Tensorflow file_io的用法

runzhliu

发布于 2020-08-06 10:13:58

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发布于 2020-08-06 10:13:58

文章被收录于专栏：容器计算

S3 对象存储的使用越来越广泛，其中的好处就不多说了，这里用 Tensorflow 举个例子。

https://github.com/tensorflow/examples/blob/master/community/en/docs/deploy/s3.md

Tensorflow 本身就支持从 S3 中读写数据的。在 TenC 弹性计算平台上，用户可以通过指定 AWS_ACCESS_KEY_ID 和 AWS_SECRET_ACCESS_KEY 两个环境变量来校验（有些场景下，已经由平台自动绑定了环境变量）。

# Credentials only needed if connecting to a private endpoint
AWS_ACCESS_KEY_ID=XXXXX                 
AWS_SECRET_ACCESS_KEY=XXXXX
# Region for the S3 bucket, this is not always needed. Default is us-east-1.
AWS_REGION=us-east-1
# The S3 API Endpoint to connect to. This is specified in a HOST:PORT format.
S3_ENDPOINT=s3.us-east-1.amazonaws.com
# Whether or not to use HTTPS. Disable with 0.
S3_USE_HTTPS=1                          
# If HTTPS is used, controls if SSL should be enabled. Disable with 0.
S3_VERIFY_SSL=1

下面是基于镜像 tensorflow/tensorflow:1.15.2-py3 的一个 demo。

export AWS_ACCESS_KEY_ID=runzhliu-demo-key
export AWS_SECRET_ACCESS_KEY=runzhliu-demo-secret
export S3_ENDPOINT=9.25.151.xxx:7480
export S3_USE_HTTPS=0

测试的程序。

from tensorflow.python.lib.io import file_io
print(file_io.stat('s3://runzhliu__demo/Tensorflow_On_S3/adobegc.log'))

详细的过程。

# export AWS_ACCESS_KEY_ID=runzhliu-demo-08ff2955
# export AWS_SECRET_ACCESS_KEY=runzhliu-demo-b4068ecf
# export S3_ENDPOINT=9.25.151.198:7480
# export S3_USE_HTTPS=0
# python
Python 3.6.9 (default, Nov  7 2019, 10:44:02)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from tensorflow.python.lib.io import file_io
>>> print(file_io.stat('s3://runzhliu__demo/Tensorflow_On_S3/adobegc.log'))
2020-02-11 01:22:56.004679: I tensorflow/core/platform/s3/aws_logging.cc:54] Found secret key
2020-02-11 01:22:56.004794: I tensorflow/core/platform/s3/aws_logging.cc:54] Connection has been released. Continuing.
2020-02-11 01:22:56.007371: I tensorflow/core/platform/s3/aws_logging.cc:54] Found secret key
2020-02-11 01:22:56.007489: I tensorflow/core/platform/s3/aws_logging.cc:54] Connection has been released. Continuing.
<tensorflow.python.pywrap_tensorflow_internal.FileStatistics; proxy of <Swig Object of type 'tensorflow::FileStatistics *' at 0x7fabbbc52180> >

下面是常见的两种错误原因。

错误情况	原因	其他
Curl returned error code 28	S3_ENDPOINT没有配置好
Curl returned error code 35	S3_USE_HTTPS需要设置为0

读取数据。

import tensorflow as tf
filenames = ["s3://bucketname/path/to/file1.tfrecord",
             "s3://bucketname/path/to/file2.tfrecord"]
dataset = tf.data.TFRecordDataset(filenames)

Tensorboard 的测试。

tensorboard --logdir s3://bucketname/path/to/model/
tensorflow_model_server --port=9000 --model_name=model --model_base_path=s3://bucketname/path/to/model/export/

在 Tensorflow on Kubernetes 训练的过程中，一般会指定一个 Chief Worker 作为输出 Checkpoint 和 SaveModel 的功能节点。可以配置两个 bucket，分别收集 Checkpoint 和 Model 数据，当需要部署服务的时候，将模型的 bucket 表示的目录，挂载到服务的 Pod。