On every machine in the cluster install openmpi and mlx-lm:
conda install conda-forge::openmpi
pip install -U mlx-lmNext download the pipeline parallel run script. Download it to the same path on every machine:
On every machine in the cluster install openmpi and mlx-lm:
conda install conda-forge::openmpi
pip install -U mlx-lmNext download the pipeline parallel run script. Download it to the same path on every machine:
Iptables performance is limited mainly by two reasons:
The kernel community moved to nftables as replacement of iptables, with the goal of removing the existing performance bottlenecks. Kubernetes has decided to implement a new nftables proxy because of this and another reasons explained in more detail in the corresponding KEP and during the Kubernetes Contributor Summit in Chicago 2023 on the session [Iptables, end of
Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962
In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.
See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix
| # Prioritize NVIDIA packages | |
| wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin | |
| sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 | |
| # Fetch NVIDIA keys | |
| sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub | |
| # Add NVIDIA repos | |
| sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /" |
Here's how I configured a GitHub Action so that a new version issued by GitHub's release interface will build a Dockerfile, tag it with the version number and upload it to Google Artifact Registry.
Before you attempt the steps below, you need the following:
Security Advisories / Bulletins / vendors Responses linked to Log4Shell (CVE-2021-44228)
| // Tailscale Frontend: It uses tailscale-as-a-library to | |
| // listen on a port, independently from the operating system network, i.e. you | |
| // can run an nginx server on :80 and :443 without impacting the frontend. | |
| // | |
| // set up DNS, e.g.: | |
| // prometheus.ts.zekjur.net A 100.117.6.125 | |
| // | |
| // frontend% TAILSCALE_USE_WIP_CODE=true tailscalefrontend -hostname=srv.example.net -allowed_user=michael@example.net | |
| // | |
| // (first login requires running with TS_LOGIN=1 environment variable to print link for the browser) |
| Device: /dev/vda | |
| Partition Table: gpt | |
| /dev/sda1 - 1MB - for UEFI/Bootloader | |
| /dev/sda2 - 10G - root partition | |
| /dev/sda3 - Remaining Space - for cinder-volumes | |
| LVM Volume Groups: |
| #!/bin/sh | |
| # Copyright 2023 Khalifah K. Shabazz | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a | |
| # copy of this software and associated documentation files (the “Software”), | |
| # to deal in the Software without restriction, including without limitation | |
| # the rights to use, copy, modify, merge, publish, distribute, sublicense, | |
| # and/or sell copies of the Software, and to permit persons to whom the | |
| # Software is furnished to do so, subject to the following conditions: |