root β /workspaces/generative_ai_with_langchain (second_edition) $ cd chapter9/ray
root β /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.
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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
π π = [1.0, 0.0]
π¬[:, d] = JuMP.VariableRef[π¬[1,2], π¬[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
- βοΈ Check my Google Docs
π’ROSLaunch
that Gazebo world that I created via udacity_office.launch
.
- π 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. SinceFine-grained tokens
are still inPreview
, 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
- π create sagemaker base environment
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
.
π for the course projcts, Unity MLAgents - Banana Collector
, etc.
π go to the Banana and VisualBanana notebooks
π go to the course repo
π check course curriculum
Window 11, VSCode, Minicoda, Powershell
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#!/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 |
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