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

» development » 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

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

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

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

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

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