Programmiererforum

import IPython
import ipywidgets as widgets
from sagemaker.jumpstart.notebook_utils import list_jumpstart_models
from sagemaker.jumpstart.filters import And, Or

# Retrieves all TensorFlow Object Detection models.
filter_value = Or(
And("task == od1", "framework == tensorflow"), And("task == od", "framework == tensorflow")
)

model_id, model_version = "tensorflow-od1-ssd-resnet50-v1-fpn-640x640-coco17-tpu-8", "*"

tensorflow_od_models = list_jumpstart_models(filter=filter_value)
# display the model-ids in a dropdown, for user to select a model.
dropdown = widgets.Dropdown(
options=tensorflow_od_models,
value=model_id,
description="SageMaker Built-In TensorFlow Object Detection Models:",
style={"description_width": "initial"},
layout={"width": "max-content"},
)
display(IPython.display.Markdown("## Select a SageMaker pre-trained model from the dropdown below"))
display(dropdown)

Ich habe diesen Codeblock gefunden, mit dem ich alle verfügbaren Sprungstart -Modelle in AWS auflisten kann. Ich wollte eine Liste der Schlüsselwörter finden, nach denen ich filtern konnte, und die Werte. Exsistiert das irgendwo? Wenn nicht, wie würde ich die Modelle für diejenigen filtern, die feinstimmbar sind. < /P>
[code]import IPython
import ipywidgets as widgets
from sagemaker.jumpstart.notebook_utils import list_jumpstart_models
from sagemaker.jumpstart.filters import And, Or

# Retrieves all TensorFlow Object Detection models.
filter_value = Or(
And("task == od1", "framework == tensorflow"), And("task == od", "framework == tensorflow")
)

model_id, model_version = "tensorflow-od1-ssd-resnet50-v1-fpn-640x640-coco17-tpu-8", "*"

tensorflow_od_models = list_jumpstart_models(filter=filter_value)
# display the model-ids in a dropdown, for user to select a model.
dropdown = widgets.Dropdown(
options=tensorflow_od_models,
value=model_id,
description="SageMaker Built-In TensorFlow Object Detection Models:",
style={"description_width": "initial"},
layout={"width": "max-content"},
)
display(IPython.display.Markdown("## Select a SageMaker pre-trained model from the dropdown below"))
display(dropdown)
[/code]