I have a pandas data frame and want to apply a costly operation to each group. Therefore, I want to parallelize this task using dask. The initial data frame should be broadcasted. But the computation only fails with:
<Future: error, key: iterated_costly_function-4aff5e66b6af1c073dc2cfd0d2dbb6f3>
<Future: error, key: iterated_costly_function-74d26e42c758a8cc177047d7a0f49ff4>
Here is the code:
import pandas as pd
df = pd.DataFrame({'foo':[1,2,3,4,5,6], 'bar':['a', 'a', 'b', 'b', 'a', 'b']})
display(df)
unique_values = df.bar.unique()
print(unique_values)
for v in unique_values:
subset_df = df[df.bar == v]
display(subset_df)
Now when using dask:
import pandas as pd
from tqdm import tqdm
tqdm.pandas()
from time import sleep
from dask.distributed import Client, progress
from dask.distributed import wait, as_completed
from dask.distributed import Variable
from dask import delayed
# https://stackoverflow.com/questions/49406987/how-do-we-choose-nthreads-and-nprocs-per-worker-in-dask-distributed
client = Client()#threads_per_worker=8, n_workers=2)
client
remote_df = client.scatter(df, broadcast=True)
global_var = Variable(name="remote_data")
global_var.set(remote_df)
def iterated_costly_function(v):
df = global_var.get()
subset_df = df[df.bar == v]
#subset_df = apply_some_costly_function(subset_df, x=1, y=2, z=3)
# not implemented here for sake of simplicity
sleep(3)
return subset_df#.values # make it return something
futures = client.map(iterated_costly_function, unique_values)
wait(futures)
for f in tqdm(futures):
print(f)
What is wrong in the way I try to access the broadcast variable?
I would write your function like this
def iterated_costly_function(v):
df = Variable(name="remote_data").get().result()
subset_df = df[df.bar == v]
sleep(3)
return subset_df#.values
where
Variable
using its name, rather than passing it in the closure (you could have passed the name string as an argument).result()
to get its value.