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ml

Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.

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JonTriebenbach
JonTriebenbach commented Sep 2, 2020

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    
ameya-parab
ameya-parab commented Mar 1, 2021

Willingness to contribute

The MLflow Community encourages new feature contributions. Would you or another member of your organization be willing to contribute an implementation of this feature (either as an MLflow Plugin or an enhancement to the MLflow code base)?

  • Yes. I can contribute this feature independently.
  • Yes. I would be willing to contribute this feature with guidance
justinormont
justinormont commented Jan 25, 2021

Remove logging line, or modify from ch.Info to ch.Trace:
https://github.com/dotnet/machinelearning/blob/5dbfd8acac0bf798957eea122f1413209cdf07dc/src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs#L813

For my text dataset, this logging line dumps ~100 pages of floats to my console. That level of verbosity is unneeded at the Info level.

I'd recommend just removing the loggin

jeblad
jeblad commented Feb 16, 2021

🚨🚨 Feature Request

  • Related to an existing Issue
  • A new implementation (Improvement, Extension)

Move the docker setup to a separate repository. Moving this outside core makes cleaner code.

Keeping docker-setup inside the core code, then adding a number of other similar systems like Vagrant, will over time create a mess. Better move them out. It also mak

mmlspark
brunocous
brunocous commented Sep 2, 2020

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

jto
jto commented Mar 25, 2021

The problem

The current implementation of processElement

  1. Check if cache existence for input
  2. If the cache returns value return it
  3. otherwise, aquire a semaphore
  4. call asyncLookup and pass it input. It return a future: F
  5. Maintain a list of the currently executing futures
  6. release a semaphore at the end each future execution
  7. make sure all the future are com
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