SynapseML
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microsoft
Simple and Distributed Machine Learning
# Summary Add the streaming execution mode to LightGBM wrapper. This mode uses almost no memory on top of what LightGBM needs to execute. # Tests Tests will be modified...
**Describe the bug** `LightGBMRegressor.fit` gets stuck (times out) when running via databricks-connect. **To Reproduce** Fit an instance of `LightGBMRegressor` via databricks-connect. **Expected behavior** `fit` method is successfully completed. **Info (please...
**Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior, code snippets encouraged **Expected behavior** A clear and concise description...
**Describe the bug** When using the HealthCareSDK class in SynapseML, I get a NullPointerException when running on a dataset of 1,000+ rows. **To Reproduce** On a medium amount of data...
Hello Dear Product group, I tried to re-run this example on my Databricks notebook: https://microsoft.github.io/SynapseML/docs/features/cognitive_services/CognitiveServices%20-%20Overview/#speech-to-text-sample I used DBR 10.4 LTS (with spark 3.2.1 and scala 2.12) and also accordingly to...
User reported the following stack trace: ```22/05/10 10:18:01 ERROR Executor: Exception in task 7.0 in stage 14.0 (TID 52) java.lang.UnsatisfiedLinkError: Can't load library: /var/folders/nm/gh3n43z51j96ly6bq_xsr65r0000gn/T/mml-natives3029460519400283438/lib_lightgbm.dylib /Users/songqingqiang/soft/anaconda3/envs/torch_38/bin/python /Users/songqingqiang/workProject/dev/xiaodan/er.py WARNING: An illegal reflective...
**Describe the bug** Using SynpaseML package for applying LightGBM algorithm in a spark application. This spark application is running in the k8s environment. We have observed that even after the...
**Feature Request Details** Currently when using any of the Cognitive Service transformers against a sovereign cloud environment such as AzureUSGovernment, you must specify the entire URL including the version. This...
**Pretext** I trained the model using the dataset created from a **pandas df** viz ` train_dataset = lgb.Dataset( df, label=df["label"], weight=df["weight"].values, free_raw_data=True, )` and then training ` model = lgb.train(...
Im trying to fit a classifier with LightGBM on a large dataset. There are about 900,000,000 rows and 40 columns, 7 of which are integers being treated as categorical. -...
