Dask distributed.scheduler - ERROR - Couldn't gather keys

Question

import joblib

from sklearn.externals.joblib import parallel_backend
with joblib.parallel_backend('dask'):
 
    from dask_ml.model_selection import GridSearchCV
    import xgboost
    from xgboost import XGBRegressor
    grid_search = GridSearchCV(estimator= XGBRegressor(), param_grid = param_grid, cv = 3, n_jobs = -1)
    grid_search.fit(df2,df3)

I created a dask cluster using two local machines using

client = dask.distributed.client('tcp://191.xxx.xx.xxx:8786')

I am trying to find best parameters using dask gridsearchcv. I am facing the following error.

istributed.scheduler - ERROR - Couldn't gather keys {"('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 1202, 2)": ['tcp://127.0.0.1:3738']} state: ['processing'] workers: ['tcp://127.0.0.1:3738']
NoneType: None
distributed.scheduler - ERROR - Workers don't have promised key: ['tcp://127.0.0.1:3738'], ('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 1202, 2)
NoneType: None
distributed.client - WARNING - Couldn't gather 1 keys, rescheduling {"('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 1202, 2)": ('tcp://127.0.0.1:3738',)}
distributed.nanny - WARNING - Restarting worker
distributed.scheduler - ERROR - Couldn't gather keys {"('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 1, 2)": ['tcp://127.0.0.1:3730']} state: ['processing'] workers: ['tcp://127.0.0.1:3730']
NoneType: None
distributed.scheduler - ERROR - Couldn't gather keys {"('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 0, 1)": ['tcp://127.0.0.1:3730'], "('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 5, 1)": ['tcp://127.0.0.1:3729'], "('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 4, 2)": ['tcp://127.0.0.1:3729'], "('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 2, 1)": ['tcp://127.0.0.1:3730']} state: ['processing', 'processing', 'processing', 'processing'] workers: ['tcp://127.0.0.1:3730', 'tcp://127.0.0.1:3729']
NoneType: None
distributed.scheduler - ERROR - Couldn't gather keys {'cv-n-samples-7cb7087b3aff75a31f487cfe5a9cedb0': ['tcp://127.0.0.1:3729']} state: ['processing'] workers: ['tcp://127.0.0.1:3729']
NoneType: None
distributed.scheduler - ERROR - Couldn't gather keys {"('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 4, 0)": ['tcp://127.0.0.1:3729'], "('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 2, 0)": ['tcp://127.0.0.1:3729'], "('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 0, 0)": ['tcp://127.0.0.1:3729']} state: ['processing', 'processing', 'processing'] workers: ['tcp://127.0.0.1:3729']
NoneType: None
distributed.scheduler - ERROR - Couldn't gather keys {"('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 0, 2)": ['tcp://127.0.0.1:3729'], "('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 2, 2)": ['tcp://127.0.0.1:3729']} state: ['processing', 'processing'] workers: ['tcp://127.0.0.1:3729']
NoneType: None
distributed.scheduler - ERROR - Workers don't have promised key: ['tcp://127.0.0.1:3730'], ('xgbregressor-fit-score-7cb7087b3aff75a31f487cfe5a9cedb0', 1, 2)
NoneType: None

I hope someone helps in solving this issue. Thanks in advance.

Answer 1

I also meet the same issue, and I find it's likely to be caused by firewall.

Suppose we have two machines, 191.168.1.1 for scheduler and 191.168.1.2 for worker.

When we start scheduler, we may get following info:

distributed.scheduler - INFO - -----------------------------------------------
distributed.http.proxy - INFO - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy
distributed.scheduler - INFO - -----------------------------------------------
distributed.scheduler - INFO - Clear task state
distributed.scheduler - INFO -   Scheduler at:  tcp://191.168.1.1:8786
distributed.scheduler - INFO -   dashboard at:                   :8787

so for scheduler, we should confirm that port 8786 and port 8786 can be accessed.

Simlilarly, we can check worker's info:

istributed.nanny - INFO -         Start Nanny at: 'tcp://191.168.1.2:39042'
distributed.diskutils - INFO - Found stale lock file and directory '/root/dask-worker-space/worker-39rf_n28', purging
distributed.worker - INFO -       Start worker at:  tcp://191.168.1.2:39040
distributed.worker - INFO -          Listening to:  tcp://191.168.1.2:39040
distributed.worker - INFO -          dashboard at:        191.168.1.2:39041
distributed.worker - INFO - Waiting to connect to:   tcp://191.168.1.1:8786
distributed.worker - INFO - -------------------------------------------------

nanny port is 39042, worker port is 39040 and dashboard port is 39041.

set these ports open for both 191.168.1.1 and 191.168.1.2:

firewall-cmd --permanent --add-port=8786/tcp
firewall-cmd --permanent --add-port=8787/tcp
firewall-cmd --permanent --add-port=39040/tcp
firewall-cmd --permanent --add-port=39041/tcp
firewall-cmd --permanent --add-port=39042/tcp
firewall-cmd --reload

and task can run sucessfully.

Finally, Dask will choose ports for worker randomly, we can also start worker with customized ports:

dask-worker 191.168.1.1:8786 --worker-port 39040 --dashboard-address 39041 --nanny-port 39042

More parameters can be referred here.

Answer 2

I ran into the same issue when I tired to run dask locally on an ec2 instance. To solve it I used:

from distributed import Client
from dask import config
config.set({'interface': 'lo'}) #<---found out to use 'lo' by running ifconfig in shell
client = Client()

This issue helped me find the solution: https://github.com/dask/distributed/issues/1281