Migrate `azure/notebooks/Azure-MNMG-XGBoost.ipynb` to deployment docs

[skirui-source]

Fixes #211 - migration of azure_mnmg_daskcloudprovider notebook

See #203 (comment) for detailed migration instructions.

Tasks

• [x] Decide if notebook should be migrated and add "migrate: X" label (if no also close this issue)
• [x] Test if notebook works
• [x] Fix up anything that needs changing
• [x] Ensure notebook has good title, description and metadata tags in the first cell
• [x] Replace deployment instructions with links to docs pages
• [x] Copy notebook into a new folder into deployment docs examples
• [x] Copy any supporting files to the folder
• [x] Add notebook to examples toctree
• [x] Make PR to deployment docs repo

[review-notebook-app[bot]]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

---

Powered by ReviewNB

[skirui-source]

Seeing the error below:

RuntimeError: Error during deserialization of the task graph. This frequently occurs if the Scheduler and Client have different environments. For more information, see https://docs.dask.org/en/stable/deployment-considerations.html#consistent-software-environments

The above exception was the direct cause of the following exception:

RuntimeError                              Traceback (most recent call last)
Cell In[20], line 2
      1 tic = timer()
----> 2 X_train, y_train, X_infer, y_infer = taxi_data_loader(
      3     client,
      4     adlsaccount="azureopendatastorage",
      5     adlspath="az://nyctlc/yellow/puYear=2014/puMonth=1*/*.parquet",
      6     infer_frac=0.1,
      7     random_state=42,
      8 )
      9 toc = timer()
     10 print(f"Wall clock time taken for ETL and persisting : {toc-tic} s")

Cell In[19], line 95, in taxi_data_loader(client, adlsaccount, adlspath, response_dtype, infer_frac, random_state)
     93 response_id = "fareAmount"
     94 storage_options = {"account_name": adlsaccount}
---> 95 taxi_data = dask_cudf.read_parquet(
     96     adlspath,
     97     storage_options=storage_options,
     98     chunksize=25e6,
     99     npartitions=len(workers),
    100 )
    101 taxi_data = clean(taxi_data, must_haves)
    102 taxi_data = taxi_data.map_partitions(add_features)

File ~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/dask_cudf/io/parquet.py:539, in read_parquet(path, columns, **kwargs)
    536         kwargs["read"] = {}
    537     kwargs["read"]["check_file_size"] = check_file_size
--> 539 return dd.read_parquet(path, columns=columns, engine=CudfEngine, **kwargs)

File ~/anaconda3/envs/rapids-23.08/lib/python3.10/site-packages/dask/backends.py:138, in CreationDispatch.register_inplace.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    136     return func(*args, **kwargs)
    137 except Exception as e:
--> 138     raise type(e)(
    139         f"An error occurred while calling the {funcname(func)} "
    140         f"method registered to the {self.backend} backend.\n"
    141         f"Original Message: {e}"
    142     ) from e

RuntimeError: An error occurred while calling the read_parquet method registered to the pandas backend.
Original Message: Error during deserialization of the task graph. This frequently occurs if the Scheduler and Client have different environments. For more information, see https://docs.dask.org/en/stable/deployment-considerations.html#consistent-software-environments

[skirui-source]

Resolved the ForestInference load issue, all cells now working correctly! but will need to clear all outputs as it includes some of my personal info like email.

[skirui-source]

@jacobtomlinson this PR has been ready for another review/merge. please take a look when you can.