Huawei Cloud Cloudrobo Train

Windows / PowerShell: Examples use bash syntax. To run on Windows PowerShell:

• Flatten \ line continuations to a single line, or end lines with a backtick.
• Set env vars with $env:NAME="value" instead of export NAME="value".
• Single-quoted JSON '{"a":"b"}' works as-is.

Overview

Manages the full lifecycle of CloudRobo model training tasks and SimRL tasks. Two training modes (MODEL_TUNING / TRAIN_FROM_SCRATCH), five methods (FFT/SFT/LORA/QLORA/DEEPSPEED). Covers creation, monitoring, diagnosis, drafts, stats, resume, and pipeline orchestration.

Two task surfaces (switchable via --sim-rl):

Scenarios: Fine-tuning, pretraining, simulation RL, draft save/resubmit, monitoring (stages/ resource/events), failure diagnosis, stats. Training runs for hours/days; poll at 30-60s intervals.

Agent → CLI (`cloudrobo train <command> [--sim-rl]`) or SDK (`TrainClient`)
      → cloudrobo-service (REST API)

All operations target cloudrobo-service and require a workspace_id. Model/dataset discovery calls cloudrobo-asset-manager via cloudrobo asset.

Prerequisites

See references/cli-installation-guide.md for CLI installation, AK/SK authentication, and workspace configuration. All training operations require a valid workspace_id.

Workspace auto-resolution: The SDK and CLI automatically resolve workspace_id in this order:

1. Explicit --workspace-id parameter (if provided)
2. Configured default workspace (via cloudrobo workspace use --workspace-id <id>)
3. Auto-query: if no workspace is configured, the SDK queries cloudrobo workspace list, picks the first available workspace, saves it as the default, and uses it

If auto-query fails (no workspaces found), the error message guides the user to create a workspace. To manually set the default workspace: cloudrobo workspace use --workspace-id <id>.

Workflow

Create Task Based on Existing Task

Scenario: user says "按照已有任务创建一个训练任务", "复制之前的任务", "create same task again", or references an existing task name. Do NOT walk through the full Task Creation Workflow. Instead, reuse the existing task's config directly.

1. Find the existing task — if user provides a task name (not ID), use cloudrobo train list-tasks to find it by name. If user provides task_id, skip to step 2.
2. Query the existing task config — cloudrobo train show-task --task-id <id> → extract the full task config (train_mode, train_method, algorithm, input_models, datasets, spec, cluster_id, worker_num, parameters, env, output_models).
3. Identify what to change — ask the user in ONE question what to modify (usually just the task name). Common changes: name, output_models[].model_name, parameters values.
4. Auto-fix conflicts — if creating a task with the same output model name, append a suffix (e.g., -2, -3, or date) to avoid "输出模型已存在" error. Check via list-tasks if needed.
5. Build new config — copy the existing config, apply user's changes. Keep all other fields identical (algorithm, datasets, spec, cluster_id, etc.).
6. Submit — cloudrobo train create-task --config '<new-json>' (CLI preferred). With --verbose/-v, show a user-friendly summary first.
7. Poll status — 30s interval until terminal state.

Key principle: When the user references an existing task, the agent MUST first query that task's config via show-task and reuse it, NOT walk through model/dataset/method selection again. The user has already made those decisions; they just want a new task with the same config.

Task Creation Workflow

Shortcut: If the user references an existing task ("按照已有任务创建", "复制之前的任务", "create same task again"), skip this workflow and use Create Task Based on Existing Task instead.

This unified workflow drives both MODEL_TUNING and TRAIN_FROM_SCRATCH task creation. The agent orchestrates discovery across cloudrobo-asset / cloudrobo-resource, presents options filtered by what the cloud actually supports, then submits silently (add --verbose/-v on the CLI call to print a user-friendly summary first and submit, no yes/no prompt).

Step 1 — Default Task Name

• Default: Train-{YYYYMMDD-HHMMSS} (e.g. Train-20260812-143000)
• Agent offers the default; user may rename. Proceed with default if user says "ok".

Step 2 — Choose Training Method

Ask exactly one question (use a single AskUserQuestion call, NEVER duplicate):

• A) 模型调优 (MODEL_TUNING) — has a base model, finetune via FFT/SFT/LORA/QLORA/DEEPSPEED
• B) 无基模型训练 (TRAIN_FROM_SCRATCH) — three sub-paths (see Step 3b)

No-repeat rule: Each step in this workflow asks the user AT MOST ONE question. Never call AskUserQuestion twice in parallel for the same topic. If a step requires multiple inputs (e.g. model_name + model_type), combine them into a single question with default options.

Step 3a — MODEL_TUNING sub-flow

1. Model source: 具身广场-模型 (Gallery, PUBLIC_MODEL_ASSET) / 空间资产-模型 (Workspace, CUSTOM_MODEL_ASSET)

   • CLI: cloudrobo asset list-publication-assets --type model (Gallery) / cloudrobo asset list-assets --type model (Workspace)
   • List output must show: model name + latest_version_id (user selects from this list)
   • Critical: When listing workspace models, filter results for status == "DRAFT" before presenting to user. Only DRAFT status models are ready for use. Models in CREATING status will cause CREATE_FAILED with error "输入模型未就绪". If no DRAFT models exist, warn the user and suggest using a Gallery model instead. Gallery models are typically all ready.
   • Gallery model (default path): Steps 2-5 below apply — algorithm comes from model's actions array
   • Workspace model (custom path): Skip Steps 2-5; algorithm config only needs algorithm_asset_id and algorithm_version_id (2-field format). The backend auto-resolves engine/command from the algorithm asset. Top-level inputs/outputs arrays are required. See Workspace Model Sub-flow below.

2. Select model → extract asset_id + latest_version_id from the selected model's list entry. Do NOT ask user for version again — auto-use latest_version_id. Only ask if user explicitly wants a non-latest version.

3. Query model version detail GET /v1/assets/{asset_id}/versions/{version_id} → get actions array. Each action: {action, algorithm:{asset_id, version_id}, status}

4. Show ONLY available training actions — filter actions where status == "ENABLE" AND action is training-related (e.g., "FFT", "SFT", "LORA", "QLORA", "DEEPSPEED"). Exclude non-training actions like "ONLINE_DEPLOYMENT". The action field value becomes the train_method in the request body. Do NOT offer actions the model does not advertise.

5. User picks method → extract matched action's action value (this is train_method) and algorithm.asset_id + algorithm.version_id. Do NOT ask user for algorithm version — it comes directly from the action. Auto-extract and proceed.

6. Query algorithm version detail GET /v1/assets/{algo_asset_id}/versions/{algo_version_id} → get ext_metadata. Extract from exact fields:

   • Hyperparams: ext_metadata.hyperparams → [{name, default, constraint, description}]
   • Environment variables: ext_metadata.environment_variables → [{name, default, description}]
   • Resource constraints: ext_metadata.resource → [{key, values, operator, constraints?}]
     • flavor_type.values: required flavor type (e.g., ["Ascend"]) — must match pool flavors
     • device_distributed_mode.constraints: {step, range:[min, max], default} — min/max NPU cards per worker; default is the recommended card count
     • host_distributed_mode.values: "singular" (single host, worker_num=1) or "multiple"
   • Note: ext_metadata.env may exist but is deprecated; always use environment_variables

7. Show env + hyperparams — present two tables:

   • Hyperparams table: | 参数名 | 默认值 | 说明 | 约束 |
   • Env vars table: | 变量名 | 默认值 | 说明 | User can modify values. Build parameters JSON string: each item {key, desc, value, constraint} (pass ALL hyperparams, required or not). Build env JSON string from environment_variables (default "[]" if empty or missing).

8. Dataset selection — three sources, each with different required fields:

   • Gallery (具身广场): source_type: "PUBLIC_DATASET_ASSET" + dataset_asset_id + version_id + dataset_name
   • Workspace (空间资产): source_type: "CUSTOM_DATASET_ASSET" + dataset_asset_id + version_id + dataset_name. Filter for status == "DRAFT" — same as models, only DRAFT datasets are ready for use.
   • OBS (对象存储): source_type: "OBS" + url_path only (e.g., "obs://bucket-name/dataset/"). No dataset_asset_id/version_id/dataset_name needed.

   Critical: Workspace datasets use CUSTOM_DATASET_ASSET (NOT DATASET). Using the wrong source_type causes RUN_FAILED with exitCode 1 in <2 minutes.

9. Resource selection — Before choosing the pool, check data_read permission for all input assets (models and datasets) that use Gallery source types (PUBLIC_MODEL_ASSET_OFFICIAL, PUBLIC_MODEL_ASSET_COMMUNITY, PUBLIC_DATASET_ASSET):

   cloudrobo asset check-permission --asset-id <id> --version-id <ver> --permissions data_read
   

   • If any asset returns data_read: deny → DEDICATED pools will fail with "专属资源池需要输入资产的可读权限" → must use SHARED pool
   • If all assets return data_read: allow → DEDICATED and SHARED pools both work
   • Workspace assets (CUSTOM_*) and OBS datasets don't need this check

   Then query pools: cloudrobo resource list-pools --resource-type MODELARTS (Note: --usages MODEL_TRAINING filter may cause 504 proxy timeout in some environments; if it fails, omit the filter and manually select pools with MODEL_TRAINING in usages array). For each pool, read config.flavor.ASCEND[] (list of spec strings like "1 * SNT9B2 | 24 vCPUs | 192 GiB"). Filter flavors using ext_metadata.resource constraints from Step 3a.6:

   • Parse the NPU count from each spec (the number before *, e.g., "2 * SNT9B2..." → 2)
   • Only show flavors where NPU count >= device_distributed_mode.constraints.range[0] (minimum) and <= range[1] (maximum). E.g., range: [2, 8] filters out 1-card flavors.
   • Default selection: the flavor matching constraints.default (e.g., default: 2 → "2 * SNT9B2 | 48 vCPUs | 384 GiB"). User can pick a different valid flavor.
   • worker_num: 1 if host_distributed_mode is "singular"; ask user if "multiple". → select pool → cluster_id (with pool- prefix). Spec: Ascend: <n> * <model> | <vCPUs> vCPUs | <GiB> GiB

10. Output model — ask save_mode once:

    • NEW_MODEL: ask model_name + model_type in one question; auto-generate version_name as "0.0.1" (user can override). Do NOT ask each field separately. Fields: save_mode + model_name + version_name + model_type + strict:false
    • NEW_VERSION: ask which existing model + version_name in one question. List existing models with their latest version; user picks and provides new version_name. Fields: save_mode + model_asset_id + model_name + version_id + version_name + model_type
    • NOT_SAVE: skip remaining output model fields. Fields: save_mode only — {"save_mode": "NOT_SAVE"}
    • Clone/Replicate task warning: If user requests to create task "based on existing task", "replicate previous task", or "create same task again", the output model name MUST be different from the original task. Check existing output model names via list-tasks or show-task before submission. If conflict detected, prompt user for new name. Error on conflict: "输出模型已存在，请更换模型名称" (Output model already exists, please change model name).

11. Submit — cloudrobo train create-task --config '<json>' (CLI) or client.create_train_task(req) (SDK). Default: silent submit (no output). With --verbose/-v, show a user-friendly summary first. NEVER print raw JSON or code. Then submit directly — no yes/no prompt.

12. Poll status — 30s interval, report state changes until terminal state

13. On completion: FINISHED → suggest export/deploy; FAILED/RUN_FAILED/CREATE_FAILED → offer logs/events for diagnosis

Step 3a-WS — Workspace model sub-flow (空间资产模型)

When the user selects a workspace model (CUSTOM_MODEL_ASSET) in Step 3a.1, the algorithm cannot be resolved from a model actions array. Instead, it must be fully configured from the workspace model's algorithm metadata. This sub-flow replaces Steps 2-5 of the Gallery path. Steps 6-13 (ext_metadata, hyperparams, dataset, resource, output model, submit, poll) are shared.

1. Select workspace model → extract asset_id + latest_version_id from the list entry. Do NOT ask user for version again.

   Critical: Workspace model must have status == "DRAFT". Models in CREATING status cannot be used as input and will cause CREATE_FAILED with error "输入模型未就绪". If no DRAFT models exist in the workspace, inform the user and suggest using a Gallery model instead.

2. Query model version detail → get ext_metadata for algorithm config (engine, image, code_dir, command, etc.). The workspace model's algorithm info is embedded in the model itself, not in a separate algorithm asset's actions array.

3. Build algorithm config — only 2 fields needed. The backend auto-resolves engine/command from the algorithm asset. Do NOT include algorithm_source_type, engine, code_dir, command, image_asset_id, or image_version_id:

   {
     "algorithm_asset_id": "<algo-asset-id>",
     "algorithm_version_id": "<algo-version-id>"
   }
   

4. Build input_models — use source_type: "CUSTOM_MODEL_ASSET" (not PUBLIC_MODEL_ASSET):

   [{"source_type": "CUSTOM_MODEL_ASSET", "model_asset_id": "...", "version_id": "...", ...}]
   

5. Build inputs/outputs arrays — workspace models require top-level inputs and outputs:

   inputs format (each item's fields depend on source_type):

   "inputs": [{
     "name": "<display-name>",
     "url_path": "obs://bucket/path/",
     "source_type": "CUSTOM_MODEL_ASSET",
     "access_method": "env",
     "local_dir": "<name>=/home/ma-user/cloudrobo/inputs/<name>_0"
   }]
   

   • access_method: "env" (环境变量) or "parameter" (超参)
   • local_dir: env mode → "<name>=<container-path>", parameter mode → "--<name>=<container-path>"

   outputs format (simpler — no source_type, always OBS path):

   "outputs": [{
     "name": "<output-name>",
     "url_path": "obs://bucket/output-path/",
     "access_method": "parameter",
     "local_dir": "--<name>=/home/ma-user/cloudrobo/outputs/<name>_0"
   }]
   

6. Continue with Steps 6-13 of the Gallery path (ext_metadata hyperparams, env vars, resource constraints, dataset selection, resource selection, output model, submit, poll). log_path is optional for MODEL_TUNING.

Key difference from Gallery model: workspace model uses CUSTOM_MODEL_ASSET source_type, 2-field algorithm format (same as Gallery), and requires top-level inputs/outputs arrays. Gallery model uses PUBLIC_MODEL_ASSET and does not require inputs/outputs arrays.

Step 3b — TRAIN_FROM_SCRATCH sub-flow

Ask which sub-path:

• A) 预制算法 (Gallery algorithm)
• B) 空间资产-算法 (Workspace algorithm)
• C) 现配置算法 (Custom config, no preset algorithm)

Common steps (all sub-paths):

• Dataset selection (same as Step 3a.8)
• Resource selection (same as Step 3a.9, using ext_metadata.resource constraints; for Custom: no constraints, show all flavors)
• Hyperparams (same as Step 3a.7; for Custom: from scratch, no defaults, build "[]" if none)
• Output model (same as Step 3a.10; NEW_MODEL only needs 4 fields: save_mode, model_name, version_name, model_type)
• Build request body: name (unique, append timestamp), train_mode: "TRAIN_FROM_SCRATCH", datasets, spec, worker_num, cluster_id, parameters, env (default "[]"), output_models, log_path (optional), enable_jupyter (optional, DEDICATED only)
• Submit (cloudrobo train create-task --config '<json>', --verbose to show summary), poll 30s until terminal state

Algorithm config differences:

Notes:

• algorithm_source_type NOT needed for Gallery/Workspace (auto-inferred from algorithm_asset_id); only required for Custom (no asset_id).
• Workspace algorithm also requires top-level inputs/outputs arrays (same format as Step 3a-WS).
• Custom config requires inputs/outputs arrays. inputs support 4 source_type values: PUBLIC_DATASET_ASSET, CUSTOM_DATASET_ASSET, OBS, CUSTOM_MODEL_ASSET. See Step 3a-WS for format.
• On failure (RUN_FAILED with exitCode 1 in <2 minutes, no log files): likely dataset format incompatibility. Check source_type and algorithm compatibility.

SimRL Workflow (Simulation Reinforcement Learning)

SimRL tasks use a different config schema from regular training tasks: config_mode + task_set + simple_params/rl_config_content instead of algorithm + parameters. SimRL tasks share the same CLI command surface via the --sim-rl flag. There is no resume for SimRL.

Step 1 — Model Discovery

Ask model source:

• 具身广场模型 (Gallery): cloudrobo asset list-publication-assets --type model --action-status ENABLE --actions LIBERO_SPATIAL,LIBERO_OBJECT,LIBERO_GOAL,LIBERO_10 --actions-operator OR → pick model → extract model_asset_id + latest_version_id. source_type: "PUBLIC_MODEL_ASSET". Do NOT ask user for version again — auto-use latest_version_id. Critical: The --actions and --action-status filters are REQUIRED — without them, the query returns ALL Gallery models, not just SimRL-compatible ones. Only models with ENABLED LIBERO_* actions can be used for SimRL tasks.
• 空间资产模型 (Workspace): cloudrobo asset list-assets --type model → pick model → extract model_asset_id + latest_version_id. source_type: "CUSTOM_MODEL_ASSET".

Query model version detail GET /v1/assets/{asset_id}/versions/{version_id} → get actions array. Each action represents a task set (e.g., LIBERO_SPATIAL, LIBERO_GOAL).

Step 2 — Select Task Set

From the actions array, show available task sets to the user. Extract the action field value (e.g., "LIBERO_SPATIAL") → this becomes task_set in the request body.

Query the selected task set detail (action detail) to get:

• ext_metadata.hyperparams → for SIMPLE mode parameter defaults
• ext_metadata.environment_variables → for SIMPLE mode env vars
• ext_metadata.yaml_config → for ADVANCED mode (full YAML config template)
• ext_metadata.resource → resource constraints (flavor type, NPU card count)

Step 3 — Config Mode Selection

Ask user which parameter configuration mode:

• 快速配置 (SIMPLE): config_mode: "SIMPLE", uses simple_params (JSON string array). Show hyperparams table from ext_metadata.hyperparams, user can modify values. Default RL_ALGO is ppo.

  "simple_params": "[{\"key\":\"RL_ALGO\",\"value\":\"ppo\",\"desc\":\"强化学习算法\"},{\"key\":\"MAX_EPOCHS\",\"value\":\"100\",\"desc\":\"训练轮数\"},...]"
  

  Each item: {key, value, desc}. simple_params accepts either a JSON string or an array; the SDK auto-serializes arrays to JSON strings.

• YAML配置 (ADVANCED): config_mode: "ADVANCED", uses rl_config_content (full YAML string). Pre-fill with ext_metadata.yaml_config content, user can adjust.

  "rl_config_content": "runner:\n  task_type: embodied\n  max_epochs: 100\n  ..."
  

Step 4 — Resource Pool Selection

Same permission check (data_read for Gallery assets) and pool query as Step 3a.9. Filter flavors by ext_metadata.resource constraints. SimRL-specific rules:

• SHARED (公共) pool: enable_jupyter must be false (JupyterLab not supported)
• DEDICATED (专属) pool: enable_jupyter can be true or false (user's choice)
• spec format: "ASCEND: <n> * <model> | <vCPUs> vCPUs | <GiB> GiB" (uppercase ASCEND)

Step 5 — Output Model

Ask save_mode:

• NEW_MODEL (新模型): 7 fields:

  {"save_mode": "NEW_MODEL", "model_name": "<name>", "version_name": "0.0.1", "model_type": "vla",
   "model_asset_id": null, "version_id": null, "strict": false, "skills": []}
  

• NEW_VERSION (已有模型新版本): 8+ fields:

  {"save_mode": "NEW_VERSION", "model_name": "<existing-model-name>", "version_name": "<new-version>",
   "model_type": "vla", "model_asset_id": "<existing-model-asset-id>", "version_id": "",
   "strict": false, "skills": [{"name": "<skill-name>", "prompt": "<skill-prompt>"}]}
  

  version_id is empty string "" (not null). skills array can contain skill definitions.

Step 6 — Build SimRL Config

Construct JSON with:

• name: unique task name
• description: optional
• workspace_id: auto-injected by SDK
• input_models: [{source_type, model_asset_id, model_name, version_id, version_name}] (PUBLIC_MODEL_ASSET for Gallery, CUSTOM_MODEL_ASSET for Workspace)
• task_set: from Step 2 (e.g., "LIBERO_SPATIAL")
• config_mode: "SIMPLE" or "ADVANCED" from Step 3
• simple_params: JSON string (SIMPLE mode only)
• rl_config_content: YAML string (ADVANCED mode only)
• spec: "ASCEND: <n> * <model> | <vCPUs> vCPUs | <GiB> GiB"
• cluster_id: pool ID with pool- prefix
• worker_num: typically 1
• output_models: from Step 5
• enable_jupyter: false for SHARED pool; true/false for DEDICATED pool

Note: SimRL does NOT use algorithm, datasets, parameters, env, or log_path fields.

Step 7 — Submit and Monitor

1. Create SimRL task — train create-task --config '<json>' --sim-rl (or save-draft --sim-rl to save a draft first). SDK: client.create_sim_rl_task(req).
2. Poll status — train show-task --task-id <id> --sim-rl or train list-tasks --sim-rl
3. Monitor — get-resource-usage --metric ... --start ... --end ... --sim-rl, get-events --start-time ... --end-time ... --sim-rl, get-logs --sim-rl, get-signed-url --file-source ... --file-name ... --sim-rl
4. Lifecycle — stop-task --sim-rl, restart-task --sim-rl, clone-task, delete-tasks --sim-rl (per-id DELETE), update-task --sim-rl
5. Stats — train stats --workspace-id <id> --sim-rl

Draft Workflow (Save & Resubmit)

Scenario: user wants to save a task config without executing immediately, then edit and submit later.

1. Prepare task config — only name + workspace_id required for draft; algorithm/spec optional
2. Save draft — train save-draft --config '<draft-json>' (or --sim-rl for SimRL draft) → returns task_id, task status = DRAFT
3. Later, edit config and resubmit:
   • SDK (recommended for draft submit): restart_train_task(task_id, req) with full TrainTaskDto body — restart endpoint edits and resubmits
   • CLI: train restart-task --task-id <draft-id> resubmits with existing config; use SDK to pass edited config. For SimRL, restart-task --sim-rl resubmits
4. After resubmit, task leaves DRAFT state → CREATING → WAITING → RUNNING → terminal

Inference note: save-draft (POST /train-tasks/draft) returns task_id in DRAFT status; restart (POST /train-tasks/{id}/restart) accepts full TrainTaskDto body and edits/resubmits the task.

Monitoring Workflow (In-Progress Task)

Scenario: task is RUNNING, track progress and resource usage.

1. Poll status — show-task --task-id <id> (30s interval)
2. Query execution stages — get-stages --task-id <id> → returns SCHEDULING → PREPARING → RUNNING → END with sub-stages and timestamps
3. Query resource usage — get-resource-usage --task-id <id> --metric <m> --start <ts> --end <ts> → CPU/GPU/NPU utilization, sample points
4. Query events — get-events --task-id <id> --start-time <ts> --end-time <ts> → INFO/WARNING/ERROR/DEBUG events with timestamps
5. Report progress to user; on WARNING/ERROR events, proactively alert

Diagnosis Workflow (Failure Diagnosis)

Scenario: task FAILED / RUN_FAILED / CREATE_FAILED → auto-analyze, locate cause, suggest fixes.

1. Get task detail to confirm failure status, failure stage, and exit code
2. Get execution stages to identify which stage failed (SCHEDULING/PREPARING/RUNNING/END)
3. Get events filtered by level=Error to find error events
4. Get logs — try multiple approaches (logs may not be available if task failed quickly):
   • get-logs --task-id <id> (CLI)
   • SDK: list_observations(task_id) to list available log files
   • SDK: get_log_signed_url(task_id, file_source, file_name) to get download URL
   • If all return empty/500: task may have failed before generating logs
5. Analyze key error patterns:
   • CREATE_FAILED → check spec format, cluster_id, resource availability, input model status
   • Input model not ready → error: "输入模型未就绪" (input model not ready). Occurs when input_models[].source_type is CUSTOM_MODEL_ASSET but the model status is CREATING (not DRAFT). Fix: wait for model to reach DRAFT status, or use a Gallery model (PUBLIC_MODEL_ASSET) instead. Check model status via cloudrobo asset show-asset --asset-id <id>
   • Task name conflict → error: "Resource has already existed" (409 Conflict). Fix: use unique task name (append timestamp suffix like Train-YYYYMMDD-HHMMSS)
   • Resource scheduling failure → check spec and worker_num, cluster capacity
   • Image pull failure → check algorithm.image_url (for MODEL_TUNING) or algorithm asset config (for TRAIN_FROM_SCRATCH)
   • Dataset access denied → check datasets[].dataset_asset_id and workspace permissions
   • Dataset format incompatibility → RUN_FAILED with exitCode 1 in <2 minutes, no log files. Occurs when dataset format doesn't match algorithm expectations (e.g., LeRobot algorithm expects specific dataset format) or when source_type is wrong (using DATASET instead of CUSTOM_DATASET_ASSET). Fix: verify dataset source_type is correct (CUSTOM_DATASET_ASSET for workspace, NOT DATASET); try using Gallery dataset (PUBLIC_DATASET_ASSET) instead; check algorithm documentation for required dataset format
   • OOM → check spec memory, worker_num, reduce batch_size in parameters
   • Algorithm error → check algorithm.command, boot_file, parameters
   • Output model exists → error: "输出模型已存在，请更换模型名称". For clone/restart, the SDK auto-increments version_name by querying the latest version from the asset service. For create-task, use unique model name (append suffix like -2, -3, or timestamp)
   • Dedicated pool asset permission → error: "专属资源池需要输入资产的可读权限". Occurs when using DEDICATED pool without proper asset permissions. Fix: switch to SHARED pool or grant read/write/usage permissions on all involved assets (input_models, algorithm, datasets)
   • Logs unavailable (API returns 500 or empty list): task failed too quickly, logs not generated. Check task execution time and exit code from events. If exitCode 1 and runtime <2 min, likely dataset format or training script error. Suggest trying different dataset or checking algorithm documentation for required dataset format.
6. Output diagnosis conclusion and fix suggestions
7. After user confirmation, fix config and restart-task or save-draft + create-task

Long-Running Task Workflow

1. Training tasks can run for hours/days; after creating, set a reasonable polling interval (60s+)
2. On each poll, report: current status, current stage, elapsed time, latest events
3. On timeout (user-defined), output current status and suggest: continue / view logs / view resource usage / stop

CLI Command Format

cloudrobo train <command> [OPTIONS] [--sim-rl]

• Subcommands: kebab-case (create-task, list-tasks, get-stages, etc.)
• --sim-rl routes to SimRL API surface; --workspace-id <id> overrides on stats
• JSON params via --config '<json>'; optionally from file via --config-file
• Output: JSON to stdout

CloudRobo CLI is a self-developed Click-based tool (not hcloud/KooCLI). SDK exposes 33 methods (12 train_* + 15 sim_rl_* + 6 unprefixed); CLI exposes 20 commands. list_observations is SDK-only; resume and checkpoint methods are train-only. See references/task-config-catalog.md for the coverage matrix.

Core Commands

CLI First: Always prefer CLI commands (cloudrobo train <command>) over direct SDK calls. Use Python SDK (TrainClient) only when: (a) CLI doesn't support the needed operation (e.g., list_observations is SDK-only), (b) cross-package queries (e.g., querying asset version detail via asset service), or (c) CLI fallback for dynamic JSON assembly. Full SDK templates in SDK Quick Start.

workspace_id auto-resolution: All commands that need workspace_id (create-task, save-draft, restart-task, list-tasks, stats) automatically resolve it from: (1) explicit --workspace-id param, (2) configured default workspace (cloudrobo workspace use), or (3) auto-query. You do NOT need to include workspace_id in the --config JSON body — the SDK injects it automatically.

SDK-level validation: SDK methods (create_train_task, create_sim_rl_task, save_draft, create_sim_rl_task_draft, restart_train_task, restart_sim_rl_task, copy_sim_rl_task, register_train_checkpoint) validate required fields before making HTTP calls. Missing fields raise ValueError. CLI automatically converts these to user-friendly click.UsageError messages. Use SDK directly when you need programmatic error handling.

Task Creation

Training mode (fine-tuning MODEL_TUNING vs pre-training TRAIN_FROM_SCRATCH), method (FFT/SFT/LORA/QLORA/DEEPSPEED), base model, dataset, and spec are all expressed through the --config JSON body — there are no dedicated finetune/pretrain subcommands.

Submit a fine-tuning task (MODEL_TUNING)

cloudrobo train create-task --config '<task-json>'

• SDK: client.create_train_task(req) — req format see Step 3a
• API: POST /v1/training/train-tasks

Submit a pretraining task (TRAIN_FROM_SCRATCH)

cloudrobo train create-task --config '<task-json>'

• SDK: client.create_train_task(req) — req format see Step 3b
• API: POST /v1/training/train-tasks
  • log_path: optional OBS path — can be omitted if user doesn't specify a log path
  • enable_jupyter: optional boolean — set to true for JupyterLab access during training
  • workspace_id: auto-injected by SDK

Create a task from full JSON config (train or SimRL)

cloudrobo train create-task --config '<task-json>' [--sim-rl]

• SDK: client.create_train_task(req) / client.create_sim_rl_task(req)
• API: POST /v1/training/train-tasks / POST /v1/training/rl-tasks/simulation

Save a draft task (train or SimRL)

cloudrobo train save-draft --config '<draft-json>' [--sim-rl]

• SDK: client.save_draft(req) / client.create_sim_rl_task_draft(req)
• API: POST /v1/training/train-tasks/draft / POST /v1/training/rl-tasks/simulation/draft

Task Management

All accept --sim-rl (except resume-task, clone-task which is SimRL-only). API prefix: /v1/training/train-tasks (train) / /v1/training/rl-tasks/simulation (SimRL).

delete-tasks behavior: For regular training tasks, the CLI auto-resolves execution_id from the provided task_id via show-task before calling batch-delete. Users can pass task IDs directly — no need to manually look up execution_id. For SimRL, task_id is used directly.

| Stop task | stop-task --task-id <id> | stop_train_task / stop_sim_rl_task | POST /{task_id}/stop | | Restart task | restart-task --task-id <id> [--config '<json>'] [--config-file <path>] [--sim-rl] | restart_train_task(task_id, req=None) / restart_sim_rl_task(task_id, req=None, task_detail=None) | POST /{task_id}/restart | | Clone task | clone-task --task-id <id> [--config '<json>'] [--config-file <path>] (SimRL-only) | copy_sim_rl_task(task_id, req=None, task_detail=None) | POST /rl-tasks/simulation/{task_id}/copy | | Resume task | resume-task --task-id <id> (train-only) | resume_train_task | POST /{task_id}/resume | | Stats | stats --workspace-id <id> [--user-id] | count_train_tasks_by_status / count_sim_rl_tasks_by_status | GET /stats |

Restart = edit & resubmit. CLI supports --config/--config-file to override fields from the original task; SDK's req param does the same. Non-DRAFT train tasks cannot modify name/train_mode/ train_method; SimRL restart requires DRAFT status. SDK's task_detail param skips the auto show call. SDK auto-cleans input_models/output_models (strips runtime fields), auto-increments version_name for save_mode=NEW_MODEL|NEW_VERSION (queries asset service for latest version), and auto-serializes simple_params/rl_config_content from array/dict to JSON string. Clone is SimRL-only (train copy API removed). SDK auto-generates a new name with -copy-{4hex} suffix (e.g., my-task-copy-a1b2) unless req provides an explicit name. Resume is train-only.

Task Monitoring

All accept --sim-rl. get-events uses milliseconds (13-digit), get-resource-usage uses seconds (10-digit).

Checkpoint Management (train-only, no --sim-rl)

cloudrobo train list-checkpoints --task-id <id> [--status] [--name] [--offset] [--limit] [--order]
cloudrobo train register-checkpoint --task-id <id> --checkpoint-name <name> [--save-mode NEW_VERSION|NEW_MODEL] [--version-name] [--model-name]

register-checkpoint: NEW_VERSION (default) adds to existing model; NEW_MODEL creates new model (requires --model-name). Returns WAITING; processed asynchronously.

Algorithm Discovery

List available algorithms

cloudrobo asset list-publication-assets --type algorithm [--name <fuzzy-name>] [--limit 20]

• SDK (cross-package): asset_client.list_publication_assets(type="algorithm", limit=20)
• API: Cross-package — calls the asset service, not the train service directly.

Each algorithm includes ext_metadata with engine.image_url, command, boot_file needed for pretrain task creation.

Query asset version detail (model / algorithm / dataset)

• API: GET /v1/assets/{asset_id}/versions/{version_id} (cloudrobo-asset-manager service)
• Model version detail returns: actions array, each action has {action, algorithm:{asset_id, version_id}, status}. The action field (e.g., FFT, LORA, ONLINE_DEPLOYMENT) becomes train_method. Filter for training-related actions with status=="ENABLE" (exclude ONLINE_DEPLOYMENT etc.)
• Algorithm version detail returns: ext_metadata.hyperparams (default hyperparameters), ext_metadata.environment_variables (env vars as [{name, default, description}]), ext_metadata.engine.image_url, ext_metadata.command, ext_metadata.inputs/outputs, ext_metadata.resource (NPU card constraints — see Step 3a.6)
• Usage: For fine-tuning, get algorithm from model actions → query algorithm version detail for hyperparams; for pretraining, query algorithm asset version detail directly for hyperparams and full ext_metadata

Submission Behavior & Confirmation

Silent submit by default. Creation commands (create-task/restart-task/ register-checkpoint/save-draft) submit without prompting. The agent MUST NOT print raw JSON or code. With --verbose/-v, present a u

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