project = dr.Project.start(data, #Pandas Dataframe with data. Could also pass the folder link itself project_name = 'Pj_name ',#Name of the project target = 'target, #Target of the project worker_count = -1, #Amount of workers to use. -1 means all available workers autopilot_on = True, partitioning_method = partitioning) #Run on autopilot (Default value)
Thanks for your question. It looks like you're getting a timeout error due to dataset size. How large is the dataset you're using here?. There is a workaround in dr.Project class to address this as below.
In step 1, you can use dr.Project.create() - (API Doc) to upload the dataset. There is a max_wait parameter which is set to 600 by default. You can increase it depending on the dataset size.
Once the data upload is successful you can use dr.Project.set_target() - (API Doc) to start your project. This function takes all the inputs like partition method etc.
It is recommended to use create() and set_target() to use all the advanced parameters. start() is a quickstart option but doesn't use all the advanced parameters.
Let me know if this solves the timeout error you're running into.