PAI-DLC Deep Learning Job Management#

Manage deep learning training jobs on Alibaba Cloud PAI-DLC (Platform for AI - Deep Learning Containers) service.

Scenario Description#

PAI-DLC is a distributed training service provided by Alibaba Cloud's AI Platform PAI, supporting:

• Job Creation and Execution — Create distributed training jobs for TensorFlow, PyTorch, XGBoost, and other frameworks
• Job Monitoring — Get job status, logs, events, and monitoring metrics
• Compute Health Check — Check health status of GPU and other compute devices
• Job Management — Update and stop jobs

Architecture: PAI Workspace + DLC Job + Computing Resources (ECS public pay-as-you-go or Lingjun dedicated quota) + AIWorkSpace catalog (images / datasets / code sources / quotas / workspaces).

Installation Requirements#

Pre-check: Aliyun CLI >= 3.3.1 required Run aliyun version to verify version >= 3.3.1. If not installed or version is too low, see references/cli-installation-guide.md for installation instructions. Then [Required] run aliyun configure set --auto-plugin-install true to enable automatic plugin installation.

Note on --user-agent: Every API-invoking aliyun command in this skill MUST include --user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job/${SESSION_ID} (unified User-Agent + session-id template — see Observability for the generation rules). Client-side helpers ( aliyun version, aliyun configure ..., aliyun plugin ..., aliyun <product> --help) do not invoke remote APIs and therefore do not require the flag.

Network timeout & retry (rule --help doesn't enforce): aliyun CLI defaults to 10s connect / 10s read with no retry. For long-running flows (large list, slow region) explicitly raise via global flags --connect-timeout 15 --read-timeout 30 --retry-count 2. Never rely on the default for user-confirmed high-risk calls ( stop-job / delete-*).

bash

aliyun version
aliyun configure set --auto-plugin-install true
aliyun pai-dlc --help
aliyun aiworkspace --help >/dev/null 2>&1 || aliyun plugin install --names aliyun-cli-aiworkspace
aliyun plugin update

Observability#

Why: every PAI-DLC API call issued by this skill MUST carry a unified User-Agent so platform-side tracing can attribute the request to this skill and correlate all calls within a single agent session.

User-Agent template#

Every API-invoking aliyun command MUST pass:

text

--user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job/{session-id}

• Prefix AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job is fixed — never alter it.
• {session-id} is a per-session identifier generated by the rules below.
• Client-side helpers ( aliyun version, aliyun configure ..., aliyun plugin ..., aliyun <product> --help) do NOT invoke remote APIs and therefore do NOT require the flag.

session-id generation rules#

1. Generate the session-id once at the start of the skill session, before the first API call.
2. Format: lowercase, no spaces, no slashes. Recommended uuidgen | tr 'A-Z' 'a-z' or $(date +%s)-$RANDOM.
3. Reuse the same session-id for every command in the session — never regenerate per call. This lets tracing group all calls of one session.
4. Export it once and reference the shell variable in every API call:

bash

SESSION_ID=$(uuidgen | tr 'A-Z' 'a-z')
export SESSION_ID
# every API-invoking command then appends:
#   --user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job/${SESSION_ID}
aliyun pai-dlc list-jobs --region <r> \
  --user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job/${SESSION_ID}

Do NOT use aliyun configure ai-mode to set the User-Agent. That mode is deprecated for skills. Always pass --user-agent explicitly per command with the session-id template, so each session is independently traceable.

text

## Environment Variables

This skill does not require any custom environment variables. Credentials are handled
by the Alibaba Cloud CLI configuration (see Authentication below). Optionally:

| Variable | Required | Purpose |
|----------|----------|---------|
| `ALIBABA_CLOUD_PROFILE` | Optional | Selects a non-default `aliyun configure` profile |
| `ALIBABA_CLOUD_REGION_ID` | Optional | Default region when `--region` is omitted (still recommended to pass `--region` explicitly) |

Do NOT export `ALIBABA_CLOUD_ACCESS_KEY_ID` / `ALIBABA_CLOUD_ACCESS_KEY_SECRET` from
within this session; configure them outside (`aliyun configure` or shell profile).

## Authentication Configuration

> **Pre-check: Alibaba Cloud Credentials Required**
>
> **Security Rules:**
> - **NEVER** read, echo, or print AK/SK values (e.g., `echo $ALIBABA_CLOUD_ACCESS_KEY_ID` is FORBIDDEN)
> - **NEVER** ask the user to input AK/SK directly in the conversation or command line
> - **NEVER** use `aliyun configure set` with literal credential values
> - **ONLY** use `aliyun configure list` to check credential status
>
> ```bash
> aliyun configure list
> ```
> Check the output for a valid profile (AK, STS, or OAuth identity).
>
> **If no valid profile exists, STOP here.**
> 1. Obtain credentials from [Alibaba Cloud Console](https://ram.console.aliyun.com/manage/ak)
> 2. Configure credentials **outside of this session** (via `aliyun configure` in terminal
>    or environment variables in shell profile)
> 3. Return and re-run after `aliyun configure list` shows a valid profile

## RAM Permissions

> **[MUST] Permission Failure Handling:** When any command or API call fails due to
> permission errors at any point during execution, follow this process:
> 1. Read `references/ram-policies.md` to get the full list of permissions required by this SKILL
> 2. Use `ram-permission-diagnose` skill to guide the user through requesting the necessary permissions
> 3. Pause and wait until the user confirms that the required permissions have been granted

For detailed permission list, see [references/ram-policies.md](references/ram-policies.md).

**Required Permissions Overview:**

| Operation | Required Permission |
|-----------|---------------------|
| Create Job | `pai:CreateJob` |
| List Jobs | `pai:ListJobs` |
| Get Job Details | `pai:GetJob` |
| Get Pod Logs | `pai:GetPodLogs` |
| Get Job Events | `pai:GetJobEvents` |
| Update Job | `pai:UpdateJob` |
| Stop Job | `pai:StopJob` |
| AIWorkSpace Resource Discovery | `paiworkspace:ListWorkspaces` / `paiimage:ListImages,GetImage` / `paidataset:ListDatasets,GetDataset` / `paicodesource:ListCodeSources,GetCodeSource` |

> **AIWorkSpace authorization note:** `Image` / `DataSourceId` / `CodeSourceId` /
> `WorkspaceId` field values for `create-job` come from the
> AIWorkSpace resource-discovery APIs. `--resource-id` (QuotaId) is manually provided by the user.
> RAM users MUST hold the corresponding
> AIWorkSpace-namespaced permissions listed above (do not abbreviate as `aiworkspace:*`).

## Parameter Confirmation

> **Authoritative parameter reference is `aliyun pai-dlc <cmd> --help`** (must-read
> before every call). This skill only documents what `--help` does **not** tell
> you: cross-field rules, cross-product dependencies, hidden behaviors, business
> labels, and reject patterns. Whenever a rule below contradicts `--help`, the
> reason is stated inline.
>
> **Confirm before call:** all user-customizable values (region, names, CIDR,
> specs, etc.) MUST be confirmed with the user — never assume defaults.

### Hard rules that override `--help`

| Rule | Why this skill overrides `--help` |
|------|-----------------------------------|
| `--workspace-id` is **always required** | `--help` marks it optional, but server silently falls back to the user's **default workspace** if omitted → job often lands in the wrong workspace. Always confirm with user. |
| `--job-specs[].Image` MUST be a verbatim `ImageUri` from `aiworkspace list-images` | Cross-product contract; `--help` only describes the field type. See §7.6 red line. |
| `--data-sources[].DataSourceId` from `aiworkspace list-datasets`; `--code-source.CodeSourceId` from `list-code-sources` | Cross-product discovery; `--help` cannot point you to the source product. |
| `--resource-id` (QuotaId) is **manually supplied** | No CLI discovery step. |

### Cross-field mutual exclusion (`--help` cannot catch these)

- `EcsSpec` ⇄ `ResourceConfig` — within a single TaskSpec, pick exactly one.
- `Uri` ⇄ `DataSourceId` — within `--data-sources[]`.
- `Uri` ⇄ `CodeSourceId` — within `--code-source`.

### `--job-type` — Worker `Type` hints per framework

`--help` lists the 9 legal enum values verbatim. What `--help` doesn't tell you
is which `JobSpecs[].Type` roles each framework expects:

| `--job-type` | Valid `JobSpecs[].Type` roles |
|---|---|
| `TFJob` | `Chief` / `PS` / `Worker` / `Evaluator` / `GraphLearn` |
| `PyTorchJob` | `Worker` (+ optional `Master`, auto-promoted) |
| `MPIJob` | `Worker` + `Master` |
| `XGBoostJob` / `OneFlowJob` / `ElasticBatchJob` | `Worker` + optional `Master` |
| `RayJob` | `Worker` |
| `SlurmJob` / `DataJuicerJob` | framework-specific roles |

> **Case-sensitive, no aliases.** `tensorflow`, `pytorch`, `tf-job`, `Pytorch`,
> `PYTORCH_JOB`, `Custom`, `CustomJob` — all rejected.
>
> **No `Custom` enum.** For single-container custom workloads, map to
> `PyTorchJob` (most permissive role set).
>
> **Locked after create:** `JobType` cannot be changed via `update-job`.

Full field reference: see [references/related-apis.md](references/related-apis.md).

## Core Workflows

### 7.1 Resource Selection Decision Guide

Before calling `create-job`, determine the resource path:

- **Public pay-as-you-go** → Use `EcsSpec` in TaskSpec; do NOT pass `--resource-id`.
  - Use cases: quick start, testing, no dedicated quota.
  - Example: `"EcsSpec": "ecs.gn6i-c4g1.xlarge"`
- **Dedicated quota** (Lingjun / enterprise quota) → Use `ResourceConfig` in TaskSpec
  AND pass `--resource-id <QuotaId>`.
  - Use cases: dedicated resource group, Lingjun smart compute, Spot bidding.
  - Example: `--resource-id quotaXXX` + `"ResourceConfig": {"CPU": "4", "Memory": "8Gi", "GPU": "1"}`

> **EcsSpec and ResourceConfig MUST NOT both appear in the same TaskSpec.**

> **Also required before `create-job`:** `--job-specs[].Image` MUST come from
> `aliyun aiworkspace list-images`; `--data-sources[].DataSourceId` from
> `list-datasets`; `--code-source.CodeSourceId` from `list-code-sources`.
> Full discovery flow → see §7.6.

**Distributed architecture choices:**

| Topology | `JobSpecs` shape |
|---|---|
| Single-node | One `Worker` only |
| TFJob PS-Worker | Both `PS` (CPU) and `Worker` (GPU) roles |
| PyTorch multi-node | One `Worker` with `PodCount > 1` |

Optional flags: `--enable-gang-scheduling true` (all-or-nothing scheduling),
`Settings.EnableRDMA: true` (high-performance network for multi-node GPU),
`Settings.EnableSanityCheck: true` (GPU health verification).

> **All commands below require `--user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job/${SESSION_ID}`** (omitted in snippets for brevity — see [Observability](#observability)).

### 7.2 Create Training Job

Minimal single-node PyTorch job (public pay-as-you-go) parameter combination:

```bash
aliyun pai-dlc create-job --region <region> --workspace-id <ws-id> \
  --display-name "my-pytorch-training" --job-type PyTorchJob \
  --job-specs '[{"Type":"Worker","PodCount":1,"Image":"<ImageUri>","EcsSpec":"ecs.gn6i-c4g1.xlarge"}]' \
  --user-command 'python train.py' \
  --user-agent AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job/${SESSION_ID}

Multi-node / Spot / RDMA / data mounting — use create-job --help. Subsequent snippets omit --user-agent for brevity — always include it.

7.3 List / Get Job#

Use --cli-query to project specific fields (essential for log/event flows):

bash

aliyun pai-dlc list-jobs --region <region> --status Running
aliyun pai-dlc get-job  --region <region> --job-id <id>
aliyun pai-dlc get-job  --region <region> --job-id <id> --cli-query "Pods[0].PodId"

7.4 Logs and Events#

Always cap return size: --max-lines 100 (logs), --max-events-num 50 (events).

Get PodId first, then query logs/events:

bash

POD_ID=$(aliyun pai-dlc get-job --region <r> --job-id <id> --cli-query "Pods[0].PodId")
aliyun pai-dlc get-pod-logs    --region <r> --job-id <id> --pod-id $POD_ID --max-lines 100
aliyun pai-dlc get-pod-events  --region <r> --job-id <id> --pod-id $POD_ID --max-events-num 20
aliyun pai-dlc get-job-events  --region <r> --job-id <id> --max-events-num 50

Diagnosis order: get-job (status) → get-job-events → get-pod-logs → get-pod-events.

7.5 Compute Health Check#

bash

aliyun pai-dlc list-job-sanity-check-results --region <r> --job-id <id>
aliyun pai-dlc get-job-sanity-check-result   --region <r> --job-id <id> --sanity-check-number 1

7.6 Pre-Create Resource Discovery (AIWorkSpace)#

Discovery flow: list-workspaces → list-image-labels → list-images → list-datasets → list-code-sources → pai-dlc create-job.

Quota ( --resource-id): user-supplied. No CLI discovery step.

bash

aliyun aiworkspace list-workspaces     --region <r>                        # → --workspace-id
aliyun aiworkspace list-image-labels   --region <r>                        # → valid label Key=Value pairs
aliyun aiworkspace list-images         --region <r> --labels "K1=V1,K2=V2" # → --job-specs[].Image (use ImageUri verbatim)
aliyun aiworkspace list-datasets       --region <r> --workspace-id <ws>    # → DataSources[].DataSourceId
aliyun aiworkspace list-code-sources   --region <r> --workspace-id <ws>    # → CodeSource.CodeSourceId

Labels rules (not in --help): comma-separated Key=Value pairs, no JSON / no spaces. Values MUST come from list-image-labels — never invent. Do not pass --workspace-id to list-images when discovering official public images (they are global). Pass --workspace-id only when filtering custom / private images scoped to a specific workspace.

RED LINE: --job-specs[].Image MUST be a verbatim ImageUri (not Name / ImageId).

Field-mapping, full parameters, and error codes: see references/related-apis.md and references/verification-method.md.

7.7 Job Lifecycle Management (Stop / Update / Web Terminal)#

Stop is a high-risk operation. Before proceeding, query status with get-job, present the result to the user, and require explicit confirmation.

Rules --help doesn't tell you ( update-job silent-no-op family):

• Stop Job applies only when status is Running or Queuing.
• update-job --priority takes effect only when (a) the job uses quota resources ( --resource-id) AND (b) status is Creating, Queuing, or EnvPreparing. Once the job enters Running or later, priority cannot be modified — the API returns 200 OK but the change is silently NOT applied. Always pre-check status with get-job.
• update-job --accessibility takes effect immediately in any status.
• update-job does NOT expose --display-name ( --help lists only --job-id, --accessibility, --description, --job-specs, --priority). To rename a job, recreate it.

For the full pre-check + confirmation + execution templates, plus the update-job low-risk path and get-web-terminal / get-token sharing commands, see references/job-management.md.

7.8 Ecs Spec Discovery#

Discover available instance types; the returned EcsSpec value goes verbatim into --job-specs[].EcsSpec.

bash

aliyun pai-dlc list-ecs-specs --region <r> --accelerator-type GPU --resource-type ECS --page-size 20
# Lingjun dedicated: --quota-id <id> (whitelisted users only)

list-ecs-specs does not support --sort-by — even values shown as valid in --help (e.g. CPU / GPU / Memory / GmtCreateTime) are rejected by the server. Always omit --sort-by here and sort the JSON output client-side with jq — e.g. ... | jq '.EcsSpecs | sort_by(-.AcceleratorNumber)'.

Success Verification Method#

For step-by-step end-to-end verification scripts (resource discovery → CreateJob → log query → cleanup), see references/verification-method.md.

Quick verification:

• get-job → Status should be Creating / Queuing / Running shortly after create-job returns.
• list-jobs --status Running → Should return the freshly created Job until it finishes or is stopped.
• get-pod-logs → Should return non-empty log content once the Pod is past EnvPreparing.

Command Tables#

The full command index (5 categories × ~40 commands, with plugin attribution) is consolidated in references/related-apis.md §1.

Best Practices#

Items below are decision rules and operational habits — not parameter values (those live in --help).

1. Job naming — use meaningful, sortable names: project-model-date (e.g. resnet50-imagenet-20260320). Recreate (not update-job) is the only way to rename.
2. Resource sizing — pick GPU type / count by model & dataset size. Verify availability with list-ecs-specs --accelerator-type GPU before picking EcsSpec (see §7.8).
3. Diagnose early — follow the order get-job → get-job-events → get-pod-logs → get-pod-events. Cap responses ( --max-lines 100, --max-events-num 50) to keep agent context lean.
4. Priority adjustment — prefer setting --priority at create-job time. Post-creation update-job --priority only works for quota jobs in Creating / Queuing / EnvPreparing phase (§7.7); once Running, priority cannot be modified.
5. Cost control — use --job-max-running-time-minutes as an auto-stop guard for every long-running experiment. Spot via SpotSpec reduces cost at the risk of preemption.
6. Health check — enable Settings.EnableSanityCheck: true for GPU training to catch faulty devices before training starts.
7. Resource cleanup — stop-job on completed jobs to free quota.
8. Idempotency on writes — PAI-DLC create-* APIs do NOT expose --client-token (verified via aliyun pai-dlc create-job --help). Network retries can therefore create duplicate Jobs. Mitigation: before re-issuing a failed create-*, run list-jobs --display-name <name> to detect a half-committed prior attempt.

Reference Links#

Reference DocumentDescriptionreferences/related-apis.mdCommand index, cross-product field map, lifecycle, red lines, error catalogreferences/ram-policies.mdRAM permission policy detailsreferences/verification-method.mdEnd-to-end verification scriptsreferences/job-management.mdHigh-risk Stop/Delete/Update flow + Web Terminalreferences/acceptance-criteria.mdSkill testing acceptance criteriareferences/cli-installation-guide.mdCLI installation guide