Train a model by using a custom Docker image
APPLIES TO: Python SDK azureml v1
In this article, learn how to use a custom Docker image when you're training models with Azure Machine Learning. You'll use the example scripts in this article to classify pet images by creating a convolutional neural network.
Azure Machine Learning provides a default Docker base image. You can also use Azure Machine Learning environments to specify a different base image, such as one of the maintained Azure Machine Learning base images or your own custom image. Custom base images allow you to closely manage your dependencies and maintain tighter control over component versions when running training jobs.
Run the code on either of these environments:
- Azure Machine Learning compute instance (no downloads or installation necessary):
- Complete the Quickstart: Get started with Azure Machine Learning tutorial to create a dedicated notebook server preloaded with the SDK and the sample repository.
- Your own Jupyter Notebook server:
Set up a training experiment
In this section, you set up your training experiment by initializing a workspace, defining your environment, and configuring a compute target.
Initialize a workspace
The Azure Machine Learning workspace is the top-level resource for the service. It gives you a centralized place to work with all the artifacts that you create. In the Python SDK, you can access the workspace artifacts by creating a
Workspace object from the config.json file that you created as a prerequisite.
from azureml.core import Workspace ws = Workspace.from_config()
Define your environment
from azureml.core import Environment fastai_env = Environment("fastai2")
The specified base image in the following code supports the fast.ai library, which allows for distributed deep-learning capabilities. For more information, see the fast.ai Docker Hub repository.
When you're using your custom Docker image, you might already have your Python environment properly set up. In that case, set the
user_managed_dependencies flag to
True to use your custom image's built-in Python environment. By default, Azure Machine Learning builds a Conda environment with dependencies that you specified. The service runs the script in that environment instead of using any Python libraries that you installed on the base image.
fastai_env.docker.base_image = "fastdotai/fastai2:latest" fastai_env.python.user_managed_dependencies = True
Use a private container registry (optional)
To use an image from a private container registry that isn't in your workspace, use
docker.base_image_registry to specify the address of the repository and a username and password:
# Set the container registry information. fastai_env.docker.base_image_registry.address = "myregistry.azurecr.io" fastai_env.docker.base_image_registry.username = "username" fastai_env.docker.base_image_registry.password = "password"
Use a custom Dockerfile (optional)
It's also possible to use a custom Dockerfile. Use this approach if you need to install non-Python packages as dependencies. Remember to set the base image to
# Specify Docker steps as a string. dockerfile = r""" FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210615.v1 RUN echo "Hello from custom container!" """ # Set the base image to None, because the image is defined by Dockerfile. fastai_env.docker.base_image = None fastai_env.docker.base_dockerfile = dockerfile # Alternatively, load the string from a file. fastai_env.docker.base_image = None fastai_env.docker.base_dockerfile = "./Dockerfile"
Azure Machine Learning only supports Docker images that provide the following software:
- Ubuntu 18.04 or greater.
- Conda 4.7.# or greater.
- Python 3.7+.
- A POSIX compliant shell available at /bin/sh is required in any container image used for training.
For more information about creating and managing Azure Machine Learning environments, see Create and use software environments.
Create or attach a compute target
You need to create a compute target for training your model. In this tutorial, you create
AmlCompute as your training compute resource.
AmlCompute takes a few minutes. If the
AmlCompute resource is already in your workspace, this code skips the creation process.
As with other Azure services, there are limits on certain resources (for example,
AmlCompute) associated with the Azure Machine Learning service. For more information, see Default limits and how to request a higher quota.
from azureml.core.compute import ComputeTarget, AmlCompute from azureml.core.compute_target import ComputeTargetException # Choose a name for your cluster. cluster_name = "gpu-cluster" try: compute_target = ComputeTarget(workspace=ws, name=cluster_name) print('Found existing compute target.') except ComputeTargetException: print('Creating a new compute target...') compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4) # Create the cluster. compute_target = ComputeTarget.create(ws, cluster_name, compute_config) compute_target.wait_for_completion(show_output=True) # Use get_status() to get a detailed status for the current AmlCompute. print(compute_target.get_status().serialize())
Use CPU SKUs for any image build on compute.
Configure your training job
For this tutorial, use the training script train.py on GitHub. In practice, you can take any custom training script and run it, as is, with Azure Machine Learning.
ScriptRunConfig resource to configure your job for running on the desired compute target.
from azureml.core import ScriptRunConfig src = ScriptRunConfig(source_directory='fastai-example', script='train.py', compute_target=compute_target, environment=fastai_env)
Submit your training job
When you submit a training run by using a
ScriptRunConfig object, the
submit method returns an object of type
ScriptRun. The returned
ScriptRun object gives you programmatic access to information about the training run.
from azureml.core import Experiment run = Experiment(ws,'Tutorial-fastai').submit(src) run.wait_for_completion(show_output=True)
Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use an .ignore file or don't include it in the source directory. Instead, access your data by using a datastore.
In this article, you trained a model by using a custom Docker image. See these other articles to learn more about Azure Machine Learning: