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a2ml-1.0.9
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مشاهده بیشترتوضیحات
A powerful API to Automate Machine Learning workflows from multiple vendors.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | a2ml-1.0.9 |
| نام | a2ml |
| نسخه کتابخانه | 1.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Auger AI |
| ایمیل نویسنده | hello@auger.ai |
| آدرس صفحه اصلی | https://a2ml.org |
| آدرس اینترنتی | https://pypi.org/project/a2ml/ |
| مجوز | Apache License 2.0 |
# a2ml - Automation of AutoML
[](https://circleci.com/gh/augerai/a2ml)
[](https://gitter.im/augerai/a2ml)
[](https://raw.githubusercontent.com/augerai/a2ml/master/LICENSE)
[](https://pypi.org/project/a2ml/)
[](https://pypi.org/project/a2ml/)
The A2ML ("Automate AutoML") project is a Python API and set of command line tools to automate Automated Machine Learning tools from multiple vendors. The intention is to provide a common API for all Cloud-oriented AutoML vendors. Data scientists can then train their datasets against multiple AutoML models to get the best possible predictive model. May the best "algorithm/hyperparameter search" win. Full documentation for A2ML is available at [a2ml.org](http://a2ml.org)
## The PREDIT Pipeline
Every AutoML vendor has their own API to manage the datasets and create and
manage predictive models. They are similar but not identical APIs. But they share a
common set of stages:
* Importing data for training
* Train models with multiple algorithms and hyperparameters
* Evaluate model performance and choose one or more for deployment
* Deploy selected models
* Predict results with new data against deployed models
* Review performance of deployed models
Since ITEDPR is hard to remember we refer to this pipeline by its conveniently mnemonic anagram: "PREDIT" (French for "predict"). The A2ML project provides classes which implement this pipeline for various Cloud AutoML providers
and a command line interface that invokes stages of the pipeline.
## Setup
A2ML is distributed as a python package, so to install it:
```sh
$ pip install -U a2ml
```
It will install Auger provider.
To use Azure AutoML:
### Mac:
```sh
$ brew install libomp
```
#### For Mac OS High Sierra and below:
```sh
$ SKLEARN_NO_OPENMP=1 pip install "scikit-learn==0.21.3"
$ pip install "a2ml[azure]" --ignore-installed onnxruntime onnx nimbusml
```
### Linux:
```sh
$ apt-get update && apt-get -y install gcc g++ libgomp1
```
```sh
$ pip install "a2ml[azure]"
```
To use Google Cloud:
```sh
$ pip install "a2ml[google]"
```
To install everything including testing and server code:
```sh
$ pip install "a2ml[all]"
```
## Development
To release a new version the flow should be:
1. Change the `__version__` variable in `a2ml/__init__.py` to match what you want to release, minus the "v". By default it would be "<current-milestone>.dev0", for example "0.3.0.dev0". This ensures we don’t accidentally release a dev version to pypi.org. So for when we’re ready to release 0.3.0, the `__version__` variable should simply be "0.3.0".
2. Commit and push the changes above.
```sh
git tag v<the-version> (for example: git tag v0.3.0)
git push --tags
```
3. verify circleci build passed and docker image tag exists:
```sh
pip install -U a2ml==0.3.0
docker pull augerai/a2ml:v0.3.0
```
4. Increment the `__version__` variable in `a2ml/__init__.py` to the next version in the current milestone. For example, "0.3.1.dev0"
نیازمندی
| مقدار | نام |
|---|---|
| ==1.23.5 | numpy |
| ==1.5.0 | pandas |
| - | joblib |
| - | ruamel.yaml |
| - | pyarrow |
| ==1.9.1 | scipy |
| - | asyncio |
| - | boto3 |
| ==0.7.4 | auger-hub-api-client |
| - | click |
| - | shortuuid |
| <0.16,>=0.10 | docutils |
| - | requests |
| ==6.2.0 | smart-open |
| - | jsonpickle |
| - | websockets |
| ==2.4.0 | liac-arff |
| ==1.2.0 | xlrd |
| <=3.7.9,>=3.1.0 | flake8 |
| - | mock |
| - | pytest |
| - | pytest-cov |
| - | pytest-runner |
| - | pytest-xdist |
| - | twine |
| - | vcrpy |
| <0.31.0,>=0.30.0 | wheel |
| - | sphinx |
| - | aioredis |
| ==5.2.7 | celery |
| ==0.85 | fastapi |
| - | gevent |
| - | redis |
| <0.5.0,>=0.4.0 | s3fs |
| - | uvicorn |
| ==1.2.0 | scikit-learn |
| ==1.29.0 | azureml-sdk[automl] |
| - | sphinx |
| - | google-cloud-automl |
| ==1.0.110 | auger.ai.predict[all] |
| ==1.0.110 | auger.ai.predict[no_cat_lgbm] |
| ==1.0.110 | auger.ai.predict[no_cat_lgbm] |
| - | catboost |
| - | aioredis |
| ==5.2.7 | celery |
| ==0.85 | fastapi |
| - | gevent |
| - | redis |
| <0.5.0,>=0.4.0 | s3fs |
| - | uvicorn |
| ==1.2.0 | scikit-learn |
| <=3.7.9,>=3.1.0 | flake8 |
| - | mock |
| - | pytest |
| - | pytest-cov |
| - | pytest-runner |
| - | pytest-xdist |
| - | twine |
| - | vcrpy |
| <0.31.0,>=0.30.0 | wheel |
نحوه نصب
نصب پکیج whl a2ml-1.0.9:
pip install a2ml-1.0.9.whl
نصب پکیج tar.gz a2ml-1.0.9:
pip install a2ml-1.0.9.tar.gz