PaddlePaddle

What is PaddlePaddle?

PaddlePaddle is an open-source deep learning platform initiated and continuously maintained by Baidu; its name in English comes from Parallel Distributed Deep Learning. It covers tensor computation, neural networks, automatic differentiation, optimizers, mixed precision, distributed training, model saving, and inference deployment, and it also provides a large number of industry-specific model kits and development tools.

PaddlePaddle can be used for teaching as well as for traditional vision, speech, and recommendation tasks; it is also applicable to the pre-training, fine-tuning, and inference of large models. Its core framework can be installed locally or on servers, while the AI Studio Xinghe community offers courses, Notebooks, datasets, models, and cloud computing resources. These two offerings should not be considered as the same paid service.

Current version: PaddlePaddle 3.3

As of this verification, both the official website documentation and the official GitHub repository list version 3.3 as the latest stable version. Version 3.x focuses on improving the unified intermediate representation for PIR, the compiler, automatic parallelization, distributed training of large models, Checkpoint compatibility, and support for various domestic hardware platforms.

The stable version is suitable for production and learning purposes, while the develop and Nightly versions are intended for testing the latest features; these versions may involve API changes and compatibility issues. When deploying, it is necessary to use a specific, fixed version number, and ensure that the environments used for training, exporting, and inference employ compatible versions.

Dynamic graphs and static graphs

The syntax for dynamic graphs is similar to that of regular Python, with calculations being executed immediately, which facilitates debugging and research. Static graphs, on the other hand, involve first constructing the complete computation graph before compiling and executing it, which makes it easier to carry out global optimization and deployment.

PaddlePaddle offers the capability to convert dynamic models into a static format suitable for inference.

The transformation from dynamic to static form imposes constraints on Python’s control flow, custom operators, and dynamic shapes. When the conversion fails, it is necessary to narrow down the scope of the problem, check for unsupported syntax, and verify accuracy using actual input data, rather than simply confirming that the model file has been exported successfully.

PIR and compilation optimization

PIR is an important unified intermediate representation in Paddle 3.x, used to link the front-end APIs, automatic parallelization mechanisms, the compiler, and the back-end execution layer. The CINN compiler is capable of performing operator fusion, graph optimization, and code generation in order to reduce intermediate memory usage and scheduling overhead.

The benefits of compilation optimization depend on the model structure, Shape, hardware, and the degree of coverage of operators. Once it is enabled, it is necessary to compare the accuracy of the results, the time required for the first compilation, the throughput, and the amount of video memory used.

When encountering an operator that is not supported, it may be necessary to fall back or disable some passes.

Automatic differentiation and neural network APIs

paddle.Tensor, paddle.nn.Layer, Loss, Optimizer, and DataLoader constitute the common training interfaces. After the user defines the forward logic of the network, the framework automatically handles the backward computation and parameter updates; optimizers such as SGD, Adam, and AdamW, along with learning rate scheduling mechanisms, can be used.

When evaluating a model, in addition to using the eval function, it is also necessary to set no_grad to turn off gradient tracking; otherwise, this may increase memory usage. When saving a model, it is important to distinguish between the parameters, the optimizer’s settings, and the model used for inference – the purposes of the files generated for resuming training and those exported for deployment are different.

Automatic mixed precision

AMP can utilize FP16 or BF16 to reduce memory usage and increase the throughput of Tensor Cores; together with GradScaler, it helps to prevent underflow that occurs at low precision levels. The training of large models can also take advantage of techniques such as recomputation, gradient accumulation, and partitioned optimizers.

Low precision can lead to NaN values, changes in convergence, or instability in certain operators. It is necessary to maintain a FP32 baseline, monitor the loss and gradients, use allowlists or blocklists to control sensitive operators, and verify everything against the final business metrics.

Distributed training and Fleet

paddle.distributed and Fleet support various modes such as multi-GPU on a single machine, multiple machines with multiple GPUs, data parallelism, model parallelism, pipeline parallelism, tensor parallelism, and parameter servers. The launcher can specify GPUs or nodes, while Fleet uses DistributedStrategy to configure settings for mixed precision, recomputation, communication optimization, and parallelization strategies.

PaddlePaddle 3.3 continues to enhance automatic parallelization and FSDP strategies, while also improving the online merging and resplitting of FlexCheckpoint. The performance in distributed environments is influenced by factors such as the network, topology, batch size, and data reading.

An increase in the number of cards does not necessarily lead to a linear acceleration; it is necessary to analyze the proportion of communication traffic and any uneven distribution of load.

Large model training

Paddle provides automatic parallelism strategy detection, distributed Checkpointing, GPU memory optimization, and high-performance operators for large language models. PaddleNLP and PaddleFleetX offer examples for pre-training, fine-tuning, alignment, and inference, which can be used with domestic and open-source large language models.

Training large models requires clear definitions regarding model licenses, data permissions, and cluster fault tolerance. It is recommended to first use a small-scale configuration to test the data pipeline and the loss function, before scaling up to multiple machines.

Checkpoints should be synchronized to reliable storage, and recovery drills should be conducted regularly.

Inference deployment

Paddle Inference is designed for high-performance inference on the server side, and it can be combined with PIR, CINN, TensorRT, MKL, as well as hardware backends for optimization. Paddle Lite is intended for use on mobile devices and edge devices, Paddle Serving is used for online services, while Paddle.js covers certain browser and front-end scenarios.

The models and operators supported by different deployment components are not exactly the same. After export, tests for correctness, cold start time, P95 latency, throughput, peak memory usage, and long-term stability should be conducted on the target device.

Hardware support

PaddlePaddle provides installation packages for CPU and NVIDIA GPUs, and it also supports compatibility with domestic hardware such as Kunlun XPU, Ascend NPU, and Haiguang DCU. The official 3.3 documentation offers separate instructions for installation, imaging, drivers, and health checks tailored for different chips.

Hardware installation must be compatible with the operating system, Python, CUDA or the vendor’s runtime, drivers, and framework packages. It is not possible to combine different backend Wheel versions in the same environment.

Container images are generally better suited for fixing dependencies, but they still require compatibility with the host machine’s drivers.

PaddleOCR

PaddleOCR is a well-known OCR and document understanding tool within the Paddle ecosystem; it covers text detection, recognition, layout analysis, as well as the processing of tables, formulas, and documents. It is suitable for structuring data from invoices, scanned documents, image-based text, and PDF files.

The accuracy of OCR is influenced by factors such as clarity, angle, language, layout, and the font used. Production systems should be evaluated using real samples, and evidence such as revisions for low confidence levels, layout coordinates, and the original images should be retained.

PaddleNLP

PaddleNLP offers tools for text classification, information extraction, embedding, pre-trained language models, and the training and deployment of large models; it includes a tokenizer, datasets, a trainer, as well as industry examples. It can be combined with Paddle’s distributed capabilities to handle large-scale training tasks.

Different models may use separate licenses and sources for weights. The fact that the PaddleNLP code is open-source does not mean that each model can be used for commercial purposes without any restrictions; it is still necessary to check the specifications of the model before downloading it.

Visual and Industrial Kits

PaddleDetection covers object detection, tracking, keypoint detection, and instance segmentation; PaddleSeg is used for image segmentation.

PaddleClas is used for classification and recognition; PaddleRec is aimed at recommendation tasks.

PaddleSpeech covers speech recognition and synthesis; PaddleHelix serves biocomputing.

PaddleX offers low-code development tools and a unified platform that facilitates the combination of various functions such as OCR, detection, classification, and segmentation to create applications. There is a compatibility matrix between the different versions of the suite and the core framework; therefore, it is necessary to choose the appropriate combination according to the relevant documentation when installing them.

Model saving and migration

During training, parameters and optimizer states can be saved to enable resuming training; before deployment, an inference model is generated using JIT or relevant export interfaces. 3.3 The distributed capability supports more flexible handling of split Checkpoints, and it is also possible to load certain Checkpoints in Hugging Face’s open format.

Format compatibility does not guarantee that the model structure and numerical results will be exactly identical. When migrating between frameworks, it is necessary to compare the tokenizer, weight mappings, position encodings, precision types, and generation parameters, and to use fixed examples for comparing the outputs at each layer or the final result.

Installation method

For the CPU version, PaddlePaddle is usually installed using pip, while for the GPU version, PaddlePaddle-GPU is used, with the official source or Wheel version corresponding to CUDA being selected. After installation, it is possible to check the version and run paddle.utils.run_check to conduct a basic health check.

Support for Windows, Linux, macOS, and various architectures varies. Before starting, use the installation selector on the official website to determine the appropriate combination of Python, operating system, and hardware; do not simply copy the CUDA suffixes from older tutorials.

AI Studio Galaxy Community

AI Studio is a learning and training community provided as part of PaddlePaddle; it offers online Notebooks, courses, projects, datasets, competitions, a model community, an application space, and cloud-based GPU resources. Beginners can run examples directly in their browsers, without the need to set up a local environment first.

The platform offers free computing power as part of various promotions, and it also provides high-performance GPUs through subscription memberships and A-coins. The benefits associated with membership status, the amount of A-coins required, and the limits set for promotions can change; therefore, the fixed number of V100 hours available per day as indicated in historical data cannot be considered a permanent commitment.

Prices and fees

The core framework of PaddlePaddle, its official source code, and most of the related repositories are available free of charge, with no subscription fees required. Users are responsible for covering the costs associated with servers, GPUs, electricity, storage, and maintenance when running it on their own.

AI Studio’s cloud computing resources are a standalone service. The current pricing structure is based on a membership system and A-coins, with high-performance resources for projects or applications being consumed in accordance with the rules specified on the relevant page. For specific membership prices and the amount of GPU credits available, please visit the real-time purchase page.

For enterprise privatization, training, and technical services, please consult separately.

GitHub and open-source licenses

The core repository is PaddlePaddle/Paddle, which is licensed under the Apache License 2.0; it allows for use, modification, and distribution as long as the requirements of the license and its associated terms are met. Documentation, PaddleOCR, PaddleNLP, PaddleDetection, and others are maintained in their respective official repositories.

A framework license does not automatically cover third-party datasets, model weights, and generated content. Commercial projects should establish separate license lists for code, models, data, and content.

Tutorial for using PaddlePaddle

Complete a basic task.

1. Register for PaddlePaddle and create an API Key intended solely for use in a testing environment;
2. Select a model based on input type, context, quality, speed, and price;
3. First, use the current version of PaddlePaddle 3.3 to submit the minimal request and examine the structure of the response received;
4. Dynamic and static diagrams are then used to test stream output, parameters, and abnormal response;
5. Record Tokens, number of calls, latency, error rate, and cost per call;
6. Move the key to the server-side key manager before integrating it into the actual application;

Create reusable professional workflows

1. Different keys and quotas are used for development, testing, and production environments;
2. Using the current version of PaddlePaddle 3.3, representative evaluation sets are created for dynamic and static graphs, as well as for PIR and compilation optimization.
3. Set timeout, concurrency, retry, throttling, and budget limits;
4. Perform checks on the output regarding facts, security, format, and sensitive information;
5. Monitor changes in model version, price, latency, and failure rate;
6. Prepare plans for downgrading the model, implementing circuit breaking, and taking manual control;

Which users is it suitable for?

• Students and teachers who are learning about deep learning and Python model training;
• Engineering teams responsible for developing OCR, vision, NLP, speech, and recommendation systems;
• Researchers who need multi-machine, multi-card setups as well as distributed training for large models;
• Companies that need to be compatible with domestic hardware such as Kunlun, Ascend, and HaiGuang;
• Developers who wish to use the AI Studio courses, Notebooks, and competition resources.

Product advantages

• It has a relatively complete set of elements, ranging from the core framework to model kits and deployment tools;
• It also supports dynamic images, static images, conversion from dynamic to static formats, as well as compilation optimization.
• Automatic parallelism, Fleet, and Checkpoint handle large-scale training;
• Industrial suites such as PaddleOCR and PaddleNLP are mature;
• Supports NVIDIA GPUs as well as a variety of domestic AI chips;
• Apache 2.0 is open-source and comes with Chinese documentation as well as a learning community.

Restrictions and Precautions

• The PaddlePaddle ecosystem offers many components, and it is a common issue for the core framework, CUDA, drivers, suites, and hardware backends to be incompatible with each other.
• The old FluidAPI is being phased out gradually, and when migrating older projects, the interfaces should be replaced in accordance with the documentation in section 3.3;
• The fact that the framework itself is free does not mean that training and cloud computing resources are also free;
• Model weights may also use other licenses;
• Before upgrading production, it is necessary to lock in dependencies, run regression tests, verify numerical accuracy, and export the model, while also preparing a rollback version.

Frequently Asked Questions

Is PaddlePaddle free?

The core framework is available freely and open-source, with no subscription fees. Training models on your own incurs hardware costs, and the high-performance cloud computing resources provided by AI Studio may require membership benefits or A coins.

What is the current latest stable version of PaddlePaddle?

As of this verification, the version is PaddlePaddle 3.3. The nightly and develop versions are updated more frequently, but it is recommended to use the stable version with a fixed minor version in production environments.

What hardware is supported by PaddlePaddle?

It supports CPUs and NVIDIA GPUs, and offers backends such as Kunlun XPU, Ascend NPU, and Hygon DCU; for specific models and requirements, please refer to the corresponding 3.3 installation documentation.

Is PaddlePaddle suitable for large models?

It is suitable. The framework offers automatic parallelization, Fleet, FSDP, mixed precision, recomputation, and distributed Checkpoints; PaddleNLP provides tools for training and deploying large models.

Can PaddlePaddle be used for commercial purposes?

The core framework is based on Apache 2.0 and can be used for commercial purposes under that license. The specific models, datasets, and third-party components require separate verification of their licenses.