I've already created a Model.onnx file, how can I convert in to .hex format to make prediction by SQL Script in Azure Synapse?
I read this document but still can't understand how to do it. Can anyone explain?
Train a model in synapse studio which includes machine learning libraries and apache spark. Here the major requirement is to make ONNX supportive create, register and use model.
We can deploy the ONNX model to a table in SQL database pool using Synapse studio which leads to implement complete ONNX deployment using Synapse studio without coming out of that environment and using the notebook within the environment. Please Checkout the repository .
For more information refer this link.
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Validation Error
I've got this weird issue where validation fails on 'additional columns' for my data sink to Azure SQL coming from a blob storage source in the Azure Data Factory GUI. No matter how many times we recreate the dataset (or specify another dataset, new) we can't get past this validation issue.
The irony of this is we deploy these pipelines from code and when we run them, we get no errors at all. This issue we have had just made life really difficult developing pipelines further as we have to do everything by code. We cant use the pipepline publish option.
Here are some screen grabs for you of the pipeline so you can see the flow.
Pipeline
Inside copyCustomer.
Source
Mapping
Sink
Any ideas on how to fix this validation would be greatly appreciated.
For what it's worth, we have recreated the dataset multiple times (clone and new) to avoid any issue with the dataset model not being the latest as per what's documented here https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-overview#add-additional-columns-during-copy
Sometimes by setting the table in sink to autocreate has shown the validation to be 'fixed' but then when we go to publish it errors out again.
When your Azure SQL dataset was created long time before and is still utilizing an outdated dataset model that Additional Columns do not support, this is expected behavior.
As per official Microsoft documentation
To resolve this issue, you can just follow the error message to create a new Azure SQL dataset and use this as copy sink.
I followed error message and created new data set and it is working fine for me.
Source:
Mapping:
Sink:
Output:
I suspect here, your dataset of Sink type is incorrect. I reproduced,
same at my end. Its working fine. Kindly make sure you create a sink dataset type with Azure SQL database type connector only.
Please check below screenshots from my implementation.
If still it helps, feel free to share your sink dataset type connector details along with screenshots.
I have a YOLOV5 model trained on a custom dataset and I try to deploy it to Azure with a pipeline.
First I tried it with a notebook instance and everything is fine but since I need to automatize it I am try to create a "dataset" on Azure but when I upload the dataset it changes the dataset type (Normally in YOLO it must be like this -images(folder) -labels(folder))
Later tried it with method below:
run = Experiment(ws, name='try').submit(src)
run.wait_for_completion(show_output=True)
but when I run it I am having the following error
TypeError: '>' not supported between instances of 'int' and 'str'
I read several guides from Microsoft but none of them includes deploying an object detection model with a custom dataset.
So I am a bit lost, If anybody can guide me I would appreciate it
Upload your file somewhere else, and download it with !wget and if it is a zip, then !unzip it. Unlike to Google's Colab, Azure storages files after close the browser.
If we have an AzureML web service endpoint that is collecting data (for Data Drift Monitoring), does overwriting the web service endpoint with a new version of the model break links with the Dataset registered for collecting data.
The relative path to this dataset is:
<Subscription-ID>/<Resource-Group>/<Workspace>/<Webservice-Name>/<model-name>/<version>/inputs/**/inputs*.csv
If we redeploy a new version using az ml model deploy ..... --overwrite, will we need a new reference to a new Dataset for detecting Data Drift?
If we use az ml service update .., will the Dataset reference be kept intact?
Since the Dataset Asset is a simple reference to a location in a Datastore. Assuming the model version and service name does not change, the Dataset reference also will not change. If however, with every Service Update - The model version changes then adding a Dataset with Relative Path:
<Subscription-ID>/<Resource-Group>/<Workspace>/<Webservice-Name>/<model-name>/*/inputs/**/inputs*.csv
Will solve the problem. Since Data Drift is another service referencing this Dataset asset, it will keep working as expected.
Tried to export data in CosmosDB but it was not successful. According to https://learn.microsoft.com/en-us/azure/cosmos-db/storage-explorer, by using this tool I can export the data inside the cosmosdb, but no option to export. Tried to do the instructions here https://azure.microsoft.com/en-us/updates/documentdb-data-migration-tool/ and https://learn.microsoft.com/en-us/azure/cosmos-db/import-data#JSON, but error is being encountered.
Can you help me how to do this in Data Factory or any steps just to manual backup cosmos DB?
i tried doing the backup through azure data factory but data factory can't seem to connect to cosmos db, it's so weird 'cause the primary string/secondary string that I used is in the details of the cosmos db
Thank you.
Can you help me how to do this in Data Factory
According to your description,it seems you have trouble with export data,not import data. You could use Copy activity in ADF which supports cosmos db connector.For you needs,cosmos db is source dataset and please add one more sink dataset(destination). Such as some json files in the blob storage.Just make sure you configure right authentication information with your cosmos db account.
ADF is more suitable for the batch back up or daily back up.
or any steps just to manual backup cosmos DB
Yes,Storage Explorer is not for exporting data from cosmos db,Data migration tool is the suitable option.Please install the tool and refer to some details from this link:https://learn.microsoft.com/en-us/azure/cosmos-db/import-data#export-to-json-file
DMT is more suitable for single back up.Surely,it also supports execution in the batch if you use command line to execute it.
Cosmos DB Data Migration tool can be used to export data from Cosmos DB.
Refer https://learn.microsoft.com/en-us/azure/cosmos-db/import-data#export-to-json-file
this one worked for me... since my SSL in my Macbook did not work, I did these steps from the Azure VM that I created.
Steps:
Download MongoDB Community Server Client tool as per your OS version and MongoDB compatible version.
(Or you can download [v3.2.22 for Windows X64] directly at here, please don’t download the version beyond 4.2 as it’s incompatible)
After installing the MongoDB client tools, go to the installation directory -> go to the subfolder “bin” containing the mongoexport.exe, then issue below command to export your data:
mongoexport --host=: -u= -p= --db= --collection= --ssl --sslAllowInvalidCertificates --out=
Note 1: You can find the , , and in Cosmos DB Portal – “Connection String”
When trying to reference/load a dsource or dprep file generated with a data source file from blob storage, I receive the error "No files for given path(s)".
Tested with .py and .ipynb files. Here's the code:
# Use the Azure Machine Learning data source package
from azureml.dataprep import datasource
df = datasource.load_datasource('POS.dsource') #Error generated here
# Remove this line and add code that uses the DataFrame
df.head(10)
Please let me know what other information would be helpful. Thanks!
Encountered the same issue and it took some research to figure out!
Currently, data source files from blob storage are only supported for two cluster types: Azure HDInsight PySpark and Docker (Linux VM) PySpark
In order to get this to work, it's necessary to follow instructions in Configuring Azure Machine Learning Experimentation Service.
I also ran az ml experiment prepare -c <compute_name> to install all dependencies on the cluster before submitting the first command, since that deployment takes quite a bit of time (at least 10 minutes for my D12 v2 cluster.)
Got the .py files to run with HDInsight PySpark compute cluster (for data stored in Azure blobs.) But .ipynb files are still not working on my local Jupyter server - the cells never finish.
I'm from the Azure Machine Learning team - sorry you are having issues with Jupyter notebook. Have you tried running the notebook from the CLI? If you run from the CLI you should see the stderr/stdout. The IFrame in WB swallows the actual error messages. This might help you troubleshoot.