PyTorch vs TensorFlow: what to choose for deep learning in 2026

This article was prepared together with Tatiana Bulgakova, an expert in data science and an AI implementation consultant, to help you navigate the flow of information and make a choice based on the engineering requirements of your project, rather than on common stereotypes.

Tatiana Bulgakova

AI implementation consultant, freelancer, instructor at Netology in courses on deep learning, ML, and data science

So, let's take a look at the history of both frameworks, their specific features, and a list of common mistakes when working with them.

What are PyTorch and TensorFlow

To understand the current state of the ecosystem, let's look at where these frameworks come from. Their history explains not only the technical differences but also why a certain perception has formed within the community.

PyTorch: research freedom that became an industrial standard

PyTorch was born in 2016 in the research laboratory of Meta* (at that time — Facebook AI Research, *recognized as an extremist organization and banned in the territory of the Russian Federation) as a successor to the earlier framework Torch. Its creators had a clear goal: to create a tool that would allow scientists and engineers to experiment as conveniently as possible.

Key breakthrough — eager execution.

Unlike the systems with static graphs that dominated at the time, including early TensorFlow, PyTorch allowed operations to be executed immediately, line by line, just like in regular Python. This radically simplified debugging, introspection, and the creation of dynamic architectures, where the computation graph can change on the fly, as in recurrent networks or reinforcement learning.

The path to universality

Initially perceived as a purely research tool, PyTorch began to rapidly gain production capabilities. Key milestones:

2018. PyTorch 1.0: introduction of TorchScript for model export and torch.jit for compilation, which became a bridge to production.

2020. TorchServe: the official framework for serving models, a response to TensorFlow Serving.

2022. PyTorch 2.0 and torch.compile: deep integration of the TorchDynamo compiler, significantly increasing performance and bringing it closer to static graphs.

2023. ExecuTorch: a specialized framework for efficient deployment on edge devices.

2026. PyTorch is a mature, universal platform that has retained its key superpowers — intuitiveness and control for the developer — and supplemented them with a full set of tools for industrial implementation.

TensorFlow: from industrial monolith to flexible ecosystem

TensorFlow was introduced at Google at the end of 2015 as a successor to the closed system DistBelief. Its DNA has been industrial from the very beginning: scalability, deployment on heterogeneous hardware — from smartphones to data centers, and support for large engineering teams.

Initially, TensorFlow 1.x required defining an immutable computation graph before training began. This allowed for optimization in production, but created a high entry barrier and made debugging quite difficult: the infamous tf.Session.run() errors. However, it was during this era that it developed a powerful ecosystem: TensorFlow Serving, TensorFlow Lite, TFX (TensorFlow Extended).

2019. The Revolution of TensorFlow 2.x

Recognizing the trend, the Google team made a radical turnaround: they adopted imperative execution by default and fully integrated Keras (originally an independent high-level API by François Chollet) as the standard way to build models. This dramatically lowered the entry barrier and brought the development experience closer to PyTorch.

2023. The Era of Keras 3 and Versatility

The latest and most significant step is the transformation of Keras into a multi-backend framework. Now the same Keras code can run on top of the TensorFlow, JAX, or (experimentally) PyTorch backends. For TensorFlow, this means its high-level API has become even more flexible and modern, while the specialized libraries KerasCV and KerasNLP provide ready-to-use, optimized, state-of-the-art models.

2026. TensorFlow is a comprehensive, modular ecosystem that offers researchers convenient high-level abstractions (Keras 3) and provides engineers with a proven, integrated path from prototype to deployment on any device.

Thus, both frameworks have come a long way towards each other:

• PyTorch has added industrial tools;

• TensorFlow has gained research flexibility and convenience.

And the choice in 2026 is between two different philosophies of development and ecosystems that cover most practical needs.

But there are still differences.

Key Differences Between PyTorch and TensorFlow

Here we see the main differences at the moment — both frameworks can technically solve the same tasks, but they offer different balances between control and automation.

1. Flexibility and Control (PyTorch):

• TorchServe and MLflow are evolving, but require more manual tuning to build a complete MLOps pipeline.

• Distributed learning (torch.distributed) requires a deep understanding but provides maximum control.

2. Integration and Automation (TensorFlow):

• TFX and TensorFlow Serving — mature, ready solutions for industrial pipelines.

• tf.distribute.Strategy — an easy-to-use API for distributed learning that abstracts complexities.

Now let's translate these differences into project requirement language.

Practical Comparison

Environment Setup and Verification

Installing PyTorch:

# For Windows/Linux with CUDA 12.x
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Installing TensorFlow:

# Installing TensorFlow with GPU support (for versions 2.15+ and CUDA 12)
pip install tensorflow[and-cuda]==2.15.*

Checking GPU:

# PyTorch
import torch
print(f"PyTorch is using GPU: {torch.cuda.is_available()}")

# TensorFlow
import tensorflow as tf
print(f"TensorFlow is using GPU: {len(tf.config.list_physical_devices('GPU')) > 0}")

One Task — Two Approaches: Implementing MNIST

The best way to understand the difference is to solve one task in two ways. Let's take the classic MNIST and implement an identical convolutional neural network (CNN) in both PyTorch and TensorFlow, comparing not only metrics but also the development experience.

Important Note: The code is simplified for clarity. In real projects, you would use more abstract APIs (Lightning for PyTorch, ready-made model.fit() for Keras).

Implementation in PyTorch — Control over Every Step

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# 1. Data preparation (explicit, step-by-step)
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

train_data = datasets.MNIST('./data', train=True, download=True, transform=transform)
test_data = datasets.MNIST('./data', train=False, transform=transform)

train_loader = DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=1000, shuffle=False)

# 2. Model definition (inheriting from nn.Module -- full freedom)
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.relu(x)
        x = self.conv2(x)
        x = torch.relu(x)
        x = torch.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        return x

# 3. Setup (everything explicit: device, optimizer, loss function)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ConvNet().to(device)  # Explicit transfer of the model to the device
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# 4. Training loop (written manually -- full control)
def train(model, device, train_loader, optimizer, criterion):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)  # Explicit transfer of data
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

# 5. Validation (also manual)
def test(model, device, test_loader):
    model.eval()
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    return 100. * correct / len(test_loader.dataset)

# 6. Running (one epoch for example)
for epoch in range(1):
    train(model, device, train_loader, optimizer, criterion)
    acc = test(model, device, test_loader)
    print(f'Epoch {epoch+1}, Accuracy: {acc:.2f}%')

What is important:

• You control everything: from data loading to the optimizer step.

• You specify where to perform computations — on CPU or GPU, using .to(device). This adds a step but gives full control and understanding of where operations are executed.

• You write the training loop (train) — it is easy to add custom logic, such as logging, mixed precision, gradient clipping.

Implementation in TensorFlow (Keras) — conciseness and integration

import tensorflow as tf
from tensorflow.keras import layers, models, datasets

# 1. Data preparation (built-in utilities, minimal code)
(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0
x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0
x_train = (x_train - 0.1307) / 0.3081  # Same normalization as in PyTorch
x_test = (x_test - 0.1307) / 0.3081

# 2. Model definition (Sequential API -- declaratively and concisely)
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Dropout(0.25),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(10, activation='softmax')
])

# 3. Compilation (everything in one line: optimizer, loss, metrics)
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# 4. Training (one call to .fit() -- the entire cycle is encapsulated)
history = model.fit(
    x_train, y_train,
    batch_size=64,
    epochs=1,
    validation_split=0.1,
    verbose=1
)

# 5. Validation (built-in method .evaluate())
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)
print(f'Test accuracy: {test_acc * 100:.2f}%')

What is important:

• Automation: no need to explicitly manage the device, TensorFlow automatically uses the GPU if available.

• Minimal boilerplate code: data is loaded in one line, the model is defined declaratively.

• Integration: the entire training cycle, logging, and validation are packed into .fit().

As seen in the examples above, the difference is not in the ability to solve the task, but in the approach.

PyTorch gives you a constructor: you see every detail, manually build the training loop, and decide where to place each tensor. This is the path of maximum control and understanding.

TensorFlow (Keras) offers a ready-made tool: you describe what should be in the model, and the framework decides how to execute it efficiently. This is the path of quick assembly and integration.

Both approaches lead to the same goal — a functioning neural network, but they create different experiences for the developer. It is this experience that should be the main criterion for choosing a tool for the specific tasks of your project.

Applications in 2026. Choosing based on project requirements

Considering all the aspects discussed above, the key to the choice is to determine the priorities of your project.

PyTorch is suitable if:

• You create non-standard, evolving architectures, for example in fundamental research. Dynamic graphs allow for changing logic on the fly.

• Deep integration with the Python ecosystem is critically important. PyTorch feels like its natural extension.

• Your workflow is built around Hugging Face or other libraries that have historically developed in the PyTorch ecosystem.

• You require direct work from .NET applications through TorchSharp (a third-party library).

• In production, you will often need to assemble a pipeline from components (TorchServe/MLflow, etc.).

TensorFlow (with Keras 3) is suitable if:

• You need one well-integrated stack from idea to production. Keras for development, TFX for pipelines, TF Serving for deployment.

• Mobile or edge deployment is an important requirement. TFLite is a mature solution for mobile/edge due to optimizations and wide hardware support.

• You primarily work in Google Cloud or plan to use TensorFlow.js for web deployment.

• You want to use state-of-the-art models for CV/NLP but value ready-made, optimized, and native APIs (through KerasCV and KerasNLP), rather than assembling from separate libraries.

Universal scenarios where both frameworks are equally suitable:

• Training standard models (CNN, RNN, Transformers). Both frameworks provide all the necessary tools. The choice may come down to team preferences.

• Scaling training across multiple GPUs/nodes. Both have mature solutions (torch.distributed, tf.distribute.Strategy).

• Exporting models for production. Through ONNX, models from both frameworks can be served in a unified environment.

Both stacks provide a complete path from idea to production. The difference lies in the distribution of effort. PyTorch allows for faster starts and experimentation but will require more work to build the pipeline. TensorFlow may take a bit longer to learn but then provides more complete solutions for scaling.

Common Mistakes

Both ecosystems have quirks that can become pitfalls. Below are the most common issues and ways to resolve them.

Tensor dimensionality mismatch. Shape mismatch

Operations require exact shape matching, but the ways to obtain these shapes differ.

Solution in PyTorch: use tensor.shape, which returns a torch.Size object (similar to a tuple). Debugging is simple: you can insert print(x.shape) anywhere in the code.

Solution in TensorFlow: use tf.shape(tensor) to get a tensor with dimensions (you may need .numpy() to get values). Most of the time, tensor.shape is sufficient, which returns TensorShape.

Model runs on CPU instead of GPU

You do not see a speedup, even though a GPU is available.

Solution in PyTorch: manually move the model and data to the device: model.to(device), data.to(device).

Solution in TensorFlow: the framework tries to use the GPU automatically. If it does not happen, check your CUDA/cuDNN installation and ensure that tf.config.list_physical_devices('GPU') returns the device.

Mismatch of tensor and model locations

In PyTorch, if the model and data are on different devices (for example, the model on GPU and the tensor on CPU), the operation will fail with an error. In TensorFlow, this occurs less frequently but can happen with manual device management.

Solution in PyTorch: always check the device of tensors using .device and explicitly move them to the desired device:

# Check the model's device
device = next(model.parameters()).device
# Move data to the same device
data = data.to(device)

Solution in TensorFlow: ensure that all operations are executed in the correct device context. Use tf.debugging to check placement:

# Temporarily enable logging of operation placements
tf.debugging.set_log_device_placement(True)
# Then run your code to see where operations are executed

Out of video memory. CUDA out of memory

Error RuntimeError: CUDA out of memory stops training.

General solution: reduce batch_size, use gradient accumulation, enable mixed precision training (torch.autocast or tf.keras.mixed_precision).

PyTorch specific solution: call torch.cuda.empty_cache() to clear the cache. Make sure you are not accumulating computation history unnecessarily: use .detach().

Specific solution for TensorFlow: enable memory growth tf.config.experimental.set_memory_growth(gpu, True).

Non-reproducibility of results

With the same seed, you get different results from run to run.

Solution in PyTorch: set torch.manual_seed() and torch.cuda.manual_seed_all(). For full determinism, add:

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

Solution in TensorFlow: set tf.random.set_seed(). For complex scenarios, you may need to set the environment variable TF_DETERMINISTIC_OPS=1.

How to make a choice in 2026: from goals to tools

If you are starting from scratch

To understand how it works, PyTorch is a good fit. Its imperative style and closeness to Python will allow you to build a model step by step, clearly seeing how data flows through each layer. This is an excellent choice for a deep dive into the mechanics of deep learning.

To quickly get a working prototype, Keras (TensorFlow) is suitable. The high-level Keras API allows you to assemble and train a model with minimal code. This will give you a quick sense of accomplishment and confidence.

If you are a professional shaping the project stack

Your choice is determined by the context of the project and long-term requirements, rather than the technical superiority of one framework over another.

The optimal choice depends on the type of your project:

• Research-oriented, focusing on unconventional architectures and maximum flexibility
  PyTorch is widely used in R&D. The ecosystem for research (Hugging Face, torchvision), dynamic graphs, and an active community make it a leader in ML research.

• Industrial, with clear deadlines, requiring a reliable pipeline from A to Z
  TensorFlow (with the Keras, TFX, TFLite stack) offers the most comprehensive and integrated path. The maturity of MLOps tools (TFX, Serving) and edge deployment (TFLite) reduces operational risks.

• Requires strategic flexibility and protection against vendor lock-in
  Consider Keras 3 as an abstract layer. By writing code in Keras, you can run it on JAX or TensorFlow backends without changes. This allows for switching the low-level engine in the future without rewriting the high-level model logic.
  
  Note: Support for PyTorch as a backend for Keras 3 remains experimental.

• Integration into the existing ecosystem (.NET, mobile applications)
  For .NET, consider the path through ONNX (suitable for both frameworks) or direct integration of PyTorch via the third-party library TorchSharp. For mobile applications, TensorFlow Lite still offers the most mature and optimized stack.

And remember: by mastering one framework, you gain the key to understanding another. Start with solving your current problem — both tools will support you in this.

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