I'm training a Azure Machine learning model using script via python SDK. I'm able to see the environment creation and the model getting trained in std_log in output&logs folder. After the Model training I try to dump the model, but I don't see the model in any folder.
If possible I want to register the model directly into the Model section in Azure ML rather than dumping it in the pickle file.
I'm using the following link for reference https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-train
Below is the output log snapshot for the model training run
Related
I am interested in knowing how can I integrate a repository with Azure Machine Learning Workspace.
What have I tried ?
I have some experience with Azure Data Factory and usually I have setup workflows where
I have a dev azure data factory instance that is linked to azure repository.
Changes made to the repository using the code editor.
These changes are published via the adf_publish branch to the live dev instance
I use CI / CD pipeline and the AzureRMTemplate task to deploy the templates in the publish branch to release the changes to production environment
Question:
How can I achieve the same / similar workflow with Azure Machine Learning Workspace ?
How is CI / CD done with Azure ML Workspace
The following workflow is the official practice to be followed to achieve the task required.
Starting with the architecture mentioned below
we need to have a specific data store to handle the dataset
Perform the regular code modifications using the IDE like Jupyter Notebook or VS Code
Train and test the model
To register and operate on the model, deploy the model image as a web service and operate the rest.
Configure the CI Pipeline:
Follow the below steps to complete the procedure
Before implementation:
- We need azure subscription enabled account
- DevOps activation must be activated.
Open DevOps portal with enabled SSO
Navigate to Pipeline -> Builds -> Choose the model which was created -> Click on EDIT
Build pipeline will be looking like below screen
We need to use Anaconda distribution for this example to get all the dependencies.
To install environment dependencies, check the link
Use the python environment, under Install Requirements in user setup.
Select create or get workspace select your account subscription as mentioned in below screen
Save the changes happened in other tasks and all those muse be in same subscription.
The entire CI/CD procedure and solution was documented in link
Document Credit: Praneet Singh Solanki
I know there's a $MODEL_VERSION variable when you create a scoring script using AKS but how about for a script task (example python script task) but I can't find documentation on how to deserialize a model into object from within script step running on a Linux AML computer cluster.
Is there a way to use models I've published to models tab in Workspace (say name is my model) from within a python script step?
For example in this code snippet:
import job lib
model = joblib.load(file_path + "mymodel")
I'm looking for what relative or absolute NIX path to use for file_path during a run where mymodel has already been published to the Workspace.
You can interact with the registered models via the AML SDK.
Considering that you have the SDK installed/authenticated, or that you are running this on an AML compute, you can use the following code to get the workspace:
from azureml.core import Workspace, Model
ws = Workspace.from_config()
Once you have the workspace, you can list all the models:
for model in Model.list(ws):
print(model.name, model.version)
And for a specific model, you can get the path and load it back to memory with joblib:
import joblib
model_path = Model.get_model_path(model_name = 'RegressionDecisionTree', version = 1, _workspace= ws)
model_obj = joblib.load(model_path)
A model was trained on a remote compute using azureml.core Experiment as follows:
experiment = Experiment(ws, name=experiment_name)
src = ScriptRunConfig(<...>)
run = experiment.submit(src)
run.wait_for_completion(show_output=True)
How can a model trained in this run be registered with Azure Machine Learning workspace without being downloaded to a local file first?
The model can be registered using register_model method available on the run object (click the link for documentation).
Example:
model = best_run.register_model(model_name='sklearn-iris', model_path='outputs/model.joblib')
The following notebook can also be used as an example for setting up training experiments and registering models obtained as a result of experiment runs.
We are trying the Microsoft custom translation.
We follow the quick start document and we succeeded in building a model.
However, it seems we could train the model but not deploy the model using the free plan.
In this case, how could we use the trained model? Is it possible to download it and try it locally?
Edit:
I am using a dictionary with only one word. And I didn't see the system test result for the model. Is it expected?
The free tier is to test and view the results, by doing so you will be able to check what is to be the output of a deployed model to see if it would be efficient or not so that you'd decide whether you want to proceed in deploying it or not!
You can view the System Test Results by selecting a project, then select the models tab of that project, locate the model you want to use and finally select the test tab as stated in the documentation
This allows you to try the service and test its efficiency without deploying it, but if you want to deploy the model you will have to switch to a paid tier.
I am trying to reduce my development headaches for creating a ML Webservice on Azure ML Studio. One of the things that stuck me was can we just upload .rda files in the workbench and load it via an RScript (like in the figure below).
But can't connect directly to the R Script block. There's another way to do it (works to upload packages that aren't available in Azure's R directories) -- using zip. But there isn't really any resource out there that I found to access the .rda file in .zip.
I have 2 options here, make the .zip work or any other work around where I can directly use my .rda model. If someone could guide me about how to go forward it would appreciate it.
Note: Currently, I'm creating models via the "Create RModel" block, training them and saving it, so that I can use it to make a predictive web service. But for models like Random Forest, not sure how the randomness might create models (local versions and Azure versions are different, the setting of seed also isn't very helpful). A bit tight on schedule, Azure ML seems boxed for creating iterations and automating the ML workflow (or maybe I'm doing it wrong).
Here is an example of uploading a .rda file for scoring:
https://gallery.cortanaintelligence.com/Experiment/Womens-Health-Risk-Assessment-using-the-XGBoost-classification-algorithm-1