A deep learning platform has been developed to forecast the occurrence of the
low visibility events or hazes. It is trained by using multi-decadal daily
regional maps of various meteorological and hydrological variables as input
features and surface visibility observations as the targets. To better preserve
the characteristic spatial information of different input features for
training, two branched architectures have recently been developed for the case
of Paris hazes. These new architectures have improved the performance of the
network, producing reasonable scores in both validation and a blind forecasting
evaluation using the data of 2021 and 2022 that have not been used in the
training and validation.

开发了一个深度学习平台，用于预测低能见度事件或雾霾的发生。它通过使用多年来各种气象和水文变量的区域地图作为输入特征和地表能见度观测作为目标进行训练。为了更好地保留不同输入特征的特征空间信息，最近为巴黎雾霾开发了两种分支架构。这些新架构改进了网络性能，在验证和使用 2021 年和 2022 年的数据进行盲预测评估时产生了合理的得分，这些数据未用于训练和验证。

一种用不同特征空间尺度的气象图预测巴黎雾霾发生的分枝深度卷积网络

A Branched Deep Convolutional Network for Forecasting the Occurrence of  Hazes in Paris using Meteorological Maps with Different Characteristic  Spatial Scales

Swift for TensorFlow is a deep learning platform that scales from mobile
devices to clusters of hardware accelerators in data centers. It combines a
language-integrated automatic differentiation system and multiple Tensor
implementations within a modern ahead-of-time compiled language oriented around
mutable value semantics. The resulting platform has been validated through use
in over 30 deep learning models and has been employed across data center and
mobile applications.

Swift for TensorFlow 是一个深度学习平台，可在数据中心的硬件加速器集群从移动设备扩展。它结合了语言集成的自动微分系统和多个 Tensor 实现，这些实现是在面向可变值语义的现代预先编译语言中实现的。该平台已经通过在 30 多个深度学习模型中的使用得到了验证，并在数据中心和移动应用程序中得到了应用。

Swift for TensorFlow：深度学习的可移植、灵活平台

Swift for TensorFlow: A portable, flexible platform for deep learning

This paper presents Tofu, a system that partitions very large DNN models
across multiple GPU devices to reduce per-GPU memory footprint. Tofu is
designed to partition a dataflow graph of fine-grained tensor operators in
order to work transparently with a general-purpose deep learning platform like
MXNet. In order to automatically partition each operator, we propose to
describe the semantics of an operator in a simple language which represents
tensors as lambda functions mapping from tensor coordinates to values. To
optimally partition different operators in a dataflow graph, Tofu uses a
recursive search algorithm that minimizes the total communication cost. Our
experiments on an 8-GPU machine show that Tofu enables the training of very
large CNN and RNN models. It also achieves 25% - 400% speedup over alternative
approaches to train very large models.

Tofu 是一个系统，它可以将超大的 DNN 模型分割到多个 GPU 设备上以减少每个 GPU 的内存占用，该系统采用数据流图的方式透明地与 MXNet 等通用深度学习平台配合使用，通过一种描述算子语义的简单语言，实现自动划分。通过递归搜索算法来优化划分，并在 8-GPU 机器上的实验中展现了训练超大 CNN 和 RNN 模型的能力，在速度方面则比其他方法提高了 25% 至 400%。