Hyperparameter Experiments with TensorFlow and Keras
BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It
BytePS is a distributed deep neural network training framework and communication library designed to scale model training across multiple GPUs and compute nodes. It functions as a GPU cluster orchestrator and RDMA network optimizer, providing the necessary primitives to synchronize gradients and data across a server cluster. The project distinguishes itself through high-performance network optimizations, utilizing remote direct memory access and page-aligned memory to reduce latency. It employs topology-aware communication tuning and CPU core affinity management to maximize hardware throughpu
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
The main features of microsoft/neuronblocks are: MLOps and Infrastructure.
Open-source alternatives to microsoft/neuronblocks include: autonomio/talos — Hyperparameter Experiments with TensorFlow and Keras. bentoml/bentoml — BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package,… bytedance/byteps — BytePS is a distributed deep neural network training framework and communication library designed to scale model… horovod/horovod — Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across… huggingface/knockknock — 🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code. allegroai/trains.