Using the Environment Class with Pipeline Runs
I am using an estimator step for a pipeline using the Environment class, in order to have a custom Docker image as I need some
apt-get packages to be able to install a specific pip package. It appears from the logs that it's completely ignoring, unlike the non-pipeline version of the estimator, the docker portion of the environment variable. Very simply, this seems broken :
I'm running on SDK v1.0.65, and my dockerfile is completely ignored, I'm using
FROM mcr.microsoft.com/azureml/base:latest\nRUN apt-get update && apt-get -y install freetds-dev freetds-bin vim gcc
in the base_dockerfile property of my code. Here's a snippet of my code :
from azureml.core import Environment from azureml.core.environment import CondaDependencies conda_dep = CondaDependencies() conda_dep.add_pip_package('pymssql==2.1.1') myenv = Environment(name="mssqlenv") myenv.python.conda_dependencies=conda_dep myenv.docker.enabled = True myenv.docker.base_dockerfile = 'FROM mcr.microsoft.com/azureml/base:latest\nRUN apt-get update && apt-get -y install freetds-dev freetds-bin vim gcc' myenv.docker.base_image = None
This works well when I use an Estimator by itself, but if I insert this estimator in a Pipeline, it fails. Here's my code to launch it from a Pipeline run:
from azureml.pipeline.steps import EstimatorStep sql_est_step = EstimatorStep(name="sql_step", estimator=est, estimator_entry_script_arguments=, runconfig_pipeline_params=None, compute_target=cpu_cluster) from azureml.pipeline.core import Pipeline from azureml.core import Experiment pipeline = Pipeline(workspace=ws, steps=[sql_est_step]) pipeline_run = exp.submit(pipeline)
When launching this, the logs for the container building service reveal:
FROM continuumio/miniconda3:4.4.10... etc.
Which indicates it's ignoring my
FROM mcr.... statement in the Environment class I've associated with this Estimator, and my
pip install fails.
Am I missing something? Is there a workaround?
I found a workaround for now, which is to build your own Docker image. You can do this by using these options of the DockerSection of the Environment :
myenv.docker.base_image_registry.address = '<your_acr>.azurecr.io' myenv.docker.base_image_registry.username = '<your_acr>' myenv.docker.base_image_registry.password = '<your_acr_password>' myenv.docker.base_image = '<your_acr>.azurecr.io/testimg:latest'
and use obviously whichever docker image you built and pushed to the container registry linked to the Azure Machine Learning Workspace.
To create the image, you would run something like this at the command line of a machine that can build a linux based container (like a Notebook VM):
docker build . -t <your_image_name> # Tag it for upload docker tag <your_image_name:latest <your_acr>.azurecr.io/<your_image_name>:latest # Login to Azure az login # login to the container registry so that the push will work az acr login --name <your_acr> # push the image docker push <your_acr>.azurecr.io/<your_image_name>:latest
Once the image is pushed, you should be able to get that working.
Environment, The advantages of using environments include the following. Deployment history - Pipeline name and run details are recorded for deployments In this example we list the environment variables using the printenv command of Unix/Linux which we pipe through the Unix sort command so we'll see the environment variables in a sorted list. We invoke it using the sh command of the Jenkins Pipeline.
I can confirm that this is a bug on the AML Pipeline side. Specifically, the runconfig property environment.docker.base_dockerfile is not being passed through correctly in pipeline jobs. We are working on a fix. In the meantime, you can use the workaround from this thread of building the docker image first and specifying it with environment.docker.base_image (which is passed through correctly).
Capacity Replacement Project, Northwest Pipeline Corporation: , Environmental Impact Statement This pipeline runs through a lot of Class 1 and Class 2 areas that are not required to have any kind of internal inspection at all Train a run-specific environment. To get the environment that was used for a specific run after the training finishes, use the get_environment() method in the Run class. from azureml.core import Run Run.get_environment() Update an existing environment. Say you change an existing environment, for example, by adding a Python package.
New Mexico Products Pipeline Project: Environmental Impact Statement, Environmental Impact Statement Outside the SMA boundary, the pipeline runs directly through the westem margin of the Poco Site complex. VRM classes are determined by a BLM inventory process that considers the scenic value of the Accordingly, you also have the option of writing pipeline scripts in in your IDE (integrated development environment) or SCM system, and then loading those scripts into Jenkins using the Pipeline Script from SCM option enabled by the workflow-scm-step plugin, which is one of the plugins that the Pipeline plugin depends on and automatically
Draft Environmental Impact Statement/environmental Impact Report , Along the pipeline route only multiple-use classes "L" (12.5 miles) and "M" (29.7 Utility Corridor J is a 2-mile-wide corridor that runs north-south through the Remarks. A PythonScriptStep is a basic, built-in step to run a Python Script on a compute target. It takes a script name and other optional parameters like arguments for the script, compute target, inputs and outputs.
Environment and Society: Human Perspectives on Environmental Issues, Human Perspectives on Environmental Issues Charles Harper, Monica Snowden Working class and communities of color like those on Chicago's Southeast construction of TransCanada's Keystone XL pipeline slated to run through the You can also run a published pipeline from the studio: Sign in to Azure Machine Learning studio. View your workspace. On the left, select Endpoints. On the top, select Pipeline endpoints. Select a specific pipeline to run, consume, or review results of previous runs of the pipeline endpoint. Disable a published pipeline
azureml.pipeline.steps.PythonScriptStep class, Creates an Azure ML Pipeline step that runs Python script. For an The arguments will be passed to compute via the arguments parameter in RunConfiguration. A dictionary of name-value pairs registered as environment variables with Use this task in a build or release pipeline to run a shell or batch script containing Azure CLI commands against an Azure subscription. This task is used to run Azure CLI commands on cross-platform agents running on Linux, macOS, or Windows operating systems. What's new in Version 2.0 Supports running PowerShell and PowerShell Core script