nov05

root ➜ /workspaces/generative_ai_with_langchain (second_edition) $ cd chapter9/ray
root ➜ /workspaces/generative_ai_with_langchain/chapter9/ray (second_edition) $ python build_index.py
2025-06-19 00:26:35,100 INFO worker.py:1841 -- Started a local Ray instance.
modules.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 349/349 [00:00<00:00, 2.21MB/s]
config_sentence_transformers.json: 100%|███████████████████████████████████████████████████████████████████████████████| 116/116 [00:00<00:00, 1.11MB/s]
README.md: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 10.4k/10.4k [00:00<00:00, 2.99MB/s]
sentence_bert_config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████| 53.0/53.0 [00:00<00:00, 422kB/s]
config.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 571/571 [00:00<00:00, 1.5

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

🟢 20250221_Import VMWare OVA to GCP VM

• ☝️ Check my Google Docs

---

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

nov05

🟢 Different Levels of AWS Resources for Machine Learning Model Training and Deployment

1. 👉 EC2 Instances: Full User Control (Least Pre-built Content)
   With EC2, you have complete control over the entire setup. You need to:
   • Start an EC2 instance (e.g., GPU-enabled for training deep learning models).
   • Install dependencies manually (e.g., Python, ML libraries like PyTorch or TensorFlow).
   • Copy or configure the training script, and handle the training data management (downloading data from S3 or other sources).
   • Run the training process manually using your own code.
   • Manage all aspects of the environment, scaling, and resource management.

nov05

• Don't use the email you registered with GitHub for commits. Instead, GitHub provides you with a proxy email for this purpose. Just go to 'Settings - Emails' in your GitHub account, and you'll find the proxy email there.
• Don't use your GitHub login password for commits. Instead, go to 'Settings - Developer Settings - Personal access tokens', create a token, and use that as your password for commits. Since Fine-grained tokens are still in Preview, I'm using a classic token for now.

nov05

• Local Install Requirements

Python 3.7
MXNet 1.8
Pandas >= 1.2.4
AutoGluon 0.2.0

• 👉 create sagemaker base environment

nov05

👉 Udacity Deep Reinforcement Learning Python Environment Setup

⚠️ Python 3.11 has to be downgraded to Python 3.10, or Multiprocessing will cause TypeError: code() argument 13 must be str, not int in both Windows and Linux. Google Colab is currently using Python 3.10 as well.

• Windows 11 (64-bit), VSCode, Powershell, Miniconda3, Python 3.10
• repo: https://github.com/Nov05/udacity-deep-reinforcement-learning
• working dir: D:\github\ udacity-deep-reinforcement-learning\python
• package deeprl is copied and modified from https://github.com/ShangtongZhang/DeepRL/tree/master/ deep_rl into .\python.

nov05

Udacity Deep Reinforcement Learning - p1 env setup

👉 for the course projcts, Unity MLAgents - Banana Collector, etc.
👉 go to the Banana and VisualBanana notebooks
👉 go to the course repo
👉 check course curriculum

---

Window 11, VSCode, Minicoda, Powershell

prabhatsharma

#!/bin/bash

# local sagemaker setup
conda update conda # update conda

conda create -n localsm python==3.7

conda activate localsm

conda install -c conda-forge jupyterlab