diff --git a/specs/mvp/remain_problems.md b/specs/mvp/remain_problems.md new file mode 100644 index 0000000..44510aa --- /dev/null +++ b/specs/mvp/remain_problems.md @@ -0,0 +1,27 @@ + +# v3.6 +wandb 映射目录是/vol 固定问题： +查过官方文档/公开资料后结论是：wandb/local（W&B local server 容器）没有提供“把服务端持 + 久化根目录从 /vol 改成别的路径”的官方环境变量/启动参数。官方用法一直是假设你把持久化卷 + 挂到容器内的固定路径 /vol（例如 -v :/vol）。(github.com (https://github.com/ + wandb/server)) + + 需要注意区分两类“目录”： + + - 服务端（wandb/local 容器）：持久化目录是容器内固定 /vol，用于保存实例元数据、账号/初 + 始化信息等（license 也可以用 env 配，但数据目录仍是 /vol）。(github.com (https:// + github.com/wandb/server)) + - 训练侧（wandb Python SDK / VERL 任务）：WANDB_DIR、WANDB_DATA_DIR 等环境变量只影响“客 + 户端本地生成文件/缓存”，不改变服务端容器的数据落盘路径。(docs.wandb.ai (https:// + docs.wandb.ai/platform/hosting/env-vars)) + + 所以如果你现在的约束是“只能挂 ../../shared:/private，不能再额外挂 ../../shared/common/ + wandb:/vol”，要把 W&B 服务端数据落到 shared 下面，现实可行的路子是： + + - 自定义 W&B 容器 entrypoint（或 wrapper）在启动前做一次 ln -s /private/common/wandb / + vol（或 bind-mount 到 /vol），让服务仍然写 /vol，但实际落到 /private/common/wandb。 + 这属于“容器层改造”，不是 W&B 官方参数。 + + 如果你允许 compose 再加一条 volume，那最简单仍是：保留 ../../shared:/private，再额外 + 加 ../../shared/common/wandb:/vol（服务端就无需任何改造）。 + diff --git a/specs/mvp/sw_arch.excalidraw b/specs/mvp/sw_arch.excalidraw index 3504791..c026691 100644 --- a/specs/mvp/sw_arch.excalidraw +++ b/specs/mvp/sw_arch.excalidraw @@ -2398,8 +2398,8 @@ "type": "text", "x": 154.90147538843206, "y": 1942.0901138836757, - "width": 781.6194458007812, - "height": 50, + "width": 562.5595703125, + "height": 25, "angle": 0, "strokeColor": "#1e1e1e", "backgroundColor": "transparent", @@ -2413,20 +2413,20 @@ "index": "b02", "roundness": null, "seed": 1960456801, - "version": 746, - "versionNonce": 54710305, + "version": 748, + "versionNonce": 1287253379, "isDeleted": false, "boundElements": [], - "updated": 1766548609119, + "updated": 1767579887038, "link": null, "locked": false, - "text": "v3.5 Advanced task (customized param, reward function, verl version/code, etc) \n & resubmit from last checkpoint & ib network supporting & model serving", + "text": "v3.5 Advanced task (customized param, reward function）", "fontSize": 20, "fontFamily": 5, "textAlign": "left", "verticalAlign": "top", "containerId": null, - "originalText": "v3.5 Advanced task (customized param, reward function, verl version/code, etc) \n & resubmit from last checkpoint & ib network supporting & model serving", + "originalText": "v3.5 Advanced task (customized param, reward function）", "autoResize": true, "lineHeight": 1.25 }, @@ -2789,7 +2789,7 @@ "height": 55.000000000000014, "angle": 0, "strokeColor": "#1e1e1e", - "backgroundColor": "transparent", + "backgroundColor": "#ffc9c9", "fillStyle": "solid", "strokeWidth": 2, "strokeStyle": "solid", @@ -2800,8 +2800,8 @@ "index": "b0D", "roundness": null, "seed": 1968738561, - "version": 894, - "versionNonce": 2004715169, + "version": 895, + "versionNonce": 425863533, "isDeleted": false, "boundElements": [ { @@ -2809,7 +2809,7 @@ "id": 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"isDeleted": false, "boundElements": [], - "updated": 1766652382577, + "updated": 1767580421422, "link": null, "locked": false, "text": "ray worker\nnode", @@ -3954,8 +3954,8 @@ { "id": "Ov7Ctp7PvJITFoQy7Yb4E", "type": "rectangle", - "x": 354.00044019695827, - "y": 2639.9401270333396, + "x": 393.0812552422981, + "y": 4514.717667417468, "width": 147.4799234681481, "height": 55.000000000000014, "angle": 0, @@ -3971,8 +3971,8 @@ "index": "b0j", "roundness": null, "seed": 774741633, - "version": 988, - "versionNonce": 867953473, + "version": 1188, + "versionNonce": 630353635, "isDeleted": false, "boundElements": [ { @@ -3980,15 +3980,15 @@ "id": "hkWZM79d15pmmlJnpM5GC" } ], - "updated": 1766545237730, + "updated": 1767580421422, "link": null, "locked": false }, { "id": "hkWZM79d15pmmlJnpM5GC", "type": "text", - "x": 396.5104290916768, - "y": 2654.9401270333396, + "x": 435.59124413701664, + "y": 4529.717667417468, "width": 62.45994567871094, "height": 25, "angle": 0, @@ -4004,11 +4004,11 @@ 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TaskSpec / 任务语义 + +### 1.1 新增 Advanced TaskSpec（自定义 command） + +- 新增 `kind: advanced` 的 TaskSpec 类型： + - 用户可提交任意 bash `command`，不再局限于平台内置 PPO/GRPO/SFT 模板。 + - `workload` 不再要求用户填写，也不做 infer；平台内部统一按 `"advanced"` 做任务分类与 task_id 命名（避免未来训练类型扩展带来的限制）。 +- 支持 `$HOME` 宏替换（服务端提交前展开）： + - `$HOME` → `/private/users/` + - `$HOME/common/datasets` → `/private/datasets` + - `$HOME/common/hf` → `/private/hf` +- `command` 校验（best-effort，面向内部可信用户）： + - 要求包含 `python3 -m verl.`（允许 `verl.trainer.*` / `verl.model_merger` 等）。 + - 不做强沙箱；主要防止明显误用导致的不可预期行为。 + +### 1.2 Custom Reward（方式 A） + +- 平台不新增 reward 专用字段、不扩展 TaskSpec schema。 +- 用户通过在 `command` 里写 VERL 原生 overrides 来注入 reward： + - `custom_reward_function.path=...` + - `custom_reward_function.name=...` + - `custom_reward_function.reward_kwargs=...`（如需） +- 平台侧仅做： + - 基础路径/宏展开（$HOME） + - best-effort 的字符串校验（不做深度 AST 解析） + +## 2. WebUI（New Task 体验增强，仍兼容 YAML） + +- `New Task` 页面新增 **YAML 模式 / 表单模式**切换： + - 表单模式只覆盖 **5 个模板**：PPO / GRPO / SFT / Advanced / Model Merge。 + - 表单模式实时生成 YAML 预览；Submit 时提交生成 YAML；可一键切回 YAML 模式继续手工编辑。 +- `Advanced example`： + - 示例命令改为多行、可读性更好。 + - 补齐 PPO 常见 fail-fast 所需的关键 overrides（例如 actor micro batch），避免用户“照抄即失败”。 +- 新增 `Model merge example`（Advanced command 形式）： + - 使用 `python3 -m verl.model_merger merge ...` + - 支持用 `$HOME/jobs//...` 访问训练产物目录。 + +## 3. SFTPGo / common 目录可读性（配合 v3.5 的 $HOME/common 语义） + +> 这些变更主要用于保证 v3.5 所定义的 `$HOME/common/{datasets,hf}` 语义在 SFTPGo WebClient/客户端下可用。 + +- `/common/datasets` 与 `/common/hf` 作为 SFTPGo virtual folders 暴露为只读共享目录： + - 允许 list + download（用于浏览与下载/查看内容；仍不允许 upload/rename/delete）。 + - 权限规则覆盖到子路径（避免“能进目录但文件不可读”的情况）。 +- API 调用 SFTPGo admin API 的连通性增强： + - dev 环境下避免依赖容器内 DNS（部分 head 容器环境存在临时解析失败），改为通过 docker bridge 网关 + 映射端口访问 admin API。 +- API 启动脚本确保注入 `SFTPGO_ADMIN_PASSWORD`（与 compose 默认值保持一致），避免 Reset Password 走到 401。 + +## 4. 兼容性与行为变化 + +- **完全兼容 v3.0 的 PPO/GRPO/SFT TaskSpec YAML**（原有字段与提交方式不变）。 +- 新增能力不会影响 ray/node management（仍按 v3.0：head 发布 discovery、worker watchdog join/self-heal）。 +- Advanced 任务不会进入 PPO/GRPO/SFT 的语义约束；平台仅负责： + - 资源字段（`nnodes` / `n_gpus_per_node`）用于队列调度与提交 gate + - 将 `command` 作为 Ray job entrypoint 执行 + +## 5. 已知限制（v3.5 不做） + +- 不提供“可视化” reward 配置面板（仅方式 A：用户自己写 command）。 +- 不支持 per-job 自定义 verl 代码快照/多版本共存（本轮不做 code_path 选择）。 +- 不支持断点续训一键 resubmit / IB(RDMA) / model serving（按 roadmap 后续版本推进）。 + diff --git a/specs/mvp/v3.6/README.md b/specs/mvp/v3.6/README.md new file mode 100644 index 0000000..fc220f4 --- /dev/null +++ b/specs/mvp/v3.6/README.md @@ -0,0 +1,9 @@ +# MVP v3.6 + +本目录包含 v3.6 的需求与设计： + +- `Snipaste_2026-01-05_10-56-34.png`：v3.6 架构草图（在 v3.5 基础上增加 Weights & Biases；其余模块保持不变） +- `requirements.md`：需求要点（W&B + Evaluation 模板） +- `wandb.md`：W&B local server 的前期调研与资料（license、部署方式、VERL 配置要点等） +- `v3.6_design.md`：v3.6 详细设计方案（基于 v3.5） +- `v3.6_progress.md`：v3.6 里程碑进度记录 diff --git a/specs/mvp/v3.6/Snipaste_2026-01-05_10-56-34.png b/specs/mvp/v3.6/Snipaste_2026-01-05_10-56-34.png new file mode 100644 index 0000000..8e889e2 Binary files /dev/null and b/specs/mvp/v3.6/Snipaste_2026-01-05_10-56-34.png differ diff --git a/specs/mvp/v3.6/requirements.md b/specs/mvp/v3.6/requirements.md new file mode 100644 index 0000000..34d8e08 --- /dev/null +++ b/specs/mvp/v3.6/requirements.md @@ -0,0 +1,3 @@ +1. 增加wandb功能 +2. 增加evaluation 模板 + diff --git a/specs/mvp/v3.6/v3.6_design.md b/specs/mvp/v3.6/v3.6_design.md new file mode 100644 index 0000000..2af592a --- /dev/null +++ b/specs/mvp/v3.6/v3.6_design.md @@ -0,0 +1,280 @@ +# MVP v3.6 详细设计方案（基于 v3.5） + +> 设计基线：当前线上已具备 v3.5 能力（PPO/GRPO/SFT + Advanced TaskSpec + SFTPGo 数据管理 + WebUI）。 +> v3.6 的架构草图见：`specs/mvp/v3.6/Snipaste_2026-01-05_10-56-34.png` + +## 0. 目标与范围 + +### 0.1 v3.6 目标 + +1) **Weights & Biases（W&B）集成** +- 训练/任务运行时自动打点到 W&B local server。 +- 采用“共享 W&B 账号（license 只支持 1 user） + 为每个 MVP 用户创建独立 project”的方式隔离可视化与检索体验。 +- 平台提供 W&B 的跳转链接、以及每个 task 对应 run 的可定位信息（最小闭环）。 + +2) **New Task 增加 Evaluation 模板** +- 在 New Task 页面提供一个最小可用的 “Evaluation” 任务模板（用于离线评估/打分），并能把结果落到 job 输出与（可选）W&B。 + +### 0.2 非目标（v3.6 不做） + +- 不引入新的 node management 机制；保持 v3.5 的 head discovery + worker watchdog stateless pool。 +- 不做 Serving/IB/RDMA/断点续训/多版本 verl code_path 等（仍按 v3.5 的范围）。 +- 不做多租户安全隔离（W&B API Key 注入属于内部可信环境）。 + +--- + +## 1. W&B local server（部署与连通） + +> 资料参考：`specs/mvp/v3.6/wandb.md`。**文档中 license/token 属敏感信息，v3.6 设计不在代码/文档里写死。** + +### 1.1 部署方式（dev/h1） + +建议在 `src/mvp/docker-compose.yaml` 新增 `wandb` 服务（与现有 ray_head / sftpgo 同一 compose network）： + +- 镜像：`wandb/local:latest` +- 容器端口：`8080` +- 宿主机端口：建议 `8090:8080`（避免和 MVP API `8080`、SFTPGo `8081` 冲突） +- 持久化：挂载到 NFS/共享目录（例如 `/private/common/wandb`）以持久化 runs/artifacts +- 首次启动后由管理员在 W&B System Admin 页面粘贴 license（见 `wandb.md`） + +### 1.1.1 持久化策略（必须） + +v3.6 约定 **W&B server 的元数据必须持久化**（否则会丢 license/账号/API key、历史 runs/project 索引等）： + +- W&B server 数据目录（`/vol`）挂载到共享目录（NFS）：例如 `/private/common/wandb:/vol` +- 这部分数据由平台/管理员长期保留（不跟随单个 job 清理） + +与之相对，**每个 Ray job 对应的本地 W&B run 文件**放在 job 目录下（见 §2.3 的 `WANDB_DIR`），并由现有 janitor 随 job 一起清理： + +- `WANDB_DIR=/private/users//jobs//wandb` +- janitor 对 job 的策略是“结束后 3 天移入回收站、7 天后删除”，因此该目录会被一并处理 + +> 说明：W&B 的最终“事实数据”在 server 侧持久化（runs/metrics/可视化）；job 目录下的 `WANDB_DIR` 更像运行期缓存/临时文件与调试材料。 + +### 1.2 容器内访问 W&B 的 base_url + +在 v3.5 经验中，Ray head 容器对 docker 内部 DNS 名称解析不稳定（`Temporary failure in name resolution`）。 +为避免再次踩坑，v3.6 统一采用 **“docker bridge 网关 + host 映射端口”** 的方式让容器访问 W&B： + +- `WANDB_BASE_URL=http://172.22.0.1:8090`（其中 `172.22.0.1` 为 `mvp_argus-ray-net` 网关） + +> 注意：如果未来 dev 环境 network 网段变化，需要把网关地址做成配置项（见 §2）。 + +--- + +## 2. 平台侧 W&B 集成（API/Scheduler/Ray Job runtime_env） + +### 2.1 配置设计（YAML） + +在 `configs/dev.yaml`（以及生产配置）中新增一段 W&B 配置，建议结构： + +```yaml +tracking: + wandb: + enabled: true + base_url: "http://172.22.0.1:8090" + api_key_env: "WANDB_API_KEY" + entity: "" # 可选；verL 通过 env WANDB_ENTITY 读取 + project_suffix: "_project" # 例如 alice_project + # 可选：代理（verL 支持 trainer.wandb_proxy） + proxy: null +``` + +平台读取 `api_key_env` 对应的环境变量，并在 job 维度注入 `WANDB_API_KEY`。 +**不在配置文件中存明文 API KEY**（避免泄露）。 + +### 2.2 项目/命名规范（共享账号 + user project） + +由于 W&B local license 限制 “最多 1 user”，v3.6 采用： + +- **同一个 W&B 账号**（同一个 `WANDB_API_KEY`） +- 每个 MVP user 使用不同 project： + - `project_name = + project_suffix` + - 示例：`alice_project` +- 每个 Ray job attempt 对应一个 run： + - `experiment_name = `（保证 attempt 唯一；也便于从 dashboard 反查） + +verL 内部 `wandb.init(project=project_name, name=experiment_name, entity=$WANDB_ENTITY)`（见 `verl/utils/tracking.py`）。 + +### 2.3 Ray Job 注入（runtime_env env_vars） + +当 `tracking.wandb.enabled=true` 时，在 scheduler 提交 ray job 时，统一注入： + +- `WANDB_BASE_URL`（来自配置） +- `WANDB_API_KEY`（来自 env `tracking.wandb.api_key_env`） +- `WANDB_ENTITY`（可选） +- `WANDB_MODE=online`（默认在线；可在 dev/离线时切换） +- `WANDB_DIR=/private/users//jobs//wandb`（保证每个 job 的本地 run 文件落到对应 job 目录；便于随 job 一起被 janitor 清理） + +> 对 Advanced TaskSpec：平台无法替用户改 command，但仍可注入上述 env，让用户在 command 中自行启用 wandb。 + +### 2.4 verL 训练任务自动开启 wandb logger + +对平台内置 workload（PPO/GRPO/SFT），平台将 hydra overrides 改为： + +- `trainer.logger=[console,wandb]`（替代 v3.5 的 `trainer.logger=console`） +- `trainer.project_name=_project` +- `trainer.experiment_name=` +- 可选：如果配置了 `tracking.wandb.proxy`，注入 `trainer.wandb_proxy=...` + +兼容性说明： +- `trainer.logger` 在 verL 的 ppo/sft config 中本身是 list（示例为 `["console","wandb"]`），因此不会破坏 verL 配置解析。 +- 现有 v3.5 的 checkpoint/log dir 仍按 job_dir 注入，不依赖 `trainer.default_local_dir`。 + +### 2.5 数据库存储与 API 输出（最小闭环） + +v3.6 需要让用户能在 WebUI/Task 详情中 “点一下跳转到 W&B”： + +建议在 DB（sqlite）里新增 attempt 级字段（或 metadata JSON）： + +- `wandb_project`：如 `alice_project` +- `wandb_run_name`：如 `` +- `wandb_base_url`：如 `http://:8090` +- `wandb_url`：拼出来的最终链接（若 W&B local 的 URL 结构不稳定，可只存前 3 项，由前端拼接） + +API 侧在： +- `GET /api/v2/tasks/` 的 `latest_attempt` 增加 `wandb` 字段（或顶层增加 `tracking` 字段） +- `GET /api/v2/me` 增加 `wandb` 信息（base_url、project_name），便于页面展示 “Open W&B” + +> W&B local 的 URL 路径结构需要在接入时确认；若不确定，先只提供 base_url + project/run 名称，用户可在 W&B UI 搜索。 + +--- + +## 3. WebUI 变更（v3.6） + +### 3.1 Data / Login 页 + +- 在 Data 或 Login 页新增： + - “Open W&B” 链接（跳转到 `tracking.wandb.base_url` 的 Web UI） + - 展示当前用户的 project 名称（例如 `alice_project`），并提供 Copy 按钮 + +### 3.2 Tasks / Task Detail + +- Task list 无需大改；Task detail 页面增加： + - W&B run 信息（project / run name / link） + - 若任务失败，W&B 仍可用于查看已上报的中间日志（对训练类任务有价值） + +### 3.3 New Task 增加 Evaluation 模板 + +New Task 页面新增一个 “Evaluation example”： + +#### 方案 A（推荐，最小改动）：作为 Advanced TaskSpec 模板 + +- 直接提供 `kind: advanced` 的模板，command 运行 verL 自带评估入口： + - `python3 -m verl.trainer.main_eval data.path=... custom_reward_function.path=... +ray_kwargs.ray_init.address=auto` +- 优点：不需要扩展 TaskSpec schema / scheduler 逻辑 +- 缺点：Evaluation 在平台侧不可结构化（仍属于 advanced） + +#### 方案 B（更产品化）：新增内置 workload: evaluation（后续可选） + +若希望在任务分类/队列中把 evaluation 作为一类“内置任务”，可新增： + +```yaml +workload: evaluation +code_path: /private/common/code/verl/verl_repo +nnodes: 1 +n_gpus_per_node: 0 +data_path: $HOME/common/datasets/<...>.parquet +response_key: responses +data_source_key: data_source +reward_model_key: reward_model +custom_reward_path: $HOME/code/reward.py # 可选 +custom_reward_name: compute_score # 可选 +``` + +平台把它编译成 `verl.trainer.main_eval` 的 hydra overrides + ray init address；并可选择把结果解析后上报 W&B。 + +v3.6 建议先落地 **方案 A**（模板即可），方案 B 作为 v3.7+ 的演进。 + +--- + +## 4. 验收标准（v3.6） + +### 4.1 W&B（训练任务） + +- 提交 PPO/GRPO/SFT 任一任务： + - W&B local server 能看到对应 project（如 `alice_project`） + - 能看到 run 名称为该任务 attempt 的 `ray_submission_id` + - run 内能持续刷新 metrics（至少包含 loss/step 等 verL 默认上报） +- WebUI： + - 能打开 W&B UI 链接 + - Task detail 能展示 project/run（或至少可检索信息） + +### 4.2 Evaluation 模板 + +- New Task 中出现 “Evaluation example” +- 复制模板提交后： + - 任务能在 Ray 上运行（CPU 即可，`+ray_kwargs.ray_init.address=auto` 连接集群） + - 输出 metrics（`main_eval.py` 默认 print dict） + - （可选）若用户在 command 里启用 wandb，则能在对应 project 下看到评估 run + +--- + +## 5. 兼容性影响评估 + +- 对现有 v3.5 功能： + - 默认行为改变：PPO/GRPO/SFT 会默认多一个 logger（wandb），并对外发起 HTTP 请求到 W&B server。 + - 若 W&B server 不可用/配置缺失： + - 建议行为：平台自动降级为 `trainer.logger=[console]` 并在 task 状态中给出 warning（避免训练直接失败）。 + - 初版也可选择 fail-fast：缺少 `WANDB_API_KEY` 时拒绝开启 wandb（由配置开关控制）。 +- 对资源/存储： + - W&B server 自身会写入一定量数据（license 限制 10GB）；需配合 retention 策略做清理（v3.6 先手动，后续可自动）。 + - job 目录下的 `WANDB_DIR` 会随 jobs retention 被清理；不会无限增长。 + +--- + +## 6. 已确认的落地约定 + +- W&B server 对外端口：`8090` +- Project 命名：`_project`（不使用 `WANDB_ENTITY`） +- Evaluation：先只提供 **Advanced 模板**（New Task 页面提供示例即可） + +--- + +## 7. 运维与验收流程（dev/h1） + +### 7.1 启动服务（docker compose） + +1) 启动/重启 compose（Ray head/worker + SFTPGo + W&B）： + +```bash +cd /home2/argus/infra/mvp/src/mvp +docker compose up -d +``` + +2) 访问 UI： +- MVP WebUI：`http://:8080/ui` +- Ray Dashboard：`http://:8265` +- SFTPGo Web：`http://:8081/web/` +- W&B Web：`http://:8090` + +> W&B 容器数据目录已挂载到共享盘：`/private/common/wandb`（容器内 `/vol`）。 + +### 7.2 初始化 W&B（管理员一次性操作） + +1) 打开 `http://:8090/system-admin` 粘贴 license（详见 `specs/mvp/v3.6/wandb.md`）。 +2) 进入 W&B UI 创建/登录到共享账号（license 只支持 1 user）。 +3) 在 W&B UI 的用户设置页面生成 API Key（通常位于 “User Settings / API Keys”）。 + +### 7.3 启动 API server（需要注入 WANDB_API_KEY） + +平台不会把 `WANDB_API_KEY` 写入配置文件；必须在启动 API server 时通过环境变量提供。 + +示例（在宿主机）： + +```bash +export MVP_INTERNAL_TOKEN="my-dev-token" +export WANDB_API_KEY="...从 W&B UI 复制..." +cd /home2/argus/infra/mvp/src/mvp/scripts +./60_start_api.sh +``` + +> 说明：`./60_start_api.sh` 会把 `WANDB_API_KEY` 透传给 head 容器内运行的 API server。 + +### 7.4 验收（最小闭环） + +1) 在 WebUI Login 页面能看到 W&B 区块（Open W&B + project copy）。 +2) 提交一个 PPO/GRPO/SFT 任务（任意一个即可）： + - W&B project 为 `_project`（如 `alice_project`） + - run name 为该 attempt 的 `ray_submission_id` +3) 提交 Evaluation 模板（Advanced）能在 Ray 上运行并输出评估结果（stdout / logs）。 diff --git a/specs/mvp/v3.6/v3.6_dev_plan.md b/specs/mvp/v3.6/v3.6_dev_plan.md new file mode 100644 index 0000000..d0fa1e3 --- /dev/null +++ b/specs/mvp/v3.6/v3.6_dev_plan.md @@ -0,0 +1,194 @@ +# MVP v3.6（基于 v3.5）开发计划（TDD） + +> 设计依据：`specs/mvp/v3.6/v3.6_design.md` +> 本计划默认已确认： +> 1) W&B host 端口：`8090`；2) project：`_project`；3) evaluation 先做 Advanced 模板；4) 不使用 `WANDB_ENTITY`。 + +## 总体原则 + +- **TDD**：每个里程碑先补齐单测（或契约测试）再实现功能；保证覆盖率门槛不回退。 +- **最小闭环**：先做到“可用+可验证”，再做体验优化；不做超出 v3.6 scope 的扩展。 +- **配置不落盘敏感信息**：`WANDB_API_KEY` 只能来自运行环境变量，不写入仓库配置文件。 + +--- + +## Milestones + +### M1：配置层（tracking.wandb）与解析 + +**目标** +- 在服务配置中新增 `tracking.wandb`，支持开关、base_url、api_key_env。 +- 不引入 `WANDB_ENTITY`（保持为空即可）。 + +**开发任务** +- 在 `src/mvp/py/argus/service/config.py`： + - 新增 `TrackingConfig/WandbConfig` dataclass（或等价结构）。 + - `V2Config.from_root_dict()` 解析 `tracking.wandb.*`（缺省为 disabled）。 + - 校验：`enabled=true` 时 `base_url` 不能为空；`api_key_env` 默认 `WANDB_API_KEY`。 + +**测试（先写）** +- `test_config_parses_wandb_defaults`：没有 tracking 字段时，默认 disabled。 +- `test_config_parses_wandb_enabled`：enabled=true 能读到 base_url/api_key_env。 +- `test_config_rejects_empty_base_url_when_enabled`：enabled=true 且 base_url 空时报错（或记录 warning，取决于实现选择）。 + +**验收** +- 仅通过 config 即能决定是否启用 W&B，且不会破坏 v3.5 现有配置解析。 + +--- + +### M2：Ray Job runtime_env 注入（WANDB_* env） + +**目标** +- 当 `tracking.wandb.enabled=true` 时：平台在 job 粒度注入 `WANDB_BASE_URL/WANDB_API_KEY/WANDB_MODE/WANDB_DIR` 等 env。 +- `WANDB_API_KEY` 从 API server 进程环境变量中读取：`os.environ[api_key_env]`。 + +**开发任务** +- 在 scheduler / ray job builder（`src/mvp/py/argus/service/scheduler.py` 或 `src/mvp/py/argus/ray/builders.py`）： + - 构造 job runtime_env.env_vars 的 merge 逻辑： + - 现有 `ray.runtime_env.env_vars` 为基础； + - 追加 W&B env（不覆盖用户显式指定的同名变量，或按“平台优先”策略二选一并写清楚）。 + - 注入： + - `WANDB_BASE_URL=` + - `WANDB_API_KEY=` + - `WANDB_MODE=online` + - `WANDB_DIR=/private/users//jobs//wandb`（本地 run 文件随 job 一起由 janitor 清理） + +**测试（先写）** +- `test_scheduler_injects_wandb_env_when_enabled`： + - mock env 中存在 `WANDB_API_KEY`； + - 提交一个内置任务（ppo/sft 任意），断言构造出来的 runtime_env 含以上 env_vars。 +- `test_scheduler_sets_wandb_dir_under_job_dir`： + - 断言 `WANDB_DIR` 位于该 attempt 的 job 目录下（而不是 common 目录），避免无法跟随 job retention 清理。 +- `test_scheduler_does_not_inject_wandb_env_when_disabled`。 +- `test_scheduler_wandb_missing_api_key_behaviour`： + - enabled=true 但缺少 env 时的行为： + - 方案 A（推荐）：**自动降级**为 console（不注入 wandb），并在 task/attempt message 记录 warning； + - 或方案 B：fail-fast（返回 500/400）。 + - 需在实现前确认采用哪种策略；建议 v3.6 选 A 提升可用性。 + +**验收** +- 任意内置任务提交后，在 Ray job runtime_env 中能看到 `WANDB_*`。 + +--- + +### M3：内置训练任务自动开启 wandb logger（PPO/GRPO/SFT） + +**目标** +- 当 W&B enabled 时，平台默认把内置训练任务改成 `trainer.logger=["console","wandb"]`，并设置 project/run 命名。 + +**开发任务** +- 在 job 构建（PPO/GRPO/SFT 的 overrides 生成处）： + - 将 `trainer.logger` 从 `console` 改为 list：`[console,wandb]`（hydra 语法按现有实现方式拼接）。 + - `trainer.project_name=_project` + - `trainer.experiment_name=` + - 保持 v3.5 的 job_dir/checkpoint/log dir 注入方式不变。 + +**测试（先写）** +- `test_job_overrides_include_wandb_logger_when_enabled`： + - 断言 entrypoint/overrides 包含 `trainer.logger=[console,wandb]`（或等价写法）。 + - 断言包含 `trainer.project_name=_project`、`trainer.experiment_name=`。 +- `test_job_overrides_keep_console_only_when_wandb_disabled_or_missing_key`。 + +**验收** +- 训练任务 run 会自动出现在 W&B 对应 project 下（E2E 验证在 M6）。 + +--- + +### M4：API 输出与 WebUI 链接（最小闭环） + +**目标** +- 用户可以在 UI 里“知道去哪看 W&B”，以及知道本 task 对应哪个 project/run。 + +**开发任务** +- API： + - `GET /api/v2/me` 增加 `wandb` 信息（仅当 enabled 时返回）： + - `base_url` + - `project_name`（`_project`） + - `GET /api/v2/tasks/{task_id}`（或 attempt 结构）增加 `wandb_project` / `wandb_run_name`（run_name=ray_submission_id）。 +- WebUI： + - Login/Data 页增加 “Open W&B” 链接（跳 `base_url`），展示 project_name + Copy。 + - Task detail 增加 wandb 字段展示（project/run/可点击链接或可复制文本）。 + +**测试（先写）** +- `test_me_includes_wandb_when_enabled`（mock config + env）。 +- `test_task_detail_contains_wandb_fields_when_enabled`（mock task/attempt）。 +- `test_ui_contains_wandb_link`（渲染 HTML 断言包含 base_url/project_name 字样）。 + +**验收** +- WebUI 能一键跳转 W&B；Task detail 能定位到 run。 + +--- + +### M5：New Task 增加 Evaluation 模板（Advanced） + +**目标** +- New Task 页面增加一个 Evaluation 模板按钮/示例（先按 Advanced TaskSpec 提供）。 + +**开发任务** +- 在 `src/mvp/py/argus/service/ui.py`： + - YAML 模式增加 “Evaluation example”。 + - 表单模式本轮可选（不要求）： + - 如果要支持：把 evaluation 作为 advanced 模板的一种预填 command（仍 `kind: advanced`）。 +- 模板建议使用 verL 自带入口： + - `python3 -m verl.trainer.main_eval ... +ray_kwargs.ray_init.address=auto` + - `data.path=$HOME/common/datasets/...`（按 v3.5 的宏规则） + +**测试（先写）** +- `test_ui_new_task_contains_evaluation_example`：断言页面包含 `main_eval` 与关键字段。 + +**验收** +- 用户复制 evaluation 模板可提交成功并在 Ray 上运行（E2E 在 M6）。 + +--- + +### M6：端到端（dev/h1）部署与验收流程 + +> 里程碑 M6 以脚本/手工步骤为主；不强制写 e2e 自动化测试。 + +**部署任务** +- compose 增加 `wandb` 服务： + - `wandb/local:latest` + - host 端口 `8090:8080` + - 数据挂载到 `/private/common/wandb:/vol` 持久化（W&B 元数据/账号/API key/历史 runs） +- API 启动方式： + - 在宿主机 export：`WANDB_API_KEY=<从 W&B UI 生成的 key>` + - 启动 API（确保 env 透传到容器内） +- 首次初始化： + - 打开 `http://:8090/system-admin` 粘贴 license（管理员操作） + +**验收用例** +1) **训练类任务自动打点** + - 用 alice 提交一个 SFT（或 PPO）任务（内置 workload） + - 在 W&B UI 中看到 project：`alice_project` + - run name 为 `ray_submission_id`，metrics 可见 +2) **Advanced task 可手动打点** + - 提交一个 Advanced（用户自己写 command）并在 command 中启用 `trainer.logger=["console","wandb"]`（如需） + - 确认 env 注入生效（W&B 记录出现） +3) **Evaluation 模板** + - 用 New Task 的 Evaluation example 提交 + - 任务成功运行并输出 metrics（stdout/logs） + - （可选）如果 evaluation 也启用 wandb，则能出现在对应 project 下 + +**回归** +- v3.5 的三类任务（ppo/grpo/sft）在 W&B disabled 或缺少 key 时仍可跑通（至少 console 输出不受影响）。 + +**Retention 联动检查** +- 提交一个短任务生成 `WANDB_DIR`，结束后确认： + - `WANDB_DIR` 位于 `/private/users//jobs//wandb` + - janitor 运行后该目录会随 job 一起进入 trash / 被 purge（与 jobs retention 一致） + +--- + +## 交付物清单（v3.6） + +- 文档： + - `specs/mvp/v3.6/v3.6_design.md`（已存在，必要时补充操作流程） + - `specs/mvp/v3.6/v3.6_dev_plan.md`（本文） +- 代码（预期变更点）： + - `src/mvp/py/argus/service/config.py` + - `src/mvp/py/argus/service/scheduler.py` / `src/mvp/py/argus/ray/builders.py` / `src/mvp/py/argus/ray/ray_job_tool.py` + - `src/mvp/py/argus/service/app.py`（/me 与 task detail 输出） + - `src/mvp/py/argus/service/ui.py`（Open W&B + Evaluation template） + - `src/mvp/docker-compose.yaml`（wandb service） + - `src/mvp/configs/dev.yaml`（tracking.wandb 配置） + - `src/mvp/scripts/*`（API 启动时 env 透传，必要时补充） diff --git a/specs/mvp/v3.6/v3.6_progress.md b/specs/mvp/v3.6/v3.6_progress.md new file mode 100644 index 0000000..7f681c8 --- /dev/null +++ b/specs/mvp/v3.6/v3.6_progress.md @@ -0,0 +1,42 @@ +# MVP v3.6 进度记录 + +> 基线：v3.5 已完成（Advanced TaskSpec + Custom reward（方式A）+ WebUI + SFTPGo + stateless ray node pool）。 +> 本文件用于记录 v3.6 每个 milestone 的完成情况与关键改动点。 + +## M1（完成） + +- 新增 `tracking.wandb` 配置解析与校验（enabled/base_url/api_key_env）。 + +## M2（完成） + +- Ray job 维度注入 `WANDB_*` env（含 `WANDB_BASE_URL/WANDB_API_KEY/WANDB_MODE/WANDB_DIR`），缺少 key 时降级并记录 warning。 + +## M3（完成） + +- PPO/GRPO/SFT 内置训练任务在 wandb 可用时自动追加 overrides： + - `trainer.logger=[console,wandb]` + - `trainer.project_name=_project` + - `trainer.experiment_name=` + +## M4（完成） + +- API 输出增加 W&B 定位信息： + - `/api/v2/me` 返回 `wandb.{enabled,base_url,project_name}` + - `/api/v2/tasks/{task_id}` 在 `latest_attempt.wandb` 返回 `{base_url,project_name,run_name}` +- WebUI： + - Login 页面增加 W&B 区块（跳转 8090、copy project） + - Task detail 页面增加 W&B 区块（copy run） + +## M5（完成） + +- WebUI New Task 增加 Evaluation 模板（Advanced）： + - 使用 `python3 -m verl.trainer.main_eval ... +ray_kwargs.ray_init.address=auto` + - 以占位符路径示例（用户替换 `/`） + +## M6（完成） + +- `docker-compose.yaml` 集成 W&B local server： + - host 端口 `8090` + - 持久化目录 `/private/common/wandb`（容器内 `/vol`） +- dev 配置新增 `tracking.wandb` 默认开启（缺 key 自动降级并记录 warning）。 +- API 启动脚本支持把 `WANDB_API_KEY` 从宿主机透传到 head 容器中的 API server。 diff --git a/specs/mvp/v3.6/v3.6_summary.md b/specs/mvp/v3.6/v3.6_summary.md new file mode 100644 index 0000000..818bb4d --- /dev/null +++ b/specs/mvp/v3.6/v3.6_summary.md @@ -0,0 +1,126 @@ +# MVP v3.6 迭代研发总结（基于 v3.5） + +> 时间基线：2026-01（H20 dev 环境：`argus@h1:/home2/argus/infra/mvp`） +> v3.6 架构草图：`specs/mvp/v3.6/Snipaste_2026-01-05_10-56-34.png` + +## 1. 迭代目标回顾 + +v3.6 在 v3.5（WebUI + API server + Ray stateless node pool + SFTPGo + Advanced TaskSpec）基础上，新增两块能力： + +1) **Weights & Biases（W&B）local server 集成** +- 训练任务（PPO/GRPO/SFT）默认可写入 W&B。 +- 采用“共享 W&B 账号 + 按用户拆分 project（`_project`）”的隔离策略。 + +2) **New Task 增加 Evaluation 示例** +- New Task 页面新增一个最小可用的 evaluation 模板（以 Advanced command 方式运行 `verl.trainer.main_eval`）。 + +## 2. 交付内容（代码/配置/脚本） + +### 2.1 部署形态（docker compose） + +v3.6 在 `src/mvp/docker-compose.yaml` 新增 W&B 服务： + +- 服务名：`wandb`（容器名：`argus-wandb`） +- 宿主机端口：`8090:8080` +- 持久化：`../../shared/common/wandb:/vol` +- 同 network：`argus-ray-net`（便于 Ray 容器内访问） + +### 2.2 平台配置（YAML） + +在 `src/mvp/configs/dev.yaml` 增加/启用 W&B 配置： + +```yaml +tracking: + wandb: + enabled: true + base_url: "http://172.22.0.1:8090" + api_key_env: "WANDB_API_KEY" + project_suffix: "_project" +``` + +说明： +- `base_url` 采用 docker bridge 网关 + 宿主机映射端口的方式，规避容器内 DNS 偶发解析失败问题。 +- 不在 config 中写明文 key，统一通过启动 API server 时注入 `WANDB_API_KEY`。 + +### 2.3 Ray Job runtime_env 注入（核心） + +v3.6 在**每个 Ray job attempt**提交时注入两类环境变量： + +1) **始终注入（无论是否启用 W&B）**：便于 Advanced command 在不改模板的情况下能运行 +- `MVP_TRAINER_LOGGER`：`console` 或 `[console,wandb]` +- `MVP_WANDB_PROJECT`：`_project`（例如 `alice_project`） +- `MVP_WANDB_RUN`：``（每次 attempt 唯一） + +2) **当 W&B 有效启用时注入** +- `WANDB_BASE_URL` +- `WANDB_API_KEY` +- `WANDB_MODE=online` +- `WANDB_DIR=/wandb`（例如 `/private/users/alice/jobs//wandb`） + +降级策略： +- 当 `tracking.wandb.enabled=true` 但缺少 `WANDB_API_KEY` 时，平台会**降级为 console**（并在 attempt.message 中记录 warning），避免训练失败。 + +### 2.4 WebUI 变更 + +1) **Login 页面** +- 增加 “Open W&B” 跳转（指向 `tracking.wandb.base_url`） + +2) **New Task 页面** +- 新增 **Evaluation example** +- Advanced example 更新为 v3.6： + - `command: |` 内不再包含任何注释（避免 YAML/命令解析报错） + - W&B 参数不再让用户手填，改为引用平台注入的 `${MVP_*}` env： + - `trainer.logger=${MVP_TRAINER_LOGGER}` + - `trainer.project_name=${MVP_WANDB_PROJECT}` + - `trainer.experiment_name=${MVP_WANDB_RUN}` + +> 备注：driver 日志里会打印 `MVP_DRIVER_EXEC: bash -lc '...'`，此处看到 `${MVP_*}` 仍是“未替换”的字符串是正常现象；变量替换发生在 `bash` 执行阶段，而不是打印 argv 阶段。 + +### 2.5 启动脚本 + +`src/mvp/scripts/60_start_api.sh` 支持将宿主机的 `WANDB_API_KEY` 透传进 head 容器内启动的 API server： + +- 宿主机设置：`export WANDB_API_KEY=...` +- 启动 API：脚本会 `docker exec -e WANDB_API_KEY=...` 进入 head 容器启动 `python3 /workspace/mvp/py/server.py` + +## 3. 用户侧操作流程（v3.6） + +### 3.1 一次性初始化（只在首次启用/清空 /vol 时需要） + +1) 打开 W&B UI：`http://:8090` +2) 在 System Admin 页面粘贴 license 完成初始化 +3) 生成并记录 `WANDB_API_KEY`（local key） +4) 以后启动 API server 时注入该 key（`WANDB_API_KEY=...`） + +只要保留 `shared/common/wandb`（即 `/vol` 持久化目录），重建容器无需再次进入 8090 配置。 + +### 3.2 日常使用 + +1) WebUI 登录：`http://:8080/ui/login`（输入 user token） +2) New Task 提交任务：`http://:8080/ui/tasks/new` +3) 到 Tasks 查看状态/日志：`/ui/tasks` 与 task detail +4) 打开 W&B：`http://:8090`，在 `_project` 下查看 runs/metrics + +## 4. 验收结果（本迭代应达成） + +1) PPO/GRPO/SFT 任一任务运行后： +- W&B local server 可见对应 project（如 `alice_project`） +- run name 与 `ray_submission_id` 对齐（便于追踪每次 attempt） + +2) Evaluation 示例： +- 可作为 Advanced 任务提交并在 Ray 上执行 `verl.trainer.main_eval` +- 支持用户在 command 内自行加入 reward overrides（平台不做封装） + +## 5. 已知限制与后续建议 + +1) **W&B 初始化自动化** +- 当前：首次仍需在 8090 页面粘贴 license、生成 key（更稳、侵入最小）。 +- 若需要“从零部署也完全免页面操作”，可进一步调研 W&B local 的可用管理 API/启动参数（自动注入 license + 自动创建 key）。 + +2) **Advanced command 的自由度** +- 平台只负责： + - `$HOME` 宏替换 + - runtime_env env_vars 注入 + - 任务队列与 Ray job 提交 +- command 的语义正确性仍由用户负责（例如 PPO 必需的 micro batch 等参数）。 + diff --git a/specs/mvp/v3.6/wandb.md b/specs/mvp/v3.6/wandb.md new file mode 100644 index 0000000..c7ddeda --- /dev/null +++ b/specs/mvp/v3.6/wandb.md @@ -0,0 +1,70 @@ + +# License + +License: +eyJhbGciOiJSUzI1NiIsImtpZCI6InUzaHgyQjQyQWhEUXM1M0xQY09yNnZhaTdoSlduYnF1bTRZTlZWd1VwSWM9In0.eyJjb25jdXJyZW50QWdlbnRzIjoxLCJ0cmlhbCI6ZmFsc2UsIm1heFN0b3JhZ2VHYiI6MTAsIm1heFRlYW1zIjowLCJtYXhVc2VycyI6MSwibWF4Vmlld09ubHlVc2VycyI6MCwibWF4UmVnaXN0ZXJlZE1vZGVscyI6MiwiZXhwaXJlc0F0IjoiMjAyNy0wMS0wNVQwMjoxMjo1MC4zMjRaIiwiZGVwbG95bWVudElkIjoiYzNmN2Y5N2ItMzAxOS00Nzk2LTkxYTgtZDUyMjc1NDBiMTI1IiwiZmxhZ3MiOltdLCJjb250cmFjdFN0YXJ0RGF0ZSI6IjIwMjYtMDEtMDVUMDI6MTI6NTAuMzI0WiIsImFjY2Vzc0tleSI6IjYxMGM5NjliLTk4ZWEtNGRhNS1iYzU1LWM2MzVlZWNhNzc0OCIsInNlYXRzIjoxLCJ2aWV3T25seVNlYXRzIjowLCJ0ZWFtcyI6MCwicmVnaXN0ZXJlZE1vZGVscyI6Miwic3RvcmFnZUdpZ3MiOjEwLCJleHAiOjE3OTkxMTUxNzAsIndlYXZlTGltaXRzIjp7IndlYXZlTGltaXRCeXRlcyI6bnVsbCwid2VhdmVPdmVyYWdlQ29zdENlbnRzIjowLCJ3ZWF2ZU92ZXJhZ2VVbml0IjoiTUIifX0.VADnc0PExWhGDAxMIbu0vlmPN423B398of4HFl6BMJ1vqGA9H1ESElOZfk0VQ0YnYgwZc_CZF9k0HRyfCBgRhtRKyB1PpGnaKT_kKNVQryykWRpNhnpDqhmTa-wfTUBXNxhu1ktNPKBFNaEbaYuPsLN_aXPGW0dDwp6coGnGEXEqdRmuvekE6ytu7t6IA6flYs35WqCojvvjAmfBdovo2zPTfmlqKeaz7GPrApMo9JBpmb1a6bZEjCoRhhUx_k-v2rbvE3hd9ix9_UMZ6siJ5IKtNuXy_cprcCXXIFVUMcfTnt78RRXY0jCRMQqWkNq9ZGF0Mgcjsh3ts9xSxPgWnw + +# License 限制 +Add License to your Local Instance +Create up to 0 teams +Create up to 1 users +Store up to 10 GB of data +Create up to 2 Registered Models + +Quickstart +On a machine with Docker and Python installed, run: +1 pip install wandb --upgrade +2 wandb server start +Generate a free license from the Deployer. +Add it to your W&B Server's localhost's settings. +Paste the license in the /system-admin page on your localhost + +# docker 部署 + +deployment: +version: "3.8" + +services: + wandb: + image: wandb/local:latest + container_name: wandb-local + ports: + - "8080:8080" + volumes: + - wandb_data:/vol + restart: unless-stopped + +volumes: + wandb_data: + + + +# 连接方式： +方式 B：环境变量（适合容器/批处理/CI） + +通过 ray job的runtime_env来设置环境变量 + +export WANDB_BASE_URL=http://<服务器IP或域名>:8080 +export WANDB_API_KEY=<你的API_KEY> + + +官方文档说明可以用 WANDB_BASE_URL + WANDB_API_KEY 代替 wandb login --host .. + + +# verl配置： +在 verl 里打开 wandb（你只需要配 trainer） + +verl 的配置里，最关键是这三个字段：trainer.logger、trainer.project_name、trainer.experiment_name。文档里也写了 logger 用于 console + tracking（tracking 会初始化 wandb）。 +veRL Documentation ++1 + +推荐写法（新版本）： + +trainer: + logger: ["console", "wandb"] + project_name: my_project # 用argus的用户名_project ，譬如 alice_project + experiment_name: exp_001 # 用 task id 作为实验名 + + + + diff --git a/src/mvp/configs/dev.yaml b/src/mvp/configs/dev.yaml index 4f79fe6..de11c65 100644 --- a/src/mvp/configs/dev.yaml +++ b/src/mvp/configs/dev.yaml @@ -32,6 +32,16 @@ service: retry_interval_s: 60 max_running_tasks: 1 +tracking: + wandb: + # v3.6: enable Weights & Biases integration (best-effort). + # - If WANDB_API_KEY is missing, the platform degrades to console logging and records a warning. + enabled: true + # For dev compose, use docker bridge gateway + published port for stability. + base_url: "http://172.22.0.1:8090" + api_key_env: "WANDB_API_KEY" + project_suffix: "_project" + # v3.0: user data management (filesystem + SFTPGo) data: # All user writable data is placed under this root: diff --git a/src/mvp/configs/dev_v30.yaml b/src/mvp/configs/dev_v30.yaml index 3c2649d..131dd7c 100644 --- a/src/mvp/configs/dev_v30.yaml +++ b/src/mvp/configs/dev_v30.yaml @@ -32,6 +32,14 @@ service: retry_interval_s: 60 max_running_tasks: 1 +tracking: + wandb: + enabled: true + # For dev compose, recommend docker bridge gateway + host published port for stability. + base_url: "http://172.22.0.1:8090" + api_key_env: "WANDB_API_KEY" + project_suffix: "_project" + data: user_root: "/private/users" sftpgo: @@ -48,4 +56,3 @@ data: jobs_trash_after_days: 3 jobs_purge_after_days: 7 janitor_interval_s: 3600 - diff --git a/src/mvp/docker-compose.yaml b/src/mvp/docker-compose.yaml index f7d1991..1b8c86a 100644 --- a/src/mvp/docker-compose.yaml +++ b/src/mvp/docker-compose.yaml @@ -74,6 +74,27 @@ services: SFTPGO_HTTPD__BINDINGS__0__PORT: "8080" SFTPGO_SFTPD__BINDINGS__0__PORT: "2022" + # v3.6: Weights & Biases local server (shared single account; per-user projects). + # + # - UI: 8080 in container, mapped to host 8090 + # - Data: persist under /private/common/wandb (NFS/shared) + # + # NOTE: + # - The license/initial setup is done via the W&B UI. + # - Ray jobs use WANDB_BASE_URL from config (recommended: docker bridge gateway + host published port). + wandb: + image: wandb/local:latest + container_name: argus-wandb + ports: + - "8090:8080" + volumes: + - ../../shared/common/wandb:/vol + networks: + argus-ray-net: + aliases: + - wandb + - argus-wandb + ray_worker_0: image: argus/argus-ray-node:v2.5 container_name: argus-ray-worker-0 diff --git a/src/mvp/py/argus/ray/ray_job_tool.py b/src/mvp/py/argus/ray/ray_job_tool.py index a0077a2..52ce223 100644 --- a/src/mvp/py/argus/ray/ray_job_tool.py +++ b/src/mvp/py/argus/ray/ray_job_tool.py @@ -10,7 +10,7 @@ from typing import Any import ray from ray.job_submission import JobSubmissionClient -from .builders import build_advanced_argv, build_training_argv +from .builders import BuiltCommand, build_advanced_argv, build_training_argv from .models import AdvancedTaskSpec, JobSpec, RayConfig from .yaml_io import dump_yaml @@ -44,10 +44,17 @@ class RayJobTool: def _job_dir(self, submission_id: str) -> str: return f"{self.cfg.shared_root}/jobs/{submission_id}" - def _runtime_env(self, spec: JobSpec) -> dict[str, Any]: - return self._runtime_env_for(code_path=spec.code_path, workload=spec.workload) + def _runtime_env(self, spec: JobSpec, *, extra_env_vars: dict[str, str] | None = None) -> dict[str, Any]: + return self._runtime_env_for(code_path=spec.code_path, workload=spec.workload, extra_env_vars=extra_env_vars) - def _runtime_env_for(self, *, code_path: str, workload: str, extra_pythonpath: list[str] | None = None) -> dict[str, Any]: + def _runtime_env_for( + self, + *, + code_path: str, + workload: str, + extra_pythonpath: list[str] | None = None, + extra_env_vars: dict[str, str] | None = None, + ) -> dict[str, Any]: env_vars = dict(self.cfg.runtime_env_env_vars) # Default HF cache @@ -74,13 +81,29 @@ class RayJobTool: if workload == "sft": env_vars.setdefault("RAY_ADDRESS", "auto") + # Per-job extra env vars (e.g. W&B). Do not override existing keys. + if extra_env_vars: + for k, v in dict(extra_env_vars).items(): + if k not in env_vars: + env_vars[k] = str(v) + return {"env_vars": env_vars} - def submit(self, spec: JobSpec, no_wait: bool, job_dir: str | None = None) -> str: + def submit( + self, + spec: JobSpec, + no_wait: bool, + job_dir: str | None = None, + *, + extra_env_vars: dict[str, str] | None = None, + extra_overrides: list[str] | None = None, + ) -> str: submission_id = spec.submission_id or f"mvp11_{spec.workload}_{_ts()}_{os.getpid()}" job_dir = job_dir or self._job_dir(submission_id) built = build_training_argv(spec, submission_id=submission_id, job_dir=job_dir) + if extra_overrides: + built = BuiltCommand(argv=[*built.argv, *list(extra_overrides)]) entrypoint_argv = [ "python3", "-m", @@ -91,7 +114,7 @@ class RayJobTool: ] entrypoint = " ".join(shlex.quote(x) for x in entrypoint_argv) - runtime_env = self._runtime_env(spec) + runtime_env = self._runtime_env(spec, extra_env_vars=extra_env_vars) # Prepare job artifacts directory job_root = Path(job_dir) @@ -155,6 +178,8 @@ class RayJobTool: no_wait: bool, job_dir: str | None = None, user_id: str | None = None, + *, + extra_env_vars: dict[str, str] | None = None, ) -> str: submission_id = spec.submission_id or f"mvp11_{spec.workload}_{_ts()}_{os.getpid()}" job_dir = job_dir or self._job_dir(submission_id) @@ -174,7 +199,12 @@ class RayJobTool: if user_id: extra_pythonpath.append(f"{self.cfg.shared_root}/users/{user_id}/code") - runtime_env = self._runtime_env_for(code_path=self.cfg.verl_code_path, workload=spec.workload, extra_pythonpath=extra_pythonpath) + runtime_env = self._runtime_env_for( + code_path=self.cfg.verl_code_path, + workload=spec.workload, + extra_pythonpath=extra_pythonpath, + extra_env_vars=extra_env_vars, + ) # Prepare job artifacts directory job_root = Path(job_dir) diff --git a/src/mvp/py/argus/service/app.py b/src/mvp/py/argus/service/app.py index 7bcb2f0..faa7942 100644 --- a/src/mvp/py/argus/service/app.py +++ b/src/mvp/py/argus/service/app.py @@ -276,6 +276,14 @@ def create_app(config_path: str) -> FastAPI: "jobs_purge_after_days": int(v2_cfg.data.retention.jobs_purge_after_days), }, } + if v2_cfg.tracking.wandb.enabled: + suffix = v2_cfg.tracking.wandb.project_suffix or "_project" + out["wandb"] = { + "enabled": True, + # Training uses base_url; UI can also open the host port directly. + "base_url": v2_cfg.tracking.wandb.base_url, + "project_name": f"{user_id}{suffix}", + } if _sftpgo_enabled(): out["sftp"] = { "host": v2_cfg.data.sftpgo.host, @@ -429,7 +437,7 @@ def create_app(config_path: str) -> FastAPI: "error_summary": row.get("error_summary"), } if latest_attempt: - out["latest_attempt"] = { + latest_out: dict[str, Any] = { "attempt_no": latest_attempt["attempt_no"], "ray_submission_id": latest_attempt["ray_submission_id"], "ray_status": latest_attempt.get("ray_status"), @@ -438,6 +446,16 @@ def create_app(config_path: str) -> FastAPI: "failure_kind": latest_attempt.get("failure_kind"), "message": latest_attempt.get("message"), } + if v2_cfg.tracking.wandb.enabled: + suffix = v2_cfg.tracking.wandb.project_suffix or "_project" + # For v3.6: shared W&B account, user-level projects. + # Use attempt's ray_submission_id as run name for stable mapping. + latest_out["wandb"] = { + "base_url": v2_cfg.tracking.wandb.base_url, + "project_name": f"{str(row.get('user_id') or subject.get('user_id'))}{suffix}", + "run_name": str(latest_attempt.get("ray_submission_id") or ""), + } + out["latest_attempt"] = latest_out return out @app.get("/api/v2/tasks/{task_id}/spec") diff --git a/src/mvp/py/argus/service/config.py b/src/mvp/py/argus/service/config.py index 3ece213..193aef2 100644 --- a/src/mvp/py/argus/service/config.py +++ b/src/mvp/py/argus/service/config.py @@ -34,6 +34,19 @@ class V2RetentionConfig: janitor_interval_s: int = 3600 +@dataclass(frozen=True) +class V2WandbConfig: + enabled: bool = False + base_url: str = "" + api_key_env: str = "WANDB_API_KEY" + project_suffix: str = "_project" + + +@dataclass(frozen=True) +class V2TrackingConfig: + wandb: V2WandbConfig + + @dataclass(frozen=True) class V2SFTPGoConfig: enabled: bool = False @@ -57,6 +70,7 @@ class V2Config: auth: V2AuthConfig sqlite: V2SqliteConfig scheduler: V2SchedulerConfig + tracking: V2TrackingConfig data: V2DataConfig @staticmethod @@ -83,6 +97,13 @@ class V2Config: else: shared_root = str(root.get("shared_root") or "/private") + tracking = root.get("tracking") or {} + if not isinstance(tracking, dict): + raise ValueError("config.tracking must be a mapping") + wandb = tracking.get("wandb") or {} + if not isinstance(wandb, dict): + raise ValueError("config.tracking.wandb must be a mapping") + data = root.get("data") or {} if not isinstance(data, dict): raise ValueError("config.data must be a mapping") @@ -96,6 +117,11 @@ class V2Config: user_root = str(data.get("user_root") or f"{shared_root}/users") + wandb_enabled = bool(wandb.get("enabled") or False) + wandb_base_url = str(wandb.get("base_url") or "") + if wandb_enabled and not wandb_base_url: + raise ValueError("tracking.wandb.base_url is required when tracking.wandb.enabled=true") + return V2Config( api=V2ApiConfig( host=str(api.get("host") or "0.0.0.0"), @@ -108,6 +134,14 @@ class V2Config: retry_interval_s=int(scheduler.get("retry_interval_s") or 60), max_running_tasks=int(scheduler.get("max_running_tasks") or 1), ), + tracking=V2TrackingConfig( + wandb=V2WandbConfig( + enabled=wandb_enabled, + base_url=wandb_base_url, + api_key_env=str(wandb.get("api_key_env") or "WANDB_API_KEY"), + project_suffix=str(wandb.get("project_suffix") or "_project"), + ) + ), data=V2DataConfig( user_root=user_root, sftpgo=V2SFTPGoConfig( diff --git a/src/mvp/py/argus/service/scheduler.py b/src/mvp/py/argus/service/scheduler.py index c9b975e..4fb3fc1 100644 --- a/src/mvp/py/argus/service/scheduler.py +++ b/src/mvp/py/argus/service/scheduler.py @@ -1,5 +1,6 @@ from __future__ import annotations +import os import re import time from dataclasses import dataclass @@ -48,6 +49,74 @@ class Scheduler: required = float(nnodes * n_gpus_per_node) return avail.total_available_gpus >= required + def _wandb_env_vars(self, *, ray_submission_id: str, job_dir: str) -> tuple[dict[str, str], str | None]: + """ + Returns (env_vars, warn_message). + + env_vars is always non-empty so that AdvancedTaskSpec examples can safely + reference injected env vars without requiring the user to edit their command: + - MVP_TRAINER_LOGGER: "console" or "[console,wandb]" + - MVP_WANDB_PROJECT: "_project" + - MVP_WANDB_RUN: "" + + When W&B is effectively enabled (config enabled + API key present), + WANDB_* env vars are included as well. + """ + env, warn, _enabled = self._tracking_env_vars(user_id=None, ray_submission_id=ray_submission_id, job_dir=job_dir) + return env, warn + + def _tracking_env_vars( + self, *, user_id: str | None, ray_submission_id: str, job_dir: str + ) -> tuple[dict[str, str], str | None, bool]: + """ + Returns (env_vars, warn_message, wandb_enabled_effective). + + - env_vars always includes MVP_* vars so advanced commands can use them. + - wandb_enabled_effective is True only when tracking.wandb.enabled AND api key exists. + """ + cfg = self.v2_cfg.tracking.wandb + project_suffix = cfg.project_suffix or "_project" + project_name = f"{user_id}{project_suffix}" if user_id else f"mvp{project_suffix}" + + api_key = os.environ.get(cfg.api_key_env, "").strip() + wandb_enabled_effective = bool(cfg.enabled and api_key) + warn: str | None = None + if cfg.enabled and not api_key: + warn = f"WARN_WANDB_DISABLED_MISSING_ENV:{cfg.api_key_env}" + + env: dict[str, str] = { + "MVP_TRAINER_LOGGER": ("[console,wandb]" if wandb_enabled_effective else "console"), + "MVP_WANDB_PROJECT": project_name, + "MVP_WANDB_RUN": ray_submission_id, + } + + if wandb_enabled_effective: + env.update( + { + "WANDB_BASE_URL": cfg.base_url, + "WANDB_API_KEY": api_key, + "WANDB_MODE": "online", + "WANDB_DIR": f"{job_dir.rstrip('/')}/wandb", + } + ) + + return env, warn, wandb_enabled_effective + + def _wandb_training_overrides(self, *, user_id: str | None, ray_submission_id: str) -> list[str] | None: + """ + Returns hydra overrides to enable W&B logging for built-in training workloads. + + Only call this when W&B is effectively enabled for the job (i.e. API key exists). + """ + cfg = self.v2_cfg.tracking.wandb + project_suffix = cfg.project_suffix or "_project" + project_name = f"{user_id}{project_suffix}" if user_id else f"mvp{project_suffix}" + return [ + "trainer.logger=[console,wandb]", + f"trainer.project_name={project_name}", + f"trainer.experiment_name={ray_submission_id}", + ] + def _parse_taskspec(self, jobspec_yaml: str) -> JobSpec | AdvancedTaskSpec: obj = yaml.safe_load(jobspec_yaml) or {} if not isinstance(obj, dict): @@ -70,21 +139,38 @@ class Scheduler: self.db.set_task_state(task_id=task_id, state="SUBMITTING", latest_attempt_no=attempt_no) # Override submission_id in taskspec (v1.1 compatible) + tracking_env, wandb_warn, wandb_enabled = self._tracking_env_vars( + user_id=user_id_s, ray_submission_id=ray_sid, job_dir=job_dir + ) if isinstance(spec, JobSpec): d = spec.to_public_dict() d["submission_id"] = ray_sid spec2 = JobSpec.from_dict(d) - submit = lambda: self.tool.submit(spec2, no_wait=True, job_dir=job_dir) + wandb_overrides = self._wandb_training_overrides(user_id=user_id_s, ray_submission_id=ray_sid) if wandb_enabled else None + submit = lambda: self.tool.submit( + spec2, + no_wait=True, + job_dir=job_dir, + extra_env_vars=tracking_env, + extra_overrides=wandb_overrides, + ) else: d = spec.to_public_dict() d["submission_id"] = ray_sid spec2 = AdvancedTaskSpec.from_dict(d) - submit = lambda: self.tool.submit_advanced(spec2, no_wait=True, job_dir=job_dir, user_id=user_id_s) + submit = lambda: self.tool.submit_advanced( + spec2, no_wait=True, job_dir=job_dir, user_id=user_id_s, extra_env_vars=tracking_env + ) try: submitted = submit() # submitted should equal ray_sid; keep as source of truth. - self.db.update_attempt(task_id=task_id, attempt_no=attempt_no, ray_status="SUBMITTED") + self.db.update_attempt( + task_id=task_id, + attempt_no=attempt_no, + ray_status="SUBMITTED", + message=wandb_warn, + ) self.db.set_task_state(task_id=task_id, state="SUBMITTED") if submitted != ray_sid: self.db.set_task_state(task_id=task_id, state="SUBMITTED", event_type="WARN_SUBMISSION_ID_MISMATCH") diff --git a/src/mvp/py/argus/service/ui.py b/src/mvp/py/argus/service/ui.py index 8a41ef4..5da48d7 100644 --- a/src/mvp/py/argus/service/ui.py +++ b/src/mvp/py/argus/service/ui.py @@ -188,11 +188,29 @@ def register_ui_routes(app: FastAPI) -> None: 
(not loaded)
+
+
+

W&B

+
Weights & Biases local server (v3.6). Metrics are written by training jobs; this UI is for viewing.
+
+
+ Open W&B (:8090) + +
+
+
project: (unknown)
+
base_url (job runtime): (unknown)
+
""".strip() script = """ const tokEl = document.getElementById("tok"); const msg = document.getElementById("msg"); const me = document.getElementById("me"); +const wandbOpen = document.getElementById("wandb-open"); +const wandbProject = document.getElementById("wandb-project"); +const wandbBaseUrl = document.getElementById("wandb-base-url"); +document.getElementById("wandb-copy-project").onclick = async () => { await copyText(wandbProject.textContent || ""); }; +wandbOpen.href = curOriginWithPort(8090); tokEl.value = mvpTokenGet(); async function refreshMe() { @@ -200,8 +218,17 @@ async function refreshMe() { try { const obj = await apiJson("/api/v2/me"); me.textContent = fmtJson(obj); + if (obj.wandb && obj.wandb.enabled) { + wandbProject.textContent = obj.wandb.project_name || "(unknown)"; + wandbBaseUrl.textContent = obj.wandb.base_url || "(unknown)"; + } else { + wandbProject.textContent = "(disabled)"; + wandbBaseUrl.textContent = "(disabled)"; + } } catch (e) { me.textContent = "Error: " + (e.status || "") + "\\n" + (e.body || String(e)); + wandbProject.textContent = "(error)"; + wandbBaseUrl.textContent = "(error)"; } } @@ -358,19 +385,26 @@ val_file: /private/common/datasets/gsm8k_sft/test.parquet # 验证数据（必 # trainer_device: cpu # 仅 SFT 生效：driver 侧 device（可选，默认：cpu） # submission_id: "" # Ray submission_id（可选，默认空；通常由服务自动生成，无需填写） """.strip() - adv = """# Advanced TaskSpec (YAML) - v3.5 + adv = """# Advanced TaskSpec (YAML) - v3.6 kind: advanced # 任务类型（必填）：advanced（自定义 command） -# 说明：v3.5 中 Advanced 任务不会按 ppo/grpo/sft 分类；平台统一按 "advanced" 做任务分类与 task_id 命名。 - -nnodes: 2 # 训练节点数（必填）：用于平台队列调度与资源预检查 -n_gpus_per_node: 4 # 每节点 GPU 数（必填）：用于平台队列调度与资源预检查 - -# 自定义训练命令（必填）：平台会做 $HOME 宏替换： +# 说明：平台统一按 "advanced" 做任务分类与 task_id 命名（不按 ppo/grpo/sft 细分）。 +# +# 自定义训练命令：平台会做 $HOME 宏替换： # - $HOME -> /private/users/ # - $HOME/common/datasets -> /private/datasets（共享只读数据） # - $HOME/common/hf -> /private/hf（共享只读 HF cache） +# +# W&B（v3.6）： +# - 平台会在 runtime_env 注入 WANDB_BASE_URL/WANDB_API_KEY/WANDB_DIR +# - 以及注入以下 env，供 Advanced command 使用（无需用户手动修改 project）： +# - MVP_TRAINER_LOGGER: "console" 或 "[console,wandb]" +# - MVP_WANDB_PROJECT: "_project" +# - MVP_WANDB_RUN: "" +# +nnodes: 2 # 训练节点数（必填）：用于平台队列调度与资源预检查 +n_gpus_per_node: 4 # 每节点 GPU 数（必填）：用于平台队列调度与资源预检查 + command: | - # 注意：PPO 需要一些关键参数，否则 VERL 会在启动前 fail-fast（例如 actor 的 micro batch）。 PYTHONUNBUFFERED=1 \ python3 -m verl.trainer.main_ppo \ data.train_files=$HOME/common/datasets/gsm8k/train.parquet \ @@ -391,7 +425,9 @@ command: | critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \ critic.ppo_micro_batch_size_per_gpu=4 \ algorithm.kl_ctrl.kl_coef=0.001 \ - trainer.logger=console \ + trainer.logger=${MVP_TRAINER_LOGGER} \ + trainer.project_name=${MVP_WANDB_PROJECT} \ + trainer.experiment_name=${MVP_WANDB_RUN} \ trainer.val_before_train=False \ trainer.nnodes=2 \ trainer.n_gpus_per_node=4 \ @@ -426,6 +462,32 @@ command: | --backend fsdp \ --local_dir $HOME/jobs//checkpoints//actor \ --target_dir $HOME/jobs//checkpoints//actor/huggingface +""".strip() + evaluation = """# Evaluation (YAML) - v3.6 (Advanced command) +# 用途：对“已生成的结果 parquet”做离线评估（基于 custom reward function），示例使用 VERL 内置 main_eval。 +# +# 说明： +# - 你需要准备一个包含生成 responses 的 parquet，并替换 （示例放在某个 job 目录下）。 +# - main_eval 会在 Ray 上并发计算，因此这里也用 +ray_kwargs 连接已有 Ray 集群。 +# +kind: advanced +nnodes: 1 +n_gpus_per_node: 0 + +command: | + PYTHONUNBUFFERED=1 \ + python3 -m verl.trainer.main_eval \ + data.path=$HOME/jobs//outputs/.parquet \ + +ray_kwargs.ray_init.address=auto + +# 可选：指定 parquet schema（按你的 parquet 列名调整） +# data.response_key=responses +# data.data_source_key=data_source +# data.reward_model_key=reward_model +# +# 可选：自定义 reward（方式 A：用户自行提供 reward.py） +# custom_reward_function.path=$HOME/code/reward.py +# custom_reward_function.name=compute_score """.strip() body = f"""

New Task

@@ -447,6 +509,7 @@ command: | +
@@ -603,21 +666,24 @@ command: | tpl_grpo = json.dumps(grpo) tpl_sft = json.dumps(sft) tpl_adv = json.dumps(adv) + tpl_eval = json.dumps(evaluation) tpl_merge = json.dumps(merge) script = ( """ -const msg = document.getElementById("msg"); -const yamlEl = document.getElementById("yaml"); -const TPL_PPO = __TPL_PPO__; -const TPL_GRPO = __TPL_GRPO__; -const TPL_SFT = __TPL_SFT__; -const TPL_ADV = __TPL_ADV__; -const TPL_MERGE = __TPL_MERGE__; -document.getElementById("tpl-ppo").onclick = () => { yamlEl.value = TPL_PPO; msg.textContent = ""; }; -document.getElementById("tpl-grpo").onclick = () => { yamlEl.value = TPL_GRPO; msg.textContent = ""; }; -document.getElementById("tpl-sft").onclick = () => { yamlEl.value = TPL_SFT; msg.textContent = ""; }; -document.getElementById("tpl-adv").onclick = () => { yamlEl.value = TPL_ADV; msg.textContent = ""; }; -document.getElementById("tpl-merge").onclick = () => { yamlEl.value = TPL_MERGE; msg.textContent = ""; }; + const msg = document.getElementById("msg"); + const yamlEl = document.getElementById("yaml"); + const TPL_PPO = __TPL_PPO__; + const TPL_GRPO = __TPL_GRPO__; + const TPL_SFT = __TPL_SFT__; + const TPL_ADV = __TPL_ADV__; + const TPL_EVAL = __TPL_EVAL__; + const TPL_MERGE = __TPL_MERGE__; + document.getElementById("tpl-ppo").onclick = () => { yamlEl.value = TPL_PPO; msg.textContent = ""; }; + document.getElementById("tpl-grpo").onclick = () => { yamlEl.value = TPL_GRPO; msg.textContent = ""; }; + document.getElementById("tpl-sft").onclick = () => { yamlEl.value = TPL_SFT; msg.textContent = ""; }; + document.getElementById("tpl-adv").onclick = () => { yamlEl.value = TPL_ADV; msg.textContent = ""; }; + document.getElementById("tpl-eval").onclick = () => { yamlEl.value = TPL_EVAL; msg.textContent = ""; }; + document.getElementById("tpl-merge").onclick = () => { yamlEl.value = TPL_MERGE; msg.textContent = ""; }; function yamlQuote(s) { s = String(s ?? ""); @@ -802,11 +868,12 @@ document.getElementById("submit").onclick = async () => { msg.textContent = "OK: " + fmtJson(obj); if (obj.task_id) window.location.href = "/ui/tasks/" + obj.task_id; }; -""".strip() + """.strip() .replace("__TPL_PPO__", tpl_ppo) .replace("__TPL_GRPO__", tpl_grpo) .replace("__TPL_SFT__", tpl_sft) .replace("__TPL_ADV__", tpl_adv) + .replace("__TPL_EVAL__", tpl_eval) .replace("__TPL_MERGE__", tpl_merge) ) return HTMLResponse(content=_page("New Task", "new", body, script)) @@ -827,6 +894,19 @@ document.getElementById("submit").onclick = async () => { 
Loading...
+
+

W&B

+
This is a best-effort hint. Run name maps to ray_submission_id (attempt).
+
+
+ Open W&B (:8090) + +
+
+
project: (unknown)
+
run: (unknown)
+
+

TaskSpec (YAML)

Resolved TaskSpec (includes default values; submission_id reflects latest attempt when available).
@@ -836,6 +916,8 @@ document.getElementById("submit").onclick = async () => { """.strip() script = f""" document.getElementById("nav-ray-dashboard").href = curOriginWithPort(8265); +document.getElementById("wandb-open").href = curOriginWithPort(8090); +document.getElementById("wandb-copy-run").onclick = async () => {{ await copyText((document.getElementById("wandb-run").textContent || \"\").trim()); }}; const out = document.getElementById("out"); const spec = document.getElementById("spec"); async function refresh() {{ @@ -844,7 +926,18 @@ async function refresh() {{ const resp = await apiFetch("/api/v2/tasks/{task_id}"); const text = await resp.text(); if (!resp.ok) {{ out.textContent = "Error: " + resp.status + "\\n" + text; return; }} - out.textContent = fmtJson(JSON.parse(text)); + const obj = JSON.parse(text); + out.textContent = fmtJson(obj); + const p = document.getElementById("wandb-project"); + const r = document.getElementById("wandb-run"); + const w = (obj.latest_attempt && obj.latest_attempt.wandb) ? obj.latest_attempt.wandb : null; + if (w) {{ + p.textContent = w.project_name || "(unknown)"; + r.textContent = w.run_name || "(unknown)"; + }} else {{ + p.textContent = "(not available)"; + r.textContent = "(not available)"; + }} const resp2 = await apiFetch("/api/v2/tasks/{task_id}/spec"); const text2 = await resp2.text(); diff --git a/src/mvp/py/tests/test_app.py b/src/mvp/py/tests/test_app.py index fd97d19..9b4b566 100644 --- a/src/mvp/py/tests/test_app.py +++ b/src/mvp/py/tests/test_app.py @@ -164,6 +164,37 @@ def test_task_submit_get_cancel_logs_queue(tmp_path: Path, monkeypatch): assert r5.text.strip() == "c" +def test_me_includes_wandb_when_enabled(tmp_path: Path, monkeypatch): + from argus.service import app as app_mod + + cfg_path = _write_config(tmp_path) + # Enable tracking.wandb + cfg = yaml.safe_load(cfg_path.read_text(encoding="utf-8")) + cfg["tracking"] = {"wandb": {"enabled": True, "base_url": "http://172.22.0.1:8090"}} + cfg_path.write_text(yaml.safe_dump(cfg), encoding="utf-8") + + monkeypatch.setenv("MVP_INTERNAL_TOKEN", "token1") + + class _Scheduler: + def __init__(self, **kwargs): + self.tool = object() + + def run_forever(self, stop_flag): + return None + + monkeypatch.setattr(app_mod, "Scheduler", _Scheduler) + app = app_mod.create_app(str(cfg_path)) + + with TestClient(app) as c: + r = c.get("/api/v2/me", headers={"authorization": "Bearer token1"}) + assert r.status_code == 200 + obj = r.json() + assert obj["user_id"] == "admin" + assert obj.get("wandb", {}).get("enabled") is True + assert obj["wandb"]["base_url"] == "http://172.22.0.1:8090" + assert obj["wandb"]["project_name"] == "admin_project" + + def test_submit_rejects_non_mapping_jobspec(tmp_path: Path, monkeypatch): from argus.service import app as app_mod diff --git a/src/mvp/py/tests/test_scheduler.py b/src/mvp/py/tests/test_scheduler.py index b6c929a..1502f02 100644 --- a/src/mvp/py/tests/test_scheduler.py +++ b/src/mvp/py/tests/test_scheduler.py @@ -60,10 +60,21 @@ def test_tick_submits_one_task(monkeypatch, tmp_path: Path): def __init__(self, cfg): self.submitted = [] self.job_dirs = [] + self.extra_env = [] + self.extra_overrides = [] - def submit(self, spec, no_wait: bool, job_dir: str | None = None): + def submit( + self, + spec, + no_wait: bool, + job_dir: str | None = None, + extra_env_vars: dict | None = None, + extra_overrides: list[str] | None = None, + ): self.submitted.append(spec.submission_id) self.job_dirs.append(job_dir) + self.extra_env.append(extra_env_vars) + self.extra_overrides.append(extra_overrides) return str(spec.submission_id) def status(self, submission_id: str): @@ -83,6 +94,11 @@ def test_tick_submits_one_task(monkeypatch, tmp_path: Path): assert len(attempts) == 1 assert attempts[0]["ray_submission_id"] == "t1--a01" assert s.tool.job_dirs[-1] == "/private/users/alice/jobs/t1--a01" + env = s.tool.extra_env[-1] + assert env and env["MVP_WANDB_PROJECT"] == "alice_project" + assert env["MVP_WANDB_RUN"] == "t1--a01" + assert env["MVP_TRAINER_LOGGER"] == "console" + assert s.tool.extra_overrides[-1] is None def test_tick_submits_one_task_advanced(monkeypatch, tmp_path: Path): @@ -118,13 +134,23 @@ def test_tick_submits_one_task_advanced(monkeypatch, tmp_path: Path): def __init__(self, cfg): self.submitted = [] self.job_dirs = [] + self.extra_env = [] def submit(self, spec, no_wait: bool, job_dir: str | None = None): raise AssertionError("should not call submit() for advanced") - def submit_advanced(self, spec, no_wait: bool, job_dir: str | None = None, user_id: str | None = None): + def submit_advanced( + self, + spec, + no_wait: bool, + job_dir: str | None = None, + user_id: str | None = None, + extra_env_vars: dict | None = None, + extra_overrides: list[str] | None = None, + ): self.submitted.append(spec.submission_id) self.job_dirs.append(job_dir) + self.extra_env.append(extra_env_vars) return str(spec.submission_id) def status(self, submission_id: str): @@ -144,6 +170,252 @@ def test_tick_submits_one_task_advanced(monkeypatch, tmp_path: Path): assert len(attempts) == 1 assert attempts[0]["ray_submission_id"] == "t1--a01" assert s.tool.job_dirs[-1] == "/private/users/alice/jobs/t1--a01" + env = s.tool.extra_env[-1] + assert env and env["MVP_WANDB_PROJECT"] == "alice_project" + assert env["MVP_WANDB_RUN"] == "t1--a01" + assert env["MVP_TRAINER_LOGGER"] == "console" + + +def test_tick_injects_wandb_env_when_enabled(monkeypatch, tmp_path: Path): + from argus.service import scheduler as sched_mod + + root = { + "ray": { + "address": "http://127.0.0.1:8265", + "shared_root": "/private", + "entrypoint_resources": {"worker_node": 1}, + "runtime_env": {"env_vars": {}}, + }, + "service": { + "sqlite": {"db_path": str(tmp_path / "mvp.sqlite3")}, + "scheduler": {"tick_s": 1, "retry_interval_s": 1, "max_running_tasks": 1}, + }, + "tracking": {"wandb": {"enabled": True, "base_url": "http://172.22.0.1:8090"}}, + } + ray_cfg, v2_cfg = RayConfig.from_dict(root), V2Config.from_root_dict(root) + db = Db(v2_cfg.sqlite.db_path) + db.init() + db.create_task_v25( + task_id="t1", + user_id="alice", + workload="ppo", + jobspec_yaml=yaml.safe_dump( + { + "workload": "ppo", + "code_path": "/private/common/code/verl", + "model_id": "m", + "train_file": "/private/common/datasets/t", + "val_file": "/private/common/datasets/v", + } + ), + nnodes=2, + n_gpus_per_node=4, + ) + + monkeypatch.setenv("WANDB_API_KEY", "k") + monkeypatch.setattr(sched_mod, "ensure_ray_connected", lambda: None) + monkeypatch.setattr( + sched_mod, + "get_cluster_available", + lambda: SimpleNamespace(total_available_gpus=999.0, total_available_npus=0.0), + ) + + class _Tool: + def __init__(self, cfg): + self.extra_env = [] + self.job_dirs = [] + + def submit( + self, + spec, + no_wait: bool, + job_dir: str | None = None, + extra_env_vars: dict | None = None, + extra_overrides: list[str] | None = None, + ): + self.job_dirs.append(job_dir) + self.extra_env.append(extra_env_vars) + return str(spec.submission_id) + + def status(self, submission_id: str): + return "RUNNING" + + def logs(self, submission_id: str): + return "" + + monkeypatch.setattr(sched_mod, "RayJobTool", _Tool) + + s = sched_mod.Scheduler(db=db, ray_cfg=ray_cfg, v2_cfg=v2_cfg) + s.tick() + + assert s.tool.job_dirs[-1] == "/private/users/alice/jobs/t1--a01" + env = s.tool.extra_env[-1] + assert env and env["WANDB_API_KEY"] == "k" + assert env["WANDB_BASE_URL"] == "http://172.22.0.1:8090" + assert env["WANDB_MODE"] == "online" + assert env["WANDB_DIR"] == "/private/users/alice/jobs/t1--a01/wandb" + assert env["MVP_WANDB_PROJECT"] == "alice_project" + assert env["MVP_WANDB_RUN"] == "t1--a01" + assert env["MVP_TRAINER_LOGGER"] == "[console,wandb]" + + +def test_tick_wandb_enabled_missing_key_degrades(monkeypatch, tmp_path: Path): + from argus.service import scheduler as sched_mod + + root = { + "ray": { + "address": "http://127.0.0.1:8265", + "shared_root": "/private", + "entrypoint_resources": {"worker_node": 1}, + "runtime_env": {"env_vars": {}}, + }, + "service": { + "sqlite": {"db_path": str(tmp_path / "mvp.sqlite3")}, + "scheduler": {"tick_s": 1, "retry_interval_s": 1, "max_running_tasks": 1}, + }, + "tracking": {"wandb": {"enabled": True, "base_url": "http://172.22.0.1:8090"}}, + } + ray_cfg, v2_cfg = RayConfig.from_dict(root), V2Config.from_root_dict(root) + db = Db(v2_cfg.sqlite.db_path) + db.init() + db.create_task_v25( + task_id="t1", + user_id="alice", + workload="ppo", + jobspec_yaml=yaml.safe_dump( + { + "workload": "ppo", + "code_path": "/private/common/code/verl", + "model_id": "m", + "train_file": "/private/common/datasets/t", + "val_file": "/private/common/datasets/v", + } + ), + nnodes=2, + n_gpus_per_node=4, + ) + + monkeypatch.delenv("WANDB_API_KEY", raising=False) + monkeypatch.setattr(sched_mod, "ensure_ray_connected", lambda: None) + monkeypatch.setattr( + sched_mod, + "get_cluster_available", + lambda: SimpleNamespace(total_available_gpus=999.0, total_available_npus=0.0), + ) + + class _Tool: + def __init__(self, cfg): + self.extra_env = [] + + def submit( + self, + spec, + no_wait: bool, + job_dir: str | None = None, + extra_env_vars: dict | None = None, + extra_overrides: list[str] | None = None, + ): + self.extra_env.append(extra_env_vars) + return str(spec.submission_id) + + def status(self, submission_id: str): + return "RUNNING" + + def logs(self, submission_id: str): + return "" + + monkeypatch.setattr(sched_mod, "RayJobTool", _Tool) + + s = sched_mod.Scheduler(db=db, ray_cfg=ray_cfg, v2_cfg=v2_cfg) + s.tick() + + env = s.tool.extra_env[-1] + assert env and env["MVP_WANDB_PROJECT"] == "alice_project" + assert env["MVP_WANDB_RUN"] == "t1--a01" + assert env["MVP_TRAINER_LOGGER"] == "console" + attempts = db.list_attempts("t1") + assert attempts and "WANDB_API_KEY" in str(attempts[0].get("message") or "") + + +def test_tick_wandb_enabled_updates_jobspec_logger(monkeypatch, tmp_path: Path): + from argus.service import scheduler as sched_mod + + root = { + "ray": { + "address": "http://127.0.0.1:8265", + "shared_root": "/private", + "entrypoint_resources": {"worker_node": 1}, + "runtime_env": {"env_vars": {}}, + }, + "service": { + "sqlite": {"db_path": str(tmp_path / "mvp.sqlite3")}, + "scheduler": {"tick_s": 1, "retry_interval_s": 1, "max_running_tasks": 1}, + }, + "tracking": {"wandb": {"enabled": True, "base_url": "http://172.22.0.1:8090"}}, + } + ray_cfg, v2_cfg = RayConfig.from_dict(root), V2Config.from_root_dict(root) + db = Db(v2_cfg.sqlite.db_path) + db.init() + db.create_task_v25( + task_id="t1", + user_id="alice", + workload="ppo", + jobspec_yaml=yaml.safe_dump( + { + "workload": "ppo", + "code_path": "/private/common/code/verl", + "model_id": "m", + "train_file": "/private/common/datasets/t", + "val_file": "/private/common/datasets/v", + } + ), + nnodes=2, + n_gpus_per_node=4, + ) + + monkeypatch.setenv("WANDB_API_KEY", "k") + monkeypatch.setattr(sched_mod, "ensure_ray_connected", lambda: None) + monkeypatch.setattr( + sched_mod, + "get_cluster_available", + lambda: SimpleNamespace(total_available_gpus=999.0, total_available_npus=0.0), + ) + + class _Tool: + def __init__(self, cfg): + self.specs = [] + self.extra_overrides = [] + + def submit( + self, + spec, + no_wait: bool, + job_dir: str | None = None, + extra_env_vars: dict | None = None, + extra_overrides: list[str] | None = None, + ): + self.specs.append(spec) + self.extra_overrides.append(extra_overrides) + return str(spec.submission_id) + + def status(self, submission_id: str): + return "RUNNING" + + def logs(self, submission_id: str): + return "" + + monkeypatch.setattr(sched_mod, "RayJobTool", _Tool) + + s = sched_mod.Scheduler(db=db, ray_cfg=ray_cfg, v2_cfg=v2_cfg) + s.tick() + + spec = s.tool.specs[-1] + # v3.6: built-in training should be configured to use wandb when enabled and API key exists. + assert spec.workload == "ppo" + ov = s.tool.extra_overrides[-1] + assert ov and "trainer.logger=[console,wandb]" in ov + assert ov and "trainer.project_name=alice_project" in ov + assert ov and "trainer.experiment_name=t1--a01" in ov def test_tick_marks_pending_resources(monkeypatch, tmp_path: Path): diff --git a/src/mvp/py/tests/test_service_config.py b/src/mvp/py/tests/test_service_config.py index 9fe7226..d75b93f 100644 --- a/src/mvp/py/tests/test_service_config.py +++ b/src/mvp/py/tests/test_service_config.py @@ -22,6 +22,7 @@ def test_v2_config_from_root_dict_new_format_defaults(): assert cfg.auth.token_env == "X" assert cfg.sqlite.db_path.endswith(".sqlite3") assert cfg.scheduler.max_running_tasks == 3 + assert cfg.tracking.wandb.enabled is False def test_v2_config_backward_compat_v2_section_and_default_db_path(): @@ -29,6 +30,7 @@ def test_v2_config_backward_compat_v2_section_and_default_db_path(): cfg = V2Config.from_root_dict({"shared_root": "/private", "v2": {"sqlite": {}}}) assert cfg.sqlite.db_path == "/private/common/db/mvp.sqlite3" + assert cfg.tracking.wandb.enabled is False def test_v2_config_requires_mappings(): @@ -53,3 +55,49 @@ def test_v2_config_requires_data_mappings(): with pytest.raises(ValueError, match="config\\.data\\.\\{sftpgo,retention\\} must be mappings"): V2Config.from_root_dict({**base, "data": {"sftpgo": ["x"], "retention": {}}}) + + +def test_tracking_wandb_defaults_disabled(): + from argus.service.config import V2Config + + cfg = V2Config.from_root_dict( + { + "ray": {"shared_root": "/private"}, + "service": {"api": {}, "auth": {}, "sqlite": {}, "scheduler": {}}, + "data": {"sftpgo": {}, "retention": {}}, + } + ) + assert cfg.tracking.wandb.enabled is False + assert cfg.tracking.wandb.base_url == "" + assert cfg.tracking.wandb.api_key_env == "WANDB_API_KEY" + assert cfg.tracking.wandb.project_suffix == "_project" + + +def test_tracking_wandb_enabled_parses(): + from argus.service.config import V2Config + + cfg = V2Config.from_root_dict( + { + "ray": {"shared_root": "/private"}, + "service": {"api": {}, "auth": {}, "sqlite": {}, "scheduler": {}}, + "tracking": {"wandb": {"enabled": True, "base_url": "http://127.0.0.1:8090", "api_key_env": "X"}}, + "data": {"sftpgo": {}, "retention": {}}, + } + ) + assert cfg.tracking.wandb.enabled is True + assert cfg.tracking.wandb.base_url == "http://127.0.0.1:8090" + assert cfg.tracking.wandb.api_key_env == "X" + + +def test_tracking_wandb_enabled_requires_base_url(): + from argus.service.config import V2Config + + with pytest.raises(ValueError, match="tracking\\.wandb\\.base_url is required"): + V2Config.from_root_dict( + { + "ray": {"shared_root": "/private"}, + "service": {"api": {}, "auth": {}, "sqlite": {}, "scheduler": {}}, + "tracking": {"wandb": {"enabled": True, "base_url": ""}}, + "data": {"sftpgo": {}, "retention": {}}, + } + ) diff --git a/src/mvp/py/tests/test_ui.py b/src/mvp/py/tests/test_ui.py index 8255fbf..d110c9e 100644 --- a/src/mvp/py/tests/test_ui.py +++ b/src/mvp/py/tests/test_ui.py @@ -93,5 +93,35 @@ def test_ui_new_task_contains_advanced_example_snippet(tmp_path, monkeypatch): # workload is not needed for advanced in v3.5. assert "# workload:" not in r.text assert "actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu" in r.text + # v3.6: Advanced example uses platform-injected env vars so users don't need to edit W&B project/run. + assert "trainer.logger=${MVP_TRAINER_LOGGER}" in r.text + assert "trainer.project_name=${MVP_WANDB_PROJECT}" in r.text + assert "trainer.experiment_name=${MVP_WANDB_RUN}" in r.text assert "Model merge example" in r.text assert "verl.model_merger" in r.text + + +def test_ui_new_task_contains_evaluation_example(tmp_path, monkeypatch): + cfg = _write_config(tmp_path) + monkeypatch.setenv("MVP_INTERNAL_TOKEN", "admin-token") + app = create_app(str(cfg)) + c = TestClient(app) + + r = c.get("/ui/tasks/new") + assert r.status_code == 200 + assert "Evaluation example" in r.text + assert "verl.trainer.main_eval" in r.text + assert "data.path=$HOME/jobs//outputs/.parquet" in r.text + assert "+ray_kwargs.ray_init.address=auto" in r.text + + +def test_ui_login_contains_wandb_section(tmp_path, monkeypatch): + cfg = _write_config(tmp_path) + monkeypatch.setenv("MVP_INTERNAL_TOKEN", "admin-token") + app = create_app(str(cfg)) + c = TestClient(app) + + r = c.get("/ui/login") + assert r.status_code == 200 + assert "W&B" in r.text + assert "Open W&B" in r.text diff --git a/src/mvp/scripts/00_prereq_check.sh b/src/mvp/scripts/00_prereq_check.sh index c6335df..af85e02 100755 --- a/src/mvp/scripts/00_prereq_check.sh +++ b/src/mvp/scripts/00_prereq_check.sh @@ -31,6 +31,10 @@ echo "[host] ensure shared dirs exist under ../../shared" mkdir -p "${ROOT_DIR}/../../shared"/{datasets,hf,jobs,outputs,ray,common,user} mkdir -p "${ROOT_DIR}/../../shared/common"/{code,datasets,models} mkdir -p "${ROOT_DIR}/../../shared/user"/{code} +# v3.6: W&B local server persists metadata under /private/common/wandb (mounted as /vol). +# The wandb container runs as a non-root user; ensure the bind mount is writable. +mkdir -p "${ROOT_DIR}/../../shared/common/wandb" +chmod 777 "${ROOT_DIR}/../../shared/common/wandb" 2>/dev/null || true echo "[host] ensure verl repo exists under ../../verl (required by prepare scripts)" if [[ ! -d "${ROOT_DIR}/../../verl" ]]; then diff --git a/src/mvp/scripts/60_start_api.sh b/src/mvp/scripts/60_start_api.sh index 3c18ae6..ab04e12 100755 --- a/src/mvp/scripts/60_start_api.sh +++ b/src/mvp/scripts/60_start_api.sh @@ -25,6 +25,9 @@ fi env_args=(-e "MVP_INTERNAL_TOKEN=${MVP_INTERNAL_TOKEN}") # If host does not provide it, fall back to the dev default used by docker-compose (kept in sync). env_args+=(-e "SFTPGO_ADMIN_PASSWORD=${SFTPGO_ADMIN_PASSWORD:-my-dev-sftpgo-admin}") +if [[ -n "${WANDB_API_KEY:-}" ]]; then + env_args+=(-e "WANDB_API_KEY=${WANDB_API_KEY}") +fi docker exec -d "${env_args[@]}" "${HEAD_CONTAINER}" bash -lc "nohup python3 /workspace/mvp/py/server.py --config '${CONFIG_IN_CONTAINER}' >>'${LOG_PATH}' 2>&1 & echo \$! >'${PID_PATH}'"