Artificial Intelligence Practice¶
Downloading Practical Material¶
To update the practical material, issue the following commands:
cd ~/ros2_ws/src/arj_packages
git checkout -- .
git pull
Installing Conda Environment¶
Anaconda (miniconda) provides an isolated virtual environment where we can install the required version of Python packages for the current work.
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init bash
The solver coordinates the versions among the packages required for the predefined environment (environment.yml
). The libmamba-solver
allows for faster coordination compared to the default solver.
source ~/.bashrc
conda config --set auto_activate_base false
conda update -n base conda
conda install -n base conda-libmamba-solver
conda config --set solver libmamba
Once Conda is installed, we create our own virtual environment:
cd ~/ros2_ws/src/arj_packages/arj_ai
conda env create -f environment.yml
Opening the Practice¶
The material can be opened as follows:
conda activate practice
cd ~/ros2_ws/src/arj_packages/arj_ai
code .
Select the environment:
Troubleshooting¶
TODO
ImportError Traceback (most recent call last)
Cell In[1], line 10
8 import matplotlib.patches as patches
9 import matplotlib.pyplot as plt
---> 10 import torch
11 import torchvision.transforms as transforms
12 from PIL import Image
File ~/miniconda3/envs/practice/lib/python3.8/site-packages/torch/__init__.py:189
187 if USE_GLOBAL_DEPS:
188 _load_global_deps()
--> 189 from torch._C import *
191 # Appease the type checker; ordinarily this binding is inserted by the
192 # torch._C module initialization code in C
193 if False:
ImportError: /home/he/miniconda3/envs/practice/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so: undefined symbol: iJIT_IsProfilingActive
Deactivating Conda¶
conda deactivate