Difference between revisions of "Data Science Platform"

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*'''[[TensorFlow]]:''' TensorFlow is an open-source software library commonly used for implementing artificial neural networks and deep learning. TensorFlow is widely used in data science for building and training complex machine learning models, offering scalable and flexible tools for deep learning, numerical computation, and large-scale optimization, with extensive support for both research and production deployments.
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*'''[[TensorFlow]]:''' TensorFlow is an open-source software library widely used for implementing artificial neural networks and deep learning. It is extensively utilized in data science to build and train complex machine learning models, providing scalable and flexible tools for deep learning, numerical computation, and large-scale optimization. TensorFlow also supports both research and production deployments.
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**<pre>module spider tensorflow</pre>
 
**<pre>module spider tensorflow</pre>
  
  
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*'''[[Pytorch]]:''' Pytorch is a Python-based scientific computing package that uses the power of graphics processing units. PyTorch can be used effectively in various data science applications, especially those that involve complex numerical computations or the development of custom machine learning models.  
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*'''[[Pytorch]]:''' PyTorch is a Python-based scientific computing package that leverages the power of graphics processing units. It is effectively used in various data science applications, particularly those involving complex numerical computations or the development of custom machine learning models.
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**<pre>module spider pytorch</pre>
 
**<pre>module spider pytorch</pre>
  
  
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*'''[[Scikit-learn]]:''' Scikit-learn is a set of python modules for machine learning and data mining. It provides simple and efficient tools for predictive data analysis. Scikit-learn is built on NumPy, SciPy, and matplotlib, and is open source and commercially usable under a BSD license.
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*'''[[Scikit-learn]]:''' Scikit-learn is a collection of Python modules for machine learning and data mining. It offers simple and efficient tools for predictive data analysis and is built on NumPy, SciPy, and Matplotlib. Scikit-learn is open source and can be used commercially under a BSD license.
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**<pre>module spider scikit-learn</pre>
 
**<pre>module spider scikit-learn</pre>
  
  
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*'''[[Rapidsai]]:''' Rapidsai accelerates end-to-end data science pipelines by providing a familiar dataframe API. Rapidsai supports machine learning integration without typical serialization costs, enabling multi-node, multi-GPU deployments for faster processing of large datasets.
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*'''[[Rapidsai]]:''' RAPIDS AI accelerates end-to-end data science pipelines by providing a familiar dataframe API. It supports machine learning integration without the typical serialization costs and enables multi-node, multi-GPU deployments for faster processing of large datasets.
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**Use the command below to list the available versions on HiPerGator-AI.  
 
**<pre>module spider rapidsai</pre>
 
**<pre>module spider rapidsai</pre>
  
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If the environments or these platforms do not have the libraries you require, you may need to create a Conda environment. See [[Conda]] and [[Managing_Python_environments_and_Jupyter_kernels]] for more details.
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*'''Note:''' If the environments or these platforms do not have the libraries you require, you may need to create a Conda environment. See [[Conda]] and [[Managing_Python_environments_and_Jupyter_kernels]] for more details.
  
 
==Examples and Reference Data==
 
==Examples and Reference Data==
  
 
Please see <code>/data/ai/</code> folder, [[AI_Examples]], and [[AI_Reference_Datasets]] for helpful resources. Addition references, such as how to run RAPIDs on HiPergator are already available in <code>/data/ai/examples/rapids</code>.
 
Please see <code>/data/ai/</code> folder, [[AI_Examples]], and [[AI_Reference_Datasets]] for helpful resources. Addition references, such as how to run RAPIDs on HiPergator are already available in <code>/data/ai/examples/rapids</code>.

Revision as of 20:30, 8 May 2024

This page describes the collection of Data Science platforms on HiperGator. Data science is the practice of using math, programming, analytics, AI, and machine learning to discover valuable insights within large data sets. Research Computing provides essential infrastructure, tools, and expertise to support data science research and accelerate impactful discoveries, or other uses via support requests or consulting.

Platform for Data Science

  • SQL: Oracle SQL Developer is a free integrated development environment that simplifies the development and management of Oracle Database in both traditional and Cloud deployments. SQLite is an in-process library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider sqldeveloper
    • module spider sqlite
  • R: R is a free software environment for statistical computing and graphics. R is favored in data science for its extensive suite of tools and packages for data manipulation, statistical modeling, and visualization, making it ideal for tasks ranging from simple data analysis to complex predictive modeling.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider R


  • Matlab: MATLAB is a powerful computing environment and programming language widely used in data science for its robust capabilities in numerical and matrix calculations, advanced data visualization, and the development of algorithms, especially useful for applications in engineering and scientific research.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider matlab
      or 
      module spider mcr


  • Python: Python has been built with extraordinary Python libraries such as NumPy, SciPy, Pandas, Matplotlib, Scrapy, and BeautifulSoup. These libraries are essential for data science and are utilized daily by programmers to solve problems.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider python


  • TensorFlow: TensorFlow is an open-source software library widely used for implementing artificial neural networks and deep learning. It is extensively utilized in data science to build and train complex machine learning models, providing scalable and flexible tools for deep learning, numerical computation, and large-scale optimization. TensorFlow also supports both research and production deployments.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider tensorflow


  • Pytorch: PyTorch is a Python-based scientific computing package that leverages the power of graphics processing units. It is effectively used in various data science applications, particularly those involving complex numerical computations or the development of custom machine learning models.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider pytorch


  • Scikit-learn: Scikit-learn is a collection of Python modules for machine learning and data mining. It offers simple and efficient tools for predictive data analysis and is built on NumPy, SciPy, and Matplotlib. Scikit-learn is open source and can be used commercially under a BSD license.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider scikit-learn


  • Rapidsai: RAPIDS AI accelerates end-to-end data science pipelines by providing a familiar dataframe API. It supports machine learning integration without the typical serialization costs and enables multi-node, multi-GPU deployments for faster processing of large datasets.
    • Use the command below to list the available versions on HiPerGator-AI.
    • module spider rapidsai

Examples and Reference Data

Please see /data/ai/ folder, AI_Examples, and AI_Reference_Datasets for helpful resources. Addition references, such as how to run RAPIDs on HiPergator are already available in /data/ai/examples/rapids.

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