InfiniTensor · Chamberlain0w0 · Mar 16, 2026 · Mar 11, 2026 · Mar 11, 2026
diff --git a/docs/dashboard.md b/docs/dashboard.md
@@ -0,0 +1,147 @@
+# InfiniMetrics Dashboard User Guide
+## 1. Dashboard Overview
+
+InfiniMetrics Dashboard provides a unified interface to visualize benchmark and evaluation results of AI accelerators across the following scenarios:
+
+- Communication (NCCL / Collective Communication)
+
+- Training (Training / Distributed Training)
+
+- Inference (Direct / Service Inference)
+
+- Operator (Core Operator Performance)
+
+The benchmark framework produces two types of outputs:
+
+```
+JSON  -> configuration / environment / scalar metrics
+CSV   -> curves / time-series data
+```
+The Dashboard automatically loads test results and provides unified analysis capabilities, including:
+
+- un ID fuzzy search: locate specific test runs using partial Run IDs
+
+- General filters: filter results by framework, model, device count, etc.
+
+- Multi-run comparison: select multiple runs to compare performance
+
+- Performance visualization: display curves such as latency / throughput / loss
+
+- Statistics and configuration view: inspect throughput statistics, runtime configuration, and environment details
+
+For example, you can enter:
+```
+allreduce
+service
+```
+to perform fuzzy matching on Run IDs
+
+Example screenshot:
+![Run_ID research](https://private-user-images.githubusercontent.com/94775646/561443423-ac18e86d-b6e3-4ae5-a386-15d9cf34bf69.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.pOFgY_W8dwv-cMMSBP4yee_2l0we_iCL4jptlMLIsnk)
+## 2. Running the Dashboard
+### 2.1 Environment Requirements
+
+Before using the Dashboard, install the following dependencies:
+```
+streamlit
+plotly
+pandas
+```
+### 2.2 Start the Dashboard
+
+Run the following command in the project root directory:
+```
+python -m streamlit run dashboard/app.py
+```
+Access URL after startup:
+```
+Local URL:    http://localhost:8501
+Network URL:  http://<server-ip>:8501
+```
+Explanation:
+
+Local URL: accessible only on the local machine
+
+Network URL: accessible from other machines within the same network
+
+## 3. Communication Test Analysis
+
+Path:
+```
+Dashboard → Communication Performance Test
+```
+Supported features:
+```
+Bandwidth analysis curve - peak bandwidth
+
+Latency analysis curve - average latency
+
+Test duration
+
+GPU memory usage
+
+Communication configuration analysis
+```
+Example screenshot:
+![Communication Test](https://private-user-images.githubusercontent.com/94775646/561443439-b3e3f409-6bb9-4c32-bee8-9928781fcd39.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.AaFHdRt4SGyldFCZR1hNBhySRoR2cGDHQ0aAVNG4dq8)
+## 4. Inference Test Analysis
+
+Path:
+```
+Dashboard → Inference Performance Test
+```
+Modes:
+```
+Direct Inference
+Service Inference
+```
+Displayed metrics:
+```
+TTFT
+
+Latency
+
+Throughput
+
+GPU memory usage
+
+Inference configuration analysis
+```
+Example screenshot:
+![Inference Test](https://private-user-images.githubusercontent.com/94775646/561443450-9b57045c-d320-4da6-9b23-6e0f9a6c96cb.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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._LAZMfKkStSoWgw-1t22fOdlbkng-xZnV9rHIpd4v-M)
+## 5. Training Test Analysis
+
+Path:
+```
+Dashboard → Training Performance Test
+```
+Supported features:
+```
+Loss curve
+
+Perplexity curve
+
+Throughput curve
+
+GPU memory usage
+
+Training configuration analysis
+```
+Example screenshot:
+![Training Test](https://private-user-images.githubusercontent.com/94775646/561443390-5a789775-9fd4-4146-932d-df2436c20977.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.3YfafricSVbHzguSeDhhKiQ0S68AQDY48a1HRpMHTj4)
+## 6. Operator Test Analysis
+
+Path:
+```
+Dashboard → Operator Performance Test
+```
+Supported metrics:
+```
+latency
+
+flops
+
+bandwidth
+```
+Example screenshot:
+![Operator Test](https://private-user-images.githubusercontent.com/94775646/561443431-58d7f944-8e6b-4f2a-8fdc-671d09653da8.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.OpDxdAaEsdHYX_jOWNmmdPB-jKp92gbLh1VFs0H2j6c)
diff --git a/docs/zh/dashboard.md b/docs/zh/dashboard.md
@@ -0,0 +1,157 @@
+# InfiniMetrics Dashboard 使用指南
+
+## 1. Dashboard 简介
+
+InfiniMetrics Dashboard 用于统一展示 AI 加速卡在以下场景下的测试与评测结果
+
+- 通信（NCCL / 集合通信）
+- 训练（Training / 分布式训练）
+- 推理（Direct / Service 推理）
+- 算子（核心算子性能）
+
+测试框架输出两类数据：
+```
+JSON  -> 配置 / 环境 / 标量指标
+CSV   -> 曲线 / 时序数据
+```
+Dashboard 会自动加载测试结果，并提供统一的分析功能，包括：
+
+- Run ID 模糊搜索：支持通过部分 Run ID 快速定位测试运行
+
+- 通用筛选器：按框架、模型、设备数量等条件筛选
+
+- 多运行对比：同时选择多个测试运行进行性能对比
+
+- 性能可视化：展示 latency / throughput / loss 等性能曲线
+
+- 统计与配置展示：查看吞吐量统计、运行配置和环境信息
+
+例如可以输入：
+```
+allreduce
+service
+```
+对 Run ID 进行模糊匹配搜索
+
+示例截图：
+![run_id搜索](https://private-user-images.githubusercontent.com/94775646/561443423-ac18e86d-b6e3-4ae5-a386-15d9cf34bf69.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.pOFgY_W8dwv-cMMSBP4yee_2l0we_iCL4jptlMLIsnk)
+## 2. 运行 Dashboard 
+### 2.1 环境依赖
+使用 Dashboard 前需要安装以下依赖：
+```
+streamlit
+plotly
+pandas
+```
+### 2.2 启动 Dashboard
+在项目根目录执行：
+```
+python -m streamlit run dashboard/app.py
+```
+访问地址，启动成功后显示：
+```
+Local URL:    http://localhost:8501
+Network URL:  http://<server-ip>:8501
+```
+说明：
+
+Local URL：仅本机访问
+
+Network URL：同一网络内其他机器可访问
+
+## 3. 通信测试分析
+路径：
+
+```
+Dashboard → 通信性能测试
+```
+
+支持：
+```
+带宽分析曲线 - 峰值带宽
+
+延迟分析曲线 - 平均延迟
+
+测试耗时
+
+显存使用
+
+通信配置解析
+```
+
+示例截图：
+
+![通信测试](https://private-user-images.githubusercontent.com/94775646/561443439-b3e3f409-6bb9-4c32-bee8-9928781fcd39.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.AaFHdRt4SGyldFCZR1hNBhySRoR2cGDHQ0aAVNG4dq8)
+## 4. 推理测试分析
+
+路径：
+
+```
+Dashboard → 推理性能测试
+```
+
+模式：
+```
+Direct Inference
+Service Inference
+```
+展示指标：
+```
+TTFT
+
+Latency
+
+Throughput
+
+显存使用
+
+推理配置解析
+```
+示例截图：
+
+![推理测试](https://private-user-images.githubusercontent.com/94775646/561443450-9b57045c-d320-4da6-9b23-6e0f9a6c96cb.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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._LAZMfKkStSoWgw-1t22fOdlbkng-xZnV9rHIpd4v-M)
+
+## 5. 训练测试分析
+路径：
+
+```
+Dashboard → 训练性能测试
+```
+
+支持：
+```
+Loss 曲线
+
+Perplexity 曲线
+
+Throughput 曲线
+
+显存使用
+
+训练配置解析
+```
+示例截图：
+
+![训练测试](https://private-user-images.githubusercontent.com/94775646/561443390-5a789775-9fd4-4146-932d-df2436c20977.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.3YfafricSVbHzguSeDhhKiQ0S68AQDY48a1HRpMHTj4)
+
+## 6. 算子测试分析
+
+路径：
+
+```
+Dashboard → 算子性能测试
+```
+
+支持：
+```
+latency
+
+flops
+
+bandwidth
+```
+
+示例截图：
+
+![算子测试](https://private-user-images.githubusercontent.com/94775646/561443431-58d7f944-8e6b-4f2a-8fdc-671d09653da8.jpg?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.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.OpDxdAaEsdHYX_jOWNmmdPB-jKp92gbLh1VFs0H2j6c)
+