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.

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

👉 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.

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.

Local Install Requirements

Python 3.7
MXNet 1.8
Pandas >= 1.2.4
AutoGluon 0.2.0

👉 create sagemaker base environment

👉 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.

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

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remote: error: GH007: Your push would publish a private email address.
remote: You can make your email public or disable this protection by visiting:
remote: http://github.com/settings/emails

	#!/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

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torchvision.models as models


	class EncoderCNN(nn.Module):
	def __init__(self, embed_size):
	# super(EncoderCNN, self).__init__()
	super().__init__()