Obtaining Training Logs
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.
- Method 1: Use the specified job_id.
from modelarts.session import Session from modelarts.estimatorV2 import Estimator session = Session() estimator = Estimator(session=session, job_id="your job id") info = estimator.get_job_log() print(info)
- Method 2: Use the training job created in Creating a Training Job.
log = job_instance.get_job_log(task_id="worker-0") print(log)
Parameters
|
Parameter |
Mandatory |
Type |
Description |
|---|---|---|---|
|
session |
Yes |
Object |
Session object. For details about the initialization method, see Session Authentication. |
|
job_id |
Yes |
String |
ID of a training job. You can obtain job_id using the training job created in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Obtaining Training Jobs. |
|
Parameter |
Mandatory |
Type |
Description |
|---|---|---|---|
|
task_id |
No |
String |
ID of a worker node for obtaining logs. It defaults to worker-0. If train_instance_count is set to 2 when you create a training job, the value of this parameter can be worker-0 or worker-1. |
|
Parameter |
Type |
Description |
|---|---|---|
|
content |
String |
Log content
|
|
current_size |
Integer |
Size of the returned log file, in bytes. The maximum value is 5 MB. |
|
full_size |
Integer |
Size of a complete log file, in bytes. |
|
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 Code. 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. |
Last Article: Terminating a Training Job
Next Article: Obtaining the Runtime Metrics of a Training Job
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