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  In this first part of a series on eBPF and Open Telemetry . We detail a couple of useful eBPF scripts tailored for an MLE with no background of the project(s). They focus on observability, performance monitoring and data collection - key areas where eBPF shines. I tried to make these as practical as possible, let's get to it. 1. Tracing Python Function Calls in ML Pipelines Monitor which Python functions are being called in an ML training script (eg. PyTorch/Tensorflow) and their execution time. This helps identity bottlenecks in data loading, preprocessing or model training. Tools: *    bpftrace (for high level scripting) *    libbpf (for custom C-based eBPF programs) *    Run the below script while your ML script is executing. sudo bpftrace -e 'uprobe:python3:PyEval_CallObject { printf("Function called: %s\n", str(arg1)); }' *    Trace all torch.Tensor method calls in a PyTorch script *    The output will show ...

  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...