vitorcalvi

TF JUPYTER LAB MAC METAL

conda create --name ollama python=3.10 -y && conda activate ollama && 
conda install -c apple tensorflow-deps -y && pip install tensorflow-macos && pip install tensorflow-metal && conda install notebook -y && pip install numpy  --upgrade && pip install pandas  --upgrade && pip install matplotlib  --upgrade && pip install scikit-learn  --upgrade && pip install scipy  --upgrade && pip install plotly  --upgrade && pip install jupyterlab && 
pip3 install chardet && conda activate ollama && sudo mkdir /Users/vitorcalvi/Desktop/JupyterNotebooks

Jupiter lab

vitorcalvi

#### Source
https://robostack.github.io/GettingStarted.html#__tabbed_1_2

# Install gazebo Mac
curl -ssL http://get.gazebosim.org | sh

# Create a ros-humble desktop environment
micromamba create -n ros_env -c conda-forge -c robostack-staging ros-humble-desktop

vitorcalvi

FROM tensorflow/tensorflow:2.14.0-gpu

ARG BUILD_DATE
ENV PORT 8888

LABEL org.opencontainers.image.authors="rinconyanezd@gmail.com" \
	org.opencontainers.image.vendor="Diego, Rincon-Yanez" \
	org.opencontainers.image.title="Tensorflow GPU Jupyter" \
	org.opencontainers.image.created=$BUILD_DATE \
	org.opencontainers.image.version="2.9.3" \

vitorcalvi

# Work in progress. V0.1
# ALL THE THINGS.
ARG     APT_PROXY         #=http://apt-cacher-ng.lan:3142/
ARG     PIP_INDEX_URL     #=http://devpi.lan:3141/root/pypi/+simple
ARG     PIP_TRUSTED_HOST  #=devpi.lan
ARG     JUPYTER_PORT=37799
ARG     LITELLM_PORT=11111

FROM    nvidia/cuda:11.8.0-devel-ubuntu22.04 as build-llama

vitorcalvi

Docker - How to cleanup (unused) resources

One line (generated by ChatGPT 3.5)

sudo docker volume ls -qf dangling=true | xargs -r sudo docker volume rm && \
sudo docker network rm $(sudo docker network ls --format "{{.ID}} {{.Name}}" | grep -v "bridge" | awk '/ / { print $1 }') && \
sudo docker images --filter "dangling=true" -q --no-trunc | xargs -r sudo docker rmi && \
sudo docker images | grep "none" | awk '/ / { print $3 }' | xargs -r sudo docker rmi && \
sudo docker rm $(sudo docker ps -qa --no-trunc --filter "status=exited")

vitorcalvi

# List all Conda environments
conda env list

# Capture the list of environment names and loop through them
while IFS= read -r line; do
    # Extract the environment name from each line
    name=$(echo "$line" | cut -d ' ' -f 1)
    
    # Skip the first line (header) and the base environment
    if [ "$name" != "#" ] && [ "$name" != "base" ]; then

vitorcalvi

#!/bin/bash

set -eo pipefail

# Function to install Xcode Command Line Tools
install_xcode_cli() {
  echo "Installing Xcode Command Line Tools..."
  xcode-select --install
}

vitorcalvi

#!/bin/bash


sudo apt update && sudo apt full-upgrade -y


# Install cockpit and its components, netdata, git-lfs, and npm
sudo apt install "cockpit" "cockpit-machines" "cockpit-storaged" "cockpit-packagekit" "cockpit-networkmanager" "cockpit-system" -y
sudo apt install netdata -y && sudo apt install git-lfs -y && sudo apt install npm -y

vitorcalvi

# Version 1
#env_name='mlx-chat-app'; 
#conda deactivate &&  conda remove --name "$env_name" --all -y && \
#conda create -n "$env_name" python=3.10 -y && conda activate "$env_name" && 
# pip install tensorflow-macos tensorflow-metal &&
# pip install -r requirements.txt

read -p "Enter the name of the conda environment: " env_name && conda deactivate && conda remove --name "$env_name" --all -y && conda create -n "$env_name" python=3.10 -y && conda activate "$env_name" && pip install -r requirements.txt

vitorcalvi

#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

# Function to print messages