Bored MLE

  Hi All Continuing from Part 1, this post details the Kubernetes and Observability configs of the project. The full source is available here . Let's break the code down shall we.  1. K3's Server Config ( infra/server-config.yaml )  write-kubeconfig-mode: "0644"  *      Sets file permissions for kubeconfig file (readable by all users in the group) *      0644 means owner can read/write, group and others can only read disable: - traefik - servicelb - local-storage - metrics-server *      Disables default k3s components that we'll replace with better alternatives. Components Disabled: *      _traefik: Replaced with ingress-nginx for better control *      _servicelb: Replaced with MetalLB or cloud load balancer *      _local-storage: Replaced with Longhon for dynamic provisioning *      _metrics-server: Replaced with Prometheus for better monit...

  Hi All  Alright today on "Bored MLE" we're doing some inference, using a LoRA optimized BERT model on GKE . Now there's a lot more to MLOps than just this (like cluster level observability for example), but for this example I'm keeping it brief.  The full code is available on GitHub with added minikube deployment instructions (I only cover the Cloud deployment here).    I assume familiarity with Python, PyTorch, K8s, FastApi, Minikube and GKE. Let's get on with it.  View the full FastApi service source below, also available here : Let's breakdown the above code, block by block. 0. Install Dependencies :  pip install fastapi uvicorn torch transformers peft accelerate  1. Imports from fastapi import FastAPI, HTTPException from pydantic import BaseModel from typing import List import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer from peft import PeftModel, PeftConfig  *      fastapi : F...

    Hi All I'm aiming to solve some tough issues in the ML backend, and heterogeneous backend support is up there as one of the most pressing . Today we'll be training BERT on a Rocm (AMD) backend and running the binary (inference) on a Nvidia backend.  Training a complex model like BERT (250M+ params) on a ROCm backend and then running the trained model on NVIDIA backend is not impossible, but it comes with some challenges and considerations. Here's a breakdown of the key aspects:  Steps to achieve Cross-Backend Training/Inference Option 1: Train on ROCm, Inference on NVIDIA (Same Framework) 1.     Train the model on AMD GPU's using PyTorch/Tensorflow with ROCm. # Example PyTorch training script (ROCm) import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # ... rest of the training code *      Note: ROCm uses cuda device string for compatibility, but maps it to AMD hardware. 2.      Save t...