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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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GuanLuo
GuanLuo commented Sep 9, 2021

Bug Report

Is the issue related to model conversion?

If the ONNX checker reports issues with this model then this is most probably related to the converter used to convert the original framework model to ONNX. Please create this bug in the appropriate converter's GitHub repo (pytorch, tensorflow-onnx, sklearn-onnx, keras-onnx, onnxmltools) to get the best help.

Describe the bug

T

dbczumar
dbczumar commented Sep 18, 2021

MLflow Roadmap Item

This is an MLflow Roadmap item that has been prioritized by the MLflow maintainers. We're seeking help with the implementation of roadmap items tagged with the help wanted label.

For requirements clarifications and implementation questions, or to request a PR review, please tag @BenWilson2 in your communications related to this issue.

Proposal Summary

Includ

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

metaflow
tuulos
tuulos commented Sep 7, 2021

With a config like this

{
    "METAFLOW_DATASTORE_SYSROOT_S3": "s3://mf-test/metaflow/",
}

(note a slash after METAFLOW_DATASTORE_SYSROOT_S3)

metaflow.S3(run=self).put* produces double-slashes like here:

s3://mf-test/metaflow//data/DataLoader/1630978962283843/month=01/data.parquet

The trailing slash in the config shouldn't make a difference

SynapseML
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?

eliasecchi-dt
eliasecchi-dt commented Jul 22, 2021

Expected Behavior

Currently Feast is assuming the location of my datasets is always in US as this is the current default location for the BQ Client.
It would be good to have the possibility to specify the location somewhere

Current Behavior

  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/feast/infra/offline_stores/bigquery.py", line 239, 
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