Chapter 19 – Training and Deploying TensorFlow Models at Scale
This notebook contains all the sample code in chapter 19.
Run in Google ColabSetup¶
First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0.
# Python ≥3.5 is required
import sys
assert sys.version_info >= (3, 5)
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
!echo "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" > /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 && apt-get install -y tensorflow-model-server
!pip install -q -U tensorflow-serving-api
IS_COLAB = True
except Exception:
IS_COLAB = False
# TensorFlow ≥2.0 is required
import tensorflow as tf
from tensorflow import keras
assert tf.__version__ >= "2.0"
if not tf.config.list_physical_devices('GPU'):
print("No GPU was detected. CNNs can be very slow without a GPU.")
if IS_COLAB:
print("Go to Runtime > Change runtime and select a GPU hardware accelerator.")
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
tf.random.set_seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "deploy"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
os.makedirs(IMAGES_PATH, exist_ok=True)
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
Deploying TensorFlow models to TensorFlow Serving (TFS)¶
We will use the REST API or the gRPC API.
Save/Load a SavedModel¶
(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.mnist.load_data()
X_train_full = X_train_full[..., np.newaxis].astype(np.float32) / 255.
X_test = X_test[..., np.newaxis].astype(np.float32) / 255.
X_valid, X_train = X_train_full[:5000], X_train_full[5000:]
y_valid, y_train = y_train_full[:5000], y_train_full[5000:]
X_new = X_test[:3]
np.random.seed(42)
tf.random.set_seed(42)
model = keras.models.Sequential([
keras.layers.Flatten(input_shape=[28, 28, 1]),
keras.layers.Dense(100, activation="relu"),
keras.layers.Dense(10, activation="softmax")
])
model.compile(loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.SGD(lr=1e-2),
metrics=["accuracy"])
model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))
np.round(model.predict(X_new), 2)
model_version = "0001"
model_name = "my_mnist_model"
model_path = os.path.join(model_name, model_version)
model_path
!rm -rf {model_name}
tf.saved_model.save(model, model_path)
for root, dirs, files in os.walk(model_name):
indent = ' ' * root.count(os.sep)
print('{}{}/'.format(indent, os.path.basename(root)))
for filename in files:
print('{}{}'.format(indent + ' ', filename))
!saved_model_cli show --dir {model_path}
!saved_model_cli show --dir {model_path} --tag_set serve
!saved_model_cli show --dir {model_path} --tag_set serve \
--signature_def serving_default
!saved_model_cli show --dir {model_path} --all
Let's write the new instances to a npy file so we can pass them easily to our model:
np.save("my_mnist_tests.npy", X_new)
input_name = model.input_names[0]
input_name
And now let's use saved_model_cli to make predictions for the instances we just saved:
!saved_model_cli run --dir {model_path} --tag_set serve \
--signature_def serving_default \
--inputs {input_name}=my_mnist_tests.npy
np.round([[1.1347984e-04, 1.5187356e-07, 9.7032893e-04, 2.7640699e-03, 3.7826971e-06,
7.6876910e-05, 3.9140293e-08, 9.9559116e-01, 5.3502394e-05, 4.2665208e-04],
[8.2443521e-04, 3.5493889e-05, 9.8826385e-01, 7.0466995e-03, 1.2957400e-07,
2.3389691e-04, 2.5639210e-03, 9.5886099e-10, 1.0314899e-03, 8.7952529e-08],
[4.4693781e-05, 9.7028232e-01, 9.0526715e-03, 2.2641101e-03, 4.8766597e-04,
2.8800720e-03, 2.2714981e-03, 8.3753867e-03, 4.0439744e-03, 2.9759688e-04]], 2)
TensorFlow Serving¶
Install Docker if you don't have it already. Then run:
docker pull tensorflow/serving
export ML_PATH=$HOME/ml # or wherever this project is
docker run -it --rm -p 8500:8500 -p 8501:8501 \
-v "$ML_PATH/my_mnist_model:/models/my_mnist_model" \
-e MODEL_NAME=my_mnist_model \
tensorflow/serving
Once you are finished using it, press Ctrl-C to shut down the server.
Alternatively, if tensorflow_model_server is installed (e.g., if you are running this notebook in Colab), then the following 3 cells will start the server:
os.environ["MODEL_DIR"] = os.path.split(os.path.abspath(model_path))[0]
%%bash --bg
nohup tensorflow_model_server \
--rest_api_port=8501 \
--model_name=my_mnist_model \
--model_base_path="${MODEL_DIR}" >server.log 2>&1
!tail server.log
import json
input_data_json = json.dumps({
"signature_name": "serving_default",
"instances": X_new.tolist(),
})
repr(input_data_json)[:1500] + "..."
Now let's use TensorFlow Serving's REST API to make predictions:
import requests
SERVER_URL = 'http://localhost:8501/v1/models/my_mnist_model:predict'
response = requests.post(SERVER_URL, data=input_data_json)
response.raise_for_status() # raise an exception in case of error
response = response.json()
response.keys()
y_proba = np.array(response["predictions"])
y_proba.round(2)
Using the gRPC API¶
from tensorflow_serving.apis.predict_pb2 import PredictRequest
request = PredictRequest()
request.model_spec.name = model_name
request.model_spec.signature_name = "serving_default"
input_name = model.input_names[0]
request.inputs[input_name].CopyFrom(tf.make_tensor_proto(X_new))
import grpc
from tensorflow_serving.apis import prediction_service_pb2_grpc
channel = grpc.insecure_channel('localhost:8500')
predict_service = prediction_service_pb2_grpc.PredictionServiceStub(channel)
response = predict_service.Predict(request, timeout=10.0)
response
Convert the response to a tensor:
output_name = model.output_names[0]
outputs_proto = response.outputs[output_name]
y_proba = tf.make_ndarray(outputs_proto)
y_proba.round(2)
Or to a NumPy array if your client does not include the TensorFlow library:
output_name = model.output_names[0]
outputs_proto = response.outputs[output_name]
shape = [dim.size for dim in outputs_proto.tensor_shape.dim]
y_proba = np.array(outputs_proto.float_val).reshape(shape)
y_proba.round(2)
Deploying a new model version¶
np.random.seed(42)
tf.random.set_seed(42)
model = keras.models.Sequential([
keras.layers.Flatten(input_shape=[28, 28, 1]),
keras.layers.Dense(50, activation="relu"),
keras.layers.Dense(50, activation="relu"),
keras.layers.Dense(10, activation="softmax")
])
model.compile(loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.SGD(lr=1e-2),
metrics=["accuracy"])
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))
model_version = "0002"
model_name = "my_mnist_model"
model_path = os.path.join(model_name, model_version)
model_path
tf.saved_model.save(model, model_path)
for root, dirs, files in os.walk(model_name):
indent = ' ' * root.count(os.sep)
print('{}{}/'.format(indent, os.path.basename(root)))
for filename in files:
print('{}{}'.format(indent + ' ', filename))
Warning: You may need to wait a minute before the new model is loaded by TensorFlow Serving.
import requests
SERVER_URL = 'http://localhost:8501/v1/models/my_mnist_model:predict'
response = requests.post(SERVER_URL, data=input_data_json)
response.raise_for_status()
response = response.json()
response.keys()
y_proba = np.array(response["predictions"])
y_proba.round(2)
Deploy the model to Google Cloud AI Platform¶
Follow the instructions in the book to deploy the model to Google Cloud AI Platform, download the service account's private key and save it to the my_service_account_private_key.json in the project directory. Also, update the project_id:
project_id = "onyx-smoke-242003"
import googleapiclient.discovery
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "my_service_account_private_key.json"
model_id = "my_mnist_model"
model_path = "projects/{}/models/{}".format(project_id, model_id)
model_path += "/versions/v0001/" # if you want to run a specific version
ml_resource = googleapiclient.discovery.build("ml", "v1").projects()
def predict(X):
input_data_json = {"signature_name": "serving_default",
"instances": X.tolist()}
request = ml_resource.predict(name=model_path, body=input_data_json)
response = request.execute()
if "error" in response:
raise RuntimeError(response["error"])
return np.array([pred[output_name] for pred in response["predictions"]])
Y_probas = predict(X_new)
np.round(Y_probas, 2)
Using GPUs¶
Note: tf.test.is_gpu_available() is deprecated. Instead, please use tf.config.list_physical_devices('GPU').
#tf.test.is_gpu_available() # deprecated
tf.config.list_physical_devices('GPU')
tf.test.gpu_device_name()
tf.test.is_built_with_cuda()
from tensorflow.python.client.device_lib import list_local_devices
devices = list_local_devices()
devices
Distributed Training¶
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
def create_model():
return keras.models.Sequential([
keras.layers.Conv2D(filters=64, kernel_size=7, activation="relu",
padding="same", input_shape=[28, 28, 1]),
keras.layers.MaxPooling2D(pool_size=2),
keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu",
padding="same"),
keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu",
padding="same"),
keras.layers.MaxPooling2D(pool_size=2),
keras.layers.Flatten(),
keras.layers.Dense(units=64, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(units=10, activation='softmax'),
])
batch_size = 100
model = create_model()
model.compile(loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.SGD(lr=1e-2),
metrics=["accuracy"])
model.fit(X_train, y_train, epochs=10,
validation_data=(X_valid, y_valid), batch_size=batch_size)
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
distribution = tf.distribute.MirroredStrategy()
# Change the default all-reduce algorithm:
#distribution = tf.distribute.MirroredStrategy(
# cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
# Specify the list of GPUs to use:
#distribution = tf.distribute.MirroredStrategy(devices=["/gpu:0", "/gpu:1"])
# Use the central storage strategy instead:
#distribution = tf.distribute.experimental.CentralStorageStrategy()
#if IS_COLAB and "COLAB_TPU_ADDR" in os.environ:
# tpu_address = "grpc://" + os.environ["COLAB_TPU_ADDR"]
#else:
# tpu_address = ""
#resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_address)
#tf.config.experimental_connect_to_cluster(resolver)
#tf.tpu.experimental.initialize_tpu_system(resolver)
#distribution = tf.distribute.experimental.TPUStrategy(resolver)
with distribution.scope():
model = create_model()
model.compile(loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.SGD(lr=1e-2),
metrics=["accuracy"])
batch_size = 100 # must be divisible by the number of workers
model.fit(X_train, y_train, epochs=10,
validation_data=(X_valid, y_valid), batch_size=batch_size)
model.predict(X_new)
Custom training loop:
keras.backend.clear_session()
tf.random.set_seed(42)
np.random.seed(42)
K = keras.backend
distribution = tf.distribute.MirroredStrategy()
with distribution.scope():
model = create_model()
optimizer = keras.optimizers.SGD()
with distribution.scope():
dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train)).repeat().batch(batch_size)
input_iterator = distribution.make_dataset_iterator(dataset)
@tf.function
def train_step():
def step_fn(inputs):
X, y = inputs
with tf.GradientTape() as tape:
Y_proba = model(X)
loss = K.sum(keras.losses.sparse_categorical_crossentropy(y, Y_proba)) / batch_size
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
return loss
per_replica_losses = distribution.experimental_run(step_fn, input_iterator)
mean_loss = distribution.reduce(tf.distribute.ReduceOp.SUM,
per_replica_losses, axis=None)
return mean_loss
n_epochs = 10
with distribution.scope():
input_iterator.initialize()
for epoch in range(n_epochs):
print("Epoch {}/{}".format(epoch + 1, n_epochs))
for iteration in range(len(X_train) // batch_size):
print("\rLoss: {:.3f}".format(train_step().numpy()), end="")
print()
Training across multiple servers¶
A TensorFlow cluster is a group of TensorFlow processes running in parallel, usually on different machines, and talking to each other to complete some work, for example training or executing a neural network. Each TF process in the cluster is called a "task" (or a "TF server"). It has an IP address, a port, and a type (also called its role or its job). The type can be "worker", "chief", "ps" (parameter server) or "evaluator":
- Each worker performs computations, usually on a machine with one or more GPUs.
- The chief performs computations as well, but it also handles extra work such as writing TensorBoard logs or saving checkpoints. There is a single chief in a cluster, typically the first worker (i.e., worker #0).
- A parameter server (ps) only keeps track of variable values, it is usually on a CPU-only machine.
- The evaluator obviously takes care of evaluation. There is usually a single evaluator in a cluster.
The set of tasks that share the same type is often called a "job". For example, the "worker" job is the set of all workers.
To start a TensorFlow cluster, you must first define it. This means specifying all the tasks (IP address, TCP port, and type). For example, the following cluster specification defines a cluster with 3 tasks (2 workers and 1 parameter server). It's a dictionary with one key per job, and the values are lists of task addresses:
cluster_spec = {
"worker": [
"machine-a.example.com:2222", # /job:worker/task:0
"machine-b.example.com:2222" # /job:worker/task:1
],
"ps": ["machine-c.example.com:2222"] # /job:ps/task:0
}
Every task in the cluster may communicate with every other task in the server, so make sure to configure your firewall to authorize all communications between these machines on these ports (it's usually simpler if you use the same port on every machine).
When a task is started, it needs to be told which one it is: its type and index (the task index is also called the task id). A common way to specify everything at once (both the cluster spec and the current task's type and id) is to set the TF_CONFIG environment variable before starting the program. It must be a JSON-encoded dictionary containing a cluster specification (under the "cluster" key), and the type and index of the task to start (under the "task" key). For example, the following TF_CONFIG environment variable defines the same cluster as above, with 2 workers and 1 parameter server, and specifies that the task to start is worker #1:
import os
import json
os.environ["TF_CONFIG"] = json.dumps({
"cluster": cluster_spec,
"task": {"type": "worker", "index": 1}
})
os.environ["TF_CONFIG"]
Some platforms (e.g., Google Cloud ML Engine) automatically set this environment variable for you.
TensorFlow's TFConfigClusterResolver class reads the cluster configuration from this environment variable:
import tensorflow as tf
resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
resolver.cluster_spec()
resolver.task_type
resolver.task_id
Now let's run a simpler cluster with just two worker tasks, both running on the local machine. We will use the MultiWorkerMirroredStrategy to train a model across these two tasks.
The first step is to write the training code. As this code will be used to run both workers, each in its own process, we write this code to a separate Python file, my_mnist_multiworker_task.py. The code is relatively straightforward, but there are a couple important things to note:
- We create the
MultiWorkerMirroredStrategybefore doing anything else with TensorFlow. - Only one of the workers will take care of logging to TensorBoard and saving checkpoints. As mentioned earlier, this worker is called the chief, and by convention it is usually worker #0.
%%writefile my_mnist_multiworker_task.py
import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
import time
# At the beginning of the program
distribution = tf.distribute.MultiWorkerMirroredStrategy()
resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
print("Starting task {}{}".format(resolver.task_type, resolver.task_id))
# Only worker #0 will write checkpoints and log to TensorBoard
if resolver.task_id == 0:
root_logdir = os.path.join(os.curdir, "my_mnist_multiworker_logs")
run_id = time.strftime("run_%Y_%m_%d-%H_%M_%S")
run_dir = os.path.join(root_logdir, run_id)
callbacks = [
keras.callbacks.TensorBoard(run_dir),
keras.callbacks.ModelCheckpoint("my_mnist_multiworker_model.h5",
save_best_only=True),
]
else:
callbacks = []
# Load and prepare the MNIST dataset
(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.mnist.load_data()
X_train_full = X_train_full[..., np.newaxis] / 255.
X_valid, X_train = X_train_full[:5000], X_train_full[5000:]
y_valid, y_train = y_train_full[:5000], y_train_full[5000:]
with distribution.scope():
model = keras.models.Sequential([
keras.layers.Conv2D(filters=64, kernel_size=7, activation="relu",
padding="same", input_shape=[28, 28, 1]),
keras.layers.MaxPooling2D(pool_size=2),
keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu",
padding="same"),
keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu",
padding="same"),
keras.layers.MaxPooling2D(pool_size=2),
keras.layers.Flatten(),
keras.layers.Dense(units=64, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(units=10, activation='softmax'),
])
model.compile(loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.SGD(lr=1e-2),
metrics=["accuracy"])
model.fit(X_train, y_train, validation_data=(X_valid, y_valid),
epochs=10, callbacks=callbacks)
In a real world application, there would typically be a single worker per machine, but in this example we're running both workers on the same machine, so they will both try to use all the available GPU RAM (if this machine has a GPU), and this will likely lead to an Out-Of-Memory (OOM) error. To avoid this, we could use the CUDA_VISIBLE_DEVICES environment variable to assign a different GPU to each worker. Alternatively, we can simply disable GPU support, like this:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
We are now ready to start both workers, each in its own process, using Python's subprocess module. Before we start each process, we need to set the TF_CONFIG environment variable appropriately, changing only the task index:
import subprocess
cluster_spec = {"worker": ["127.0.0.1:9901", "127.0.0.1:9902"]}
for index, worker_address in enumerate(cluster_spec["worker"]):
os.environ["TF_CONFIG"] = json.dumps({
"cluster": cluster_spec,
"task": {"type": "worker", "index": index}
})
subprocess.Popen("python my_mnist_multiworker_task.py", shell=True)
That's it! Our TensorFlow cluster is now running, but we can't see it in this notebook because it's running in separate processes (but if you are running this notebook in Jupyter, you can see the worker logs in Jupyter's server logs).
Since the chief (worker #0) is writing to TensorBoard, we use TensorBoard to view the training progress. Run the following cell, then click on the settings button (i.e., the gear icon) in the TensorBoard interface and check the "Reload data" box to make TensorBoard automatically refresh every 30s. Once the first epoch of training is finished (which may take a few minutes), and once TensorBoard refreshes, the SCALARS tab will appear. Click on this tab to view the progress of the model's training and validation accuracy.
%load_ext tensorboard
%tensorboard --logdir=./my_mnist_multiworker_logs --port=6006
That's it! Once training is over, the best checkpoint of the model will be available in the my_mnist_multiworker_model.h5 file. You can load it using keras.models.load_model() and use it for predictions, as usual:
from tensorflow import keras
model = keras.models.load_model("my_mnist_multiworker_model.h5")
Y_pred = model.predict(X_new)
np.argmax(Y_pred, axis=-1)
And that's all for today! Hope you found this useful. 😊