From efc193ad49bf06b78bc6fb8b47507df755aedd3c Mon Sep 17 00:00:00 2001 From: wyoung1 <1569907421@qq.com> Date: Sat, 21 Sep 2024 00:13:01 +0800 Subject: [PATCH] update framework image in proposal Signed-off-by: wyoung1 <1569907421@qq.com> --- ...rk Inference for High Mobility Scenarios.md | 8 ++++---- .../multi-edge-inference/images/framework.png | Bin 0 -> 183729 bytes .../images/new_process.png | Bin 141393 -> 0 bytes .../images/old_process.png | Bin 123789 -> 0 bytes .../multi-edge-inference/images/process.png | Bin 0 -> 63334 bytes 5 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 docs/proposals/algorithms/multi-edge-inference/images/framework.png delete mode 100644 docs/proposals/algorithms/multi-edge-inference/images/new_process.png delete mode 100644 docs/proposals/algorithms/multi-edge-inference/images/old_process.png create mode 100644 docs/proposals/algorithms/multi-edge-inference/images/process.png diff --git a/docs/proposals/algorithms/multi-edge-inference/Heterogeneous Multi-Edge Collaborative Neural Network Inference for High Mobility Scenarios.md b/docs/proposals/algorithms/multi-edge-inference/Heterogeneous Multi-Edge Collaborative Neural Network Inference for High Mobility Scenarios.md index 84037d3d..e33dfb29 100644 --- a/docs/proposals/algorithms/multi-edge-inference/Heterogeneous Multi-Edge Collaborative Neural Network Inference for High Mobility Scenarios.md +++ b/docs/proposals/algorithms/multi-edge-inference/Heterogeneous Multi-Edge Collaborative Neural Network Inference for High Mobility Scenarios.md @@ -52,9 +52,6 @@ def load(self, model_url=None): raise Exception(f"model url is None") ``` -The overall process is illustrated in the following diagram: -![old process](images/old_process.png) - Based on the above process analysis, we find that the existing multi-edge inference benchmarking job only uses Ianvs to create and manage test cases, where the core algorithmic processes such as multi-device parallelism and model partitioning are left to the user to implement. It is also worth mentioning that the nn.DataParallel(self.model) used in this case only achieves data parallelism, and for scenarios with low computing power on the edge and large models, relying solely on data parallelism is obviously insufficient to support edge inference needs. Therefore, this project needs to implement model parallel capabilities based on model partitioning and encapsulate these capabilities (partitioning and scheduling) into an function, separated from the user's code, as an optional feature in the multiedge_inference paradigm supported by Ianvs. ## Module Design and Code Integration @@ -110,7 +107,10 @@ Further, provide the load method of the BaseModel class in the benchmarking job At the same time, the corresponding multi-edge inference benchmarking job for high-mobility scenarios will be provided in the _examples_ folder. In conjunction with the process design, the newly added automatic partitioning module will be inserted into the position indicated in the diagram below, thereby forming a new complete flowchart: -![new process](images/new_process.png) +![process](images/process.png) + +The following diagram illustrates the framework of the entire system after the modifications: +![framework](images/framework.png) ## Method Desgin ![image](images/partition_method.png) diff --git a/docs/proposals/algorithms/multi-edge-inference/images/framework.png b/docs/proposals/algorithms/multi-edge-inference/images/framework.png new file mode 100644 index 0000000000000000000000000000000000000000..a00f91532b8900287cfcf7097abe12e53c5ab096 GIT binary patch literal 183729 zcmd43WmuNm7B)&shlEH8(jg_Cf`o*mbc#rKcM3>KNOyM#NT+~EcSuWjcb~~xVtn7; z`}{dS&blrx-UScuJLi}q?s1QM>MtYp0tFEd5ds1NMNCvk4gvzM83F=E4ju~pL_Vyr z90CHF&rncMModtUSjNg+&(K5{0z%Y3QVCA^RmX!QweV1W14x(*?->|KYRC-lekf4} 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