Package: bluemist
Bluemist AIBluemist AI is an advanced, open-source, low-code machine learning library developed in Python. Its primary purpose is
to facilitate the development, evaluation, and deployment of automated machine learning models and pipelines.The library acts as a convenient wrapper service, integrating seamlessly with popular Python libraries such as
scikit-learn, NumPy, pandas, mlflow, and FastAPI. For visualization purposes, Bluemist AI leverages the capabilities of
pandas-profiling, sweetviz, dtale, and autoviz.Key FeaturesBluemist AI offers a range of powerful features, including:Native Data Integration: Seamlessly extract data from various sources, including MySQL, PostgreSQL, MS SQL, Oracle, MariaDB, Amazon Aurora, and Amazon S3, providing a unified interface for working with diverse datasets.Exploratory Data Analysis (EDA): Conduct thorough EDA on your data, enabling comprehensive insights into the characteristics, distributions, and relationships within the dataset.Data Preprocessing: Streamline the preprocessing pipeline by leveraging Bluemist AI's built-in preprocessing capabilities, allowing you to handle missing data, perform feature scaling, encoding, and other essential preprocessing tasks efficiently.Algorithm Selection and Comparison: Bluemist AI facilitates training data across multiple algorithms, enabling you to compare and evaluate various models using comprehensive metrics and insights.Hyperparameter Tuning: Optimize model performance with built-in hyperparameter tuning functionalities, automatically searching for the optimal combination of hyperparameters to achieve the best results.Experiment Tracking: Keep track of your experiments effortlessly, storing essential metadata, parameters, and results, allowing for easy reproducibility and experimentation management.API Deployment: Bluemist AI simplifies the process of deploying machine learning models as APIs, making it easier to integrate your models into production systems and applications.These features make Bluemist AI a powerful tool for developing, evaluating, and deploying machine learning models efficiently and effectively.Getting StartedFor the detailed list of supported and upcoming features, visithttps://www.bluemist-ai.oneFull documentation is available @https://bluemist-ai.readthedocs.ioUser installationMethod 1To install minimal version of the package with hard dependencies listed inrequirements.txtpip install -U bluemistTo install the complete package including optional dependencies listed inrequirements-optional.txt. ReferMinimal package vs Full packagefor more details.pip install -U bluemist[complete]Method 2 (recommended)It is advised to set up a separate Python environment to avoid conflicts with package dependencies. Follow the steps below:Install the packagevirtualenvpip install virtualenvCreate a separate directory where bluemist environment will be createdmkdir /path/to/bluemist-ai
cd /path/to/bluemist-aiCreate the bluemist environmentvirtualenv bluemist-envActivate the environment and install bluemistsource bluemist-env/bin/activate
pip install -U bluemistMethod 3bluemist package can be installed usingpipxutility. It automatically creates an isolated environment to run the
bluemist packagepip install pipx
pipx install bluemist
pipx upgrade bluemistSupport for GPU/CPU AccelerationGPU AccelerationIf your system is equipped with an NVIDIA GPU and you want to utilize GPU acceleration for model training, you can
install the RAPIDS cuML package by executing the following command:pip install cudf-cu11 cuml-cu11 --extra-index-url=https://pypi.nvidia.comThecumlpackage provides GPU-accelerated implementations of various machine learning algorithms. By installing this
package, you can leverage the power of your GPU to significantly improve the speed and performance of your machine
learning workflows.For the list of supported algorithms, please
referhttps://docs.rapids.ai/api/cuml/stable/api/#regression-and-classificationAcceleration extensions can ben enabled by passing the parameterenable_acceleration_extensions=Trueduring
theinitializephase.CPU AccelerationIf your system is equipped with an Intel CPU and you want to utilize CPU acceleration for model training, you can
install the Intel® Extension for Scikit-learn package by executing the following command:pip install -U scikit-learn-intelexThescikit-learn-intelexpackage provides CPU-accelerated implementations of Scikit-learn algorithms. By installing
this package, you can leverage the power of your Intel CPU to significantly improve the speed and performance of your
machine learning workflows.For the list of supported algorithms, please refer to
theIntel® Extension for Scikit-learn documentation.Acceleration extensions can ben enabled by passing the parameterenable_acceleration_extensions=Trueduring
theinitializephase.Minimal package vs Full packageBelow functionalities are available only with complete package installationData extraction from RDBMS or cloudEDA Visualizations using dtale and sweetvizCPU/GPU AccelerationAlternatively a single optional package can be installed using thepipcommand. For example, if you would like to
extract data from Amazon S3 but do not wish to install other optional packages :pip install boto3LicenseThis project is licensed under theMIT License.SeeThird Party Librariesfor license details of third party libraries included in the distribution.MIT LicenseCopyright (c) 2022-2023 Bluemist AIPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.