Halaman ini diterjemahkan oleh Cloud Translation API.
Switch to English

Latih dan sajikan model TensorFlow dengan TensorFlow Serving

Panduan ini melatih model jaringan saraf untuk mengklasifikasikan gambar pakaian, seperti sepatu kets dan kemeja , menyimpan model terlatih, lalu menyajikannya dengan Penyajian TensorFlow . Fokusnya adalah pada Penyajian TensorFlow, bukan pemodelan dan pelatihan di TensorFlow, jadi untuk contoh lengkap yang berfokus pada pemodelan dan pelatihan, lihat contoh Klasifikasi Dasar .

Panduan ini menggunakan tf.keras , API level tinggi untuk membuat dan melatih model di TensorFlow.

import sys

# Confirm that we're using Python 3
assert sys.version_info.major is 3, 'Oops, not running Python 3. Use Runtime > Change runtime type'
# TensorFlow and tf.keras
print("Installing dependencies for Colab environment")
!pip install -Uq grpcio==1.26.0

import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
import os
import subprocess

print('TensorFlow version: {}'.format(tf.__version__))
Installing dependencies for Colab environment
[K     |████████████████████████████████| 2.4MB 4.6MB/s 
[?25hInstalling TensorFlow
TensorFlow 2.x selected.
TensorFlow version: 2.1.0-rc1

Buat model Anda

Impor set data Fashion MNIST

Panduan ini menggunakan dataset Fashion MNIST yang berisi 70.000 gambar grayscale dalam 10 kategori. Gambar menunjukkan setiap artikel pakaian dengan resolusi rendah (28 kali 28 piksel), seperti yang terlihat di sini:

Fashion MNIST sprite
Gambar 1. Sampel Fashion-MNIST (oleh Zalando, MIT Licence).

Fashion MNIST dimaksudkan sebagai pengganti drop-in untuk kumpulan data MNIST klasik — sering digunakan sebagai "Halo, Dunia" dari program pembelajaran mesin untuk computer vision. Anda dapat mengakses Fashion MNIST langsung dari TensorFlow, cukup impor dan muat datanya.

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# scale the values to 0.0 to 1.0
train_images = train_images / 255.0
test_images = test_images / 255.0

# reshape for feeding into the model
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

print('\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype))
print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype))
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
32768/29515 [=================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26427392/26421880 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
8192/5148 [===============================================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4423680/4422102 [==============================] - 0s 0us/step

train_images.shape: (60000, 28, 28, 1), of float64
test_images.shape: (10000, 28, 28, 1), of float64

Latih dan evaluasi model Anda

Mari gunakan CNN yang sesederhana mungkin, karena kita tidak fokus pada bagian pemodelan.

model = keras.Sequential([
  keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3, 
                      strides=2, activation='relu', name='Conv1'),
  keras.layers.Flatten(),
  keras.layers.Dense(10, activation=tf.nn.softmax, name='Softmax')
])
model.summary()

testing = False
epochs = 5

model.compile(optimizer='adam', 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=epochs)

test_loss, test_acc = model.evaluate(test_images, test_labels)
print('\nTest accuracy: {}'.format(test_acc))
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Conv1 (Conv2D)               (None, 13, 13, 8)         80        
_________________________________________________________________
flatten (Flatten)            (None, 1352)              0         
_________________________________________________________________
Softmax (Dense)              (None, 10)                13530     
=================================================================
Total params: 13,610
Trainable params: 13,610
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples
Epoch 1/5
60000/60000 [==============================] - 11s 185us/sample - loss: 0.5466 - accuracy: 0.8087
Epoch 2/5
60000/60000 [==============================] - 5s 79us/sample - loss: 0.4032 - accuracy: 0.8580
Epoch 3/5
60000/60000 [==============================] - 5s 76us/sample - loss: 0.3613 - accuracy: 0.8712
Epoch 4/5
60000/60000 [==============================] - 5s 75us/sample - loss: 0.3406 - accuracy: 0.8797
Epoch 5/5
60000/60000 [==============================] - 4s 75us/sample - loss: 0.3247 - accuracy: 0.8848
10000/10000 [==============================] - 1s 73us/sample - loss: 0.3510 - accuracy: 0.8747

Test accuracy: 0.8747000098228455

Simpan model Anda

Untuk memuat model terlatih kita ke dalam Penyajian TensorFlow, pertama-tama kita harus menyimpannya dalam format SavedModel . Ini akan membuat file protobuf dalam hierarki direktori yang terdefinisi dengan baik, dan akan menyertakan nomor versi. TensorFlow Serving memungkinkan kita memilih versi model mana, atau "servable" yang ingin kita gunakan saat membuat permintaan inferensi. Setiap versi akan diekspor ke sub-direktori yang berbeda di bawah jalur yang diberikan.

# Fetch the Keras session and save the model
# The signature definition is defined by the input and output tensors,
# and stored with the default serving key
import tempfile

MODEL_DIR = tempfile.gettempdir()
version = 1
export_path = os.path.join(MODEL_DIR, str(version))
print('export_path = {}\n'.format(export_path))

tf.keras.models.save_model(
    model,
    export_path,
    overwrite=True,
    include_optimizer=True,
    save_format=None,
    signatures=None,
    options=None
)

print('\nSaved model:')
!ls -l {export_path}
export_path = /tmp/1

Warning:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Assets written to: /tmp/1/assets

Saved model:
total 84
drwxr-xr-x 2 root root  4096 Jan  7 23:15 assets
-rw-r--r-- 1 root root 74086 Jan  7 23:15 saved_model.pb
drwxr-xr-x 2 root root  4096 Jan  7 23:15 variables

Periksa model simpanan Anda

Kami akan menggunakan utilitas baris perintah saved_model_cli untuk melihat MetaGraphDefs (model) dan SignatureDefs (metode yang dapat Anda panggil) di SavedModel kami. Lihat diskusi CLI SavedModel ini di Panduan TensorFlow.

saved_model_cli show --dir {export_path} --all

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['Conv1_input'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 28, 28, 1)
        name: serving_default_Conv1_input:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['Softmax'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 10)
        name: StatefulPartitionedCall:0
  Method name is: tensorflow/serving/predict
WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          inputs: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='inputs')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #3
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
    Option #4
      Callable with:
        Argument #1
          Conv1_input: TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name='Conv1_input')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None

Itu memberi tahu kita banyak hal tentang model kita! Dalam hal ini kami hanya melatih model kami, jadi kami sudah mengetahui input dan outputnya, tetapi jika tidak, ini akan menjadi informasi penting. Itu tidak memberi tahu kita segalanya, seperti fakta bahwa ini adalah data gambar skala abu-abu misalnya, tetapi ini adalah awal yang bagus.

Sajikan model Anda dengan Penyajian TensorFlow

Tambahkan URI distribusi TensorFlow Serving sebagai sumber paket:

Kami sedang mempersiapkan untuk menginstal TensorFlow Serving menggunakan Aptitude karena Colab ini berjalan di lingkungan Debian. Kami akan menambahkan paket tensorflow-model-server ke daftar paket yang diketahui Aptitude. Perhatikan bahwa kami menjalankan sebagai root.

# This is the same as you would do from your command line, but without the [arch=amd64], and no sudo
# You would instead do:
# echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list && \
# curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -

!echo "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | tee /etc/apt/sources.list.d/tensorflow-serving.list && \
curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add -
!apt update
deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  2943  100  2943    0     0  11496      0 --:--:-- --:--:-- --:--:-- 11496
OK
Get:1 http://storage.googleapis.com/tensorflow-serving-apt stable InRelease [3,012 B]
Get:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ InRelease [3,626 B]
Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease
Ign:4 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease
Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release
Get:6 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release [564 B]
Get:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release.gpg [833 B]
Hit:8 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease
Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease
Get:10 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]
Get:11 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server amd64 Packages [354 B]
Get:12 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ Packages [81.6 kB]
Get:13 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]
Get:14 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server-universal amd64 Packages [364 B]
Get:15 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic InRelease [15.4 kB]
Get:17 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Packages [30.4 kB]
Get:18 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]
Get:19 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic/main Sources [1,749 kB]
Get:20 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [796 kB]
Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [1,073 kB]
Get:22 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [776 kB]
Get:23 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [21.3 kB]
Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [10.8 kB]
Get:25 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [1,324 kB]
Get:26 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [35.5 kB]
Get:27 http://ppa.launchpad.net/marutter/c2d4u3.5/ubuntu bionic/main amd64 Packages [844 kB]
Fetched 7,019 kB in 4s (1,913 kB/s)
Reading package lists... Done
Building dependency tree       
Reading state information... Done
21 packages can be upgraded. Run 'apt list --upgradable' to see them.

Instal Penyajian TensorFlow

Ini semua yang Anda butuhkan - satu baris perintah!

apt-get install tensorflow-model-server
Reading package lists... Done
Building dependency tree       
Reading state information... Done
The following package was automatically installed and is no longer required:
  libnvidia-common-430
Use 'apt autoremove' to remove it.
The following NEW packages will be installed:
  tensorflow-model-server
0 upgraded, 1 newly installed, 0 to remove and 21 not upgraded.
Need to get 140 MB of archives.
After this operation, 0 B of additional disk space will be used.
Get:1 http://storage.googleapis.com/tensorflow-serving-apt stable/tensorflow-model-server amd64 tensorflow-model-server all 2.0.0 [140 MB]
Fetched 140 MB in 2s (78.8 MB/s)
Selecting previously unselected package tensorflow-model-server.
(Reading database ... 145674 files and directories currently installed.)
Preparing to unpack .../tensorflow-model-server_2.0.0_all.deb ...
Unpacking tensorflow-model-server (2.0.0) ...
Setting up tensorflow-model-server (2.0.0) ...

Mulai jalankan Penyajian TensorFlow

Di sinilah kami mulai menjalankan Penyajian TensorFlow dan memuat model kami. Setelah dimuat, kita dapat mulai membuat permintaan inferensi menggunakan REST. Ada beberapa parameter penting:

  • rest_api_port : Port yang akan Anda gunakan untuk permintaan REST.
  • model_name : Anda akan menggunakan ini di URL permintaan REST. Bisa apa saja.
  • model_base_path : Ini adalah jalur ke direktori tempat Anda menyimpan model Anda.
os.environ["MODEL_DIR"] = MODEL_DIR
nohup tensorflow_model_server \
  --rest_api_port=8501 \
  --model_name=fashion_model \
  --model_base_path="${MODEL_DIR}" >server.log 2>&1
Starting job # 0 in a separate thread.

tail server.log
[warn] getaddrinfo: address family for nodename not supported
[evhttp_server.cc : 238] NET_LOG: Entering the event loop ...

Buat permintaan ke model Anda di TensorFlow Serving

Pertama, mari kita lihat contoh acak dari data pengujian kita.

def show(idx, title):
  plt.figure()
  plt.imshow(test_images[idx].reshape(28,28))
  plt.axis('off')
  plt.title('\n\n{}'.format(title), fontdict={'size': 16})

import random
rando = random.randint(0,len(test_images)-1)
show(rando, 'An Example Image: {}'.format(class_names[test_labels[rando]]))

png

Oke, kelihatannya menarik. Seberapa sulit bagi Anda untuk mengenali? Sekarang mari buat objek JSON untuk kumpulan tiga permintaan inferensi, dan lihat seberapa baik model kita mengenali berbagai hal:

import json
data = json.dumps({"signature_name": "serving_default", "instances": test_images[0:3].tolist()})
print('Data: {} ... {}'.format(data[:50], data[len(data)-52:]))
Data: {"signature_name": "serving_default", "instances": ...  [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]]]}

Buat permintaan REST

Versi terbaru dari servable

Kami akan mengirimkan permintaan prediksi sebagai POST ke titik akhir REST server kami, dan memberikan tiga contoh. Kami akan meminta server kami untuk memberi kami versi terbaru dari server kami dengan tidak menentukan versi tertentu.

!pip install -q requests

import requests
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']

show(0, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
  class_names[np.argmax(predictions[0])], np.argmax(predictions[0]), class_names[test_labels[0]], test_labels[0]))

png

Versi tertentu dari servable

Sekarang mari tentukan versi tertentu dari servable kita. Karena kita hanya punya satu, pilih versi 1. Kita juga akan melihat ketiga hasil.

headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/fashion_model/versions/1:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)['predictions']

for i in range(0,3):
  show(i, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(
    class_names[np.argmax(predictions[i])], np.argmax(predictions[i]), class_names[test_labels[i]], test_labels[i]))

png

png

png