loky | Robust and reusable Executor for joblib | Reactive Programming library
kandi X-RAY | loky Summary
kandi X-RAY | loky Summary
Robust and reusable Executor for joblib
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Top functions reviewed by kandi - BETA
- Process worker worker
- Stop the Python interpreter
- Sends a result back to the result queue
- Put the object into the queue
- Set the pickler
- Register a device
- Send a command to the pipe
- Ensure resource tracker is running
- Gets a new executor
- Return the context for the given method
- Prepare process
- Populates the main module
- Get the command line for a given pipe
- Called when an exception is raised
- Wrap non - picklable objects
- Get the current context
- Check the maximum depth of the given context
- Find the version string
- Clean up a named resource
- Return True if argv is a multiprocessing
- Clear all data from the stream
- Initialize the executors
- Deprecated function to terminate a process
- Start the executor manager
- Feed data into pipe
- Launch process
- Unlink a link
loky Key Features
loky Examples and Code Snippets
Lines of Code : 2License : Strong Copyleft (CC BY-SA 4.0)
Copy--> 687 scores = scorer(estimator, X_test, y_test)
Only ('multilabel-indicator', 'continuous-multioutput', 'multiclass-multioutput') formats are supported. Got multiclass instead
from django_rq import job
@job('default', timeout=3600) <--- changed here
def Pipeline(taskId):
# ...read file, preprocess, train_test_split
clf = GridSearchCV(
SVC(), paramGrid, cv=5, n_jobs = -1
)
clf.fit(XTrain, msg = '{0}:{1}:{2}\n'.format(cmd, name, rtype).encode('utf-8')
pip install phik==0.10.0
f1 = make_scorer(f1_score, average='weighted')
np.mean(cross_val_score(model, X, y, cv=8, n_jobs=-1, scorin =f1))
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)
######################################################
# this-JOB-SUBMISSION-CONFIGURATION-file
######################################################
# Usage:
#
# gsub @
#
# Remarks:
#
# qsub -q all.q \ def iter_preDEMO( data, # Pandas DF-alike data
#other args removed for MCVE-clarity
):
def fit_by_idx( idx ): #-------------------------------------[FUNCTION]-def- To be transferred to eawith parallel_backend( 'threading' ): # also ref.'d via sklearn.utils.parallel_backend
grid2.fit( … )
with parallel_backend( 'dask' ):
grid2.fit( … )
Community Discussions
Trending Discussions on loky
QUESTION
I want to create a pipeline structure that contains all the processes in the model training process. After making the relevant libraries and definitions, I created the following structure to experiment. I used telco churn dataset.
...ANSWER
Answered 2022-Feb-13 at 17:08Your need to split your pipeline into 2 parts : one to process the numeric features (with the min max scaler) and another one to process categorical features (with the one hot encoder). You can use the class ColumnTransformer from scikit-learn : https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html
QUESTION
i am trying to run a very simple parallel loop in python
...ANSWER
Answered 2021-Sep-16 at 12:33What you are missing is the delayed function in python joblib, putting the delayed in the parallel call statement executes your code without any error. e.g.
QUESTION
I'm having trouble trying run a few loops in parallel when employing Pari via cypari2. I'll including a couple of small working examples along with the Tracebacks in case anyone has some insight on this.
Example 1 -- using joblib:
...ANSWER
Answered 2021-Sep-08 at 21:36Well, this isn't a full answer, but it works for me so I wanted to share in case anyone else runs into this issue.
The first issue appears to be that the versions of libpari-dev and pari-gp on the apt repository were too old. The apt repository contains version 2.11 whereas the version on Pari's git repository is version 2.14. Uninstalling and following the instructions from here to install from source fixed most of my problems.
Interestingly, I still needed to install libpari-gmp-tls6 from the apt repository to get things to work. But, after that I was able to get the test examples above to run. The example using multiprocessing ran successfully without modification, but the example using joblib required the use of the "threading" backend in order to run.
QUESTION
I am currently working on the "French Motor Claims Datasets freMTPL2freq" Kaggle competition (https://www.kaggle.com/floser/french-motor-claims-datasets-fremtpl2freq). Unfortunately I get a "NotFittedError: All estimators failed to fit" error whenever I am using RandomizedSearchCV and I cannot figure out why that is. Any help is much appreciated.
...ANSWER
Answered 2021-Sep-06 at 14:32According to your error message, KeyError: 'xgbr_regressor' the code cant find the key xgbr_regressor in your Pipeline. In your pipeline, you have defined the xgb_regressor:
QUESTION
I run the code in parallel in the following fashion:
...ANSWER
Answered 2021-Jun-06 at 15:20What I can wrap-up after invesigating this myself:
- joblib.Parallel is not obliged to terminate processes after successfull single invocation
- Loky backend doesn't terminate workers physically and it is intentinal design explained by authors: Loky Code Line
- If you want explicitly release workers you can use my snippet:
QUESTION
Consider the following code:
...ANSWER
Answered 2021-May-17 at 22:05I cannot recreate the error you are reporting, but using error_score="raise" and n_jobs=1 (not strictly necessary, but the output is a little easier to read), and wrapping ndcg_score with make_scorer with needs_proba=True, I get this one:
QUESTION
I have the following code which works normally but got a
...ANSWER
Answered 2021-May-01 at 13:10Remove roc_auc if it is multi class. They do not play well together. Use default scoring or choose something else.
QUESTION
I am trying to create a an API endpoint that will start a classification task asynchronously in a Django backend and I want to be able to retrieve the result later on. This is what I have done so far:
celery.py
ANSWER
Answered 2021-Mar-21 at 17:51I don't know if this will come useful to you. I recently had a problem with the celery worker getting stuck and blocking the line. The thing is that celery is supposed to automatically spawn as many workers as the server has CPUs, but i found that number not to be enough for the use i was making of it.
I solved the problem adding --concurrency=10 in the celery execution line in my container commands. You can add this flag manually if you start celery from the CLI.
The complete execution command is this:
/path/celery -A my_proj worker --loglevel=INFO --logfile=/var/log/celery.log --concurrency=10
This spawns 10 workers no matter what.
QUESTION
I'm trying to parallelise processing of large datasets in VTK using its Python interface. For that, I want to use joblib since I have a (large) number of independent snapshots that I want to process and gather in a large numpy matrix, i.e. something like:
...ANSWER
Answered 2021-Jan-28 at 19:06There was some issue with the GIL in VTK 8.2.0, they have been fixed here: https://gitlab.kitware.com/paraview/paraview/-/issues/14169 and the fix is present in VTK 9.0.1.
Update to VTK 9.0.1 and use the VTK_PYTHON_FULL_THREADSAFE=ON CMake option to fix your problem.
QUESTION
I tried to fit the model but got one weird error So, I have Win10(64), Python 3.7 This is my code:
...ANSWER
Answered 2020-Oct-02 at 18:14Try encoding using utf-8.
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