Help Center> ModelArts> DevEnviron> JupyterLab> Using ModelArts SDK
Updated on 2024-04-19 GMT+08:00
View PDF

Using ModelArts SDK

Notebook instances allow you to use ModelArts SDK to manage OBS, training jobs, models, and real-time services.

Your notebook instances have automatically obtained your AK/SK for authentication and the region. Therefore, SDK sessions are automatically authenticated.

Example Code

  • Create a training job. 
     1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
    		 
    from modelarts.session import Session
from modelarts.estimator import Estimator
session = Session()
estimator = Estimator(
                      modelarts_session=session,
                      framework_type='PyTorch',                                     # AI engine name
                      framework_version='PyTorch-1.0.0-python3.6',                  # AI engine version
                      code_dir='/obs-bucket-name/src/',                             # Training script directory
                      boot_file='/obs-bucket-name/src/pytorch_sentiment.py',        # Training boot script directory
                      log_url='/obs-bucket-name/log/',                              # Training log directory
                      hyperparameters=[
                                       {"label":"classes",
                                        "value": "10"},    
                                       {"label":"lr",
                                        "value": "0.001"}
                                       ],
                      output_path='/obs-bucket-name/output/',                         # Training output directory
                      train_instance_type='modelarts.vm.gpu.p100',                  # Training environment specifications
                      train_instance_count=1,                                       # Number of training nodes
                      job_description='pytorch-sentiment with ModelArts SDK')       # Training job description
job_instance = estimator.fit(inputs='/obs-bucket-name/data/train/', wait=False, job_name='my_training_job')
  • Obtain a model list. 
    1
2
3
4
    		 
    from modelarts.session import Session 
from modelarts.model import Model
session = Session() 
model_list_resp = Model.get_model_list(session, model_status="published", model_name="digit", order="desc")
  • Obtain service details. 
    1
2
3
4
5
    		 
    from modelarts.session import Session
from modelarts.model import Predictor
session = Session()
predictor_instance = Predictor(session, service_id="input your service_id")
predictor_info_resp = predictor_instance.get_service_info()
Parent topic: JupyterLab