和 TensorFlow 集成¶
这篇指引会介绍如何在 Mars 里集成 TensorFlow 。
本指引基于 TensorFlow 2.0。
安装¶
如果你尝试在单机比如你的笔记本上使用 Mars,确保 TensorFlow 已经安装。
通过 pip 安装 TensorFlow:
pip install tensorflow
访问 TensorFlow 安装指引 获取更多信息
另一方面,如果你打算在集群中使用 Mars,确保 TensorFlow 在每个 worker 上安装。
准备数据¶
这里我们使用 ionosphere 数据集,点击链接下载数据。
编写 TensorFlow 脚本¶
现在我们创建一个命名为 tf_demo.py 的 Python 文件,它包含了 TensorFlow 的处理逻辑。
import os
import mars.dataframe as md
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
def prepare_data():
df = md.read_csv('ionosphere.data', header=None)
# split into input and output columns
X = df.iloc[:, :-1].to_tensor().astype('float32')
y = df.iloc[:, -1].to_tensor()
# convert Mars tensor to numpy ndarray
X, y = X.to_numpy(), y.to_numpy()
# encode string to integer
y = LabelEncoder().fit_transform(y)
# split into train and test datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
print(X_train.size, X_test.size, y_train.size, y_test.size)
return X_train, X_test, y_train, y_test
def get_model(n_features):
model = Sequential()
model.add(Dense(10, activation='relu', kernel_initializer='he_normal',
input_shape=(n_features,)))
model.add(Dense(8, activation='relu', kernel_initializer='he_normal'))
model.add(Dense(1, activation='sigmoid'))
# compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
def train():
X_train, X_test, y_train, y_test = prepare_data()
model = get_model(X_train.shape[1])
# fit model
model.fit(X_train, y_train, epochs=150, batch_size=32, verbose=0)
# evaluate
loss, acc = model.evaluate(X_test, y_test, verbose=0)
print('Test accuracy: %.3f' % acc)
if __name__ == '__main__':
if 'TF_CONFIG' in os.environ:
# distributed TensorFlow
multiworker_strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
with multiworker_strategy.scope():
train()
else:
train()
Mars DataFrame 等模块可以直接在脚本里使用,以处理大规模数据和加速预处理。
通过 Mars 运行 TensorFlow 脚本¶
现在可以通过 run_tensorflow_script()
提交 TensorFlow 脚本。
In [1]: from mars.learn.contrib.tensorflow import run_tensorflow_script
In [2]: run_tensorflow_script('tf_demo.py', n_workers=1)
2020-04-28 15:40:38.284763: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
sh: sysctl: command not found
2020-04-28 15:40:38.301635: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd29699c020 executing computations on platform Host. Devices:
2020-04-28 15:40:38.301656: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version
2020-04-28 15:40:38.303779: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:258] Initialize GrpcChannelCache for job worker -> {0 -> localhost:2221}
2020-04-28 15:40:38.304476: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:365] Started server with target: grpc://localhost:2221
7990 3944 235 116
WARNING:tensorflow:`eval_fn` is not passed in. The `worker_fn` will be used if an "evaluator" task exists in the cluster.
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
WARNING:tensorflow:ModelCheckpoint callback is not provided. Workers will need to restart training if any fails.
WARNING:tensorflow:`eval_fn` is not passed in. The `worker_fn` will be used if an "evaluator" task exists in the cluster.
WARNING:tensorflow:`eval_strategy` is not passed in. No distribution strategy will be used for evaluation.
WARNING:tensorflow:ModelCheckpoint callback is not provided. Workers will need to restart training if any fails.
Test accuracy: 0.931
2020-04-28 15:40:45.906407: W tensorflow/core/common_runtime/eager/context.cc:290] Unable to destroy server_ object, so releasing instead. Servers don't support clean shutdown.
Out[2]: {'status': 'ok'}
分布式训练和推理¶
部署参考 在集群中部署 部分,在 Kubernetes 上运行参考 在 Kubernetes 中部署 部分。
As you can tell from tf_demo.py
, Mars will set environment variable
TF_CONFIG
automatically. TF_CONFIG
contains cluster and task
information. Thus you don’t need to worry about the distributed setting, what
you need do is to choose a proper distributed strategy.
一旦一个集群存在,你可以要么设置默认 session,训练和预测就会自动提交到集群,要么你可以通过 session=***
显示指定运行的 session。
# A cluster has been configured, and web UI is started on <web_ip>:<web_port>
import mars
# set the session as the default one
sess = mars.new_session('http://<web_ip>:<web_port>')
# submitted to cluster by default
run_tensorflow_script('tf_demo.py', n_workers=1)
# Or, session could be specified as well
run_tensorflow_script('tf_demo.py', n_workers=1, session=sess)