Search Results for author:
Found 8 papers, 4 papers with code
On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
In this paper, we relax the requirement of having a central machine for the target subset by proposing a novel distributed bounding algorithm with provable approximation guarantees.
Modyn: A Platform for Model Training on Dynamic Datasets With Sample-Level Data Selection
Machine learning training data is often dynamic in real-world use cases, i. e., data is added or removed and may experience distribution shifts over time.
DeltaZip: Multi-Tenant Language Model Serving via Delta Compression
Fine-tuning large language models (LLMs) for downstream tasks can greatly improve model quality, however serving many different fine-tuned LLMs concurrently for users in multi-tenant environments is challenging.
tf.data service: A Case for Disaggregating ML Input Data Processing
To avoid data stalls, the host CPU and RAM required for input data processing per accelerator core used for ML computations varies across jobs.
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch.
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Our analysis of over two million ML jobs in Google datacenters reveals that a significant fraction of model training jobs could benefit from faster input data pipelines.
Towards Demystifying Serverless Machine Learning Training
The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML).
tf.data: A Machine Learning Data Processing Framework
Finally, we characterize machine learning input pipelines for millions of jobs that ran in Google's fleet, showing that input data processing is highly diverse and consumes a significant fraction of job resources.
