_path',
type=str,
default=None,
help='Save path of the trained model.')
args = parser.parse_args()
logger = logging.getLogger('print')
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler())
logger.propagate = False
if args.no_cuda:
tf.config.set_visible_devices([], 'GPU')
gpus = tf.config.get_visible_devices('GPU')
if gpus:
# Print GPU info
logger.info('NVIDIA_VISIBLE_DEVICES: {}'.format(
os.getenv('NVIDIA_VISIBLE_DEVICES')))
logger.info('T9K_GPU_PERCENT: {}'.format(os.getenv('T9K_GPU_PERCENT')))
logger.info('Visible GPUs: {}'.format(
tf.config.get_visible_devices('GPU')))
# Set memory growth
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# # Set GPU memory limit
# tf.config.set_logical_device_configuration(
# gpus[0], [tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
strategy = tf.distribute.MultiWorkerMirroredStrategy()
# Get information for current worker.
tf_config = json.loads(os.environ['TF_CONFIG'])
world_size = len(tf_config['cluster']['worker'])
task_index = tf_config['task']['index']
params = {
# Search space:
# 'batch_size': ...
# 'learning_rate': ...
# 'conv_channels1': ...
'epochs': 10,
'conv_channels2': 64,
'conv_channels3': 64,
'conv_kernel_size': 3,
'maxpool_size': 2,
'linear_features1': 64,
'seed': 1,
}
with strategy.scope():
model = models.Sequential([
layers.Conv2D(params['conv_channels1'],
params['conv_kernel_size'],
activation='relu',
input_shape=(28, 28, 1)),
layers.MaxPooling2D((params['maxpool_size'], params['maxpool_size'])),
layers.Conv2D(params['conv_channels2'],
params['conv_kernel_size'],
activation='relu'),
layers.MaxPooling2D((params['maxpool_size'], params['maxpool_size'])),
layers.Conv2D(params['conv_channels3'],
params['conv_kernel_size'],
activation='relu'),
layers.Flatten(),
layers.Dense(params['linear_features1'], activation='relu'),
layers.Dense(10, activation='softmax'),
])
model.compile(
optimizer=optimizers.Adam(learning_rate=params['learning_rate']),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
(train_images, train_labels), (test_images,
test_labels) = datasets.mnist.load_data(
path=os.path.join(os.getcwd(), 'mnist.npz'))
train_images = train_images.reshape((60000, 28, 28, 1)).astype("float32") / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype("float32") / 255
train_images, val_images = train_images[:48000], train_images[48000:]
train_labels, val_labels = train_labels[:48000], train_labels[48000:]
train_dataset = tf.data.Dataset.from_tensor_slices(
(train_images, train_labels)).shuffle(
48000, seed=params['seed']).repeat().batch(params['batch_size'])
val_dataset = tf.data.Dataset.from_tensor_slices(
(val_images, val_labels)).batch(400)
test_dataset = tf.data.Dataset.from_tensor_slices(
(test_images, test_labels)).batch(1000)
model.fit(train_images,
train_labels,
batch_size=params['batch_size'],
epochs=params['epochs'],
validation_split=0.2,
verbose=2)
# TODO: Automatically save best n models.
# if args.save_path and task_index == 0:
# t9k.autotune.utils.save_best_n_models(model, args.save_path)
model.evaluate(test_images, test_labels, callbacks=test_callbacks, verbose=2)
if task_index > 0:
# wait a while for index 0
time.sleep(1)
在上述脚本中导入 t9k.tuner 模块，在训练模型之前调用 get_next_parameter() 函数获取训练超参数，替换原来的参数。
from t9k import tuner
def main():
...
tuner_params = tuner.get_next_parameter()
params.update(tuner_params)
...
在训练过程中，添加 AutoTuneCallback 上传实验指标。
train_callbacks = []
test_callbacks = []
if task_index == 0:
from t9k.tuner.keras import AutoTuneFitCallback, AutoTuneEvalCallback
train_callbacks.append(AutoTuneFitCallback(metric='accuracy'))
test_callbacks.append(AutoTuneEvalCallback(metric='accuracy'))
if args.log_dir:
tensorboard_callback = callbacks.TensorBoard(log_dir=args.log_dir)
train_callbacks.append(tensorboard_callback)
如下为修改后的训练脚本：
import argparse
import json
import logging
import os
import time
import tensorflow as tf
from tensorflow.keras import callbacks, datasets, layers, models, optimizers
from t9k import tuner
parser = argparse.ArgumentParser(
description='Distributed training of Keras model for MNIST with '
'MultiWorkerMirroredStrategy.')
parser.add_argument('--log_dir',
type=str,
default=None,
help='Path of the TensorBoard log directory.')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='Disable CUDA training.')
parser.add_argument('--save_path',
type=str,
default=None,
help='Save path of the trained model.')
args = parser.parse_args()
logger = logging.getLogger('print')
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler())
logger.propagate = False
if args.no_cuda:
tf.config.set_visible_devices([], 'GPU')
gpus = tf.config.get_visible_devices('GPU')
if gpus:
# Print GPU info
logger.info('NVIDIA_VISIBLE_DEVICES: {}'.format(
os.getenv('NVIDIA_VISIBLE_DEVICES')))
logger.info('T9K_GPU_PERCENT: {}'.format(os.getenv('T9K_GPU_PERCENT')))
logger.info('Visible GPUs: {}'.format(
tf.config.get_visible_devices('GPU')))
# Set memory growth
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
# # Set GPU memory limit
# tf.config.set_logical_device_configuration(
# gpus[0], [tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
strategy = tf.distribute.MultiWorkerMirroredStrategy()
# Get information for current worker.
tf_config = json.loads(os.environ['TF_CONFIG'])
world_size = len(tf_config['cluster']['worker'])
task_index = tf_config['task']['index']
tuner_params = tuner.get_next_parameter()
params = {
# Search space:
# 'batch_size': ...
# 'learning_rate': ...
# 'conv_channels1': ...
'epochs': 10,
'conv_channels2': 64,
'conv_channels3': 64,
'conv_kernel_size': 3,
'maxpool_size': 2,
'linear_features1': 64,
'seed': 1,
}
params.update(tuner_params)
with strategy.scope():
model = models.Sequential([
layers.Conv2D(params['conv_channels1'],
params['conv_kernel_size'],
activation='relu',
input_shape=(28, 28, 1)),
layers.MaxPooling2D((params['maxpool_size'], params['maxpool_size'])),
layers.Conv2D(params['conv_channels2'],
params['conv_kernel_size'],
activation='relu'),
layers.MaxPooling2D((params['maxpool_size'], params['maxpool_size'])),
layers.Conv2D(params['conv_channels3'],
params['conv_kernel_size'],
activation='relu'),
layers.Flatten(),
layers.Dense(params['linear_features1'], activation='relu'),
layers.Dense(10, activation='softmax'),
])
model.compile(
optimizer=optimizers.Adam(learning_rate=params['learning_rate']),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
(train_images, train_labels), (test_images,
test_labels) = datasets.mnist.load_data(
path=os.path.join(os.getcwd(), 'mnist.npz'))
train_images = train_images.reshape((60000, 28, 28, 1)).astype("float32") / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype("float32") / 255
train_images, val_images = train_images[:48000], train_images[48000:]
train_labels, val_labels = train_labels[:48000], train_labels[48000:]
train_dataset = tf.data.Dataset.from_tensor_slices(
(train_images, train_labels)).shuffle(
48000, seed=params['seed']).repeat().batch(params['batch_size'])
val_dataset = tf.data.Dataset.from_tensor_slices(
(val_images, val_labels)).batch(400)
test_dataset = tf.data.Dataset.from_tensor_slices(
(test_images, test_labels)).batch(1000)
train_callbacks = []
test_callbacks = []
if task_index == 0:
from t9k.tuner.keras import AutoTuneFitCallback, AutoTuneEvalCallback
train_callbacks.append(AutoTuneFitCallback(metric='accuracy'))
test_callbacks.append(AutoTuneEvalCallback(metric='accuracy'))
if args.log_dir:
tensorboard_callback = callbacks.TensorBoard(log_dir=args.log_dir)
train_callbacks.append(tensorboard_callback)
model.fit(train_images,
train_labels,
batch_size=params['batch_size'],
epochs=params['epochs'],
validation_split=0.2,
callbacks=train_callbacks,
verbose=2)
# TODO: Automatically save best n models.
# if args.save_path and task_index == 0:
# t9k.autotune.utils.save_best_n_models(model, args.save_path)
model.evaluate(test_images, test_labels, callbacks=test_callbacks, verbose=2)
if task_index > 0:
# wait a while for index 0
time.sleep(1)
在 Notebook 中创建文件 main.py，写入上述脚本并保存文件。
创建 download_dataset.py 文件，写入并执行以下脚本来下载实验数据。
import os
import tensorflow as tf
_, _ = tf.keras.datasets.mnist.load_data(os.path.join(os.getcwd(), 'mnist.npz'))
准备数据库
你可以使用实验管理平台（以下称 EM）记录超参数调优实验中的超参数组合和训练结果。（如果你选择不使用 EM 持久记录 AutoTune 实验结果，请忽略这一步，并在开始实验时删掉 AutoTuneExperiment 的 spec.aistore 字段）
在实验管理控制台中新建文件夹
EM 的实验数据是以文件夹形式管理的，所以你首先需要在实验管理控制台点击右上角的 + 新建一个文件夹。
进入文件夹，点击 ID 来复制该文件夹的 ID。
获得访问 EM 所需的 API Key
在超参数优化实验中，如果你希望使用 EM 来存储实验数据，需要生成一个具有访问 EM 文件夹权限的 API Key，你可以通过这个 API Key 上传实验数据。
你需要按照生成 API Key 教程中的步骤，在安全控制台中生成一个 API Key，其中必须勾选 AIStore 选项。
生成 API Key 之后，点击复制按钮复制该 API Key。
然后，你需要按照管理 Secret 教程中的步骤，在模型构建控制台中将所复制的 API Key 存入名为 aistore 的 Secret 中，以方便后续实验使用。
开始实验
在模型构建控制台的 AutoTune 列表页面，点击右上角的创建 AutoTuneExperiment 进入 AutoTuneExperiment 创建页面。
在 AutoTuneExperiment 创建页面，点击预览 YAML，输入以下配置，点击创建：
apiVersion: tensorstack.dev/v1beta1
kind: AutoTuneExperiment
metadata:
name: autotune-mnist-keras
spec:
maxExecSeconds: 3600
maxTrialNum: 20
trialConcurrency: 3
storage: 100Mi
aistore:
secret: 'aistore'
folder: 'b6c17378-965c-4467-9a43-eed65597f976'
searchSpace: |-
{
"batch_size": {"_type": "choice", "_value": [16, 32, 64, 128]},
"learning_rate": {"_type": "choice", "_value": [0.0001, 0.001, 0.01, 0.1]},
"conv_channels1": {"_type": "choice", "_value": [16, 32, 64, 128]}
}
trainingConfig:
type: tensorflow
tasks:
- type: worker
replicas: 4
template:
spec:
securityContext:
runAsUser: 1000
containers:
- command:
- sh
- -c
- "python3 main.py --log_dir /mnt/log --no_cuda"
workingDir: /mnt/
imagePullPolicy: IfNotPresent
image: t9kpublic/tensorflow-2.5.1:20220216
name: tensorflow
resources:
requests:
cpu: 2000m
memory: 2Gi
limits:
cpu: 4000m
memory: 4Gi
volumeMounts:
- mountPath: /mnt
name: data
volumes:
- name: data
persistentVolumeClaim:
claimName: autotune-mnist-keras
tuner:
builtinTunerName: TPE
classArgs: |-
{
"optimize_mode": "maximize",
"constant_liar_type": "min"
}
在此例中，实验绑定了准备过程中创建的名为 autotune-mnist-keras 的 PVC，其中存有在 Notebook 中编辑的训练脚本；使用了名为 aistore 的存有 API Key 的 Secret；填入了前面创建的 EM Folder 的 ID。
查看实验
你可以在 AutoTune 列表页面看到已创建的 AutoTuneExperiment，点击连接进入实验详情页面。
下图为实验详情页面，你可以在该页面查看实验基本信息、各试验的参数与结果，以及查看试验之间的对比。
