TensorFlow Versioning

Introduction

Understanding TensorFlow's versioning system is a crucial skill for any machine learning developer. As TensorFlow evolves, its APIs, features, and performance characteristics change. This can affect how your code runs and whether it remains compatible with the latest TensorFlow releases.

In this guide, you'll learn about:

• How TensorFlow uses semantic versioning
• Major TensorFlow version differences
• How to check your TensorFlow version
• Managing compatibility between versions
• Best practices for version control in ML projects

TensorFlow Semantic Versioning

TensorFlow follows a semantic versioning pattern, which consists of three numbers: MAJOR.MINOR.PATCH. For example, in TensorFlow 2.8.0:

• MAJOR (2): Indicates major changes that are often not backward compatible
• MINOR (8): Represents new features added in a backward-compatible manner
• PATCH (0): Indicates backward-compatible bug fixes

TensorFlow 1.x vs 2.x

The shift from TensorFlow 1.x to 2.x represented the most significant change in the framework's history, with fundamental changes to the programming model:

TensorFlow 1.x	TensorFlow 2.x
Session-based execution	Eager execution by default
Complex graph building	Simpler, more Pythonic API
tf.contrib package	Reorganized modules
Manual device placement	Automatic device placement

Checking Your TensorFlow Version

Let's see how to check which version of TensorFlow you have installed:

python

import tensorflow as tf

# Print TensorFlow version
print(f"TensorFlow version: {tf.__version__}")

# Print additional version information
print(f"Keras version: {tf.keras.__version__}")
print(f"GPU available: {tf.config.list_physical_devices('GPU')}")

Output:

TensorFlow version: 2.8.0
Keras version: 2.8.0
GPU available: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Installing Specific TensorFlow Versions

You can install a specific version of TensorFlow using pip:

bash

# Install a specific version
pip install tensorflow==2.8.0

# Install the latest stable version
pip install tensorflow

# Install the nightly build
pip install tf-nightly

Version Compatibility

API Compatibility

TensorFlow maintains backward compatibility within major versions, but API changes between major versions can break your code. Let's see an example of code that works differently between TensorFlow 1.x and 2.x:

TensorFlow 1.x style:

python

# TensorFlow 1.x
import tensorflow as tf

# Create a session
sess = tf.Session()

# Define constants
a = tf.constant(5.0)
b = tf.constant(6.0)

# Define computation
c = a * b

# Run the computation
print(sess.run(c))  # Output: 30.0

# Close the session
sess.close()

TensorFlow 2.x style:

python

# TensorFlow 2.x
import tensorflow as tf

# Define constants
a = tf.constant(5.0)
b = tf.constant(6.0)

# Computation executes eagerly
c = a * b

# Print result
print(c)  # Output: tf.Tensor(30.0, shape=(), dtype=float32)

Using TensorFlow 1.x Code in TensorFlow 2.x

TensorFlow 2.x provides compatibility features to run 1.x code:

python

# Enable TF1.x compatibility mode
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

# Now you can use TF1.x style code
sess = tf.Session()
a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b
print(sess.run(c))  # Output: 30.0
sess.close()

Saved Model Compatibility

Models saved with one version of TensorFlow might not load correctly in another version. Here's how to save and load models with version compatibility in mind:

python

import tensorflow as tf

# Create a simple model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(5,)),
    tf.keras.layers.Dense(1)
])

# Save the model with a version tag
tf.saved_model.save(model, "./saved_model_tf2.8")

# When loading, you can specify the tags
loaded_model = tf.saved_model.load("./saved_model_tf2.8")

Version Constraints in requirements.txt

When sharing your project, it's a good practice to specify TensorFlow version constraints:

# Exact version
tensorflow==2.8.0

# Minimum version
tensorflow>=2.8.0

# Range of versions
tensorflow>=2.8.0,<3.0.0

Practical Example: Version-Aware Text Classification

Let's build a simple text classification model that works across TensorFlow versions:

python

import tensorflow as tf

# Version check and adjustments
if tf.__version__.startswith('1.'):
    print("Using TensorFlow 1.x style")
    # TF 1.x specific imports and setup
    import tensorflow.compat.v1 as tf
    tf.disable_v2_behavior()
else:
    print("Using TensorFlow 2.x style")
    # TF 2.x is good to go!

# Create a simple text classification model that works in both versions
def create_text_classification_model(vocab_size=10000, embedding_dim=16):
    if tf.__version__.startswith('1.'):
        # TF 1.x style model building
        inputs = tf.keras.Input(shape=(None,))
        x = tf.keras.layers.Embedding(vocab_size, embedding_dim)(inputs)
        x = tf.keras.layers.GlobalAveragePooling1D()(x)
        x = tf.keras.layers.Dense(16, activation='relu')(x)
        outputs = tf.keras.layers.Dense(1, activation='sigmoid')(x)
        model = tf.keras.Model(inputs=inputs, outputs=outputs)
        model.compile(optimizer='adam',
                     loss='binary_crossentropy',
                     metrics=['accuracy'])
    else:
        # TF 2.x style model building
        model = tf.keras.Sequential([
            tf.keras.layers.Embedding(vocab_size, embedding_dim),
            tf.keras.layers.GlobalAveragePooling1D(),
            tf.keras.layers.Dense(16, activation='relu'),
            tf.keras.layers.Dense(1, activation='sigmoid')
        ])
        model.compile(optimizer='adam',
                     loss='binary_crossentropy',
                     metrics=['accuracy'])
    return model

# Create the model
model = create_text_classification_model()
print(model.summary())

Best Practices for TensorFlow Version Management

1. Always specify the TensorFlow version in your project documentation and requirements
2. Test your code on multiple versions if you expect it to be used across different environments
3. Use version checks in your code to handle version-specific logic
4. Consider Docker containers to ensure consistent environments across development and production
5. Follow the migration guides when upgrading to newer TensorFlow versions

Handling Breaking Changes

When you encounter breaking changes between versions, consider these approaches:

1. Check the migration guide: TensorFlow provides detailed migration guides for major version changes
2. Use compatibility modules: Like tensorflow.compat.v1 for backwards compatibility
3. Refactor your code: Sometimes it's best to embrace the new paradigm
4. Pin your dependencies: If you can't update right away, pin to a working version

Here's a simple example of handling a breaking change:

python

import tensorflow as tf

# Check TensorFlow version and use appropriate API
if tf.__version__.startswith('1.'):
    # TF 1.x way to get variable scope
    var_scope = tf.variable_scope
else:
    # TF 2.x way to get variable scope
    var_scope = tf.compat.v1.variable_scope if hasattr(tf.compat, 'v1') else tf.variable_scope
    
# Now use var_scope in a version-agnostic way
with var_scope('my_model'):
    # Your model code here
    pass

Summary

Understanding TensorFlow versioning is essential for building reliable machine learning applications. In this guide, you've learned:

• How TensorFlow's semantic versioning works and the major differences between versions
• How to check and install specific TensorFlow versions
• Techniques for maintaining compatibility across different versions
• Best practices for version management in your ML projects

Managing TensorFlow versions properly helps ensure your code remains functional and maintainable as the framework evolves.

Additional Resources

• TensorFlow's official versioning policy
• TensorFlow 1.x to 2.x migration guide
• TensorFlow release notes

Exercises

1. Check which version of TensorFlow you currently have installed and list three key features that were introduced in that version.
2. Write a simple script that detects the TensorFlow version and runs appropriate code for both 1.x and 2.x versions.
3. Create a small model using TensorFlow 2.x features, then modify it to be compatible with TensorFlow 1.x.
4. Set up a virtual environment with a specific older version of TensorFlow and test if your current code works with it.