DP(Data Parallelism):早期数据并行模式,一般采用参数服务器(Parameters Server)这一编程框架。实际中多用于单机多卡。DDP(Distributed Data Parallelism):分布式数据并行,采用Ring AllReduce的通讯方式,多用于多机多卡场景。 模型并行( model parallesim) 当模型参数过大,单个 GPU无法容纳模型参数时,就需要模型并行,将模...
model, eval_dataloader = accelerator.prepare(model, eval_dataloader) 注意事项: 不支持 DeepSpeed Pipeline Parallelism: 当前的集成不支持 DeepSpeed 的 Pipeline Parallelism(管道并行)。 不支持 mpu: 当前集成不支持 mpu,从而限制了在 Megatron-LM 中支持的张量并行性。 不支持多模型: 当前集成不支持多个模型。
Figure 1: Example 3D parallelism with 32 workers. Layers of the neural network are divided among four pipeline stages. Layers within each pipeline stage are further partitioned among four model parallel workers. Lastly, each pipeline is replicated across two data parallel instances, and ZeRO partiti...
model_compression add zeroquant-lkd example (deepspeedai#214) Nov 19, 2022 pipeline_parallelism add local rank explicitly for mpirun (deepspeedai#72) Dec 29, 2020 .gitignore Initial commit Jan 30, 2020 .pre-commit-config.yaml DeepSpeed 0.2 support (deepspeedai#21) May 15, 2020 CODEOWNERS ...
DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infini...
DeepSpeed provides memory-efficient data parallelism and enables training models without model parallelism. For example, DeepSpeed can train models with up to 6 billion parameters on NVIDIA V100 GPUs with 32GB of device memory. In comparison, existing frameworks (e.g., PyTorch's Distributed Data Pa...
which may be of broader interest to the deep learning (DL) community. As an example, we use it to trainZ-codeMoE, a production-quality, multilingual, and multitask language model with 10 billion parameters, achieving state-of-the-art results on machine translation and cross-lingual summa...
DeepSpeed version of NVIDIA's Megatron-LM that adds additional support for several features such as MoE model training, Curriculum Learning, 3D Parallelism, and others. The examples_deepspeed/ folder includes example scripts about the features supported by DeepSpeed....
In particular, we use the Deep Java Library (DJL) serving and tensor parallelism techniques from DeepSpeed to achieve under 0.1 second latency in a text generation use case with 6 billion parameter GPT-J. Complete example can be seen on our GitHub repository. Large...
In particular, we use the Deep Java Library (DJL) serving and tensor parallelism techniques from DeepSpeed to achieve under 0.1 second latency in a text generation use case with 6 billion parameter GPT-J. Complete example can be seen on our GitHub repository. Lar...