REVOLUTIONIZING DEEP LEARNING WITH SYSML: Unleashing the Power of TensorFlow Eager | by Ansh Mittal | Jan, 2024

Hello, this is the fifth article exploring the research in the domain of MLSys. Read the complete article list to catch up on the previous articles. We dive into “TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning.” TL;DR: TensorFlow Eager is an innovative domain-specific language (DSL) embedded in Python (and now in Tensorflow Core) that revolutionized the building and execution of ML models. It combined the flexibility of eager execution with the efficiency of graph execution. This multi-stage DSL allowed intuitive coding, efficient debugging, and high-performance model execution. This blog explores the core concepts, architecture, and benefits of TensorFlow Eager! (Though it moved out of tf.contrib.modules now, it helped me understand various things related to TfGraphs, TfCore, and TfAPIs)

TensorFlow has been one of the household names in the ML community. However, TensorFlow Eager, introduced in this paper, takes it to the next level. It addresses the limitations of the traditional TensorFlow programming model and provides a more Pythonic way of building models. A multi-stage programming model that combines eager execution with graph construction helps achieve this. We discuss this in different components.


Eager execution helps execute operations immediately after they are invoked. This execution makes the development process more intuitive and easier to debug. However, it can be less efficient when running large-scale models.


On the other hand, graph execution builds a computational graph that represents the model. The graph is executed all at once after this build, which can be more efficient but less flexible and involves harder debugging.

TensorFlow Eager brings these two paradigms together, allowing you to write code in a more Pythonic way while still taking advantage of the performance benefits of graph execution.

x =  tf.constant(3.0)
with tf.GradientTape() as t1:
with tf.GradientTape() as t2:
y = x * x
dy_dx = t2.gradient(y, x) #6.0
d2y_dx2 = t1.gradient(dy_dx, x)

TensorFlow Eager architecture provides both flexibility and performance. It consists of three main stages: Python front-end, tracing JIT compiler, and the TensorFlow runtime.

Python Front-end

The Python front-end is where you write your code. It’s designed to be intuitive and familiar to those who have experience with Python. You can use Python control flow, data structures, and libraries seamlessly.

Tracing JIT Compiler

The tracing Just-In-Time (JIT) compiler is where the magic happens. It traces the execution of the Python code and constructs a computational graph. This graph is further optimized for performance.

TensorFlow Runtime

Finally, the TensorFlow runtime executes the optimized graph. It’s highly efficient and can take advantage of hardware accelerators like GPUs and TPUs.

class Net(tf.Keras.Model):
def __init__(self):
super(Net, self).__init__()
self.v = tf.Variable(1.)
self.out = tf.layers.Dense(1)

def call(self, x):
return self.out(
tf.nn.softplus(x * self.v))

Fig 2. Visualization of the dependency graph for Listing 2 (above), with fill-in intermediate nodes and nodes without the fill-containing state. [SOURCE]
Fig 3. Architecture for Training and Deployment of Tensorflow 2.0 (The training part of this diagram focuses on the Python APIs, but TensorFlow also supports training models. Other language bindings also exist with various degrees of support, including Swift, R, and Julia) [SOURCE]

TensorFlow Eager execution model follows three guiding principles:

  1. Privilege imperative execution: TensorFlow Eager operates imperatively by default and executes operations as called. This is in contrast to the traditional TensorFlow programming model, which requires building a computational graph for execution. Imperative execution makes TensorFlow Eager feel more like a NumPy-like library for hardware-accelerated numerical computation and machine learning.
  2. Seamlessly embedded into Python: TensorFlow Eager is embedded into Python seamlessly. You can write TensorFlow Eager code using familiar Python constructs like native control flow (e.g. if statements and while loops), recursion, arbitrary data structures, and even Python debugging tools. This integration is more than just syntactic sugar; it greatly simplifies the implementation of data-dependent models like segmental recurrent neural networks and recursive neural networks.
  3. Stage imperative code as dataflow graphs: To leverage the benefits of dataflow graphs, TensorFlow Eager provides a mechanism to trace Python functions and stage their operations as graph functions. This tracing operation is achieved through a tracing Just-In-Time (JIT) compiler that records TensorFlow operations and the tensors flowing between them in a dataflow graph. This allows TensorFlow Eager to remove the overhead of the Python interpreter and take advantage of optimizations like constant-folding and buffer reuse.

TensorFlow Eager provides two ways of executing operations in the multi-stage programming model: imperatively or as part of a static dataflow graph. Both execution models have access to the same set of operations and kernels but differ in how they dispatch kernels.

  • Imperative execution: By default, TensorFlow Eager executes operations immediately. For example, library functions such as tf.matmul construct operations and immediately execute their kernels afterward. This mode resembles a NumPy-like library for hardware-accelerated numerical computation and machine learning.
  • Staged execution: While imperative execution simplifies prototyping, the overhead of going back and forth into the Python interpreter limits its performance. Therefore, TensorFlow Eager provides a mechanism to stage computations as dataflow graphs. In particular, TensorFlow Eager provides a decorator function to trace the execution of a Python function, recording all TensorFlow operations and the tensors flowing between them in a dataflow graph.

This architecture allows TensorFlow Eager to provide an intuitive coding experience without sacrificing performance. It’s like having your cake and eating it too!

Fig 4. The TfRuntime’s role in graph and eager execution within the TensorFlow training stack [SOURCE]

TensorFlow Eager has various benefits that make it a powerful tool for machine learning practitioners.


You can write code more naturally using Python constructs and libraries. This coding process makes the development process more intuitive and accessible, especially for those new to TensorFlow.


The immediate execution of operations allows for easier debugging. You can use standard Python debugging tools and get meaningful error messages.


Despite the flexibility, TensorFlow Eager does not compromise on performance. The tracing JIT compiler and TensorFlow runtime ensure that your models get executed efficiently.


TensorFlow Eager works seamlessly with the existing TensorFlow ecosystem. You can use TensorFlow datasets, metrics, and other components without hassle.


TensorFlow Eager gives you more control over the model execution. You can easily customize gradients, write custom layers, and create custom training loops.

Table 1. Comparing Lazy Execution and Eager Execution [SOURCE]

The reasons for its discontinuation in tf.contrib.modules are attributed to the following:

  • Integration into Core TensorFlow: TensorFlow Eager execution, initially part of the tf.contrib modules, was integrated into the core TensorFlow library. This integration aimed to make Eager execution a fundamental part of TensorFlow’s user experience, rather than an optional add-on.
  • Streamlining the TensorFlow API: The removal of Eager execution from tf.contrib.module was part of a broader effort to streamline TensorFlow API and reduce redundancy. By integrating Eager execution into the core, TensorFlow aimed to simplify the user experience caused by multiple ways of accomplishing the same task.
  • Focus on Improved Performance and Flexibility: The development team shifted their focus towards improving the performance and flexibility of the core TensorFlow library. This effort included enhancing the Eager execution mode in the core library, making the version in tf.contrib redundant.
  • Deprecation of tf.contrib: The entire tf.contrib.module was eventually deprecated. This update was due to the maintenance challenges and the fact that tf.contrib became a repository for experimental and unsupported code, which did not align with its goals of stability and reliability.
  • Encouraging Community Contributions more structurally: By deprecating tf.contrib, TensorFlow encouraged community contributions in a more structured and sustainable way. This effort involved contributing directly to the core TensorFlow library or developing separate, standalone projects.
  • Alignment with TensorFlow 2.0 Philosophy: With the release of TensorFlow 2.0, there was a significant shift in the philosophy, focusing on simplicity and ease of use. The Eager execution integration into the core was a step towards this philosophy, making advanced functionalities accessible to the broader TensorFlow community.

TensorFlow Eager is not just a theoretical concept; it is used (without our knowledge) in real-world applications to solve complex problems. From image synthesis and LLMs to reinforcement learning (RLHF) and LoRA-like models, TensorFlow Eager empowered developers to build state-of-the-art models with ease and efficiency and is now doing the same without our knowledge.

Fig 5. Examples per second when training ResNet-50 on a GPU (top). Percent improvement over TensorFlow Eager (bottom).

TensorFlow Eager is a groundbreaking addition to the TensorFlow family. Its multi-stage, Python-embedded DSL bridges the gap between the flexibility of eager execution and the efficiency of graph execution. Whether you are a beginner just getting started with TensorFlow or an experienced practitioner looking to optimize your models, TensorFlow Eager is a tool that deserves a place in your ML toolkit.

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