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wall-e's repositories

loom

The LOOM IR instrumentation framework

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Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

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clusterdata

cluster data collected from production clusters in Alibaba for cluster management research

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pulsar

Apache Pulsar - distributed pub-sub messaging system

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tensorflow-windows-wheel

Tensorflow prebuilt binary for Windows

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grafana-openfalcon-datasource

openfalcon plugin for grafana

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java-callgraph

Programs for producing static and dynamic (runtime) call graphs for Java programs

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EagleEye

A new SaaS based deep performance diagnosis framework

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blockade

Docker-based utility for testing network failures and partitions in distributed applications

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serverless-offline

Emulate AWS λ and API Gateway locally when developing your Serverless project

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Deep-Reinforcement-Learning

Code of the book <Deep Reinforcement Learning: Principles and Practices>

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DeepLog

Pytorch implementation of DeepLog.

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incubator-skywalking

APM, Application Performance Monitoring System

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analytics-zoo

Distributed Tensorflow, Keras and BigDL on Apache Spark

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cloudpickle

Extended pickling support for Python objects

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autoconfig

The source code for ASE 2018 paper: AutoConfig: Automatic Configuration Tuning for Distributed Message Systems

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MARL-Papers

Paper list of multi-agent reinforcement learning (MARL)

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depends

Depends is a fast, comprehensive code dependency analysis tool

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ScaleX

ScaleX: Auto-scaling Performance Measurement Tool (Previous Name APMT)

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kubernetes-handbook

Kubernetes中文指南/云原生应用架构实践手册 - https://jimmysong.io/kubernetes-handbook

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matrixprofile-ts

A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile

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causal_inference_python_code

Python code for part 2 of the book Causal Inference, by Miguel Hernán and James Robins

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ad_examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.