| apiVersion: batch/v1 | |
| kind: CronJob | |
| metadata: | |
| name: clickhouse-backup-cron | |
| spec: | |
| # every day at 00:00 | |
| # schedule: "0 0 * * *" | |
| schedule: "* * * * *" | |
| concurrencyPolicy: "Forbid" | |
| jobTemplate: |
| // 3D Dom viewer, copy-paste this into your console to visualise the DOM as a stack of solid blocks. | |
| // You can also minify and save it as a bookmarklet (https://www.freecodecamp.org/news/what-are-bookmarklets/) | |
| (() => { | |
| const SHOW_SIDES = false; // color sides of DOM nodes? | |
| const COLOR_SURFACE = true; // color tops of DOM nodes? | |
| const COLOR_RANDOM = false; // randomise color? | |
| const COLOR_HUE = 190; // hue in HSL (https://hslpicker.com) | |
| const MAX_ROTATION = 180; // set to 360 to rotate all the way round | |
| const THICKNESS = 20; // thickness of layers | |
| const DISTANCE = 10000; // ¯\\_(ツ)_/¯ |
Each day at our company, developers are required to document their activities, painstakingly jotting down their daily work and future plans. A monotonous chore that I just really dislike.
So now, there's a scribe for that :
This worked on 14/May/23. The instructions will probably require updating in the future.
llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)
Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.
It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.
- Clone llama.cpp from git, I am on commit
08737ef720f0510c7ec2aa84d7f70c691073c35d.
| #!/usr/bin/env python3 | |
| from yaml import load, dump, FullLoader | |
| import sys | |
| file_name = sys.argv[1] | |
| def process_ua(data): | |
| for node in data: | |
| if 'family_replacement' not in node: |
Sometimes we need to add redundancy to some service or server which happen to be a public-facing entry point of our infrastructure. For example, imagine we want to add a high availability pair for a load balancer which sits on the edge of network and forwards traffic to alive backend servers.
┌─────────────┐
│ │
┌─────►│ Backend 1 │
│ │ │
│ └─────────────┘
Copyright 2020 Glia Inc.
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:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER D
In order to compile a fully static binary when using Cgo you'll need to link in a C library like musl.
I find it convenient to have a Docker image ready for building these artifacts.
FROM golang
RUN wget https://www.musl-libc.org/releases/musl-1.2.0.tar.gz && \
tar -zf musl-1.2.0.tar.gz && \
cd musl-1.2.0 && \
./configure --enable-static --disable-shared && \