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Found 6 papers, 1 papers with code
PartIR: Composing SPMD Partitioning Strategies for Machine Learning
Training of modern large neural networks (NN) requires a combination of parallelization strategies encompassing data, model, or optimizer sharding.
Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR
Large neural network models are commonly trained through a combination of advanced parallelism strategies in a single program, multiple data (SPMD) paradigm.
RLFlow: Optimising Neural Network Subgraph Transformation with World Models
Our proposed approach RLFlow can learn to perform neural network subgraph transformations, without the need for expertly designed heuristics to achieve a high level of performance.
Model-based Reinforcement Learning
reinforcement-learning
+1
BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters
Then it applies the expert-provided knowledge to the graph to further contextualize the system behavior.
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB
The model is then incorporated in a standard Bayesian Optimization loop to find parameters that maximize RocksDB's IO throughput.
RLCache: Automated Cache Management Using Reinforcement Learning
We also introduce a framework to simplify the modelling of computer systems problems as a reinforcement learning task.
