PyTorch Beginner's Guide: A Complete Analysis of Deep Learning Framework Selection and Evolution

Why Choose PyTorch? Starting from the History of Deep Learning Frameworks

The history of deep learning can be traced back to 1956, but the development of deep learning frameworks only truly began in the 21st century. Understanding the evolution of these frameworks helps us better understand why PyTorch has become today's most mainstream choice.

Early Stage: The Difficult Years of Manual Implementation

Before deep learning frameworks existed, researchers had to write code from scratch in C++. Even classic networks like AlexNet were originally implemented line by line.

Representative tools from this era include:

MATLAB: Widely used between 2011-2016, it packaged many algorithm APIs and was suitable for students to learn and understand concepts. However, it was closed-source, making it impossible to examine internal implementation details, and it didn't support GPU acceleration.
Torch/OpenNN: Implemented in C++, meaning users had to master C++ first, creating a high barrier to entry.

These early tools shared several common pain points: complex APIs, no GPU support, and the need to manually implement network construction, optimizer configuration, backpropagation, and all other components. Backpropagation is the core algorithm for training neural networks, formally proposed by Rumelhart, Hinton, and Williams in 1986. Its essence is the systematic application of the Chain Rule on computational graphs—starting from the loss function and computing the gradient of each parameter with respect to the loss, layer by layer in reverse. In early frameworks, researchers needed to manually derive and write gradient computation code for every layer, which was not only tedious but also error-prone. Modern frameworks like PyTorch have completely solved this problem through Automatic Differentiation: when you perform forward computation on a Tensor, PyTorch automatically builds a computational graph recording all operations; when you call .backward(), the system automatically computes all gradients along this graph in reverse. This means researchers only need to focus on the forward computation logic, while gradient computation is entirely handled by the framework.

2012-2017: A Flourishing of Frameworks

2012 was a pivotal year for deep learning—AlexNet burst onto the scene, proving for the first time the feasibility of multi-GPU training. AlexNet is a convolutional neural network proposed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) by an overwhelming margin, reducing the Top-5 error rate from 26% to 15.3%. AlexNet's breakthrough significance lies not only in its network architecture itself (comprising 5 convolutional layers and 3 fully connected layers), but more importantly in its first successful use of GPUs (two NVIDIA GTX 580s) for large-scale neural network training, demonstrating the enormous acceleration that GPU parallel computing brings to deep learning. This achievement directly triggered widespread attention to deep learning from both academia and industry, and is regarded as the starting point of the modern deep learning wave.

This gave birth to a batch of new frameworks:

Caffe: Once one of the most popular deep learning frameworks, but modifying network structures was extremely difficult—you had to disassemble the entire computational graph to make adjustments.
Theano and other frameworks also appeared during this period, but all eventually exited the stage.

Since none of the existing frameworks were user-friendly enough, many companies chose to develop their own internal deep learning frameworks—a common phenomenon between 2012-2017.

2016-2019: The Era of TensorFlow Dominance

TensorFlow, released by Google around 2016, quickly captured the market, with peak market share exceeding 80%. TensorFlow 1.x made numerous improvements over Caffe, making the framework more user-friendly.

However, TensorFlow had obvious shortcomings—it used a static graph mechanism, making it impossible to obtain intermediate results from each neuron in real-time during training, which was inconvenient for debugging and research. A Static Computational Graph requires users to completely define the entire computational graph structure before executing any computation, then feed data into this predefined graph for execution. TensorFlow 1.x adopted this approach, where users needed to first define inputs with tf.placeholder, define computation processes with various ops, and finally execute through Session.run(). The advantage of this approach is that the compiler can perform global optimization on the entire graph, improving execution efficiency, but the downside is difficult debugging—you cannot insert print statements in the middle of the graph to inspect intermediate values.

2017 to Present: The Rise of PyTorch

Facebook (now Meta) released PyTorch in 2017, adopting a clever market strategy—starting with promotion in universities.

PyTorch's core advantages over TensorFlow include:

Dynamic graph mechanism: Allows real-time inspection of intermediate results at every layer, facilitating debugging and research. Dynamic Computational Graph (also called Eager Execution) is a compute-as-you-define execution mode where each line of code produces immediate results, just like a regular Python program. PyTorch adopted dynamic graphs from the beginning, enabling researchers to use Python's if/for control flow statements, print intermediate results at any time, and use standard debuggers (like pdb or IDE breakpoint debugging) to step through code, greatly reducing the difficulty of experimentation and debugging.
More Pythonic: Code style better aligns with Python developers' habits
High flexibility: Researchers can conveniently modify any part of the network

This strategy proved highly effective—students used PyTorch during their university years and naturally brought it into industry after graduation. By 2022-2023, PyTorch's market share had far surpassed TensorFlow, reaching over 80%.

The Rise and Fall of Other Deep Learning Frameworks

Keras: The Lesson of Over-Encapsulation

Keras drew inspiration from scikit-learn's design philosophy, making it extremely simple to use—build, fit, predict in three steps. But this very over-encapsulation brought a fatal problem: programmers couldn't flexibly adjust training parameters, and customizability was too limited. Even after being integrated with TensorFlow 2.0, it failed to reverse its decline.

TensorFlow 2.0: A Bloated Compromise

TensorFlow 2.0 attempted to maintain backward compatibility with 1.x code, add new 2.x features, and integrate Keras simultaneously. The result was an extremely bloated framework that consumed massive resources after installation, actually accelerating user attrition.

Development of Chinese Deep Learning Frameworks

PaddlePaddle: Developed by Baidu, widely used in Chinese universities and Baidu-affiliated companies, supported by national policies
MindSpore: Developed by Huawei
OneFlow: Another well-developed framework from China
MXNet: Supported by Amazon and led by Mu Li, but failed in market promotion
CNTK: Developed by Microsoft, now essentially discontinued

PyTorch Core Concept: Tensor Explained in Detail

PyTorch's name can be understood as a combination of Python + Torch, possessing dual capabilities of data processing (similar to NumPy) and algorithm implementation (deep learning).

In NumPy, data exists as ndarrays, while in PyTorch all data is unified as Tensors:

Scalar (0-dimensional tensor)
Vector (1-dimensional tensor)
Matrix (2-dimensional tensor)
Multi-dimensional array (higher-dimensional tensor)

Tensor is a core concept in linear algebra, essentially a mathematical generalization of multi-dimensional arrays. In physics, tensors are used to describe quantities that follow specific transformation rules under coordinate transformations. In the deep learning context, the concept of tensors is simplified to numerical arrays of arbitrary dimensions. For example, an RGB color image can be represented as a 3-dimensional tensor with shape (3, H, W), where 3 represents color channels, and H and W represent height and width respectively; a batch of images is a 4-dimensional tensor with shape (B, 3, H, W). PyTorch's Tensor is highly similar to NumPy's ndarray in functionality, but with two key differences: first, Tensors can operate on GPUs, leveraging CUDA for large-scale parallel computation; second, Tensors support automatic differentiation (autograd), capable of automatically recording all operations on tensors and computing gradients, which is the foundation for implementing the backpropagation algorithm.

This unified data abstraction makes PyTorch more natural and efficient when handling various deep learning tasks.

PyTorch Installation and Version Selection Guide

Installing PyTorch is very simple using pip:

pip install torch==1.10.0

Key Considerations for Version Selection

PyTorch is not forward-compatible: A project developed with 1.6.0 may not run in a 1.10 environment
1.12 is a dividing line: Version 1.12 and later have significant differences from earlier versions
Version 2.0: Although officially claimed to be compatible with 1.12/1.13, compatibility issues still exist in actual testing
Recommendation: Beginners are advised to start with version 1.10, staying consistent with most tutorials and projects

GPU Acceleration and the CUDA Ecosystem

An important consideration when installing PyTorch is GPU support. GPUs (Graphics Processing Units) are suitable for deep learning because the core operations of neural networks—matrix multiplication and convolution—are essentially large-scale parallel computations. A modern GPU has thousands of computing cores that can simultaneously process thousands of floating-point operations, while CPUs typically have only dozens of cores. NVIDIA's CUDA (Compute Unified Device Architecture) is currently the most mainstream GPU programming platform in deep learning. PyTorch calls GPUs through cuDNN (CUDA Deep Neural Network library) at the lower level for efficient tensor operations. This is also why NVIDIA holds an absolute dominant position in the AI chip market. When installing PyTorch, you need to pay attention to matching the CUDA version with your graphics driver—different versions of PyTorch correspond to different CUDA versions, and choosing incorrectly will prevent GPU acceleration from working.

Future Trends: Cloud-Based Deep Learning

Deep learning's demand for computational resources is growing ever larger, and GPUs are being updated at a rapid pace. The future trend is cloud-based training—where major companies like Tencent, Alibaba, Google, and Meta provide cloud computing resources, and users only need to purchase computing power services without procuring and maintaining expensive hardware themselves.

The core architecture of cloud-based training typically includes several layers: at the bottom are large-scale GPU clusters (such as servers composed of NVIDIA A100/H100), the middle layer consists of distributed training frameworks (such as PyTorch's DistributedDataParallel), and the upper layer is resource scheduling and task management systems (such as Kubernetes). Users submit training tasks through APIs or web interfaces, and the cloud platform automatically allocates GPU resources, manages data storage, and handles model checkpoints. Major cloud GPU services include AWS SageMaker, Google Cloud's Vertex AI, and Alibaba Cloud's PAI platform. The economic logic of this model is clear: a top-tier GPU (like the H100) costs over $30,000, and new generations are released every 18-24 months. For individuals and small-to-medium enterprises, on-demand rental is far more economical than self-procurement.

Summary

For learners at the current stage, PyTorch is the deep learning framework most worth investing time to learn. Not only does it have the highest market share, but after mastering PyTorch, migrating to PaddlePaddle, TensorFlow, or other frameworks becomes very easy—because the core concepts (such as backpropagation) are universal, with only API interfaces differing.