What Are Some of the Most Popular Deep Learning Frameworks?

Picking a deep learning framework can feel like choosing your first programming language all over again — everyone has an opinion, the “best” answer depends heavily on what you’re trying to build, and the ecosystem keeps shifting under your feet. Having worked across several of these frameworks, I can say confidently that there isn’t one universally “correct” choice — but there is usually a right fit for your specific project. This article walks through the major players, what makes each one distinct, and how to decide between them.

Table of Contents

1. Why the Framework You Choose Matters
2. PyTorch
3. TensorFlow
4. Keras
5. JAX
6. Other Notable Frameworks
7. Feature Comparison Table
8. Code Comparison: The Same Model in PyTorch and TensorFlow
9. Performance and Ecosystem Considerations
10. Which Framework Should You Learn First?
11. Advantages and Disadvantages Summary
12. Best Practices
13. Summary

1. Why the Framework You Choose Matters

A deep learning framework handles the heavy lifting behind training neural networks: automatic differentiation (computing gradients), GPU/TPU acceleration, pre-built layers and optimizers, and tools for deployment. Building all of this from scratch for every project would be impractical, so choosing a framework that fits your workflow — research vs. production, Python-first vs. cross-platform, academic vs. industrial deployment — has a real impact on productivity.

2. PyTorch

Developed by Meta AI (formerly Facebook AI Research) and released in 2016, PyTorch has become the dominant framework in research and, increasingly, in production. Its defining feature is dynamic computation graphs — the graph of operations is built on the fly as code executes, which makes debugging far more intuitive since you can use standard Python tools (like print statements or a debugger) at any point in the model.

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(10, 32),
    nn.ReLU(),
    nn.Linear(32, 1)
)
x = torch.randn(5, 10)
output = model(x)   # graph is built dynamically as this executes
print(output.shape)

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(10, 32),
    nn.ReLU(),
    nn.Linear(32, 1)
)
x = torch.randn(5, 10)
output = model(x)   # graph is built dynamically as this executes
print(output.shape)

PyTorch is the framework behind most major open research releases in recent years, including large language models, diffusion models, and vision transformers, largely due to its flexibility and the strength of its research community.

3. TensorFlow

Developed by Google Brain and released in 2015, TensorFlow was originally built around static computation graphs — you’d define the entire graph first, then run data through it. This made early TensorFlow fast for production but harder to debug. TensorFlow 2.x addressed this by adopting eager execution by default, making it feel much more like PyTorch in day-to-day use, while still supporting graph compilation (tf.function) for performance-critical deployment.

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu', input_shape=(10,)),
    tf.keras.layers.Dense(1)
])
x = tf.random.normal((5, 10))
output = model(x)
print(output.shape)

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(32, activation='relu', input_shape=(10,)),
    tf.keras.layers.Dense(1)
])
x = tf.random.normal((5, 10))
output = model(x)
print(output.shape)

TensorFlow’s biggest strength remains its production ecosystem — TensorFlow Serving, TensorFlow Lite (mobile/embedded deployment), and TensorFlow.js (browser deployment) give it broad reach across deployment targets that other frameworks don’t match as comprehensively.

4. Keras

Keras began as a standalone, framework-agnostic high-level API and is now tightly integrated as TensorFlow’s official high-level interface (tf.keras), while also supporting PyTorch and JAX backends as of Keras 3. Its focus is developer productivity — building and training a model in Keras typically takes far fewer lines of code than the equivalent in raw TensorFlow or PyTorch.

from tensorflow import keras

model = keras.Sequential([
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10)

from tensorflow import keras

model = keras.Sequential([
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10)

Keras is often the best starting point for beginners because of this simplicity, while still scaling up to complex custom architectures when needed.

5. JAX

JAX, developed by Google Research, takes a different philosophy: it’s essentially NumPy with automatic differentiation and just-in-time (JIT) compilation via XLA, built for high-performance numerical computing rather than being a traditional “deep learning framework” out of the box. Libraries like Flax and Haiku build neural network APIs on top of JAX.

import jax.numpy as jnp
from jax import grad

def loss_fn(w, x, y):
    pred = jnp.dot(x, w)
    return jnp.mean((pred - y) ** 2)

grad_fn = grad(loss_fn)   # automatic differentiation

import jax.numpy as jnp
from jax import grad

def loss_fn(w, x, y):
    pred = jnp.dot(x, w)
    return jnp.mean((pred - y) ** 2)

grad_fn = grad(loss_fn)   # automatic differentiation

JAX has become popular in cutting-edge research, particularly for large-scale training on TPUs, due to its composable function transformations (grad, vmap, jit) and excellent performance characteristics.

6. Other Notable Frameworks

7. Feature Comparison Table

8. Code Comparison: The Same Model in PyTorch and TensorFlow

To see the philosophical difference directly, here’s a simple training step in both frameworks:

PyTorch:

optimizer.zero_grad()
output = model(x_batch)
loss = criterion(output, y_batch)
loss.backward()
optimizer.step()

optimizer.zero_grad()
output = model(x_batch)
loss = criterion(output, y_batch)
loss.backward()
optimizer.step()

TensorFlow (with GradientTape, the eager-mode equivalent):

with tf.GradientTape() as tape:
    output = model(x_batch)
    loss = loss_fn(y_batch, output)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))

with tf.GradientTape() as tape:
    output = model(x_batch)
    loss = loss_fn(y_batch, output)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))

Both achieve the same result — forward pass, loss computation, gradient calculation, and weight update — but PyTorch’s backward() and TensorFlow’s GradientTape context manager reflect each framework’s underlying design philosophy.

9. Performance and Ecosystem Considerations

• Community and pretrained models: PyTorch dominates on Hugging Face Hub, the largest repository of pretrained models, which matters enormously if you plan to fine-tune existing models rather than train from scratch.
• Deployment targets: If you need mobile or browser deployment, TensorFlow’s ecosystem (TFLite, TF.js) is more mature, though PyTorch’s ExecuTorch is closing the gap.
• Enterprise/production pipelines: TensorFlow’s serving infrastructure has a longer track record in large-scale production environments, though PyTorch is now heavily used in production at major companies as well (including Meta, Tesla, and OpenAI’s earlier work).
• Cutting-edge research: JAX is increasingly the framework of choice for large-scale research requiring TPU pods and highly composable transformations.

10. Which Framework Should You Learn First?

Given the current landscape, most educators and practitioners recommend:

1. Start with PyTorch if you’re interested in research, NLP, computer vision, or plan to work with Hugging Face — it has become the de facto standard for most published research and pretrained models.
2. Start with Keras/TensorFlow if you want the gentlest learning curve and plan to deploy models to mobile, web, or embedded devices.
3. Learn JAX later, once you’re comfortable with the fundamentals, if your work moves toward large-scale research or TPU-based training.

10b. Framework Choice by Project Stage

A perspective that’s often missing from “which framework is best” discussions is that the right answer can change depending on what stage your project is in:

Prototyping and experimentation: Keras or PyTorch with high-level wrappers (like Lightning) let you iterate on architecture ideas quickly, without worrying about deployment concerns yet. Speed of iteration matters more than production readiness here.

Research and novel architectures: PyTorch’s flexibility and dynamic graphs make it far easier to implement unusual, non-standard model structures (custom attention mechanisms, novel loss functions, dynamic control flow) — this is a major reason it dominates academic paper releases.

Large-scale distributed training: JAX’s functional design and native support for pmap/vmap transformations make it a strong choice when scaling training across many TPU or GPU cores, particularly for very large models where every bit of compute efficiency counts.

Production deployment: TensorFlow’s mature serving infrastructure (TF Serving, TFLite for mobile, TF.js for browsers) remains a strong choice when deployment targets span multiple platforms. That said, PyTorch has closed much of this gap with tools like TorchServe and ExecuTorch, and is now a fully viable production choice for many teams, especially those already using PyTorch for training.

Fine-tuning existing models: Since the vast majority of pretrained, open-source models are released in PyTorch format (particularly via Hugging Face), sticking with PyTorch avoids unnecessary conversion steps.

11. Advantages and Disadvantages Summary

11b. Beyond the Core Frameworks: The Supporting Ecosystem

Choosing a core framework is only part of the picture — a rich ecosystem of supporting libraries has grown around each one, and knowing about them can save significant development time:

For most practitioners today, working effectively means combining a core framework (PyTorch, TensorFlow, or JAX) with one or more of these supporting tools, rather than writing every piece of training and deployment infrastructure from scratch.

12. Best Practices

• Choose a framework based on your goal (research vs. production vs. learning), not just popularity.
• If you plan to use pretrained models heavily, check Hugging Face Hub compatibility — most models ship in PyTorch first.
• Don’t be afraid to use Keras on top of TensorFlow, PyTorch, or JAX for rapid prototyping, then drop down to lower-level APIs when you need more control.
• Learn the underlying autodiff and tensor operations of at least one framework deeply, rather than only learning high-level APIs — this understanding transfers across frameworks.
• Keep an eye on deployment requirements early — the framework that’s easiest for training isn’t always the easiest to deploy on your target platform (mobile, web, edge devices).

12b. A Brief History: How We Got Here

Understanding a bit of history helps explain why the current landscape looks the way it does. Early deep learning frameworks like Theano (developed at the University of Montreal, first released 2007) and Caffe (Berkeley, 2013) were among the first tools to make GPU-accelerated neural network training broadly accessible to researchers, but both required relatively low-level, verbose code to define models.

TensorFlow’s 2015 release brought Google’s engineering resources to bear on the problem, offering a more complete ecosystem but with a static-graph design that many researchers found cumbersome for experimentation. PyTorch’s 2016 release, building on the earlier Torch library (which used the Lua programming language), directly addressed this pain point with dynamic graphs and a design philosophy that felt much closer to writing ordinary Python code — a major reason it was quickly and enthusiastically adopted by the research community.

Since then, the landscape has continued to consolidate: Theano development ceased in 2017, Caffe’s development slowed considerably as its original team moved toward Caffe2 (later merged into PyTorch), and TensorFlow itself adopted eager execution by default starting with TensorFlow 2.0 in 2019, largely converging toward the dynamic, Pythonic style PyTorch had popularized. JAX’s rise since roughly 2018 represents the most recent major shift, driven by demand for extreme performance and composability in large-scale research settings, particularly at Google DeepMind. This consolidation is worth keeping in mind: the “best” framework has shifted meaningfully every few years as the field has matured, and staying flexible enough to learn a new one when the ecosystem shifts is arguably a more durable skill than mastering any single framework permanently.

13. Summary

There is no single “best” deep learning framework — PyTorch leads in research flexibility and pretrained model availability, TensorFlow offers the most mature production and deployment ecosystem, Keras provides the gentlest on-ramp for beginners, and JAX delivers cutting-edge performance for large-scale, TPU-based research. The good news is that the underlying concepts — tensors, automatic differentiation, layers, optimizers — transfer across all of them, so learning one deeply makes picking up the others significantly easier down the line.

13b. Skills That Transfer Regardless of Framework

Given how much the framework landscape has shifted over the past decade — and will likely continue to shift — it’s worth closing on what actually transfers between frameworks, since that’s a more durable investment than deep expertise in any single tool. Understanding tensors and their operations (reshaping, broadcasting, matrix multiplication), automatic differentiation and how gradients flow through a computational graph, the structure of common layers (convolutional, recurrent, attention), and how training loops orchestrate forward passes, loss computation, and optimizer steps — all of this knowledge maps directly from PyTorch to TensorFlow to JAX with only syntax changes. Practitioners who understand these underlying concepts deeply tend to pick up a new framework in days or weeks, while those who’ve only memorized framework-specific API calls without understanding what’s happening underneath often struggle far more when the ecosystem shifts, as it reliably does every few years.

References

• PyTorch Official Documentation. https://pytorch.org/docs/stable/index.html
• TensorFlow Official Documentation. https://www.tensorflow.org/api_docs
• Keras Official Documentation. https://keras.io/
• JAX Official Documentation. https://jax.readthedocs.io/
• Hugging Face Transformers Documentation. https://huggingface.co/docs/transformers/index