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OBS Not supported Not supported Method 1: Modify Service Information on the Service Management Page Log in to the ModelArts management console and choose Service Deployment from the left navigation pane. Go to the service management page of the target service.
After the model is saved, it must be uploaded to the OBS directory before being published. The config.json and customize_service.py files must be contained during publishing. For details about the definition method, see Introduction to Model Package Specifications.
After the permission is granted, you can access OBS and SWR of IAM users in a notebook instance.
After the permission is granted, you can access OBS and SWR of IAM users in a notebook instance.
After the model is saved, it must be uploaded to the OBS directory before being published. The config.json configuration and the customize_service.py inference code must be included during the publishing.
The options are as follows: DIR: Data is exported to OBS (default value).
Non-custom images are declared in runtime. output_types Array of strings Input and output type in asynchronous mode and video service scenarios, such as OBS and DIS. This parameter is used for importing custom images.
# Build a local image and save to local path and OBS ma-cli image build .ma/customize_from_ubuntu_18.04_to_modelarts/Dockerfile --target .
However, the original data in the dataset and the labeled data that has been accepted are still stored in the corresponding OBS bucket. Parent topic: Team Labeling
# Directories for storing the label.txt file on OBS and in the model package # with open(os.path.join(self.model_path, 'label.txt')) as f: # self.label = json.load(f) # Load the model in saved_model format in non-blocking mode to prevent blocking
Storage resource fee: fee for storing data in OBS, EVS, or SFS Table 1 Billing items Billing Item Description Billing Mode Billing Formula Compute resource Public resource pools Usage of compute resources. For details, see ModelArts Pricing Details.
Currently, obs, flavor, train_flavor, swr, and pacific are supported. No str delay Whether parameters are set when the workflow is running. The default value is False, indicating that parameters are set before the workflow runs.
By default, this parameter is left blank. src_path Yes String OBS path of the input data of a batch job dest_path Yes String OBS path of the output data of a batch job req_uri Yes String Inference API called in a batch task, that is, the RESTful API exposed in the model image.
Not Using Hard-coded Credentials During Development If you want to develop an algorithm and publish it to the production environment in ModelArts Standard Notebook, you shall check the password, AK/SK, database connection, OBS connection, and SWR connection information used in the
15099239923, "resource_id": "4787c885-e18d-4ef1-aa12-c4ed0c364b27", "duration": 1502323, "job_desc": "This is a visualization job", "service_url": "https://console.huaweicloud.com/modelarts/tensoarbod/xxxx/111", "train_url": "/obs
However, the original data in the dataset and the labeled data that has been accepted are still stored in the corresponding OBS bucket. Parent topic: Team Labeling
Currently, only the value 0 is supported, indicating that the OBS file size is limited. import_data Boolean Whether to import data.
Enter an existing OBS path in the format of /OBS bucket name/Folder path/. Dataset object: Enter the dataset name and version number. Training flavor: Configure GPU resources since the algorithm in this example can run only on GPUs.
The work directory of a table dataset cannot be an OBS path in a KMS-encrypted bucket. Only one data source can be imported at a time. dataset_name Yes String Dataset name. The value contains 1 to 100 characters.
# Define the input OBS object. obs_data = wf.data.OBSPlaceholder(name="obs_placeholder_name", object_type="directory") # Use JobStep to define a training phase, and use OBS to store the output. job_step = wf.steps.JobStep( name="training_job", # Name of a training phase.
