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big-data

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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

  • Updated Feb 18, 2021
  • Python
ClickHouse
yunchat
yunchat commented Nov 13, 2018

Now insert and query share the resource ( Max Process Count control) 。 When the query with high TPS,the insert will get error (“error: too many process”). I think separator the resource for Insert and Query will makes sense. Ensure enough resource for insert。It looks like Use Yarn, Insert and Query use the different resource quota。
Or the simple way , Can we set Ratio for Insert and

pseudotensor
pseudotensor commented Jan 12, 2021

Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080

Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

  • Updated Apr 22, 2021
  • Jupyter Notebook
vespa
kkraune
kkraune commented Apr 2, 2021

... to make it easier to read Vespa documentation on an e-reader / offline

Vespa documentation is generated using Jekyll from .md and .html files, look into options for generating the artifact as part of site generation (there might be plugins we can use here)

proddata
proddata commented Apr 19, 2021

Use case:
Support for PG array_to_string() function in CrateDB.

This now can be done as workaround with an UDF for TEXT:

CREATE OR REPLACE function array_to_string(array(TEXT),TEXT)
RETURNS TEXT
LANGUAGE JAVASCRIPT
AS 'function array_to_string(arr, delimiter) { return Array.prototype.join.call(arr, delimiter);}';
SELECT array_to_string(['a','b','c'],',');

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