This guide walks researchers through selecting a Tencent Cloud GN7 GPU instance, installing NVIDIA drivers, CUDA 10.2, cuDNN, setting up PyTorch and Jupyter, and configuring remote development with PyCharm, enabling efficient, cost‑effective AI development on a Tesla T4 GPU server.
This article introduces Boa, a Node.js library that bridges CPython, enabling JavaScript developers to import and use Python packages—especially machine‑learning libraries like PyTorch—seamlessly within Node.js, complete with practical code examples and API explanations.
This article explains PyTorch's autograd system, covering tensor creation, the requires_grad flag, detaching tensors, disabling gradient tracking with no_grad, the Function class and computational graph, and demonstrates forward and backward passes with code examples illustrating gradient computation and Jacobian‑vector products.
dl_inference is an open‑source inference platform that simplifies deployment of TensorFlow and PyTorch models in production, offering unified gRPC access, load‑balanced multi‑node serving, GPU/CPU options, customizable pre‑ and post‑processing, and extensible architecture for future AI workloads.
A detailed case study reveals why PyTorch training on UFS file storage suffers severe I/O bottlenecks, compares it with local SSD and SSHFS, and presents practical optimizations such as using cv2.imdecode, caching DataLoader handles, and converting small‑file datasets into large UFS files to close the performance gap.
This article explains the concept, mathematics, and practical PyTorch implementation of dilated (or atrous) convolutions, showing how to keep image dimensions unchanged while dramatically increasing the receptive field and discussing their advantages and typical applications.
The article presents a comprehensive overview of 58.com’s AI platform built on Kubernetes, detailing its layered architecture, resource scheduling, offline training pipelines, debugging environment, distributed TensorFlow/PyTorch training, performance benchmarks, and online inference services, highlighting how the system empowers various business units with scalable AI capabilities.
This article introduces ten essential Python libraries—TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano, and Pandas—detailing their features, typical use cases, and adoption in machine‑learning and data‑science projects, while highlighting each library's performance advantages, community support, and integration capabilities to help developers choose the right tool for their AI workflows.
This article provides an in‑depth overview of BERT, covering its NLP background, GLUE benchmark achievements, Transformer‑based architecture, pre‑training strategies (MLM and NSP), downstream fine‑tuning methods, and includes detailed PyTorch code implementations of its core components.
The article explains how transpose (de)convolution works as the spatial inverse of standard convolution, detailing its relationship to fully‑connected layers, padding, stride, output size formulas, odd‑case handling, and practical implementation in frameworks like PyTorch and TensorFlow.
This article reviews major deep‑learning object detection approaches—including one‑stage YOLO and SSD and two‑stage RCNN, Fast RCNN, and Faster RCNN—then provides a step‑by‑step explanation of Faster RCNN’s architecture, region‑proposal network, RoI pooling, loss functions, and sample PyTorch code.
This article introduces five highly rated Python libraries—PyWren, Tfdeploy, Luigi, Kubelib, and PyTorch—that streamline data handling, cloud execution, workflow orchestration, and GPU acceleration, helping machine‑learning engineers boost productivity and tackle complex projects more efficiently.
