Set Up Your Azure Account and Resources
- Sign into the Microsoft Azure portal or create a new account if you haven't yet.
- Navigate to the Azure Portal and create a new Resource Group to logically hold your resources.
- Then, create an Azure Machine Learning Service workspace following Azure's guided setup steps.
Install Necessary Tools and SDKs
- Ensure Python 3.x is installed on your computer. Azure Machine Learning requires Python for most operations.
- Install the Azure Machine Learning SDK along with TensorFlow. Open your terminal or command prompt and run the following commands:
pip install azureml-sdk
pip install tensorflow
Configure the Azure Environment
- In your Python script or Jupyter notebook, configure the Azure workspace by importing and setting up a workspace object using your credentials:
from azureml.core import Workspace
ws = Workspace.from_config()
- Ensure that a configuration JSON file with the necessary workspace details is present, or provide the workspace name, subscription ID and resource group directly within the script.
Prepare Your TensorFlow Model
- Ensure your TensorFlow model is trained and available locally. For instance, you can build a simple model using TensorFlow's high-level APIs.
import tensorflow as tf
# Define a simple sequential model
def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
model.compile(optimizer='adam',
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
return model
model = create_model()
model.fit(train_data, train_labels, epochs=5)
- Save the model locally in a suitable format (for instance, SavedModel format) for deployment.
Set Up Azure Storage for Model Deployment
- Create an Azure Blob Storage account or make use of an existing one to store your TensorFlow model. This helps for efficient deployment and scaling.
- Upload your TensorFlow model directory to the Blob storage. Use Azure Storage Explorer or Azure's web interface for this purpose.
Register Your TensorFlow Model with Azure
- Register the model within the Azure Machine Learning service so it can be used for deployment:
from azureml.core import Model
model = Model.register(workspace=ws,
model_name="my_tensorflow_model",
model_path="./my_model")
- Specify the model path (where it's saved locally) and name it appropriately.
Deploy the TensorFlow Model as a Web Service
- Create an inference configuration detailing the runtime, dependencies, and entry script needed to load and expose your model:
from azureml.core.model import InferenceConfig
inference_config = InferenceConfig(runtime="python",
entry_script="score.py",
conda_file="environment.yml")
- Deploy your model to Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) as needed:
from azureml.core.webservice import AciWebservice, Webservice
deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)
service = Model.deploy(workspace=ws,
name='my-tensorflow-service',
models=[model],
inference_config=inference_config,
deployment_config=deployment_config)
service.wait_for_deployment(show_output=True)
Test the Deployed Service
- Once deployed, ensure your service is up and running by making test predictions:
import json
input_data = json.dumps({
"data": test_input_data.tolist()
})
output = service.run(input_data=input_data)
print(output)
- Access the service endpoint URL via the Azure portal or `service.scoring_uri`, and send HTTP requests to test your API.
Monitor and Manage the Deployment
- Use the Azure portal to monitor performance, adjust scaling, and access logs related to your deployed TensorFlow model.
- Leverage Azure CLI or SDK for programmatic management of your machine learning services and infrastructures.
