When working with deep learning models, deploying these models efficiently is as crucial as the model training itself. TensorFlow provides various tools and techniques to streamline and optimize this process. One such tool is the TensorFlow Graph Util which offers an efficient way of managing and deploying models. This article will guide you through leveraging TensorFlow Graph Util for better model deployment, focusing on how to freeze graphs and optimize them for performance.
Understanding TensorFlow Graph Util
At the heart of TensorFlow, computations are represented as dataflow graphs. Optimizing these graphs for deployment means simplifying them while maintaining performance integrity. TensorFlow includes a module called 'graph_util' which allows for various graph manipulations including turning variables into constants (also known as freezing the graph).
Why Freeze a Graph?
Freezing a graph involves converting your trained model's variables into constants. This change results in fewer dependencies at runtime and leads to a lightweight, deployable version of your model.
Freezing a TensorFlow Model
Let’s dive into the code to see how we can freeze and optimize a TensorFlow model. Assume we already have a trained TensorFlow model saved on disk.
import tensorflow as tf
from tensorflow.python.framework import graph_util
# Load the saved model
output_node_names = ['output_node']
with tf.Session() as sess:
# Restore the model's metagraph and weights
saver = tf.train.import_meta_graph('/model-path/model.meta')
saver.restore(sess, "/model-path/model")
# Retrieve the graph definition
graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
# Freeze the graph: Convert variables into constants
frozen_graph_def = graph_util.convert_variables_to_constants(
sess,
input_graph_def,
output_node_names
)
# Serialize and dump the frozen graph to the filesystem
with tf.io.gfile.GFile('/frozen-model-path/frozen_graph.pb', 'wb') as f:
f.write(frozen_graph_def.SerializeToString())
Key Steps Explained
1. Load the Saved Model: Begin by loading your trained model using tf.train.import_meta_graph. 2. Obtain Graph Definition: Access the current graph's definition which lays out operations needed to perform computations.
3. Convert Variables to Constants: This step is where TensorFlow’s convert_variables_to_constants() method comes into play, transposing variables within the session to constants facilitating optimization.
4. Serialize the Graph: Finally, serialize and save this optimized representation to a file, enabling deployment without requiring the initial training checkpoint files.
Optimizing the Frozen Graph
Beyond freezing, TensorFlow models can be subject to further optimization steps like pruning operations (removing unnecessary computations) and quantization (reducing model size with minimal performance loss).
TensorFlow Model Optimization Toolkit provides several utilities for these steps. Here’s a simplistic view:
from tensorflow_model_optimization.sparsity import keras as sparsity
# Define pruning schedule
pruning_schedule = sparsity.PolynomialDecay(initial_sparsity=0.0,
final_sparsity=0.5,
begin_step=2000,
end_step=8000)
# Modify and train model with pruning
model = ... # Original model
pruned_model = sparsity.prune_low_magnitude(model,
pruning_schedule=pruning_schedule)
pruned_model.compile(...)
pruned_model.fit(...)
Conclusion
The process of freezing and further optimizing your model with the TensorFlow Graph Util and associated optimization libraries prepares it for deployment keeping efficiency in check. This conversion eases the workloads as it loads lighter models into production environments, leading to performance speed-ups and reduced resource consumption. Employ these techniques for seamless transition from model development to serving pipeline.
