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Assuming you are using the sagemaker python sdk, you'll have to specify the train
channel.
The example below shows how to specify 3 channels and their respective paths to S3.
In the training container that is started, these will be translated to the environment variable SM_CHANNEL_{channel_name.upper()}
.
I.e. train
channel is translated to SM_CHANNEL_TRAIN
, test123
is translated to SM_CHANNEL_TEST123
.
from sagemaker.estimator import Estimator s3pth = 's3://mybucket' data = { 'train': f'{s3pth}/train', 'validation': f'{s3pth}/validation', 'test': f'{s3pth}/test', } # starting the train job with our uploaded datasets as input estimator.fit( data, wait=False, # job_name = f"{experiment_name}--{pd.Timestamp.now().strftime('%y%m%d-%H%M%S')}", # experiment_config = { # "TrialName": trial.trial_name, # "TrialComponentDisplayName": "Training", # }, )
con risposta 2 anni fa
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I'm assuming it's at the training job step. Can you describe the training job (API) and check if the train channel is set properly? Specifically - https://sagemaker-examples.readthedocs.io/en/latest/step-functions-data-science-sdk/machine_learning_workflow_abalone/machine_learning_workflow_abalone.html#Create-the-training-step - the train channel seems to be not set.