Machine Learning Frameworks Support

This topic lists supported versions and features. Machine learning frameworks support basic tensor operations math behind algorithms automatic differentiation to prevent calculation errors through backpropagation a method of doing forward and then backward passes to compute the gradient and systems techniques like minibatching and Adam optimization.

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Each runtime supports models that are created in the following frameworks.

Machine learning frameworks support. However you will find key machine learning libraries such as a gradient boosting framework LightGBM and support for a few deep learning frameworks like Torch and Theano. TensorFlow is one of the most popular machine learning and deep learning frameworks used by developers and researchers. Initially launched in 2007 by the Google Brain team TensorFlow has matured to become an end-to-end machine learning platform.

Available deep learning frameworks and tools on Azure Data Science Virtual Machine. IBM Watson Machine Learning You can use IBM Watson Machine Learning to perform payload logging feedback logging and. Yes With Parallel Computing Toolbox.

Yes Microsoft Cognitive Toolkit CNTK Microsoft Research. Some familiar machine learning frameworks include PyTorch TensorFlow Scikit-learn ONNX format FireBase ML Kit CAFFE Convolutional Architecture for Fast Feature Embedding Apache Spark and likewise. One of the first machine learning frameworks to support the Open Neural Network Exchange ONNX model interchange format CNTK has support for MATLAB Caffe 2 Keras Chainer PyTorch and several other formats.

Supported frameworks Watson Machine Learning You can use popular tools libraries and frameworks to build machine learning models using IBM Watson Machine Learning. Integration with leading data science frameworks like Apache Spark cuPY Dask XGBoost and Numba as well as numerous deep learning frameworks such as PyTorch TensorFlow and Apache MxNet broaden adoption and encourage integration with others. No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder.

C C Java MATLAB. Azure supports all popular machine learning frameworks. Having a container handle the dependencies reduces a significant amount of friction.

Nearly all Azure Machine Learning Compute targets support using Docker. The design of DMTK allows for users to make the most of clusters with limited resources. No Linux macOS Windows.

Azure ML Studio You can use Microsoft Azure ML. MATLAB Deep Learning Toolbox MathWorks. Having a container handle the dependencies reduces a significant amount of friction.

Whether you are developing models in deep learning frameworks like PyTorch or TensorFlow taking advantage of Azure automated machine learning capabilities or training traditional machine learning models in scikit-learn you will be able to support your workloads on Azure. It goes beyond training to support data preparation feature engineering and model serving.

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