PaddlePaddle 2.3.0 introduces the PHI operator library, a significant architectural update designed to lower development costs and improve performance. The release also enhances distributed training capabilities and adds support for new hardware like Huawei Ascend 910 and GraphCore IPU.
PaddlePaddle 2.3.1 introduces official support for CUDA 11.6, significant upgrades to quantization workflows including AMP support, and performance improvements in attention and linear operators.
PaddlePaddle 2.4.0 introduces a new dynamic graph architecture, 55 sparse computing APIs, and a GPU training engine for large-scale graph neural networks, alongside significant enhancements to distributed training and deployment.
PaddlePaddle 2.4.2 introduces Cutlass-based kernel fusion for sparse convolutions and fixes critical bugs in distributed training and mixed-precision inference.
PaddlePaddle 2.5.0 introduces a unified dynamic-static execution model, native 0D tensor support, and significant performance improvements for ResNet50 and BERT models. The release also deprecates legacy APIs like paddle.fluid and removes older data parallel interfaces, requiring code updates for multi-node setups.
PaddlePaddle 2.6.0 introduces a new Intermediate Representation (PIR) and integrates the CINN compiler to enhance extensibility and performance. The update focuses on large model support through Auto Parallelism, advanced distributed training strategies, and optimized inference capabilities.
Baidu’s PaddlePaddle framework updates to v3.1.1, focusing on robust large model training, FP8 optimization, and new API features.
PaddlePaddle 3.2.2 introduces optimizations for distributed training, including HuggingFace checkpoint support and fixes for safetensor loading, alongside updates to the XPU memory monitoring module.
PaddlePaddle 2.2.0 introduces 100+ new APIs for scientific computing and signal processing, enhances dynamic-to-static graph conversion for mixed precision, and advances distributed training with 4D hybrid parallelism. The release also adds TensorRT 8.x support and dynamic shape auto-configuration for easier deployment.
PaddlePaddle 2.3.0‑rc0 brings more than 100 new APIs, a brand‑new PHI operator library, major distributed‑training upgrades, Java and ONNX inference support, and native compatibility with a range of new AI accelerators.
