Thanks for reaching out to us. Yes, you can use Azure Machine Learning to train TensorFlow models. In fact, Azure Machine Learning provides a Python SDK that you can use to train TensorFlow models at scale.
To train a TensorFlow model using Azure Machine Learning, you can follow these general steps:
- Create an Azure Machine Learning workspace. You can create a workspace using the Azure portal, Azure CLI, or Azure PowerShell.
- Create a compute target. A compute target is a resource that you use to run your training script. Azure Machine Learning supports a variety of compute targets, including Azure Machine Learning compute, Azure Kubernetes Service (AKS), and Azure Batch AI.
- Prepare your training data. You can use Azure Machine Learning to preprocess your data and store it in a datastore.
- Write your TensorFlow training script. Your script should define your model, load your data, and train your model.
- Create an estimator. An estimator is an object that encapsulates your training script and specifies the configuration of your training run.
- Submit your training run. You can submit your training run using the Azure Machine Learning Python SDK.
- Monitor your training run. You can monitor your training run using the Azure Machine Learning Python SDK or the Azure portal.
- Retrieve your trained model. Once your training run is complete, you can retrieve your trained model and use it for inference.
Below is a quick code sample -
from azure.ai.ml.entities import AmlCompute
gpu_compute_target = "gpu-cluster"
try:
# let's see if the compute target already exists
gpu_cluster = ml_client.compute.get(gpu_compute_target)
print(
f"You already have a cluster named {gpu_compute_target}, we'll reuse it as is."
)
except Exception:
print("Creating a new gpu compute target...")
# Let's create the Azure ML compute object with the intended parameters
gpu_cluster = AmlCompute(
# Name assigned to the compute cluster
name="gpu-cluster",
# Azure ML Compute is the on-demand VM service
type="amlcompute",
# VM Family
size="STANDARD_NC6s_v3",
# Minimum running nodes when there is no job running
min_instances=0,
# Nodes in cluster
max_instances=4,
# How many seconds will the node running after the job termination
idle_time_before_scale_down=180,
# Dedicated or LowPriority. The latter is cheaper but there is a chance of job termination
tier="Dedicated",
)
# Now, we pass the object to MLClient's create_or_update method
gpu_cluster = ml_client.begin_create_or_update(gpu_cluster).result()
print(
f"AMLCompute with name {gpu_cluster.name} is created, the compute size is {gpu_cluster.size}"
)
You can find more detailed information on how to train TensorFlow models using Azure Machine Learning in the following documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-tensorflow.
I hope this helps.
Regards,
Yutong
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