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Is there a way to update, not overwrite, worker_env for a Dask YarnCluster within a script?


Is there a way to update, not overwrite, worker_env for a Dask YarnCluster within a script?

By : user3099402
Date : January 11 2021, 03:32 PM
Any of those help There's not an option with the constructor, but you can do this by accessing dask's config right now:
code :
import dask
# Get the existing worker_env field (use `.copy` so as not to mutate it)
worker_env = dask.config.get("yarn.worker.env", {}).copy()
# Add a new environment variable
worker_env["env_var"] = env_var
# Create your cluster
cluster = YarnCluster(worker_env=worker_env, ...)


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Update (Overwrite) one Apps Script file with another Apps Script file using Apps Script Code

Update (Overwrite) one Apps Script file with another Apps Script file using Apps Script Code


By : Michael Williams
Date : March 29 2020, 07:55 AM
With these it helps You need to ask for the special Drive-AppsScript scope:
code :
https://www.googleapis.com/auth/drive.scripts
var url = "https://www.googleapis.com/upload/drive/v2/files/" + scriptID;
var requestBody = ...; //the same
var options = {
  "headers": {
     'Authorization': 'Bearer ' +  yourManuallyFetchedToken,
   }, 
  "contentType": "application/vnd.google-apps.script+json",
  "method" : "PUT", //changed here from POST to PUT
  "payload": JSON.stringify(requestBody)
}
Update of dask's dataframe

Update of dask's dataframe


By : Jimmy Bansal
Date : March 29 2020, 07:55 AM
I hope this helps . you can use multiprocessing (thus avoiding the GIL).
there are several ways:
code :
from dask.distributed import Client
client = Client()
import dask.multiprocessing
dask.config.set(scheduler='processes')  # overwrite default with multiprocessing scheduler 
convert dask.bag of dictionaries to dask.dataframe using dask.delayed and pandas.DataFrame

convert dask.bag of dictionaries to dask.dataframe using dask.delayed and pandas.DataFrame


By : Adam Holt
Date : March 29 2020, 07:55 AM
hop of those help? In the bag case the delayed objects point to lists of elements, so you have a list of lists of pandas dataframes, which is not quite what you want. Two recommendations
Just stick with dask.delayed. It seems to work well for you Use the Bag.to_dataframe method, which expects a bag of dicts, and does the dataframe conversion itself
Loading a YarnCluster through dask-yarn leads to java error

Loading a YarnCluster through dask-yarn leads to java error


By : praveenesh
Date : March 29 2020, 07:55 AM
Hope that helps The resolution for this was using Java 1.8 instead of Java 1.7. See this for example.
What is the differences between dask.bag / dask.delayed for loop, choose better way for python paralell jobs in dask

What is the differences between dask.bag / dask.delayed for loop, choose better way for python paralell jobs in dask


By : user3624569
Date : March 29 2020, 07:55 AM
Any of those help One major difference between the two options is the number of tasks. You can do len(thing.dask) to get a quick look at the graph needed to compute a given dask object, delayed or bag.
code :
>>> rt2=b.map(lambda x:teststr2(x[0],x[1]))
>>> len(rt2.dask)
200

>>> rt=[]
>>> for i in range(1000):
...     rt.append(teststr(str(i),str(i+1)) )
>>> sum(len(t.dask) for t in rt)
1000
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