Package Centre > Docker > Install
/docker/gitea/postgresql
/docker/gitea/gitea
env PYTHON_CONFIGURE_OPTS='--enable-optimizations --with-lto' \ | |
PYTHON_CFLAGS='-march=native -mtune=native' MAKEFLAGS="-j$(nproc)" pyenv install 3.12 |
DOMAIN=mydomain.com | |
STORAGE_ROOT=/mnt/data/ai |
mkdir -p ~/.docker/cli-plugins | |
REPO_NAME=buildx | |
LATEST_URL=`curl -Ls -o /dev/null -w %{url_effective} https://github.com/docker/${REPO_NAME}/releases/latest` | |
LATEST_VERSION=${LATEST_URL##*/} | |
DOWNLOAD_URL=https://github.com/docker/${REPO_NAME}/releases/download/${LATEST_VERSION}/${REPO_NAME}-${LATEST_VERSION}.`uname -s`-`uname -m` | |
curl -L ${DOWNLOAD_URL} -o ~/.docker/cli-plugins/docker-${REPO_NAME} | |
chmod +x ~/.docker/cli-plugins/docker-${REPO_NAME} | |
docker buildx install |
""" | |
WARNING: dont use loguru, use structlog | |
https://gist.github.com/nkhitrov/38adbb314f0d35371eba4ffb8f27078f | |
Configure handlers and formats for application loggers. | |
""" | |
import logging | |
import sys | |
from pprint import pformat |
See nomic-ai/gpt4all for canonical source.
~/GPT4All
. Adjust the following commands as necessary for your own environment.conda env create -f conda-macos-arm64.yaml
and then use with conda activate gpt4all
.Simple script to detect operating system, GPU architecture, and return device
for CUDA usage. Useful for when running on MacOS with Apple Silicon and need to swap out hardcoded cuda:0
type strings for mps
.
"""Uses some sensible logic to determine platform and best available device for pytorch.
Assumed combinations (in order of preference):
* CUDA (nvidia GPU) / AMD (ROCm)
This repository outlines what I have done in order to get Stable Diffusion running on a MacBook Pro with a M2 chip.
Exact Specs:
Key | Value |
---|---|
Model | MacBook Pro |
Screen | 14" |
Microk8s is a Canonical project to provide a kubernetes environment for local development, similar to minikube but without requiring a separate VM to manage. These instructions describe setting it up for common development use cases with Cilium and may be helpful in particular for testing BPF kernel extensions with Cilium.
Microk8s will run its own version of docker for the kubernetes runtime, so if you have an existing docker installation then this may be confusing, for instance when building images the image may be stored with one of these installations and not the other. This guide assumes you will run both docker daemon instances, and use your existing docker-ce for building Cilium while using the microk8s.docker daemon instance for the runtime of your kubernetes pods.