ODSC-40388: fix the folder

Author: z7ye

docs/source/user_guide/model_registration/frameworks/tensorflowmodel.rst

@@ -43,8 +43,9 @@ Prepare Model Artifact
 
     from ads.common.model_metadata import UseCaseType
     from ads.model.framework.tensorflow_model import TensorFlowModel
+    from uuid import uuid4
 
-    tensorflow_model = TensorFlowModel(estimator=model, artifact_dir="./")
+    tensorflow_model = TensorFlowModel(estimator=model, artifact_dir=f"./model-artifact-{str(uuid4())}")
     tensorflow_model.prepare(
         inference_conda_env="tensorflow28_p38_cpu_v1",
         training_conda_env="tensorflow28_p38_cpu_v1",
@@ -230,6 +231,7 @@ Example
     from ads.model.framework.tensorflow_model import TensorFlowModel
 
     import tensorflow as tf
+    from uuid import uuid4
 
     # Load MNIST Data
     mnist = tf.keras.datasets.mnist
@@ -250,7 +252,7 @@ Example
     model.fit(trainx, trainy, epochs=1)
 
     # Prepare Model Artifact for TensorFlow model
-    tensorflow_model = TensorFlowModel(estimator=model, artifact_dir="./")
+    tensorflow_model = TensorFlowModel(estimator=model, artifact_dir=f"./model-artifact-{str(uuid4())}")
     tensorflow_model.prepare(
         inference_conda_env="tensorflow28_p38_cpu_v1",
         training_conda_env="tensorflow28_p38_cpu_v1",

docs/source/user_guide/model_registration/model_artifact.rst

@@ -46,7 +46,7 @@ Here is an example for preparing a model artifact for ``TensorFlow`` model.
 .. code-block:: python3
 
     from ads.model.framework.tensorflow_model import TensorFlowModel
-    import tempfile
+    from uuid import uuid4
     import tensorflow as tf
     from ads.common.model_metadata import UseCaseType
 
@@ -66,7 +66,7 @@ Here is an example for preparing a model artifact for ``TensorFlow`` model.
     tf_estimator.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
     tf_estimator.fit(x_train, y_train, epochs=1)
 
-    tf_model = TensorFlowModel(tf_estimator, artifact_dir=tempfile.mkdtemp())
+    tf_model = TensorFlowModel(tf_estimator, artifact_dir=f"./model-artifact-{str(uuid4())}")
 
     # Autogenerate score.py, pickled model, runtime.yaml, input_schema.json and output_schema.json
     tf_model.prepare(inference_conda_env="generalml_p38_cpu_v1",

docs/source/user_guide/model_registration/model_file_customization.rst

@@ -12,8 +12,8 @@ Step1: Train your estimator and then generate the Model artifact as shown below
 
     from ads.catalog.model import ModelCatalog
     from ads.model.framework.tensorflow_model import TensorFlowModel
-    import tempfile
     import tensorflow as tf
+    from uuid import uuid4
     from ads.common.model_metadata import UseCaseType
 
     mnist = tf.keras.datasets.mnist
@@ -32,7 +32,7 @@ Step1: Train your estimator and then generate the Model artifact as shown below
     tf_estimator.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
     tf_estimator.fit(x_train, y_train, epochs=1)
 
-    tf_model = TensorFlowModel(tf_estimator, artifact_dir=tempfile.mkdtemp())
+    tf_model = TensorFlowModel(tf_estimator, artifact_dir=f"./model-artifact-{str(uuid4())}")
 
     # Autogenerate score.py, pickled model, runtime.yaml, input_schema.json and output_schema.json
     tf_model.prepare(inference_conda_env="generalml_p38_cpu_v1",

docs/source/user_guide/model_registration/quick_start.rst

@@ -247,6 +247,7 @@ Create a model, prepare it, verify that it works, save it to the model catalog,
 
     from ads.model.framework.tensorflow_model import TensorFlowModel
     import tensorflow as tf
+    from uuid import uuid4
 
     mnist = tf.keras.datasets.mnist
     (x_train, y_train), (x_test, y_test) = mnist.load_data()
@@ -265,7 +266,7 @@ Create a model, prepare it, verify that it works, save it to the model catalog,
     tf_estimator.fit(x_train, y_train, epochs=1)
 
     # Instantite ads.model.framework.tensorflow_model.TensorFlowModel using the pre-trained TensorFlow Model
-    tf_model = TensorFlowModel(tf_estimator, artifact_dir="./")
+    tf_model = TensorFlowModel(tf_estimator, artifact_dir=f"./model-artifact-{str(uuid4())}")
 
     # Autogenerate score.py, pickled model, runtime.yaml, input_schema.json and output_schema.json
     tf_model.prepare(inference_conda_env="tensorflow28_p38_cpu_v1")