ml-for-systems

There are 1 repository under ml-for-systems topic.

mit-han-lab / torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
pytorch-quantum quantum quantum-machine-learning neural-network machine-learning quantum-computing pytorch deep-learning system ml-for-systems quantum-simulation quantum-neural-network parameterized-quantum-circuit
Language:Jupyter Notebook 1180
neurocard / neurocard
State-of-the-art neural cardinality estimators for join queries
ml-for-systems learned-database cardinality-estimation query-optimization deep-generative-model self-supervised-learning transformers data-sampling probabilistic-models
Language:Python 60
Peregrine
microsoft / Peregrine
Peregrine is a workload optimization platform for cloud query engines. The goal of Peregrine is three-fold: 1. make it easier to ingest and analyze query workload telemetry into a common engine-agnostic representation, 2. help developers to quickly build workload optimization applications to reduce overall costs and improve operational efficiency, and 3. providing better experience to the customers in the form of workload insights, actionable recommendations, and self-tuning capabilities.
workload-optimization ml-for-systems cloud-data-systems big-data
Language:Python 21

mit-han-lab / torchquantum