Creating a Training Job

Sample Code

In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.

Example 1: Create a training job using a common AI engine.

If both framework_type and framework_version are specified in estimator, a training job will be created using a common AI engine.

from modelarts.session import Session
from modelarts.train_params import TrainingFiles
from modelarts.train_params import OutputData
from modelarts.train_params import InputData
from modelarts.estimatorV2 import Estimator
session = Session()
# Parameters received in the training script (set based on the site requirements):

parameters = [{"name": "mod", "value":"gpu"}, 
              {"name": "epoc_num", "value":2}]
estimator = Estimator(session=session,
                      training_files=TrainingFiles(code_dir= "obs://bucket_name/code_dir/", boot_file="boot_file.py"),
                      outputs=[OutputData(obs_path="obs://bucket_name/output/", name="output_dir")],
                      parameters=parameters,
                      framework_type='PyTorch',                      # Common AI engine
                      framework_version='PyTorch-1.4.0-python3.6',   # Version of the AI engine
                      train_instance_type="modelarts.p3.large.public",
                      train_instance_count=1,
                      log_url="obs://bucket_name/log/",
                      env_variables={"USER_ENV_VAR": "customize environment variable"},
                      working_dir="/home/ma-user/modelarts/user-job-dir",
                      local_code_dir="/home/ma-user/modelarts/user-job-dir",
                      job_description='This is an image net train job')
job_instance = estimator.fit(inputs=[InputData(obs_path="obs://bucket_name/input/", name="data_url")],
                             job_name="job_name_1")

Example 2: Create a training job using a custom image.

If both user_image_url and user_command are specified in estimator, a training job will be created using a custom image and started using a custom boot command.

from modelarts.session import Session
from modelarts.train_params import TrainingFiles
from modelarts.train_params import OutputData
from modelarts.train_params import InputData
from modelarts.estimatorV2 import Estimator
session = Session()
# Parameters received in the training script (set based on the site requirements):

parameters = [{"name": "mod", "value":"gpu"}, 
              {"name": "epoc_num", "value":2}]
estimator = Estimator(session=session,
                      training_files=TrainingFiles(code_dir= "obs://bucket_name/code_dir/", boot_file="boot_file.py"),
                      outputs=[OutputData(obs_path="obs://bucket_name/output/", name="output_dir")],
                      parameters=parameters,
                      user_image_url="sdk-test/pytorch1_4:1.0.1",     # URL of the custom image
                      user_command="/home/ma-user/anaconda3/envs/PyTorch-1.4/bin/python /home/ma-user/modelarts/user-job-dir/train/test-pytorch.py",  # Custom boot command
                      train_instance_type="modelarts.p3.large.public",
                      train_instance_count=1,
                      log_url="obs://bucket_name/log/",
                      env_variables={"USER_ENV_VAR": "customize environment variable"},
                      working_dir="/home/ma-user/modelarts/user-job-dir",
                      local_code_dir="/home/ma-user/modelarts/user-job-dir",
                      job_description='This is an image net train job')
job_instance = estimator.fit(inputs=[InputData(obs_path="obs://bucket_name/input/", name="data_url")],
                             job_name="job_name_2")

Example 3: Creating a training job in a dedicated resource pool

from modelarts.session import Session
from modelarts.train_params import TrainingFiles
from modelarts.train_params import OutputData
from modelarts.train_params import InputData
from modelarts.estimatorV2 import Estimator
session = Session()
# Parameters received in the training script (set based on the site requirements):

parameters = [{"name": "mod", "value":"gpu"}, 
              {"name": "epoc_num", "value":2}]
estimator = Estimator(session=session,
                      training_files=TrainingFiles(code_dir= "obs://bucket_name/code_dir/", boot_file="boot_file.py"),
                      outputs=[OutputData(obs_path="obs://bucket_name/output/", name="output_dir")],
                      parameters=parameters,
                      framework_type='PyTorch',                       
                      framework_version='PyTorch-1.4.0-python3.6',    
                      pool_id="your pool id",                                 # Dedicated resource pool ID
                      train_instance_type="modelarts.pool.visual.xlarge",     # VM flavor of the dedicated pool
                      train_instance_count=1,
                      log_url="obs://bucket_name/log/",
                      env_variables={"USER_ENV_VAR": "customize environment variable"},
                      working_dir="/home/ma-user/modelarts/user-job-dir",
                      local_code_dir="/home/ma-user/modelarts/user-job-dir",
                      job_description='This is an image net train job')
job_instance = estimator.fit(inputs=[InputData(obs_path="obs://bucket_name/input/", name="data_url")],
                             job_name="job_name_3")

Example 4: Create a training job using a dataset.

from modelarts.session import Session
from modelarts.train_params import TrainingFiles
from modelarts.train_params import OutputData
from modelarts.train_params import InputData
from modelarts.estimatorV2 import Estimator
session = Session()
# Parameters received in the training script (set based on the site requirements):
parameters = [{"name": "model_name", "value":"s"}, 
              {"name": "batch-size", "value": 32},
              {"name": "epochs", "value":100},
              {"name": "img-size", "value":"640,640"} ]
estimator = Estimator(session=session,
                      training_files=TrainingFiles(code_dir= "obs://bucket_name/code_dir/", boot_file="boot_file.py"),
                      outputs=[OutputData(obs_path="obs://bucket_name/output/", name="output_dir")],
                      parameters=parameters,
                      framework_type='PyTorch',                       # Common AI engine
                      framework_version='PyTorch-1.4.0-python3.6',    # Version of the AI engine
                      train_instance_type="modelarts.p3.large.public",
                      train_instance_count=1,
                      log_url="obs://bucket_name/log/",
                      working_dir="/home/ma-user/modelarts/user-job-dir",
                      local_code_dir="/home/ma-user/modelarts/user-job-dir",
                      job_description='This is an image net train job')
job_instance = estimator.fit(dataset_id="your dataset id",
                             dataset_version_id="your dataset version id",
                             job_name="job_name_5")

Parameters

**Table 1** Estimator request parameters
Parameter	Mandatory	Type	Description
session	Yes	Object	Session object. For details about the initialization method, see Session Authentication.
training_files	No	TrainingFiles Object	Path to the training script in OBS. For details, see Table 2.
outputs	No	Array of OutputData objects	Training output path. For details, see Table 3.
parameters	No	JSON Array	Running parameters of a training job. The format is as follows: [{"name":"your name", "value": "your value"}]. The value can be a string or an integer.
train_instance_type	Yes	String	Resource flavor selected for a training job. For details, see Obtaining Resource Flavors.
train_instance_count	Yes	Int	Number of compute nodes in a training job
framework_type	No	String	Engine type selected for a training job. For details, see Obtaining Engine Types.
framework_version	No	String	Engine version selected for a training job. For details, see Obtaining Engine Types.
user_image_url	No	String	SWR URL of the custom image used by a training job
user_command	No	String	Command for starting a training job created using a custom image
log_url	No	String	OBS path for storing training job logs, for example, obs://xx/yy/zz/
local_code_dir	No	String	Local directory to the training container to which the algorithm code directory is downloaded. Note: The directory must be under /home. In v1 compatibility mode, this parameter does not take effect. When code_dir is prefixed with file://, this parameter does not take effect.
working_dir	No	String	Work directory where an algorithm is executed. Note that this parameter does not take effect in v1 compatibility mode.
job_description	No	String	Description of a training job
volumes	No	JSON Array	Information of the disks attached for a training job in the following example format: [{ "nfs": { "local_path": "/xx/yy/zz", "read_only": False, "nfs_server_path": "xxx.xxx.xxx.xxx:/" } }]
env_variables	No	Dict	Environment variables of a training job
pool_id	No	String	ID of the resource pool for a training job. To obtain the ID, do as follows: Log in to the ModelArts management console, choose Dedicated Resource Pools in the navigation pane on the left, and view the resource pool ID in the dedicated resource pool list.

**Table 2** Parameters for initializing **TrainingFiles**
Parameter	Mandatory	Type	Description
code_dir	Yes	String	Code directory of a training job, which is an OBS path and must start with obs:/, for example, obs://xx/yy/
boot_file	Yes	String	Boot file of a training job, which must be stored in the code directory. You can enter a relative path, for example, boot_file.py, or an absolute path, for example, obs://xx/yy/boot_file.py.

**Table 3** Parameters for initializing **OutputData**
Parameter	Mandatory	Type	Description
obs_path	Yes	String	OBS path to which data is exported
name	Yes	String	Keyword parameter name of the output data, for example, output_dir

**Table 4** **fit** request parameters
Parameter	Mandatory	Type	Description
inputs	No	Array of InputData Object	Input data of a training job stored in OBS Either inputs or dataset_id/dataset_version_id can be configured.
wait	No	Boolean	Whether to wait for the completion of a training job. It defaults to False.
job_name	No	String	Name of a training job
show_log	No	Boolean	Whether to output training job logs after a job is submitted. It defaults to False.
dataset_id	No	String	Dataset ID of a training job. For details, see Data Management. This parameter must be used with dataset_version_id, but cannot be used with inputs.
dataset_version_id	No	String	Dataset version ID of a training job. For details, see Data Management. This parameter must be used with dataset_id, but cannot be used with inputs.

**Table 5** Parameters for initializing **InputData**
Parameter	Mandatory	Type	Description
obs_path	Yes	String	OBS path to the dataset required by a training job, for example, obs://xx/yy/
name	Yes	String	Keyword parameter name of the input data, for example, data_url.

**Table 6** Response for creating a training job
Parameter	Type	Description
TrainingJob	Object	Training object, which contains attributes such as job_id. When you perform operations on a training job, for example, obtain information of, update, or delete a training job, you can use job_instance.job_id to obtain the ID of the training job.

**Table 7** Response for the failure to call a training API
Parameter	Type	Description
error_msg	String	Error message when calling an API failed. This parameter is unavailable if an API is successfully called.
error_code	String	Error code when calling an API failed. For details, see "Error Codes" in ModelArts API Reference. This parameter is unavailable if an API is successfully called.
error_solution	String	Solution to an API calling failure. This parameter is unavailable if an API is successfully called.

Parent topic: Training Jobs

Previous topic: Training Jobs

Next topic: Debugging a Training Job

Feedback

Was this page helpful?

Helpful Not helpful

Provide feedback

Thank you very much for your feedback. We will continue working to improve the documentation.

The system is busy. Please try again later.