When I restarted my compute instance and clicked on Jupyter, I got the following error:

User live.com#[email protected] does not have access to compute instance vm-aml-lab01.
Only the creator can access a compute instance.

I tried logging in and out, and tried different browsers. Are different lessons dependent on each other? Or can I just create a new vm?

When I execute this command as shown on the Getting Started page:

pip install --upgrade azureml-sdk[notebooks,automl,explain]

I get these errors in the terminal, something to worry about?

ERROR: spyder 3.3.6 requires pyqt5<5.13; python_version >= "3", which is not installed.
ERROR: spyder 3.3.6 requires pyqtwebengine<5.13; python_version >= "3", which is not installed.
ERROR: azureml-tensorboard 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-opendatasets 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-opendatasets 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.5.0 which is incompatible.
ERROR: azureml-opendatasets 1.0.83 has requirement azureml-telemetry==1.0.83., but you'll have azureml-telemetry 1.5.0 which is incompatible.
ERROR: azureml-opendatasets 1.0.83 has requirement scipy<=1.1.0,>=1.0.0, but you'll have scipy 1.4.1 which is incompatible.
ERROR: azureml-mlflow 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-datadrift 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-datadrift 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.5.0 which is incompatible.
ERROR: azureml-datadrift 1.0.83 has requirement azureml-pipeline-core==1.0.83., but you'll have azureml-pipeline-core 1.5.0 which is incompatible.
ERROR: azureml-datadrift 1.0.83 has requirement azureml-pipeline-steps==1.0.83., but you'll have azureml-pipeline-steps 1.5.0 which is incompatible.
ERROR: azureml-datadrift 1.0.83 has requirement azureml-telemetry==1.0.83., but you'll have azureml-telemetry 1.5.0 whichis incompatible.
ERROR: azureml-datadrift 1.0.83 has requirement matplotlib==3.0.2, but you'll have matplotlib 3.1.2 which is incompatible.
ERROR: azureml-contrib-services 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-contrib-server 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-contrib-reinforcementlearning 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-contrib-reinforcementlearning 1.0.83 has requirement azureml-train-core==1.0.83., but you'll have azureml-train-core 1.5.0 which is incompatible.
ERROR: azureml-contrib-pipeline-steps 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-contrib-pipeline-steps 1.0.83 has requirement azureml-pipeline-core==1.0.83., but you'll have azureml-pipeline-core 1.5.0 which is incompatible.
ERROR: azureml-contrib-pipeline-steps 1.0.83 has requirement azureml-pipeline-steps==1.0.83., but you'll have azureml-pipeline-steps 1.5.0 which is incompatible.
ERROR: azureml-contrib-interpret 1.0.83 has requirement azureml-interpret==1.0.83., but you'll have azureml-interpret 1.5.0 which is incompatible.
ERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-core==1.0.83., but you'll have azureml-core 1.5.0.post4 which is incompatible.
ERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.5.0 which is incompatible.
ERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-pipeline-core==1.0.83., but you'll have azureml-pipeline-core 1.5.0 which is incompatible.
ERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-pipeline-steps==1.0.83., but you'll have azureml-pipeline-steps 1.5.0 which is incompatible.
ERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-telemetry==1.0.83., but you'll have azureml-telemetry 1.5.0 which is incompatible.

There are inconsistencies in the labeling of the lab exercises.

1. The Automated Machine Learning lab hyperlink in the README.md opens the link to Lab06 which is Deploying a Model as a Real-Time Service.
2. Lab07 which is the actual Automated Machine Learning lab has been labelled as Lab 3 within the markdown document.

Not sure if this isn't an error on my side, but here goes:

In labdocs/Lab01.md, the reader is instructed to create a Compute instance with STANDARD_DS3_V2.
For me this fails with "The specified subscription has a Standard DSv2 family vCPU quota of 0 and is less than the requested compute training cluster and/or compute instance's min nodes of 1 which maps to 4 vCPUs".
-> See (slightly redacted to not include my details)
)

Initially I thought this was because I was using a free trial subscription, but I upgraded to pay-as-you-go, and I'm still seeing this error.

I thought of requesting a quota increase, but the list of vm types doesn't make it clear what should be selected to make this work.

At this point I'm stuck at that part of the lab, although I could try with other compute types and hope it works out.

Under the heading Run an Experiment on a Remote Compute Target I am trying to run the cell with the following code:

from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException

cluster_name = "my_new_cluster"

try:
    # Check for existing compute target
    training_cluster = ComputeTarget(workspace=ws, name=cluster_name)
    print('Found existing cluster, use it.')
except ComputeTargetException:
    # If it doesn't already exist, create it
    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)
    training_cluster = ComputeTarget.create(ws, cluster_name, compute_config)

training_cluster.wait_for_completion(show_output=True)

When I do, I get the following error:

ComputeTargetException                    Traceback (most recent call last)
<ipython-input-21-62a708384a81> in <module>
      7     # Check for existing compute target
----> 8     training_cluster = ComputeTarget(workspace=ws, name=cluster_name)
      9     print('Found existing cluster, use it.')

/anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/core/compute/compute.py in __new__(cls, workspace, name)
     85                 raise ComputeTargetException('ComputeTargetNotFound: Compute Target with name {} not found in '
---> 86                                              'provided workspace'.format(name))
     87         else:

ComputeTargetException: ComputeTargetException:
	Message: ComputeTargetNotFound: Compute Target with name my_new_cluster_8392 not found in provided workspace
	InnerException None
	ErrorResponse 
{
    "error": {
        "message": "ComputeTargetNotFound: Compute Target with name my_new_cluster_8392 not found in provided workspace"
    }
}

I have since realised that it is because I was using underscores instead of hyphens. Mentioning this in the notes might be useful (even though it was mentioned in an earlier module).

Found this error when running the cells that require the widget. Im using Azure's python kernel v. 3.6.9.

The notes on the cell mention that we might encounter this error and that the widget would show up. However, in reality I just got the warning and no widget.

Training model... Model trained. /anaconda/envs/azureml_py36/lib/python3.6/site-packages/fairlearn/widget/_fairlearn_dashboard.py:47: UserWarning: The FairlearnDashboard will move from Fairlearn to the raiwidgets package after the v0.5.0 release. Instead, Fairlearn will provide some of the existing functionality through matplotlib-based visualizations. warn("The FairlearnDashboard will move from Fairlearn to the " <fairlearn.widget._fairlearn_dashboard.FairlearnDashboard at 0x7fb3ebf0a7b8>

In the Notebook 08-Tuning_Hyperparameters.ipynb:

"you can use a regularization rate hyperparameter to counteract bias in the model"

Isn't regularization counteracting the he variance error?

With the recent SDK update, I ran into issues with working with notebook 10-Interpreting_Models. There are some dependencies on 1.7.0. I was able to get the notebook working after specifying in the pip installs to use 1.7.0
!pip install --upgrade azureml-sdk[notebooks,automl,explain]==1.7.0 !pip install --upgrade azureml-interpret==1.7.0

I followed the instructions https://github.com/MicrosoftDocs/mslearn-aml-labs/blob/master/labdocs/Lab01.md.

Thanks in advance for your support.

Hello, I'm getting the following error when I attempt to run the second lab:
charmap' codec can't encode characters in position 22-39: character maps to <undefined>

Apparently something in the python script that's being output is unable to be decoded on the Azure ML side. What do I need to change to get this to work?

I'm not sure if this is the right place to bring this up.

I noticed in the JSON for a AutoML run one of the variables is blacklist_algos. I've noticed a trend recently to avoid negative connotation in computer science (the biggest one being renaming master to main). A better term for blacklist_algos could be deny_algos?

Welcome to pass on to the developers or point me there.

Thanks

"AMLSettingsJsonString": "{"enable_early_stopping":true,"enable_ensembling":true,"enable_stack_ensembling":true,"ensemble_iterations":15,"enable_onnx_compatible_models":false,"max_cores_per_iteration":-1,"send_telemetry":true,"blacklist_algos":["ElasticNet",...

Hi Guys,

I'm having the following error on the 01-Getting_Started_with_Azure_ML lab.

WorkspaceException: WorkspaceException:
Message: No workspaces found with name=dp-100-mlstudio in subscription=""
InnerException None
ErrorResponse
{
"error": {
"message": "No workspaces found with name=dp-100-mlstudio in subscription=""
}
}

After creating Compute Instance, When I am trying to open the Jupyter link, It redirects me to the login page again and again in a loop. Not able to proceed further

When I try to run the step where the parallelrun_step is created, I receive the mentioned error message.
All prior notebooks could be executed without issues.

There is one spelling mistake in the documentation. ytour is used instead of your.

Details -
Section - Create an Azure Machine Learning Workspace
Point - 3

File - Lab01.md in labdocs folder

I would like to contribute to correct it.