Advanced Level:
1. Custom Training Loops:
- Understand and implement custom training loops for greater control.
python code
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
def train_step(inputs, targets):
with tf.GradientTape() as tape:
predictions = model(inputs)
loss = loss_object(targets, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
Explanation:
- Creates an instance of the Sparse Categorical Crossentropy loss function. This loss function is commonly used for classification problems when the labels are integers (e.g., 0, 1, 2) representing class indices.
- Defines a function called
train_stepthat takesinputsandtargetsas parameters. - Inside the function, a
tf.GradientTapeis used to record operations for automatic differentiation. predictions = model(inputs): Passes the input data (inputs) through the model to get predictions.loss = loss_object(targets, predictions): Calculates the loss between the true labels (targets) and the model predictions.gradients = tape.gradient(loss, model.trainable_variables): Computes the gradients of the loss with respect to the trainable variables (model parameters) using backpropagation.optimizer.apply_gradients(zip(gradients, model.trainable_variables)): Applies the computed gradients to update the model’s trainable variables using the specified optimizer. This step is part of the optimization process.
2. TensorFlow Serving:
- Deploy a TensorFlow model using TensorFlow Serving.
docker run -p 8501:8501 --name=tf_serving_container --mount type=bind,source=$(pwd)/model,target=/models/model -e MODEL_NAME=model -t tensorflow/serving
Explanation:
docker run: This command is used to run a Docker container.-p 8501:8501: Maps port 8501 from the container to port 8501 on the host machine. This is the default port for TensorFlow Serving.--name=tf_serving_container: Assigns a name (tf_serving_container) to the running container.--mount type=bind,source=$(pwd)/model,target=/models/model: Mounts a bind mount at the specified source and target paths. This is used to make the model from the host machine available to the container. The model is assumed to be in the current working directory ($(pwd)/model) on the host, and it will be available inside the container at the path/models/model.-e MODEL_NAME=model: Sets an environment variable (MODEL_NAME) inside the container with the valuemodel. This specifies the name of the model that TensorFlow Serving should serve.-t tensorflow/serving: Specifies the Docker image to use (tensorflow/serving). This image provides a TensorFlow Serving environment.
3. TensorFlow Extended (TFX):
- Explore TensorFlow Extended for end-to-end ML production pipelines.
python code
import tensorflow as tf
import tfx
from tfx.components import CsvExampleGen, StatisticsGen, SchemaGen, ExampleValidator, Transform, Trainer
from tfx.components.trainer.executor import GenericExecutor
from tfx.components.evaluator.component import Evaluator
from tfx.components.infra_validator.component import InfraValidator
from tfx.components.tuner.component import Tuner
from tfx.components.tuner.driver.driver import TunerDriver
from tfx.components.tuner.tuner import TunerFnResult
from tfx.components.tuner import utils as tuner_utils
from tfx.orchestration import metadata, pipeline
from tfx.proto import infra_validator_pb2
from tfx.proto import trainer_pb2
# Define your TFX pipeline components
def create_tfx_pipeline():
# ExampleGen component
example_gen = CsvExampleGen(input_base="/path/to/data")
# Other TFX components (StatisticsGen, SchemaGen, etc.)
# ...
# Trainer component
trainer = Trainer(
module_file="/path/to/trainer_module.py",
custom_executor_spec=GenericExecutor(),
examples=example_gen.outputs["examples"],
transform_output=transform.outputs["transform_output"],
schema=schema_gen.outputs["schema"],
train_args=trainer_pb2.TrainArgs(num_steps=100),
eval_args=trainer_pb2.EvalArgs(num_steps=50),
)
# Other TFX components (Evaluator, InfraValidator, etc.)
# ...
return [example_gen, ... , trainer, ...]
# Define and run the TFX pipeline
def run_tfx_pipeline():
components = create_tfx_pipeline()
pipeline_name = "my_tfx_pipeline"
pipeline_root = "/path/to/pipeline_root"
tfx_pipeline = pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True
)
metadata_connection_config = metadata.sqlite_metadata_connection_config(
os.path.join(pipeline_root, 'metadata.sqlite'))
with metadata.Metadata(metadata_connection_config) as metadata_handler:
tfx.orchestration.LocalDagRunner().run(
tfx_pipeline,
metadata_handler=metadata_handler
)
# Deploy the trained model using TensorFlow Serving
def deploy_model_with_serving():
model_path = "/path/to/saved_model"
docker_command = f"docker run -p 8501:8501 --name=tf_serving_container \
--mount type=bind,source={model_path},target=/models/model \
-e MODEL_NAME=model -t tensorflow/serving"
# Run the TensorFlow Serving container
os.system(docker_command)
# Run the TFX pipeline
run_tfx_pipeline()
# Deploy the trained model using TensorFlow Serving
deploy_model_with_serving()
Explanation:
- Import necessary TensorFlow and TFX modules for defining and running TFX pipelines.
- Define a function (
create_tfx_pipeline) that creates TFX pipeline components, such as ExampleGen, Trainer, and other components based on your specific use case. - Define a function (
run_tfx_pipeline) that assembles the TFX pipeline components, specifies pipeline metadata configuration, and runs the pipeline locally using theLocalDagRunner. - Define a function (
deploy_model_with_serving) that deploys the trained model using TensorFlow Serving in a Docker container. - Execute the TFX pipeline by calling
run_tfx_pipeline. - Deploy the trained model using TensorFlow Serving by calling
deploy_model_with_serving.
This tutorial provides a structured progression from basic TensorFlow concepts to more advanced topics, including building models, handling data, transfer learning, custom training loops, deployment, and production pipelines. You can further explore each topic based on your specific interests and project requirements.