nov05

✅ Autonomous Vehicle Risk Assessment Framework, Installation in Windows 11

Repo:
https://github.com/sisl/AutonomousRiskFramework.jl

Environment:
Windows 11, VS Code, Miniconda, Jalia 1.11.5, CARLA 0.9.13, scenario_runner 0.9.13

---

nov05

👉 𝐝 = [1.0, 0.0]
   𝐬[:, d] = JuMP.VariableRef[𝐬[1,2], 𝐬[2,2]]
------------------------------------------------------------------
	       SCS v3.2.7 - Splitting Conic Solver
	(c) Brendan O'Donoghue, Stanford University, 2012
------------------------------------------------------------------
problem:  variables n: 14, constraints m: 26
cones: 	  z: primal zero / dual free vars: 2
	  l: linear vars: 24

nov05

• PDF
• YouTube video

Here’s the extracted OCR text from your PDF, “The Quantum Eraser”:

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The Quantum Eraser

1. Initial Superposition State

nov05

🟢 Hidden Beauty Behind Generative AI

Artem Kirsanov
https://www.youtube.com/watch?v=laaBLUxJUMY

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Derivation Explanation

nov05

🟢 ChatGPT output (could be wrong. verify carefully)

To export SAP HANA data in real time continuously, you can use several methods depending on the target system and the purpose. Here are some of the most common approaches:

1. Smart Data Integration (SDI)

• Use Case: Real-time data replication and transformation.
• How: SDI allows you to create real-time data replication tasks between SAP HANA and other systems. You can define data flows that continuously export data from HANA and send it to another system, such as another HANA instance or a non-HANA database.
• Steps:
  1. Set up a Data Provisioning Agent.

2. Configure the SDI connection to the target system.

nov05

🟢 20250221_Import VMWare OVA to GCP VM

• ☝️ Check my Google Docs

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🟢⚠️ It is hard to ROSLaunch that Gazebo world that I created via udacity_office.launch.

nov05

⚠️ Issue

(awsmle_py310) PS D:\github\udacity-cd13926-Building-Apps-Amazon-Bedrock-exercises\Experiments> aws bedrock invoke-model `
>> --model-id anthropic.claude-3-5-sonnet-20240620-v1:0 `
>> --body file://claude_input.json output.json

usage: aws [options] <command> <subcommand> [<subcommand> ...] [parameters]
To see help text, you can run:

nov05

• Google Docs: 20250125_AWS SageMaker Input Mode, WebDataset

✅✅✅ My working code: Create WebDataset from local data files to local .tar files

## example code for webdataset
import webdataset as wds
import io
import json

nov05

• ⚠️🟢 Issue: training error

[1,mpirank:0,algo-1]<stderr>:../aten/src/ATen/native/cuda/Loss.cu:242: nll_loss_forward_reduce_cuda_kernel_2d: block: [0,0,0], thread: [0,0,0] Assertion `t >= 0 && t < n_classes` failed.
[1,mpirank:0,algo-1]<stderr>:../aten/src/ATen/native/cuda/Loss.cu:242: nll_loss_forward_reduce_cuda_kernel_2d: block: [0,0,0], thread: [6[1,mpirank:0,algo-1]<stderr>:,0,0] Assertion `t >= 0 && t < n_classes` failed.
[1,mpirank:0,algo-1]<stderr>:../aten/src/ATen/native/cuda/Loss.cu:242: nll_loss_forward_reduce_cuda_kernel_2d: block: [0,0,0], thread: [30,0,0] Assertion `t >= 0 && t < n_classes` failed.
...
[1,mpirank:1,algo-2]<stdout>:  File "train.py", line 675, in <module>
[1,mpirank:1,algo-2]<stdout>:    main(task)
[1,mpirank:1,algo-2]<stdout>:  File "train.py", line 572, in main

nov05

• Uninstall all VS Code extensions
  Delete C:\Users\*\.vscode\extensions folder
  Reinstall extensions

• Remove Jupyter kernels

(base) PS D:\github\udacity-nd009t-capstone-starter> jupyter kernelspec list
Available kernels: