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alibabacloud-pai-dlc-job

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person作者: alibabacloud-skillshubOpenAPI
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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. 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-*).

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

aliyun configure ai-mode enable
aliyun configure ai-mode set-user-agent --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-pai-dlc-job"
# After session: aliyun configure ai-mode disable

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
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
  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.

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.

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 (omitted in snippets for brevity — see Installation Requirements).

7.2 Create Training Job

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

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

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):

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:

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

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.

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.

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 Document | Description | |--------------------|-------------| | references/related-apis.md | Command index, cross-product field map, lifecycle, red lines, error catalog | | references/ram-policies.md | RAM permission policy details | | references/verification-method.md | End-to-end verification scripts | | references/job-management.md | High-risk Stop/Delete/Update flow + Web Terminal | | references/acceptance-criteria.md | Skill testing acceptance criteria | | references/cli-installation-guide.md | CLI installation guide |