# 通过API创建自定义镜像Tensorboard训练作业场景 本节通过调用一系列API，以训练模型为例介绍ModelArts API的使用流程。 #### 概述 使用PyTorch框架创建训练作业的流程如下： 1. 调用[认证鉴权](https://support.huaweicloud.com/api-modelarts/modelarts_03_0004.html)接口获取用户Token，在后续的请求中需要将Token放到请求消息头中作为认证。 2. 调用[创建训练作业](https://support.huaweicloud.com/api-modelarts/CreateTrainingJob.html)接口使用刚创建的算法返回的uuid创建一个训练作业，记录训练作业id。 3. 调用[查询训练作业详情](https://support.huaweicloud.com/api-modelarts/ShowTrainingJobDetails.html)接口使用刚创建的训练作业返回的id查询训练作业状态和Tensorboard地址。 4. 打开Tensorboard Endpoint查看可视化指标数据。 5. 当训练作业使用完成或不再需要时，调用[删除训练作业](https://support.huaweicloud.com/api-modelarts/DeleteTrainingJob.html)接口删除训练作业。 #### 前提条件 - 已获取[IAM的EndPoint](https://developer.huaweicloud.com/endpoint?IAM)和[ModelArts的EndPoint](https://support.huaweicloud.com/api-modelarts/modelarts_03_0141.html)。 - 确认服务的部署区域，[获取项目ID和名称](https://support.huaweicloud.com/api-modelarts/modelarts_03_0147.html)、[获取账号名和ID](https://support.huaweicloud.com/api-modelarts/modelarts_03_0148.html)和[获取用户名和用户ID](https://support.huaweicloud.com/api-modelarts/modelarts_03_0006.html)。 - 已准备好Tensorboard日志保存目录，例如OBS并行文件系统的"obs://cnnorth4-job-pfs/summary"目录下 #### 操作步骤 1. 调用[认证鉴权](https://support.huaweicloud.com/api-modelarts/modelarts_03_0004.html)接口获取用户的Token。 1. 请求消息体： URI格式：POST https://***{iam_endpoint}***/v3/auth/tokens 请求消息头：Content-Type →application/json 请求Body： ``` { "auth": { "identity": { "methods": ["password"], "password": { "user": { "name": "user_name", "password": "user_password", "domain": { "name": "domain_name" } } } }, "scope": { "project": { "name": "cn-north-1" } } } } ``` 其中，加粗的斜体字段需要根据实际值填写： - ***iam_endpoint***为IAM的终端节点。 - ***user_name***为IAM用户名。 - ***user_password***为用户登录密码。 - ***domain_name***为用户所属的账号名。 - ***cn-north-1***为项目名，代表服务的部署区域。 2. 返回状态码"201 Created"，在响应Header中获取"X-Subject-Token"的值即为Token，如下所示： ``` x-subject-token →MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX... ``` 2. 调用[创建训练作业](https://support.huaweicloud.com/api-modelarts/CreateTrainingJob.html)接口使用刚创建的算法返回的uuid创建一个训练作业，记录训练作业id。 1. 请求消息体： URI格式：POST https://***{ma_endpoint}*** /v2/***{project_id}***/training-jobs 请求消息头： - X-Auth-Token →***MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...*** - Content-Type →application/json 其中，加粗的斜体字段需要根据实际值填写。 请求Body： ``` { "kind": "job", "metadata": { "name": "test-pytorch-cpu01", "description": "test pytorch work cpu", "annotations": { "tensorboard/enable": "true" } }, "algorithm": { "engine": { "image_url": "atelier/pytorch_cuda:pytorch_2.7.0-cuda_12.8-py_3.11.10-ubuntu_22.04-x86_64-20251215163925-4e5422a" }, "command": "sleep 1h", "summary": { "data_sources": [ { "pfs": { "pfs_path": "obs://cnnorth4-job-pfs/summary/" } } ] } }, "spec": { "resource": { "node_count": 1, "pool_id": "pool-maostest-train-06024304be00d5092fbdc0013d201342" } } } ``` 其中，加粗的斜体字段需要根据实际值填写： - ***"kind"***填写训练作业的类型，默认为job。 - ***"metadata"*** 下的***"name"*** 和***"description"***填写训练作业的名称和描述。 - ***"metadata"*** 下的***"annotations"***填写"tensorboard/enable": "true"表示启用tensorboard。 - ***"algorithm"*** 下的***"image_url"***填写训练作业镜像的地址。 - ***"algorithm"*** 下的***"command"***填写训练作业启动命令。 - ***"algorithm"*** 下的***"data_sources"***填写Tensorboard日志的保存目录。 - ***"spec"*** 字段下的***"pool_id"*** 表示训练作业所依赖的资源池ID。***"node_count"*** 表示训练是否需要多机训练（分布式训练），此处为单机情况使用默认值"1"。***"log_export_path"***用于指定用户需要上传日志的obs目录。 2. 返回状态码"201 Created"，表示训练作业创建成功，响应Body如下所示： ``` { "kind": "job", "metadata": { "id": "dc0e330d-c6a8-4f1d-9cd3-1f108c3bbaf9", "name": "test-pytorch-cpu01", "description": "test pytorch work cpu", "create_time": 1777545590838, "workspace_id": "0", "ai_project": "default-ai-project", "labels": { "training-job": "modelarts-os" }, "user_name": "", "annotations": { "job_template": "Template DL", "key_task": "worker", "tensorboard/enable": "true" }, "training_experiment_reference": {}, "tags": [] }, "status": { "phase": "Pending", "secondary_phase": "Creating", "pending_time": 2000, "duration": 0, "is_hanged": false, "retry_count": 0, "start_time": 0, "node_count_metrics": null, "tasks": [ "worker-0" ], "metrics_statistics": { "cpu_usage": { "average": -1, "max": -1, "min": -1 }, "mem_usage": { "average": -1, "max": -1, "min": -1 } } }, "algorithm": { "command": "sleep 1h", "engine": { "engine_id": "", "engine_name": "", "engine_version": "", "v1_compatible": false, "image_url": "atelier/pytorch_cuda:pytorch_2.7.0-cuda_12.8-py_3.11.10-ubuntu_22.04-x86_64-20251215163925-4e5422a", "non_swr_image": false, "run_user": "", "image_source": true, "image_repo_id": "", "image_id": "" }, "summary": { "data_sources": [ { "pfs": { "pfs_path": "obs://cnnorth4-job-pfs/summary/" } } ] } }, "spec": { "resource": { "pool_id": "pool-maostest-train-06024304be00d5092fbdc0013d201342", "pool_resource_flavor": "", "node_count": 1, "pool_info": { "cpu_arch": "x86", "core_num": 5, "mem_size": 22, "cache_size": 0, "accelerator": "", "accelerator_num": 0, "accelerator_type": "", "accelerator_size": 0, "variant": "", "huge_pages": 0, "x_parameter_plane": "", "use_privileged": false, "use_host_network": false, "use_ib_network": false, "project_id": "", "pool_resource_flavor": "liumuqi-eni-test", "pool_id": "pool-maostest-train-06024304be00d5092fbdc0013d201342", "cluster_id": "", "maos_pool": true, "quota_id": "", "maos_migrated": false, "detect_all_in_int": false, "pool_type": "", "enable_cabinet": false, "enable_memarts": false, "enable_ems": false, "empty_dir_size": 0 }, "main_container_allocated_resources": { "cpu_arch": "x86", "cpu_core_num": 4, "mem_size": 20, "accelerator_num": 0, "accelerator_type": "" } }, "is_hosted_log": true, "runtime_type": "production" }, "endpoints": { "tensorboard": {} }, "ftjob_config": { "checkpoint_config": { "save_checkpoints_max": 0, "checkpoint_id": "", "skipped_steps": 0, "restore_training": 0 }, "task_env": { "envs": null } } } ``` - 记录***"metadata"*** 下的***"id"***（训练作业的任务ID）字段的值便于后续步骤使用。 - "Status"下的"phase"和"secondary_phase"为表示训练作业的状态和下一步状态。示例中"Creating"表示训练作业正在创建中。 3. 调用[查询训练作业详情](https://support.huaweicloud.com/api-modelarts/ShowTrainingJobDetails.html)接口使用刚创建的训练作业返回的uuid查询训练作业状态。 1. 请求消息体： URI格式：GET https://***{ma_endpoint}*** /v2/***{project_id}*** /training-jobs/***{training_job_id}*** 请求消息头：X-Auth-Token →***MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...*** 其中，加粗的斜体字段需要根据实际值填写： ***"training_job_id"*** 为[2]记录的训练作业的任务ID。 2. 返回状态码"200 OK"，响应Body如下所示： ``` { "kind": "job", "metadata": { "id": "dc0e330d-c6a8-4f1d-9cd3-1f108c3bbaf9", "name": "test-pytorch-cpu01", "description": "test pytorch work cpu", "create_time": 1777545590838, "workspace_id": "0", "ai_project": "default-ai-project", "labels": { "training-job": "modelarts-os" }, "user_name": "ei_modelarts_y00218826_05", "annotations": { "job_template": "Template DL", "key_task": "worker", "tensorboard/enable": "true" }, "training_experiment_reference": {}, "tags": [] }, "status": { "phase": "Running", "secondary_phase": "Running", "pending_time": 77162, "duration": 181000, "is_hanged": false, "retry_count": 0, "task_ips": [ { "task": "worker-0", "ip": "172.16.0.79", "host_ip": "192.168.209.44", "schedule_count": 1 } ], "start_time": 1777545668000, "node_count_metrics": [ [ 1777545658000, 0 ], [ 1777545667000, 0 ], [ 1777545668000, 1 ], [ 1777545848000, 1 ], [ 1777545849000, 1 ] ], "tasks": [ "worker-0" ], "metrics_statistics": { "cpu_usage": { "average": -1, "max": -1, "min": -1 }, "mem_usage": { "average": -1, "max": -1, "min": -1 } }, "running_records": [ { "start_at": 1777545675, "start_type": "init_or_rescheduled" } ] }, "algorithm": { "command": "sleep 1h", "engine": { "engine_id": "", "engine_name": "", "engine_version": "", "v1_compatible": false, "image_url": "atelier/pytorch_cuda:pytorch_2.7.0-cuda_12.8-py_3.11.10-ubuntu_22.04-x86_64-20251215163925-4e5422a", "non_swr_image": false, "run_user": "", "image_source": true, "image_repo_id": "", "image_id": "" }, "summary": { "data_sources": [ { "pfs": { "pfs_path": "obs://cnnorth4-job-pfs/summary/" } } ] } }, "spec": { "resource": { "pool_id": "pool-maostest-train-06024304be00d5092fbdc0013d201342", "pool_resource_flavor": "", "node_count": 1, "pool_info": { "cpu_arch": "x86", "core_num": 5, "mem_size": 22, "cache_size": 0, "accelerator": "", "accelerator_num": 0, "accelerator_type": "", "accelerator_size": 0, "variant": "", "huge_pages": 0, "x_parameter_plane": "", "use_privileged": false, "use_host_network": false, "use_ib_network": false, "project_id": "", "pool_resource_flavor": "liumuqi-eni-test", "pool_id": "pool-maostest-train-06024304be00d5092fbdc0013d201342", "cluster_id": "", "maos_pool": true, "quota_id": "", "maos_migrated": false, "detect_all_in_int": false, "pool_type": "", "enable_cabinet": false, "enable_memarts": false, "enable_ems": false, "empty_dir_size": 0 }, "main_container_allocated_resources": { "cpu_arch": "x86", "cpu_core_num": 4, "mem_size": 20, "accelerator_num": 0, "accelerator_type": "" } }, "is_hosted_log": true, "runtime_type": "production" }, "endpoints": { "tensorboard": { "url": "https://authoring-modelarts-cnnorth7.ulanqab.huawei.com/3efaddb9-dc0e330d-c6a8-4f1d-9cd3-1f108c3bbaf9/proxy/6006/", "token": "fa8e321f772xxxxxxxxxxx3f1844d06" } }, "ftjob_config": { "checkpoint_config": { "save_checkpoints_max": 0, "checkpoint_id": "", "skipped_steps": 0, "restore_training": 0 }, "task_env": { "envs": null } } } ``` 根据响应可以了解训练作业的版本详情，其中***"status"***为"Running"表示训练作业正在运行。当训练作业运行后，开始启动Tensorboard，Tensorboard启动成功后作业详情中的endpoints会返回tensorboard的打开地址和token。 4. 打开Tensorboard Endpoint查看可视化指标数据。 URI格式：GET ***{tensorboard_endpoint}*** ?token=***{tensorboard_token}*** 其中，加粗的斜体字段需要根据实际值填写: - ***"tensorboard_endpoint"***为训练作业的Tensorboard打开地址。 - ***"tensorboard_token"***训练作业的Tensorboard访问凭证。 5. 当训练作业使用完成或不再需要时，调用[删除训练作业](https://support.huaweicloud.com/api-modelarts/DeleteTrainingJob.html)接口删除训练作业。 1. 请求消息体： URI格式：DELETE https://***{ma_endpoint}*** /v2/***{project_id}*** /training-jobs/***{training_job_id}*** 请求消息头：X-Auth-Token →***MIIZmgYJKoZIhvcNAQcCoIIZizCCGYcCAQExDTALBglghkgBZQMEAgEwgXXXXXX...*** 其中，加粗的斜体字段需要根据实际值填写。 2. 返回状态码"202 No Content"响应，则表示删除作业成功。