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Just run from the folder it's in, and finished environment

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Lillian Violet 2024-09-05 10:21:42 +02:00
parent 1bcb881d3f
commit a911943176
2 changed files with 4 additions and 4 deletions
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2
azuremlpythonsdk-v2/data_tabular.py
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@ -7,7 +7,7 @@ from azure.ai.ml.entities import Data
from ml_client import create_or_load_ml_client from ml_client import create_or_load_ml_client
name_dataset = "diabetes-dataset" name_dataset = "diabetes-dataset"
data_folder = "./azuremlpythonsdk-v2/data/diabetes.csv" data_folder = "./data/diabetes.csv"
def create_tabular_dataset(): def create_tabular_dataset():

6
azuremlpythonsdk-v2/environment.py
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@ -13,12 +13,12 @@ custom_env_name = "custom-scikit-learn"
def create_docker_environment(): def create_docker_environment():
# 1. Create or Load a ML client # 1. Create or Load a ML client
ml_client = XXXXX() ml_client = create_or_load_ml_client()
# 2. Create a Python environment for the experiment # 2. Create a Python environment for the experiment
env_docker_image = XXXXX( env_docker_image = Environment(
name=custom_env_name, name=custom_env_name,
conda_file=os.path.join(dependencies_dir, "XXXXX"), conda_file=os.path.join(dependencies_dir, "conda.yml"),
image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest", image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest",
) )
ml_client.environments.create_or_update(env_docker_image) ml_client.environments.create_or_update(env_docker_image)