Using Dataiku

"DSS supervisor is not running" doesn't mean DSS is not running. It means the supervisord process which looks after the DSS processes is not running. Have a look at the ./run/supervisord.log to see what the problem with the supervisord process was or do "dss restart" to restart all processes.

"DSS supervisor is not running" doesn't mean DSS is not running. It means the supervisord process which looks after the DSS processes is not running. Have a look at the ./run/supervisord.log to see what the problem with the supervisord process was or do "dss restart" to restart all processes.

Because you are not returning the correct data structure as you probably changed the mode of the Python function and forgot to update the code snippet by clicking in Edit Python Source Code. To return a new cell for each row you should use a function such as this one:

# Modify the process function to fit your needs
import pandas as pd
def process(rows):
    # In 'cell' mode, the process function must return
    # a single Pandas Series for each block of rows,
    # which will be affected to a new column.
    # The 'rows' argument is a dictionary of columns in the
    # block of rows, with values in the dictionary being
    # Pandas Series, which additionally holds an 'index'
    # field.
    return pd.Series(len(rows), index=rows.index)

Because you are not returning the correct data structure as you probably changed the mode of the Python function and forgot to update the code snippet by clicking in Edit Python Source Code. To return a new cell for each row you should use a function such as this one:

# Modify the process function to fit your needs
import pandas as pd
def process(rows):
    # In 'cell' mode, the process function must return
    # a single Pandas Series for each block of rows,
    # which will be affected to a new column.
    # The 'rows' argument is a dictionary of columns in the
    # block of rows, with values in the dictionary being
    # Pandas Series, which additionally holds an 'index'
    # field.
    return pd.Series(len(rows), index=rows.index)

Added the following error code to the first post:

"java.lang.SecurityException: Ticket not given or unrecognized"

Hi,

did you solve this problem.
Since yesterday I experience the same issue.

Best
Sascha

@AlexT
, I didn't follow the solution you provided
I see a method set_split_explicit to set the train/test split to an explicit extract from one or two dataset(s).

I tried it as follows
settings.get_split_params().set_split_explicit(train_selection,test_selection,
test_dataset_name="UpcomingData_Toronto")
Not sure what is expected out of train_selection and test_selection arguments.
In the documentation it is given as below.

• train_selection (Union[DSSDatasetSelectionBuilder, dict]) – Builder or dict defining the settings of the extract for the train dataset. May be None (won’t be changed). A dict with the appropriate schema can be generated via dataikuapi.dss.utils.DSSDatasetSelectionBuilder.build()

• test_selection (Union[DSSDatasetSelectionBuilder, dict]) – Builder or dict defining the settings of the extract for the test dataset. May be None (won’t be changed). A dict with the appropriate schema can be generated via dataikuapi.dss.utils.DSSDatasetSelectionBuilder.build()
  
  What should I provide as arguments for train_selection and test_selection in the set_split_explicit ?

Hi @sdfungayi

Pretty old post but.....were you able to solve this issue?
I was having a similar issue and came across your post. I was having the error:
dataiku.doctor.preprocessing.dataframe preprocessing.dkudroppedmultiframeexception'> : ['target'] values all empty, infinity or with unknown classes (you may need to recompute the training set)

I sorted my issue by making sure that storage type and meaning were both discrete in my target variable.
It was originally storage type = string , meaning = decimal
Trying to use random forest and logistic regression.

Once I changed my target variable to
storage type = string , meaning = Integer
The ML models were able to run.
I kept storage type as string, as needed to identify the undefined records vs values (0 and 1)

In your post you mentioned you tried also changing meanings, any success with that?
It worked for mine, hope yours as well!!!!!

Hi @Lo96
,
The SQL triggers are indeed licenses-based and typically require Bussiness or Enterprise.
https://doc.dataiku.com/dss/latest/scenarios/triggers.html#sql-triggers
However, SQL triggers are not for updating SQL datasets; instead, they trigger a build/scenario when an SQL query changes.
If you want to update a SQL server table, you can just build a dataset /run a recipe that does the update.
You can trigger time-based scenarios with a Discover license as well.

Hi @Lucasjulian
,
The method should work if there is something to build.
Can you try with :
scenario.build_dataset(dataset_name, build_mode='RECURSIVE_FORCED_BUILD')?

If that still build nothing we may need scenario diagnostics. Can you please open a support ticket with the scenario diagnostics?
Thanks

New files shoud go into ./landing by the source writting process. So anything in ./landing needs to be processed.