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Parallel SIG 聚焦于利用分布式并行技术加速AI大模型训练和推理。具体包括以下几点:
SIG为上述领域的专家、爱好者提供了一个交流、合作的平台。我们的愿景是打造根植于MindSpore的世界领先的分布式并行技术。Parallel SIG的目标有两个:
① 开源实习任务
时间:每季度一次
负责人:Maintainers、Committers
时间:下午3点
例会内容:①对齐SIG成员领取的开发任务进度;②SIG成员论文分享;③赋能
任务认领情况:
SIG/特性组 | 题目 | 分值 | 预期完成时间 | 开发语言 | 详情链接 | 任务状态 | 任务认领人 |
---|---|---|---|---|---|---|---|
Parallel SIG | 共享参数预训练初始化 | 50 | 2022-8-31 | C++、Python | 详情 | 未认领 | - |
Parallel SIG | 通过FFN近似计算实现Transformer网络训练加速 | 50 | 2022-8-31 | Python | 详情 | 未认领 | - |
Parallel SIG | 通过attention近似计算实现Transformer网络训练加速 | 50 | 2022-8-31 | Python | 详情 | 未认领 | - |
Parallel SIG | MoE(混合专家)中路由策略(optimal_transport路由策略)实现 | 50 | 2022-8-31 | Python | 详情 | 未认领 | - |
Parallel SIG | MoE(混合专家)中路由策略(hashing路由策略)实现 | 50 | 2022-8-31 | Python | 详情 | 未认领 | - |
Parallel SIG | MoE(混合专家)中路由策略(ntlb路由策略)实现 | 50 | 2022-8-31 | Python | 详情 | 未认领 | - |
负责人:Maintainers、Committers轮流组织。
轮值表:jiahongQian、Xiaoda、wangshengnan123、youhui
SIG的成员身份包括Members、Contributors、Maintainers,各角色的描述和申请条件如下:
MindSpore community
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