Should I develop in TensorFlow or PyTorch?

Throughout my first year coding in ML frameworks I have worked almost exclusively in classic TensorFlow. At the same time, I'm often hearing glowing recommendations and encouragement by peers and other professionals to give PyTorch a go. Curiosity piqued, I recently took the opportunity to write a project experimenting with GAN (Generative Adversarial Network) architectures in PyTorch.

Here are my thoughts.

TLDR:

The choice of framework boils down to performance, flexibility, and ease of use trade-offs that really depend on your project's specific needs. Writing in PyTorch I found in addition to being incredibly flexible, my code more closely resembled the mathematical notation from the papers I work from. This made my codebase easier to debug and rapidly iter-able for experimentation, which as a student is a complete game-changer! But, that isn't the whole story.

The Dynamic vs Static Graph Paradigm

The most fundamental difference between these frameworks lies in how they construct computational graphs. You can think about it like mapping out a route for a road trip.

With classic TensorFlow you're creating a detailed, fixed itinerary with all stops and routes before you start driving. Once the plan is set, you must follow the route exactly unless you stop and create a whole new plan.

PyTorch builds computational graphs dynamically as the code executed, like getting on the road and deciding your route and stops as you go This approach allows you to make changes on the fly based on real-time considerations. This makes it exceptionally intuitive for research.(GeeksforGeeks, 2024)

Take a look at the following (simplified) equivalent training setups:

TensorFlow

Notice how the model is completely defined first, and can only run with tf.session() after the entire structure is established.

# Define symbolic placeholders for input and target
input_placeholder = tf.placeholder(tf.float32, shape=[None, input_dim])
target_placeholder = tf.placeholder(tf.float32, shape=[None, output_dim])

# Forward pass (defined once, before execution)
output = model(input_placeholder)
loss = tf.losses.mean_squared_error(target_placeholder, output)

# Define the optimization operation
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

# Execute the graph inside a session
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    # For each training iteration
    for epoch in range(epochs):
        for batch_input, batch_target in data_loader:
            # Execute the graph with specific data
            _, loss_value = sess.run(
                [train_op, loss], 
                feed_dict={
                    input_placeholder: batch_input,
                    target_placeholder: batch_target
                }
            )        

PyTorch

Notice that computation happens immediately and works with real values when you call model(input_tensor). This enables building a model layer by layer in a loop! In a loop, each variable can be dynamically defined by your looping logic and diagnostic prints execute as the model is being built!

for step in range(num_steps):
	
	# Dynamic input and target
	input_tensor = torch.randn((batch_size, channels, height, width))
	target = torch.randn((batch_size, channels, height, width))

	optimizer.zero_grad()

	# Forward pass 
	output = model(input_tensor)         
	loss = nn.MSELoss(output, target)

	# Backward pass
	loss.backward()            	     			
	optimizer.step()           	     			

	if step % 10 == 0:
		print(f"Step {step}, Loss: {loss.item()}")	# Print status updates through execution
        

Debugging: A World of Difference

For my GAN project experiments, I worked from the "ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks" (Wang et al., 2018) paper. As the paper's authors note: architectural innovations like Residual-in-Residual Dense Blocks require extensive experimentation.

This is where PyTorch's incredible flexibility really shines!

When debugging complex GAN architectures, PyTorch's eager execution mode allowed me to inspect tensor values at any point in the computational graph. I can insert print statements or set breakpoints anywhere, and tensors behave like regular NumPy arrays. This made troubleshooting ESRGAN'S relativistic discriminator loss calculations or perceptual loss behaviors significantly more developer-friendly.

The contrast demonstrates why PyTorch gained popularity: the ability to define computations on-the-fly without pre-compiling a static graph makes the code more Pythonic and easier to debug. Each operation is executed immediately rather than being compiled first, giving immediate feedback during development.

The TensorFlow 2.0 Response

I'm sure developers in ML frameworks will point out that TensorFlow 2.++ implements many of these flexibility features with tf.function decorators and AutoGraph, these approaches were largely influenced by PyTorch's success in research and educational settings (F22 Labs, 2024).

PyTorch's intuitive design prioritized this dynamic approach from the beginning, making debugging and prototyping significantly more intuitive, which contributed to its rapid adoption among researchers and students (F22 Labs, 2024).

The Rest of the Story

While PyTorch won over researchers with its intuitive design and dynamic computation graphs, TensorFlow 2.0's adoption of eager execution has narrowed the gap considerably. Yet, the frameworks still maintain distinct personalities that reflect their origins.

PyTorch continues to feel more Pythonic and research-oriented. Its design philosophy prioritized developer experience from the beginning rather than retrofitting it later. Personally I find PyTorch's approach more naturally aligned with how I tend to conceptualize and experiment with models.

TensorFlow, meanwhile, has maintained its production strengths while improving its research capabilities. This dual focus makes it particularly valuable in end-to-end ML pipelines where experimentation must eventually transition to deployment (DigitalOcean, 2024).

Making Your Choice: A Framework Decision Guide

After implementing ESRGAN in PyTorch and spending a year in TensorFlow, I've found the decision comes down to three key considerations:

1. Your Development Phase

For research and rapid prototyping, PyTorch's immediate execution model remains unmatched for debugging complex architectures. When troubleshooting my GAN's relativistic discriminator, being able to inspect tensor values at any point was invaluable.

TensorFlow excels when your models are ready for production deployment, offering superior tooling with TF Serving, TF Lite, and seamless cloud integration (DigitalOcean, 2024).

2. Learning Curve and Team Experience

PyTorch tends to be more intuitive for those with strong Python backgrounds, as its API closely mirrors NumPy and follows Python's imperative programming style. In my view TensorFlow 2.0 has improved but still carries some conceptual overhead from its declarative roots.

3. Deployment Requirements

If your destination is mobile devices, edge computing, or browser-based applications, TensorFlow's deployment ecosystem offers more mature solutions. For server-side API deployment, both frameworks are now equally capable, with PyTorch's TorchServe closing the previous gap.

Conclusion: The Best of Both Worlds

Rather than viewing these frameworks as competitors, I think they can be complementary tools. Especially since ONNX (Open Neural Network Exchange) now makes it possible to develop in PyTorch for its research-friendly interface and deploy with TensorFlow's production ecosystem.

For newcomers to ML engineering (like me!), I recommend starting with the framework that best aligns with your immediate goals—research or production—while keeping an open mind to learn from both ecosystems.

The "framework wars" have ultimately benefited everyone by driving improvements in usability, performance, and flexibility. I'm grateful for the strengths of both approaches and excited to see how they continue to evolve in response to each other.

References:

• GeeksforGeeks. (2024). Dynamic vs Static Computational Graphs in ML
• ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
• F22 Labs. (2024). PyTorch vs. TensorFlow: Choosing Your Deep Learning Framework
• DigitalOcean. (2024). PyTorch vs TensorFlow: Which One Should You Choose?