Topic

Kubeflow

2 posts about Kubeflow. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Posts

Red Hat
Distributed training on OpenShift AI 3.4 with Kubeflow Trainer v2
Red Hat OpenShift AI 3.4 introduces Kubeflow Trainer v2, a unified training API that replaces framework-specific CRDs like PyTorchJob and TFJob with a single TrainJob resource. Under the hood, each TrainJob is translated into a JobSet, a Kubernetes-native API for managing groups of Jobs as a single unit, which handles headless services for pod discovery and automatic failure recovery. The post validates the stack through two phases: manual validation with a minimal NCCL all-reduce test across 2 nodes, and real-world training of an embedding-based neural network on a fraud detection dataset using 1, 2, and 4 NVIDIA A10G GPUs. Benchmarking shows near-linear Distributed Data Parallel scaling at 95% efficiency, successful checkpoint recovery after pod kills, and GPU utilization confirmed via DCGM Exporter. The TrainJob API simplifies distributed training by automatically configuring torchrun, environment variables, and pod coordination, while raw JobSets offer more control for multi-stage pipelines and differentiated failure handling.

Red Hat
Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI
Training an English-centered text-to-speech model for Turkish can produce fluent but hard-to-understand pronunciation for native speakers. This tutorial fine-tunes unsloth/orpheus-3b-0.1-pretrained with about 81,000 Turkish text-audio pairs on Red Hat OpenShift AI using a Kubeflow Trainer TrainJob distributed across two nodes with two A100 GPUs each. LoRA freezes the 3-billion-parameter base and trains 97.3M adapter parameters, while preprocessing converts text to Llama-3 tokenizer tokens and audio to three-layer SNAC codes, masking text labels during scoring. The merged model is evaluated against the base on ten Turkish sentences using Whisper transcription and WER/CER metrics. Mean WER falls from 2.44 to 0.35 and CER from 1.59 to 0.09, though the authors describe the small test as directional.

The best engineering reads, once a week.

Topic

2 posts about Kubeflow. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Red Hat
Distributed training on OpenShift AI 3.4 with Kubeflow Trainer v2
Red Hat OpenShift AI 3.4 introduces Kubeflow Trainer v2, a unified training API that replaces framework-specific CRDs like PyTorchJob and TFJob with a single TrainJob resource. Under the hood, each TrainJob is translated into a JobSet, a Kubernetes-native API for managing groups of Jobs as a single unit, which handles headless services for pod discovery and automatic failure recovery. The post validates the stack through two phases: manual validation with a minimal NCCL all-reduce test across 2 nodes, and real-world training of an embedding-based neural network on a fraud detection dataset using 1, 2, and 4 NVIDIA A10G GPUs. Benchmarking shows near-linear Distributed Data Parallel scaling at 95% efficiency, successful checkpoint recovery after pod kills, and GPU utilization confirmed via DCGM Exporter. The TrainJob API simplifies distributed training by automatically configuring torchrun, environment variables, and pod coordination, while raw JobSets offer more control for multi-stage pipelines and differentiated failure handling.

Red Hat
Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI
Training an English-centered text-to-speech model for Turkish can produce fluent but hard-to-understand pronunciation for native speakers. This tutorial fine-tunes unsloth/orpheus-3b-0.1-pretrained with about 81,000 Turkish text-audio pairs on Red Hat OpenShift AI using a Kubeflow Trainer TrainJob distributed across two nodes with two A100 GPUs each. LoRA freezes the 3-billion-parameter base and trains 97.3M adapter parameters, while preprocessing converts text to Llama-3 tokenizer tokens and audio to three-layer SNAC codes, masking text labels during scoring. The merged model is evaluated against the base on ten Turkish sentences using Whisper transcription and WER/CER metrics. Mean WER falls from 2.44 to 0.35 and CER from 1.59 to 0.09, though the authors describe the small test as directional.

The best engineering reads, once a week.