MLReef Integrations

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.

Scikit-image is a collection algorithm for image processing. It is free to download and without restriction. We are proud of our high-quality code that has been peer-reviewed and is written by a large community of volunteers. Scikit-image is a Python library that provides a variety of image processing routines. This library is being developed by its community. Contributions are most welcome! Scikit-image is a reference library for scientific image analysis using Python. This is achieved by making it easy to use and easy to install. We take care when adding new dependencies. Sometimes we remove existing ones or make them optional. Our API has detailed docstrings that clarify the expected inputs and outputs for all functions. Conceptually identical arguments share the same name and position within a function signature. The library has close to 100% test coverage and all code is reviewed by at minimum two core developers before it is included.

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

Docker eliminates repetitive, tedious configuration tasks and is used throughout development lifecycle for easy, portable, desktop, and cloud application development. Docker's complete end-to-end platform, which includes UIs CLIs, APIs, and security, is designed to work together throughout the entire application delivery cycle. Docker images can be used to quickly create your own applications on Windows or Mac. Create your multi-container application using Docker Compose. Docker can be integrated with your favorite tools in your development pipeline. Docker is compatible with all development tools, including GitHub, CircleCI, and VS Code. To run applications in any environment, package them as portable containers images. Use Docker Trusted Content to get Docker Official Images, images from Docker Verified Publishings, and more.

Greater security. More packages. Newer tools. All your open source software, from cloud to edge. Secure your open source apps. For CVE compliance, patch the entire stack, including libraries and applications. Auditors and governments have certified Ubuntu for FedRAMP and FISMA. Rethink the possibilities with Linux and open-source. Canonical is engaged by companies to reduce open-source operating costs. Automate everything: multicloud operations, bare-metal provisioning, edge clusters, and IoT. Ubuntu is the perfect platform for anyone who needs a powerful machine to do their work, including a mobile app developer, engineer manager, music or video editor, or financial analyst with large-scale models. Because of its reliability, versatility, continually updated features, extensive developer libraries, and widespread use, Ubuntu is used by thousands around the globe.

The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.