semvec/pypi-packages huggingface dataset:
autogluon.cloud 0.4.2
Train and deploy AutoGluon backed models on the cloud
License: Apache-2.0
Requires Python: <3.12,>=3.8

<div align="left">
  <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
</div>

# AutoGluon-Cloud

[![Continuous Integration](https://github.com/autogluon/autogluon-cloud/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon-cloud/actions/workflows/continuous_integration.yml)

[AutoGluon-Cloud Documentation](https://auto.gluon.ai/cloud/stable/index.html) | [AutoGluon Documentation](https://auto.gluon.ai)

AutoGluon-Cloud aims to provide user tools to train, fine-tune and deploy [AutoGluon](https://auto.gluon.ai/stable/index.html) backed models on the cloud. With just a few lines of codes, users could train a model and perform inference on the cloud without worrying about MLOps details such as resource management.

Currently, AutoGluon-Cloud supports [AWS SageMaker](https://aws.amazon.com/sagemaker/) as the cloud backend.

## Installation
```bash
pip install -U pip
pip install -U setuptools wheel
pip install autogluon.cloud
```

## Example
```python

from autogluon.cloud import TabularCloudPredictor
import pandas as pd
train_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv")
test_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv")
test_data.drop(columns=['class'], inplace=True)
predictor_init_args = {"label": "class"}  # init args you would pass to AG TabularPredictor
predictor_fit_args = {"train_data": train_data, "time_limit": 120}  # fit args you would pass to AG TabularPredictor
cloud_predictor = TabularCloudPredictor(cloud_output_path='YOUR_S3_BUCKET_PATH')
cloud_predictor.fit(predictor_init_args=predictor_init_args, predictor_fit_args=predictor_fit_args)
cloud_predictor.deploy()
result = cloud_predictor.predict_real_time(test_data)
cloud_predictor.cleanup_deployment()
# Batch inference
result = cloud_predictor.predict(test_data)
```
cycquery 0.1.8
A tool to query EHR databases
License: Apache-2.0
Requires Python: <3.12,>=3.9

cycquery
--------------------------------------------------------------------------------

[![PyPI](https://img.shields.io/pypi/v/cycquery)](https://pypi.org/project/cycquery)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cycquery)
[![code checks](https://github.com/VectorInstitute/cycquery/actions/workflows/code_checks.yml/badge.svg)](https://github.com/VectorInstitute/cycquery/actions/workflows/code_checks.yml)
[![integration tests](https://github.com/VectorInstitute/cycquery/actions/workflows/integration_tests.yml/badge.svg)](https://github.com/VectorInstitute/cycquery/actions/workflows/integration_tests.yml)
[![docs](https://github.com/VectorInstitute/cycquery/actions/workflows/docs_deploy.yml/badge.svg)](https://github.com/VectorInstitute/cycquery/actions/workflows/docs_deploy.yml)
[![codecov](https://codecov.io/gh/VectorInstitute/cycquery/branch/main/graph/badge.svg)](https://codecov.io/gh/VectorInstitute/cycquery)

``cycquery`` is a tool for querying relational databases using a simple Python API. It is specifically developed to query
Electronic Health Record (EHR) databases. The tool is a wrapper around [SQLAlchemy](https://www.sqlalchemy.org/) and can be used
to write SQL-like queries in Python, including joins, conditions, groupby aggregation and many more.

## 🐣 Getting Started

### Installing cycquery using pip

```bash
python3 -m pip install cycquery
```

### Query postgresql database

```python
from cycquery import DatasetQuerier
import cycquery.ops as qo


querier = DatasetQuerier(
    dbms="postgresql",
    port=5432,
    host="localhost",
    database="dbname",
    user="usename",
    password="password",
)
# List all tables.
querier.list_tables()

# Get some table.
table = querier.schema.sometable()
# Filter based on some condition (e.g. substring match).
table = table.ops(qo.ConditionSubstring("col1", "substr"))
# Run query to get data as a pandas dataframe.
df = table.run()

# Create a sequential list of operations to perform on the query.
ops = qo.Sequential(
	qo.ConditionIn("col2", [1, 2]),
	qo.DropNulls("col3"),
	qo.Distinct("col1")
)
table = table.ops(ops)
# Run query to get data as a pandas dataframe.
df = table.run()
```

## 🧑🏿‍💻 Developing

### Using poetry

The development environment can be set up using
[poetry](https://python-poetry.org/docs/#installation). Hence, make sure it is
installed and then run:


```bash
python3 -m poetry install
source $(poetry env info --path)/bin/activate
```

In order to install dependencies for testing (codestyle, unit tests, integration tests),
run:

```bash
python3 -m poetry install --with test
```

API documentation is built using [Sphinx](https://www.sphinx-doc.org/en/master/) and
can be locally built by:

```bash
python3 -m poetry install --with docs
cd docs
make html SPHINXOPTS="-D nbsphinx_allow_errors=True"
```

### Contributing

Contributing to ``cycquery`` is welcomed.
See [Contributing](https://vectorinstitute.github.io/cycquery/api/contributing.html) for
guidelines.


## 📚 [Documentation](https://vectorinstitute.github.io/cycquery/)