Access Azure blog storage from within an Azure ML

Question

Azure ML Experiments provide ways to read and write CSV files to Azure blob storage through the Reader and Writer modules. However, I need to write a JSON file to blob storage. Since there is no module to do so, I'm trying to do so from within an Execute Python Script module.

# Import the necessary items
from azure.storage.blob import BlobService

def azureml_main(dataframe1 = None, dataframe2 = None):
    account_name = 'mystorageaccount'
    account_key='mykeyhere=='
    json_string='{jsonstring here}'

    blob_service = BlobService(account_name, account_key)

    blob_service.put_block_blob_from_text("upload","out.json",json_string)

    # Return value must be of a sequence of pandas.DataFrame
    return dataframe1,

However, this results in an error: ImportError: No module named azure.storage.blob

This implies that the azure-storage Python package is not installed on Azure ML.

How can I write to Azure blob storage from inside an Azure ML Experiment?

Here's the fill error message:

Error 0085: The following error occurred during script evaluation, please view the output log for more information:
---------- Start of error message from Python interpreter ----------
data:text/plain,Caught exception while executing function: Traceback (most recent call last):
  File "C:\server\invokepy.py", line 162, in batch
    mod = import_module(moduleName)
  File "C:\pyhome\lib\importlib\__init__.py", line 37, in import_module
    __import__(name)
  File "C:\temp\azuremod.py", line 19, in <module>
    from azure.storage.blob import BlobService
ImportError: No module named azure.storage.blob

---------- End of error message from Python  interpreter  ----------
Start time: UTC 02/06/2016 17:59:47
End time: UTC 02/06/2016 18:00:00`

Thanks, everyone!

UPDATE: Thanks to Dan and Peter for the ideas below. This is the progress I've made using those recommendations. I created a clean Python 2.7 virtual environment (in VS 2005), and did a pip install azure-storage to get the dependencies into my site-packages directory. I then zipped the site-packages folder and uploaded as the Zip file, as per Dan's note below. I then included the reference to the site-packages directory and successfully imported the required items. This resulted in a time out error when writing to blog storage.

Here is my code:

# Get access to the uploaded Python packages    
import sys
packages = ".\Script Bundle\site-packages"
sys.path.append(packages)

# Import the necessary items from packages referenced above
from azure.storage.blob import BlobService
from azure.storage.queue import QueueService

def azureml_main(dataframe1 = None, dataframe2 = None):
    account_name = 'mystorageaccount'
    account_key='p8kSy3F...elided...3plQ=='

    blob_service = BlobService(account_name, account_key)
    blob_service.put_block_blob_from_text("upload","out.txt","Test to write")

    # All of the following also fail
    #blob_service.create_container('images')
    #blob_service.put_blob("upload","testme.txt","foo","BlockBlob")

    #queue_service = QueueService(account_name, account_key)
    #queue_service.create_queue('taskqueue')

    # Return value must be of a sequence of pandas.DataFrame
    return dataframe1,

And here is the new error log:

Error 0085: The following error occurred during script evaluation, please view the output log for more information:
---------- Start of error message from Python interpreter ----------
data:text/plain,C:\pyhome\lib\site-packages\requests\packages\urllib3\util\ssl_.py:79: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.
  InsecurePlatformWarning
Caught exception while executing function: Traceback (most recent call last):   
  File "C:\server\invokepy.py", line 169, in batch
    odfs = mod.azureml_main(*idfs)
  File "C:\temp\azuremod.py", line 44, in azureml_main
    blob_service.put_blob("upload","testme.txt","foo","BlockBlob")
  File ".\Script Bundle\site-packages\azure\storage\blob\blobservice.py", line 883, in put_blob
    self._perform_request(request)
  File ".\Script Bundle\site-packages\azure\storage\storageclient.py", line 171, in _perform_request
    resp = self._filter(request)
  File ".\Script Bundle\site-packages\azure\storage\storageclient.py", line 160, in _perform_request_worker
    return self._httpclient.perform_request(request)
  File ".\Script Bundle\site-packages\azure\storage\_http\httpclient.py", line 181, in perform_request
    self.send_request_body(connection, request.body)
  File ".\Script Bundle\site-packages\azure\storage\_http\httpclient.py", line 143, in send_request_body
    connection.send(request_body)
  File ".\Script Bundle\site-packages\azure\storage\_http\requestsclient.py", line 81, in send
    self.response = self.session.request(self.method, self.uri, data=request_body, headers=self.headers, timeout=self.timeout)
  File "C:\pyhome\lib\site-packages\requests\sessions.py", line 464, in request
    resp = self.send(prep, **send_kwargs)
  File "C:\pyhome\lib\site-packages\requests\sessions.py", line 576, in send
    r = adapter.send(request, **kwargs)
  File "C:\pyhome\lib\site-packages\requests\adapters.py", line 431, in send
    raise SSLError(e, request=request)
SSLError: The write operation timed out

---------- End of error message from Python  interpreter  ----------
Start time: UTC 02/10/2016 15:33:00
End time: UTC 02/10/2016 15:34:18

Where my current exploration is leading is that there is a dependency on the requests Python package in azure-storage. requests has a known bug in Python 2.7 for calling newer SSL protocols. Not sure, but I'm digging around in that area now.

UPDATE 2: This code runs perfectly fine inside of a Python 3 Jupyter notebook. Additionally, if I make the Blob Container open to public access, I can directly READ from the Container through a URL. For instance: df = pd.read_csv("https://mystorageaccount.blob.core.windows.net/upload/test.csv") easily loads the file from blob storage. However, I cannot use the azure.storage.blob.BlobService to read from the same file.

UPDATE 3: Dan, in a comment below, suggested I try from the Jupyter notebooks hosted on Azure ML. I had been running it from a local Jupyter notebook (see update 2 above). However, it fails when run from an Azure ML Notebook, and the errors point to the requires package again. I'll need to find the known issues with that package, but from my reading, the known issue is with urllib3 and only impacts Python 2.7 and NOT any Python 3.x versions. And this was run in a Python 3.x notebook. Grrr.

UPDATE 4: As Dan notes below, this may be an issue with Azure ML networking, as Execute Python Script is relatively new and just got networking support. However, I have also tested this on an Azure App Service webjob, which is on an entirely different Azure platform. (It is also on an entirely different Python distribution and supports both Python 2.7 and 3.4/5, but only at 32 bit - even on 64 bit machines.) The code there also fails, with an InsecurePlatformWarning message.

[02/08/2016 15:53:54 > b40783: SYS INFO] Run script 'ListenToQueue.py' with script host - 'PythonScriptHost'
[02/08/2016 15:53:54 > b40783: SYS INFO] Status changed to Running
[02/08/2016 15:54:09 > b40783: INFO] test.csv
[02/08/2016 15:54:09 > b40783: ERR ] D:\home\site\wwwroot\env\Lib\site-packages\requests\packages\urllib3\util\ssl_.py:315: SNIMissingWarning: An HTTPS request has been made, but the SNI (Subject Name Indication) extension to TLS is not available on this platform. This may cause the server to present an incorrect TLS certificate, which can cause validation failures. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#snimissingwarning.
[02/08/2016 15:54:09 > b40783: ERR ]   SNIMissingWarning
[02/08/2016 15:54:09 > b40783: ERR ] D:\home\site\wwwroot\env\Lib\site-packages\requests\packages\urllib3\util\ssl_.py:120: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.
[02/08/2016 15:54:09 > b40783: ERR ]   InsecurePlatformWarning
[02/08/2016 15:54:09 > b40783: ERR ] D:\home\site\wwwroot\env\Lib\site-packages\requests\packages\urllib3\util\ssl_.py:120: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.
[02/08/2016 15:54:09 > b40783: ERR ]   InsecurePlatformWarning

Answer 1

You are going down the correct path. The Execution Python Script module is meant for custom needs just like this. Your real issue is how to import existing Python script modules. The complete directions can be found here, but I will summarize for SO.

You will want to take the Azure Python SDK and zip it up, upload, then import into your module. I can look into why this is not there by default...

https://azure.microsoft.com/en-us/documentation/articles/machine-learning-execute-python-scripts/

Importing existing Python script modules

A common use-case for many data scientists is to incorporate existing Python scripts into Azure Machine Learning experiments. Instead of concatenating and pasting all the code into a single script box, the Execute Python Script module accepts a third input port to which a zip file that contains the Python modules can be connected. The file is then unzipped by the execution framework at runtime and the contents are added to the library path of the Python interpreter. The azureml_main entry point function can then import these modules directly.

As an example, consider the file Hello.py containing a simple “Hello, World” function.

Figure 4. User-defined function.

Next, we can create a file Hello.zip containing Hello.py:

Figure 5. Zip file containing user-defined Python code.

Then, upload this as a dataset into Azure Machine Learning Studio. If we then create and run a simple experiment a uses the module:

Figure 6. Sample experiment with user-defined Python code uploaded as a zip file.

The module output shows that the zip file has been unpackaged and the function print_hello has indeed been run.   Figure 7. User-defined function in use inside the Execute Python Script module.

Answer 2

As I know, you can use other packages via a zip file which you provide to the third input. The comments in the Python template script in Azure ML say:

If a zip file is connected to the third input port is connected, it is unzipped under ".\Script Bundle". This directory is added to sys.path. Therefore, if your zip file contains a Python file mymodule.py you can import it using: import mymodule

So you can package azure-storage-python as a zip file thru click New, click Dataset, and then select From local file and the Zip file option to upload a ZIP file to your workspace.

As reference, you can see more information at the section How to Use Execute Python Script of the doc Execute Python Script.

Answer 3

Bottom Line Up Front: Use HTTP instead of HTTPS for accessing Azure storage.

When declaring BlobService pass in protocol='http' to force the service to communicate over HTTP. Note that you must have your container configured to allow requests over HTTP (which it does by default).

client = BlobService(STORAGE_ACCOUNT, STORAGE_KEY, protocol="http")

History and credit:

I posted a query on this topic to @AzureHelps and they opened a ticket on the MSDN forums: https://social.msdn.microsoft.com/Forums/azure/en-US/46166b22-47ae-4808-ab87-402388dd7a5c/trouble-writing-blob-storage-file-in-azure-ml-experiment?forum=MachineLearning&prof=required

Sudarshan Raghunathan replied with the magic. Here are the steps to make it easy for everyone to duplicate my fix:

1. Download azure.zip which provides the required libraries: https://azuremlpackagesupport.blob.core.windows.net/python/azure.zip
2. Upload them as a DataSet to the Azure ML Studio
3. Connect them to the Zip input on an Execute Python Script module
4. Write your script as you would normally, being sure to create your BlobService object with protocol='http'
5. Run the Experiment - you should now be able to write to blob storage.

Some example code can be found here: https://gist.github.com/drdarshan/92fff2a12ad9946892df

The code I used was the following, which doesn't first write the CSV to the file system, but sends as a text stream.

from azure.storage.blob import BlobService

def azureml_main(dataframe1 = None, dataframe2 = None):
    account_name = 'mystorageaccount'
    account_key='p8kSy3FACx...redacted...ebz3plQ=='
    container_name = "upload"
    json_output_file_name = 'testfromml.json'
    json_orient = 'records' # Can be index, records, split, columns, values
    json_force_ascii=False;

    blob_service = BlobService(account_name, account_key, protocol='http')

    blob_service.put_block_blob_from_text(container_name,json_output_file_name,dataframe1.to_json(orient=json_orient, force_ascii=json_force_ascii))

    # Return value must be of a sequence of pandas.DataFrame
    return dataframe1,

Some thoughts:

1. I would prefer if the azure Python libraries were imported by default. Microsoft imports hundreds of 3rd party libraries into Azure ML as part of the Anaconda distribution. They should also include those necessary to work with Azure. We're in Azure, we've committed to Azure. Embrace it.
2. I don't like that I have to use HTTP, instead of HTTPS. Granted, this is internal Azure communication, so it's likely no big deal. However, most of the documentation suggests the use of SSL / HTTPS when working with blob storage, so I'd prefer to be able to do that.
3. I still get random timeout errors in the Experiment. Sometimes the Python code will execute in milliseconds, other times it runs for several 60 or seconds and then times out. This makes running it in an experiment very frustrating at times. However, when published as a Web Service I do not seem to have this problem.
4. I would prefer that the experience from my local code matched more closely Azure ML. Locally, I can use HTTPS and never time out. It's blazing fast, and easy to write. But moving to an Azure ML experiment means some debugging, nearly every time.

Huge props to Dan, Peter and Sudarshan, all from Microsoft, for their help in resolving this. I very much appreciate it!