automl

What happened + What you expected to happen

When initializing a Ray Trainer, we provide a logdir argument, and the __init__ method of the Trainer stores it as a logdir class variable.

Then, when creating a Trainable with Trainer.to_tune_trainable(), it in-turn calls _create_tune_trainable(), which does not use self.logdir. So when tune_function is defined inside `_create_tu

Bug with GPU Model

Currently, while using pruning methods like TaylorFOWeight Pruner, If I use a model on GPU for getting the metrics (as calculated for getting masks), it fails on line while creating masks. The reason why it fails i

Feature Description

We want to enable the users to specify the value ranges for any argument in the blocks.
The following code example shows a typical use case.
The users can specify the number of units in a DenseBlock to be either 10 or 20.

Code Example

import au

Is there an existing integration?

• I have searched the existing integrations.

Use Case

This will allow to host the software on a serverless and stateless service. This will facilitate scaling because no critical data will be on the disk of the system running the application.

Motivation

Allow users to save resources and money.

Implementation

No response

Anything e

The program throws an error at runtime, how can this error be solved?

[ERROR] [2022-06-27 14:13:51,391:asyncio.events] 
Traceback (most recent call last):
  File "/home/xumaoyuan/.virtualenvs/lib/python3.8/site-packages/distributed/utils.py", line 761, in wrapper
    return await func(*args, **kwargs)
  File "/home/xumaoyuan/.virtualenvs/lib/python3.8/site-packages/distributed/clie

In the check_schema_version utility function, there is custom code to determine whether saved schema versions are older or newer than the current schema version. This comparison could likely be simplified significantly by using the packaging library performing the version comparison instead of the custom code.

Current code:

        current = SCHEMA_VERSION.split(".")
        saved = ve

Add PECOS model to TabularPredictor.

If you'd like to work on this, please respond to this GitHub issue.

It is recommended to follow the custom model tutorial for implementing the model into AutoGluon.

We would like to forward a particular 'key' column which is part of the features to appear alongside the predictions - this is to be able to identify to which set of features a particular prediction belongs to. Here is an example of predictions output using the tensorflow.contrib.estimator.multi_class_head:

{"classes": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
 "scores": [0.068196

Hello everyone,

First of all, I want to take a moment to thank all contributors and people who supported this project in any way ;) you are awesome!

If you like the project and have any interest in contributing/maintaining it, you can contact me here or send me a msg privately:

• Email: [email protected]

PS: You need to be familiar with python and machine learning

Contact Details [Optional]

[email protected]

Describe the feature you'd like

When someone uses the zenml secret register --help command, the formatting of the CLI help information comes out wrong with line breaks not really happening. It isn't possible to read the text printed to the terminal as executed within [the register_secret function](https://github.com/zenml-io/zenml/blob/373

Problem
Some of our transformers & estimators are not thoroughly tested or not tested at all.

Solution
Use OpTransformerSpec and OpEstimatorSpec base test specs to provide tests for all existing transformers & estimators.

I trained models on Windows, then I tried to use them on Linux, however, I could not load them due to an incorrect path joining. During model loading, I got learner_path in the following format experiments_dir/model_1/100_LightGBM\\learner_fold_0.lightgbm. The last two slashes were incorrectly concatenated with the rest part of the path. In this regard, I would suggest adding something like `l

ray.init(n_cpus=16)
should be
ray.init(num_cpus=16) in the documentation page
https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning

https://www.usenix.org/conference/opml19/program

Prepare to read papers