PyTorch vs. statsmodels Comparison


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About Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.	About statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. An extensive list of result statistics is available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open-source Modified BSD (3-clause) license. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings. You can also use numpy arrays instead of formulas. The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users.
Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Not Supported On-Premises Not Supported iPhone Supported iPad Supported Android Supported Chromebook Not Supported	Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Not Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment	Audience Users and anyone in search of a solution to calculate the estimation of many different statistical models
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported
API Offers API Not Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Not Supported Free Trial Not Supported	Pricing Free Free Version Supported Free Trial Not Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software

Training Documentation Supported Webinars Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported
Company Information PyTorch Founded: 2016 pytorch.org	Company Information statsmodels www.statsmodels.org/stable/index.html
Alternatives TensorFlow 5.0 out of 5 stars	Alternatives Bokeh
Core ML Apple	broot
Create ML Apple	imageio
DeepSpeed Microsoft	NumPy
AWS Neuron Amazon Web Services View All	kondeor.ai Kondeor View All
Categories AI Development Supported AI/ML Model Training Supported Machine Learning Supported Neural Network Supported	Categories Component Libraries Supported

Integrations Akira AI Supported Amazon EC2 P5 Instances Supported Amazon S3 Express One Zone Supported Bayesforge Supported BentoML Supported Compute with Hivenet Supported EasyODM Supported ExecuTorch Supported Fuzzball Supported Google Cloud Deep Learning VM Image Supported Groq Supported HunyuanWorld Supported NodeShift Supported Sesterce Supported Skyportal Supported SuperDuperDB Supported Superwise Supported SynapseAI Supported TensorWave Supported ZenML Supported Show More Integrations View All 151 Integrations	Integrations Akira AI Not Supported Amazon EC2 P5 Instances Not Supported Amazon S3 Express One Zone Not Supported Bayesforge Not Supported BentoML Not Supported Compute with Hivenet Not Supported EasyODM Not Supported ExecuTorch Not Supported Fuzzball Not Supported Google Cloud Deep Learning VM Image Not Supported Groq Not Supported HunyuanWorld Not Supported NodeShift Not Supported Sesterce Not Supported Skyportal Not Supported SuperDuperDB Not Supported Superwise Not Supported SynapseAI Not Supported TensorWave Not Supported ZenML Not Supported Show More Integrations View All 2 Integrations
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