Day: June 1, 2023

Top 20 Deep Learning Frameworks

by rajeshkumarUncategorizedLeave a Comment on Top 20 Deep Learning Frameworks

Deep Learning Frameworks

Deep Learning Frameworks In addition to speeding up the process of creating machine or deep learning algorithms, the frameworks offer accurate and research-backed ways to do it, making the end product far more accurate than if one was to build the entirety of the model themselves.

Here is a list of 20 popular deep learning frameworks, in no particular order:

  1. TensorFlow
  2. PyTorch
  3. Keras
  4. Caffe
  5. MXNet
  6. Theano
  7. Microsoft Cognitive Toolkit (CNTK)
  8. Chainer
  9. Torch
  10. Caffe2
  11. Deeplearning4j
  12. PaddlePaddle
  13. ONNX
  14. TensorRT
  15. Gluon
  16. Fast.ai
  17. BigDL
  18. Neon
  19. Lasagne
  20. TFLearn

1. TensorFlow

TensorFlow: Developed by Google Brain, TensorFlow is one of the most widely used deep learning frameworks. It provides a flexible ecosystem for building and training various types of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. TensorFlow supports both low-level programming and high-level APIs like Keras for easier model development.

Key features:

  • Programming language variety. Developers most often use TensorFlow in Python to ensure stability. Support for other languages is available, too, such as JavaScipt, C++, and Java. Versatility in programming languages allows a broader range of industry applications.
  • Eager Execution. The eager execution environment provides immediate results, allowing insight into a neural network operation as it happens.
  • Built-in primitive neural network executions. TensorFlow features many built-in functions used in programming a neural network. One such feature is the tf.nn module for various neural network operations.

2. PyTorch

PyTorch, developed by Facebook’s AI Research lab (FAIR), is a dynamic deep learning framework known for its ease of use and Pythonic programming interface. It allows for dynamic computation graphs and provides seamless integration with the Python data science ecosystem. PyTorch is widely used in research and supports a wide range of network architectures and advanced features.

Key features:

  • It provides flexibility and speed due to its hybrid front end.
  • Enables scalable distributed training and performance optimization in research and production using the “torch distributed” backend.
  • Deep integration with Python allows popular libraries and packages to quickly write neural network layers in Python.

3. Keras

Keras is a high-level neural networks API written in Python. Initially developed as a user-friendly interface on top of other deep learning frameworks, such as TensorFlow and Theano, Keras is now part of TensorFlow as its official high-level API. Keras provides a simple and intuitive way to build and train deep learning models, making it popular among beginners and experienced practitioners alike.

Key features:

  • User-friendly, as it offers simple APIs and provides clear and actionable feedback upon user error
  • Provides modularity as a sequence or a graph of standalone, fully-configurable modules that can be combined with as few restrictions as possible
  • Easily extensible as new modules are simple to add, making Keras suitable for advanced research

4. Caffe

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR). It is designed for efficiency, particularly in computer vision tasks, and offers a declarative model specification through configuration files. Caffe is known for its fast training and inference times and is widely adopted in industry and academia.

Key features:

  • Used in academic research projects, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia
  • Supports GPU- and CPU-based acceleration computational kernel libraries, such as NVIDIA, cuDNN, and IntelMLK
  • Can process over 60M images per day with a single NVIDIA K40 GPU

5. MXNet

MXNet is a deep learning framework that emphasizes efficiency and scalability. It supports multiple programming languages, including Python, R, and Scala, and provides a flexible interface for building neural networks. MXNet’s dynamic computation graph allows for dynamic model building and easy debugging.

6. Theano

Theano is a deep learning framework that focuses on numerical computation and mathematical expressions. It is known for its efficiency and ability to optimize computations on both CPUs and GPUs. While Theano is no longer actively developed, its concepts and optimizations have influenced the development of other deep-learning frameworks.

7. Microsoft Cognitive Toolkit (CNTK)

CNTK, now known as Microsoft Machine Learning for Apache Spark, is a deep learning framework developed by Microsoft. It provides efficient parallelization across CPUs and GPUs and supports distributed training. While CNTK’s development has shifted to Microsoft Machine Learning for Apache Spark, it remains a powerful tool for deep learning tasks.

Key features:

  • Highly efficient and scalable for multiple machines
  • Supported by interfaces such as Python, C++, and Command Line
  • Fit for image, handwriting, and speech recognition use cases
  • Supports both RNN and CNN types of neural networks

8. Chainer:

Chainer is an open-source deep-learning framework that was developed by Preferred Networks, a Japanese AI company. It is designed to provide a flexible and intuitive platform for neural network development, emphasizing dynamic computation graphs and rapid prototyping.

Key features:

  • Supports CUDA computation
  • Requires only a few lines of code to leverage a GPU
  • uns on multiple GPUs with little effort
  • Provides various network architectures, including feed-forward nets, convents, recurrent nets, and recursive nets

9. Torch

Torch, often referred to as Torch7, is a popular open-source deep learning framework widely used for scientific computing and machine learning tasks. It is built on Lua, a lightweight scripting language, and provides a high-level interface for building and training deep neural networks.

Key features:

  • It provides flexibility and speed due to its hybrid front end.
  • Enables scalable distributed training and performance optimization in research and production using the “torch distributed” backend.
  • Deep integration with Python allows popular libraries and packages to quickly write neural network layers in Python.

10. Caffe2

Caffe2 is an open-source deep learning framework that was developed by Facebook AI Research (FAIR) as an extension of the original Caffe framework. It is designed to be efficient, flexible, and scalable, with a focus on production deployment and mobile applications.

11. Deeplearning4j:

Deeplearning4j (DL4J) is an open-source deep learning framework specifically designed for Java and the Java Virtual Machine (JVM) ecosystem. It provides a comprehensive set of tools, libraries, and algorithms for building and training deep neural networks.

Key features:

  • A distributed computing framework as training with DL4J occurs in a cluster
  • An n-dimensional array class using ND4J that allows scientific computing in Java and Scala
  • A vector space modeling and topic modeling toolkit that is designed to handle large text sets and perform NLP

12. PaddlePaddle

PaddlePaddle, also known as Paddle, is an open-source deep learning framework developed by Baidu’s AI research team. It is designed to provide an easy-to-use platform for building, training, and deploying deep learning models, with a particular emphasis on large-scale and industrial applications.

13. ONNX

ONNX (Open Neural Network Exchange) is an open-source format and ecosystem designed to enable interoperability between different deep learning frameworks. It allows models to be trained in one framework and then exported and used in another framework for inference without the need for extensive rework or retraining.

Key features:

  • Provides interoperability and flexibility
  • Provides compatible runtimes and libraries
  • Liberty of using the preferred framework with a selected inference engine
  • Maximizes performance across hardware

14. TensorRT

TensorRT (Tensor Runtime) is a high-performance deep learning inference optimizer and runtime library developed by NVIDIA. It is designed to maximize the efficiency of deep learning models during inference, delivering fast and low-latency performance on NVIDIA GPUs.

15. Gluon

Gluon is a deep learning framework that offers a flexible and intuitive interface for building, training, and deploying machine learning models. It was developed by the Apache MXNet community and offers support for both imperative and symbolic programming paradigms.

Key features:

  • Since Gluon allows users to define and manipulate ML/DL models just like any other data structure, it is a versatile tool for beginners who are new to Machine Learning.
  • Thanks to Gluon’s high flexibility quotient, it is straightforward to prototype and experiment with neural network models.

16. Fast.ai

Fast.ai is a deep learning library and educational platform that aims to make deep learning accessible to a wide range of users, including beginners and non-experts. It provides a high-level API built on top of popular deep learning frameworks like PyTorch, simplifying the process of building and training deep learning models.

17. BigDL

BigDL is an open-source deep-learning library specifically designed for distributed training and inference on big data platforms. Developed by Intel, BigDL aims to enable deep learning models to run efficiently on Apache Spark and Hadoop clusters, leveraging the power of distributed computing for large-scale data processing.

18. Neon

Neon is a deep learning framework developed by Nervana Systems, which was acquired by Intel. It is designed to optimize performance on Intel architecture, particularly for training deep neural networks. Neon provides a high-level API that simplifies the process of building, training, and deploying deep learning models.

19. Lasagne

Lasagne is a lightweight and flexible deep-learning library built on top of Theano. It provides a high-level interface for building and training neural networks, making it easy to experiment with different network architectures and configurations.

20. TFLearn

TFLearn is a high-level deep-learning library built on top of TensorFlow. It provides a simplified interface for building, training, and deploying deep neural networks, making it easy to develop machine learning models even for users with limited experience in deep learning.

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Top 20 Machine Learning Frameworks

by rajeshkumarUncategorized1 Comment on Top 20 Machine Learning Frameworks

What is Machine Learning?

Machine Learning, ML for short, is an area of computational science that deals with the analysis and interpretation of patterns and structures in large volumes of data. Through it, we can infer insightful patterns from data sets to support business decision-making – without or with very little need for human interface.

In Machine Learning, we feed large volumes of data to a computer algorithm that then trains on it, analyzing it to find patterns and generating data-driven decisions and recommendations. If there are any errors or outliers in information identified, the algorithm is structured to take this new information as input to improve its future output for recommendations and decision-making.

Simply put, ML is a field in AI that supports organizations to analyze data, learn, and adapt on an ongoing basis to help in decision-making. It’s also worth noting that deep learning is a subset of machine learning.

What is a Machine Learning Framework?

A simplified definition would describe machine learning frameworks as tools or libraries that allow developers to easily build ML models or Machine Learning applications, without having to get into the nuts and bolts of the base or core algorithms. It provides more of an end-to-end pipeline for machine learning development.

Here are the top 20 machine learning frameworks:

  1. TensorFlow
  2. PyTorch
  3. scikit-learn
  4. Keras
  5. MXNet
  6. Caffe
  7. Theano
  8. Microsoft Cognitive Toolkit (CNTK)
  9. Spark MLlib
  10. H2O.ai
  11. LightGBM
  12. XGBoost
  13. CatBoost
  14. Fast.ai
  15. Torch
  16. CNTK (Microsoft Cognitive Toolkit)
  17. Deeplearning4j
  18. Mahout
  19. Accord.NET
  20. Shogun

1. TensorFlow:

Developed by Google’s Brain Team, TensorFlow is one of the most widely used machine learning frameworks. It provides a comprehensive ecosystem for building and deploying machine learning models, including support for deep learning. TensorFlow offers high-level APIs for ease of use and low-level APIs for customization.

Key Features:

  • Based on JavaScript
  • Open source and has extensive APIs
  • Can be used via script tags or via installation through npm
  • Runs on CPUs and GPUs
  • Extremely popular and has lots of community support

2. PyTorch:

PyTorch is a popular open-source machine learning framework developed by Facebook’s AI Research Lab. It has gained significant popularity due to its dynamic computational graph, which enables more flexibility during model development. PyTorch is widely used for research purposes and supports both deep learning and traditional machine learning models.

Key Features:

  • Supports cloud-based software development
  • Suitable for designing neural networks and Natural Language Processing
  • Used by Meta and IBM
  • Good for designing computational graphs
  • Compatible with Numba and Cython

3. scikit-learn:

scikit-learn is a Python library that provides a simple and efficient set of tools for data mining and machine learning. It offers a wide range of algorithms for classification, regression, clustering, and dimensionality reduction. scikit-learn is known for its user-friendly API and extensive documentation.

Key Features:

  • Works well with Python
  • The top framework for data mining and data analysis
  • Open-source and free

4. Keras:

Keras is a high-level neural networks API written in Python. Initially developed as a user-friendly interface for building deep learning models on top of TensorFlow, Keras has evolved into an independent framework. It provides an intuitive and modular approach to building neural networks and supports both convolutional and recurrent networks.

5. MXNet:

MXNet is a deep learning framework that emphasizes efficiency, scalability, and flexibility. It offers both imperative and symbolic programming interfaces, allowing developers to choose the approach that best suits their needs. MXNet is known for its support of distributed training, which enables training models on multiple GPUs or across multiple machines.

Key Features:

  • Adopted by Amazon for AWS
  • Supports multiple languages, including Python, JavaScript, Julia, C++, Scala, and Perl
  • Microsoft, Intel, and Baidu also support Apache MXNet
  • Also used by the University of Washington and MIT

6. Caffe:

Keeping speed, modularity, and articulation in mind, Berkeley Vision and Learning Center (BVLC) and community contributors came up with Caffe, a Deep Learning framework. Its speed makes it ideal for research experiments and production edge deployment. It comes with a BSD-authorized C++ library with a Python interface, and users can switch between CPU and GPU. Google’s DeepDream implements Caffe. However, Caffe is observed to have a steep learning curve, and it is also difficult to implement new layers with Caffe.

7. Theano:

Theano was developed at the LISA lab and was released under a BSD license as a Python library that rivals the speed of the hand-crafted implementations of C. Theano is especially good with multidimensional arrays and lets users optimize mathematical performances, mostly in Deep Learning with efficient Machine Learning Algorithms. Theano uses GPUs and carries out symbolic differentiation efficiently.

Several popular packages, such as Keras and TensorFlow, are based on Theano. Unfortunately, Theano is now effectively discontinued but is still considered a good resource in ML.

8. Microsoft Cognitive Toolkit (CNTK):

CNTK is a deep learning framework developed by Microsoft. It provides high-level abstractions and supports both convolutional and recurrent neural networks. CNTK is known for its scalability and performance, particularly in distributed training scenarios.

9. Spark MLlib :

Spark MLlib is a machine learning library provided by Apache Spark, an open-source big data processing framework. Spark MLlib offers a wide range of tools and algorithms for building scalable and distributed machine learning models. It is designed to work seamlessly with the Spark framework, enabling efficient processing of large-scale datasets.

10. H2O.ai :

H2O.ai is an open-source machine-learning platform that provides a range of tools and frameworks for building and deploying machine-learning models. It aims to make it easy for data scientists and developers to work with large-scale data and build robust machine-learning pipelines.

11. LightGBM:

LightGBM is an open-source gradient-boosting framework developed by Microsoft. It is specifically designed to be efficient, scalable, and accurate, making it a popular choice for various machine-learning tasks.

12. XGBoost:

XGBoost (Extreme Gradient Boosting) is a powerful and widely used open-source gradient boosting framework that has gained significant popularity in the machine learning community. It is designed to be efficient, scalable, and highly accurate for a variety of machine-learning tasks.

13. CatBoost:

CatBoost is an open-source gradient-boosting framework developed by Yandex, a Russian technology company. It is specifically designed to handle categorical features in machine learning tasks, making it a powerful tool for working with structured data.

14. Fast.ai:

Fast.ai is a comprehensive deep-learning library and educational platform that aims to democratize and simplify the process of building and training neural networks. It provides a high-level API on top of popular deep learning frameworks like PyTorch, allowing users to quickly prototype and iterate on their models.

15. Torch:

Torch, or PyTorch, is a widely used open-source deep learning framework that provides a flexible and efficient platform for building and training neural networks. It is developed and maintained by Facebook’s AI Research Lab (FAIR).

16. CNTK (Microsoft Cognitive Toolkit):

CNTK (Microsoft Cognitive Toolkit), now known as Microsoft Machine Learning for Apache Spark, is an open-source deep learning framework developed by Microsoft. It provides a flexible and scalable platform for building, training, and deploying deep learning models.

17. Deeplearning4j:

Deeplearning4j (DL4J) is an open-source deep-learning library specifically designed for Java and the Java Virtual Machine (JVM) ecosystem. It provides a comprehensive set of tools and capabilities for building and training deep neural networks in Java, while also supporting integration with other JVM-based languages like Scala and Kotlin.

18. Mahout:

Apache Mahout is an open-source machine learning library and framework designed to provide scalable and distributed implementations of various machine learning algorithms. It is part of the Apache Software Foundation and is built on top of Apache Hadoop and Apache Spark, making it well-suited for big data processing.

19. Accord.NET:

Accord.NET is an open-source machine learning framework for .NET developers. It provides a wide range of libraries and algorithms for various machine-learning tasks, including classification, regression, clustering, neural networks, image processing, and more. Accord.NET aims to make machine learning accessible and easy to use within the .NET ecosystem.

20. Shogun:

Shogun is an open-source machine-learning library that provides a comprehensive set of algorithms and tools for a wide range of machine-learning tasks. It is implemented in C++ and offers interfaces for several programming languages, including Python, Java, Octave, and MATLAB.

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Jenkins Error: no known implementation of class hudson.model.ParameterDefinition is named ChoiceParameter

by rajeshkumarUncategorizedLeave a Comment on Jenkins Error: no known implementation of class hudson.model.ParameterDefinition is named ChoiceParameter

Error

java.lang.UnsupportedOperationException: no known implementation of class hudson.model.ParameterDefinition is named ChoiceParameter
	at org.jenkinsci.plugins.structs.describable.DescribableModel.resolveClass(DescribableModel.java:549)
	at org.jenkinsci.plugins.structs.describable.DescribableModel.coerce(DescribableModel.java:473)
	at org.jenkinsci.plugins.structs.describable.DescribableModel.coerceList(DescribableModel.java:585)
	at org.jenkinsci.plugins.structs.describable.DescribableModel.coerce(DescribableModel.java:458)
	at org.jenkinsci.plugins.structs.describable.DescribableModel.buildArguments(DescribableModel.java:409)
	at org.jenkinsci.plugins.structs.describable.DescribableModel.instantiate(DescribableModel.java:329)
Caused: java.lang.IllegalArgumentException: Could not instantiate {parameterDefinitions=[{$class=ChoiceParameter, choiceType=PT_SINGLE_SELECT, description=Select Build Type, name=build_type, randomName=choice-parameter-5631314439613978, script={$class=GroovyScript, fallbackScript={classpath=[], sandbox=true, script=return['Could not get build type']}, script={classpath=[], sandbox=true, script=

Solution

INSTALL THESE PLUGINS

To use the ChoiceParameter in a Jenkins pipeline, you need to install the “Extended Choice Parameter” plugin. This plugin provides additional parameter types, including the choice parameter.

To install the “Extended Choice Parameter” plugin in Jenkins, follow these steps:

  1. Access the Jenkins web interface.
  2. Click on “Manage Jenkins” on the left sidebar.
  3. Select “Manage Plugins” from the dropdown menu.
  4. In the “Available” tab, search for “Extended Choice Parameter” in the search box.
  5. Check the checkbox next to the “Extended Choice Parameter” plugin.
  6. Click on the “Install without restart” button.

After the plugin is installed, you should be able to use the ChoiceParameter in your Jenkins pipeline by specifying the parameter type as “Choice Parameter” and providing the necessary configuration.