AutoMLPipeline vs NVIDIA Deep Learning AMI
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Detailed Comparison
It is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification. | It is an optimized environment for running the Deep Learning, Data Science, and HPC containers available from NVIDIA's NGC Catalog. The Docker containers available on the NGC Catalog are tuned, tested, and certified by NVIDIA to take full advantage of NVIDIA Ampere, Volta and Turing Tensor Cores, the driving force behind artificial intelligence. Deep Learning, Data Science, and HPC containers from the NGC Catalog require this AMI for the best GPU acceleration on AWS P4D, P3 and G4 instances. |
Pipeline API that allows high-level description of processing workflow;
Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc;
Symbolic pipeline parsing for easy expression of complexed pipeline structures;
Easily extensible architecture by overloading just two main interfaces: fit! and transform!;
Meta-ensembles that allow composition of ensembles of ensembles (recursively if needed) for robust prediction routines;
Categorical and numerical feature selectors for specialized preprocessing routines based on types | Provides AI researchers with fast and easy access to NVIDIA A100, V100 and T4 GPUs in the cloud, with performance-engineered deep learning framework containers that are fully integrated, optimized, and certified by NVIDIA;
Optimized for highest performance across a wide range of workloads on NVIDIA GPUs;
NVIDIA accelerates innovation by eliminating the complex do-it-yourself task of building and optimizing a complete deep learning software stack tuned specifically for GPUs |
Statistics | |
GitHub Stars 368 | GitHub Stars - |
GitHub Forks 28 | GitHub Forks - |
Stacks 0 | Stacks 13 |
Followers 7 | Followers 11 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to AutoMLPipeline, NVIDIA Deep Learning AMI?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
