编写工作流 from modelarts import workflow as wf # 定义统一存储对象管理输出目录 output_storage = wf.data.OutputStorage(name="output_storage", description="输出目录统一配置") # 创建标注任务 data = wf.data.DatasetPlaceholder(name="input_data") label_step = wf.steps.LabelingStep( name="labeling", title="数据标注", properties=wf.steps.LabelTaskProperties( task_type=wf.data.LabelTaskTypeEnum.IMAGE_CLASSIFICATION, task_name=wf.Placeholder(name="task_name", placeholder_type=wf.PlaceholderType.STR, description="请输入一个只包含大小写字母、数字、下划线、中划线或者中文字符的名称。填写已有标注任务名称，则直接使用该标注任务；填写新标注任务名称，则自动创建新的标注任务") ), inputs=wf.steps.LabelingInput(name="labeling_input", data=data), outputs=wf.steps.LabelingOutput(name="labeling_output"), ) # 对标注任务进行发布 release_step = wf.steps.ReleaseDatasetStep( name="release", title="数据集版本发布", inputs=wf.steps.ReleaseDatasetInput(name="input_data", data=label_step.outputs["labeling_output"].as_input()), outputs=wf.steps.ReleaseDatasetOutput(name="labeling_output", dataset_version_config=wf.data.DatasetVersionConfig(train_evaluate_sample_ratio="0.8")), depend_steps=[label_step] ) # 创建训练作业 job_step = wf.steps.JobStep( name="training_job", title="图像分类训练", algorithm=wf.AIGalleryAlgorithm( subscription_id="***", # 订阅算法的ID，自行补充 item_version_id="10.0.0", # 订阅算法的版本ID parameters=[ wf.AlgorithmParameters(name="task_type", value="image_classification_v2"), wf.AlgorithmParameters(name="model_name", value="resnet_v1_50"), wf.AlgorithmParameters(name="do_train", value="True"), wf.AlgorithmParameters(name="do_eval_along_train", value="True"), wf.AlgorithmParameters(name="variable_update", value="horovod"), wf.AlgorithmParameters(name="learning_rate_strategy", value=wf.Placeholder(name="learning_rate_strategy", placeholder_type=wf.PlaceholderType.STR, default="0.002", description="训练的学习率策略(10:0.001,20:0.0001代表0-10个epoch学习率0.001，10-20epoch学习率0.0001),如果不指定epoch, 会根据验证精度情况自动调整学习率，并当精度没有明显提升时，训练停止")), wf.AlgorithmParameters(name="batch_size", value=wf.Placeholder(name="batch_size", placeholder_type=wf.PlaceholderType.INT, default=64, description="每步训练的图片数量（单卡）")), wf.AlgorithmParameters(name="eval_batch_size", value=wf.Placeholder(name="eval_batch_size", placeholder_type=wf.PlaceholderType.INT, default=64, description="每步验证的图片数量（单卡）")), wf.AlgorithmParameters(name="evaluate_every_n_epochs", value=wf.Placeholder(name="evaluate_every_n_epochs", placeholder_type=wf.PlaceholderType.FLOAT, default=1.0, description="每训练n个epoch做一次验证")), wf.AlgorithmParameters(name="save_model_secs", value=wf.Placeholder(name="save_model_secs", placeholder_type=wf.PlaceholderType.INT, default=60, description="保存模型的频率（单位：s)")), wf.AlgorithmParameters(name="save_summary_steps", value=wf.Placeholder(name="save_summary_steps", placeholder_type=wf.PlaceholderType.INT, default=10, description="保存summary的频率（单位：步)")), wf.AlgorithmParameters(name="log_every_n_steps", value=wf.Placeholder(name="log_every_n_steps", placeholder_type=wf.PlaceholderType.INT, default=10, description="打印日志的频率（单位：步)")), wf.AlgorithmParameters(name="do_data_cleaning", value=wf.Placeholder(name="do_data_cleaning", placeholder_type=wf.PlaceholderType.STR, default="True", description="是否进行数据清洗, 数据格式异常会导致训练失败，建议开启，保证训练稳定性。数据量过大时，数据清洗可能耗时较久，可自行线下清洗（支持BMP.JPEG,PNG格式, RGB三通道）。建议用JPEG格式数据")), wf.AlgorithmParameters(name="use_fp16", value=wf.Placeholder(name="use_fp16", placeholder_type=wf.PlaceholderType.STR, default="True", description="是否使用混合精度, 混合精度可以加速训练，但是可能会造成一点精度损失，如果对精度无极严格的要求，建议开启")), wf.AlgorithmParameters(name="xla_compile", value=wf.Placeholder(name="xla_compile", placeholder_type=wf.PlaceholderType.STR, default="True", description="是否开启xla编译，加速训练，默认启用")), wf.AlgorithmParameters(name="data_format", value=wf.Placeholder(name="data_format", placeholder_type=wf.PlaceholderType.ENUM, default="NCHW", enum_list=["NCHW", "NHWC"], description="输入数据类型，NHWC表示channel在最后，NCHW表channel在最前，默认值NCHW（速度有提升）")), wf.AlgorithmParameters(name="best_model", value=wf.Placeholder(name="best_model", placeholder_type=wf.PlaceholderType.STR, default="True", description="是否在训练过程中保存并使用精度最高的模型，而不是最新的模型。默认值True,保存最优模型。在一定误差范围内，最优模型会保存最新的高精度模型")), wf.AlgorithmParameters(name="jpeg_preprocess", value=wf.Placeholder(name="jpeg_preprocess", placeholder_type=wf.PlaceholderType.STR, default="True", description="是否使用jpeg预处理加速算子(仅支持jpeg格式数据)，可加速数据读取，提升性能，默认启用。如果数据格式不是jpeg格式，开启数据清洗功能即可使用")) ] ), inputs=[wf.steps.JobInput(name="data_url", data=release_step.outputs["labeling_output"].as_input())], outputs=[wf.steps.JobOutput(name="train_url", obs_config=wf.data.OBSOutputConfig(obs_path=output_storage.join("/train_output/")))], spec=wf.steps.JobSpec( resource=wf.steps.JobResource( flavor=wf.Placeholder(name="training_flavor", placeholder_type=wf.PlaceholderType.JSON, description="训练资源规格" ) ) ), depend_steps=[release_step] ) model_name = wf.Placeholder(name="model_name", placeholder_type=wf.PlaceholderType.STR, description="请输入一个1至64位且只包含大小写字母、中文、数字、中划线或者下划线的名称。工作流第一次运行建议填写新的模型名称，后续运行会自动在该模型上新增版本") # 模型注册 model_step = wf.steps.ModelStep( name="model_step", title="模型注册", inputs=[wf.steps.ModelInput(name="model_input", data=job_step.outputs["train_url"].as_input())], outputs=[wf.steps.ModelOutput(name="model_output", model_config=wf.steps.ModelConfig(model_name=model_name, model_type="TensorFlow"))], depend_steps=[job_step] ) # 服务部署 service_step = wf.steps.ServiceStep( name="service_step", title="服务部署", inputs=[wf.steps.ServiceInput(name="service_input", data=wf.data.ServiceInputPlaceholder(name="service_model", model_name=model_name))], outputs=[wf.steps.ServiceOutput(name="service_output")], depend_steps=[model_step] ) # 构建工作流对象 workflow = wf.Workflow(name="image-classification-ResNeSt", desc="this is a image classification workflow", steps=[label_step, release_step, job_step, model_step, service_step], storages=[output_storage] ) 在工作流编写完成后可自行进行调试、发布等操作。