mi-prometheus - Enabling reproducible Machine Learning research

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MI-Prometheus (Machine Intelligence - Prometheus), an open-source framework aiming at accelerating Machine Learning Research, by fostering the rapid development of diverse neural network-based models and facilitating their comparison. In its core, to accelerate the computations on their own, MI-Prometheus relies on PyTorch and extensively uses its mechanisms for the distribution of computations on CPUs/GPUs. In MI-Prometheus, the training & testing mechanisms are no longer pinned to a specific model or problem, and built-in mechanisms for easy configuration management & statistics collection facilitate running experiments combining different models with problems.




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prometheus - A docker-compose stack for Prometheus monitoring


Before we get started installing the Prometheus stack. Ensure you install the latest version of docker and docker swarm on your Docker host machine. Docker Swarm is installed automatically when using Docker for Mac or Docker for Windows.Clone the project locally to your Docker host.

node_exporter - Exporter for machine metrics

  •    Go

Prometheus exporter for hardware and OS metrics exposed by *NIX kernels, written in Go with pluggable metric collectors.The WMI exporter is recommended for Windows users.

Seq2Seq-PyTorch - Sequence to Sequence Models with PyTorch

  •    Python

A vanilla sequence to sequence model presented in https://arxiv.org/abs/1409.3215, https://arxiv.org/abs/1406.1078 consits of using a recurrent neural network such as an LSTM (http://dl.acm.org/citation.cfm?id=1246450) or GRU (https://arxiv.org/abs/1412.3555) to encode a sequence of words or characters in a source language into a fixed length vector representation and then deocoding from that representation using another RNN in the target language. An extension of sequence to sequence models that incorporate an attention mechanism was presented in https://arxiv.org/abs/1409.0473 that uses information from the RNN hidden states in the source language at each time step in the deocder RNN. This attention mechanism significantly improves performance on tasks like machine translation. A few variants of the attention model for the task of machine translation have been presented in https://arxiv.org/abs/1508.04025.

thanos - Highly available Prometheus setup with long term storage capabilities.

  •    Go

Thanos is a set of components that can be composed into a highly available metric system with unlimited storage capacity. It can be added seamlessly on top of existing Prometheus deployments and leverages the Prometheus 2.0 storage format to cost-efficiently store historical metric data in any object storage while retaining fast query latencies. Additionally, it provides a global query view across all Prometheus installations and can merge data from Prometheus HA pairs on the fly.

GDB/Machine Interface library

  •    C

A GDB/MI interface library for C and C++. It implements the GDB/MI (GNU DeBugger/Machine Interface) protocol as a library so you can create a GDB frontend without writing the quot;dialogquot; with GDB. Unlinke CLI the MI is intended for programs and not hu

fairseq-py - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

  •    Python

This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross. The toolkit implements the fully convolutional model described in Convolutional Sequence to Sequence Learning and features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French and English to German translation. Currently fairseq-py requires PyTorch version >= 0.3.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.

MIEngine - The Visual Studio MI Debug Engine ("MIEngine") provides an open-source Visual Studio Debugger extension that works with MI-enabled debuggers such as gdb, lldb, and clrdbg

  •    CSharp

The Visual Studio MI Debug Engine ("MIEngine") provides an open-source Visual Studio extension that enables debugging with debuggers that support the gdb Machine Interface ("MI") specification such as GDB, LLDB, and CLRDBG.Before you contribute, please read through the contributing and developer guides to get an idea of requirements for pull requests.

parameter_server - moved to https://github.com/dmlc/ps-lite

  •    C++

The parameter server is a distributed system scaling to industry size machine learning problems. It provides asynchronous and zero-copy key-value pair communications between worker machines and server machines. It also supports flexible data consistency model, data filters, and flexible server machine programming.

stockroom - πŸ—ƒ Offload your store management to a worker easily.

  •    Javascript

Offload your store management to a worker. Stockroom seamlessly runs a Unistore store (and its actions) in a Web Worker, setting up optimized bidirectional sync so you can also use & subscribe to it on the main thread.

seq2seq - Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch

  •    Python

Minimal Seq2Seq model with attention for neural machine translation in PyTorch. This implementation relies on torchtext to minimize dataset management and preprocessing parts.

miio - Control Mi Home devices, such as Mi Robot Vacuums, Mi Air Purifiers, Mi Smart Home Gateway (Aqara) and more

  •    Javascript

Control Mi Home devices that implement the miIO protocol, such as the Mi Air Purifier, Mi Robot Vacuum and Mi Smart Socket. These devices are commonly part of what Xiaomi calls the Mi Ecosystem which is branded as MiJia. miio is MIT-licensed and requires at least Node 6.6.0. As the API is promise-based Node 8 is recommended which provides support async and await that greatly simplifies asynchronous handling.

torchMoji - πŸ˜‡A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

  •    Python

It's been a year since TorchMoji and DeepMoji were released. We're trying to understand how it's being used such that we can make improvements and design better models in the future. Read our blog post about the implementation process here.

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

  •    Python

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

prometheus-operator - Prometheus Operator creates/configures/manages Prometheus clusters atop Kubernetes

  •    Go

Project status: beta Not all planned features are completed. The API, spec, status and other user facing objects may change, but in a backward compatible way.The Prometheus Operator for Kubernetes provides easy monitoring definitions for Kubernetes services and deployment and management of Prometheus instances.

test-tube - Python library to easily log, track machine learning code, experiments and parallelize hyperparameter search

  •    HTML

Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. If you're a researcher, test-tube is highly encouraged as a way to post your paper's training logs to help add transparency and show others what you've tried that didn't work.

fairseq - Facebook AI Research Sequence-to-Sequence Toolkit

  •    Lua

This is fairseq, a sequence-to-sequence learning toolkit for Torch from Facebook AI Research tailored to Neural Machine Translation (NMT). It implements the convolutional NMT models proposed in Convolutional Sequence to Sequence Learning and A Convolutional Encoder Model for Neural Machine Translation as well as a standard LSTM-based model. It features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French, English to German and English to Romanian translation. Note, there is now a PyTorch version fairseq-py of this toolkit and new development efforts will focus on it.

awesome-prometheus - A curated list of awesome Prometheus resources, projects and tools.


A curated list of awesome Prometheus resources, projects and tools. Prometheus is an open-source systems monitoring and alerting toolkit.

loki - Like Prometheus, but for logs.

  •    Go

Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost effective and easy to operate, as it does not index the contents of the logs, but rather a set of labels for each log stream. Loki is like Prometheus, but for logs: we prefer a multidimensional label-based approach to indexing, and want a single-binary, easy to operate system with no dependencies. Loki differs from Prometheus by focussing on logs instead of metrics, and delivering logs via push, instead of pull.

go-grpc-prometheus - Prometheus monitoring for your gRPC Go servers.

  •    Go

Prometheus monitoring for your gRPC Go servers and clients. A sister implementation for gRPC Java (same metrics, same semantics) is in grpc-ecosystem/java-grpc-prometheus.

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