I often run into issues by accidentally starting a new cluster when one is already running. Then, this later causes problems when I try to connect and there are two running clusters. I'm then forced to ray stop both clusters and ray start my new one again.

My workflow would be improved if I just got an error when trying to start the second cluster and knew to immediately tear down the exist

What would you like to be added: As title

Why is this needed: All pruning schedule except AGPPruner only support level, L1, L2. While there are FPGM, APoZ, MeanActivation and Taylor, it would be much better if we can choose any pruner with any pruning schedule.

**Without this feature, how does current nni

with the Power Transformer.

Motivation

From following resources, the search space of examples/xgboost_simple.py seems not to be practical.

• https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
• https://www.youtube.com/watch?v=VC8Jc9_lNoY
• https://www.amazon.co.jp/dp/B07YTDBC3Z/

Description

Improve the search space of examples/xgboost_simple.py.

There can be a situation when all features are dropped during feature selection. Need to handle it. Maybe by throwing exception or raising a warning.

Code to reproduce:

import numpy as np
from supervised import AutoML

X = np.random.uniform(size=(1000, 31))
y = np.random.randint(0, 2, size=(1000,))

automl = AutoML(
    algorithms=["CatBoost", "Xgboost", "LightGBM"],
    model_t

If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.

We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing

Need further investigation.

Related:

• Epistimio/orion#335
• Epistimio/orion#359