Ben Gamble Ugbot

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

README

Create a template service file at /etc/systemd/system/secure-tunnel@.service. The template parameter will correspond to the name of target host:

[Unit]
Description=Setup a secure tunnel to %I
After=network.target

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	#include <assert.h>

	#define COBJMACROS
	#include <windows.h>
	#include <d3d11_1.h>
	#include <d3dcompiler.h>

	//////////////////////////////////////////////////////////////////////////////////////////////////////////////

	#define TITLE "D3D11 Triangle C"

	package block_allocator

	// Allocator based on Sebastian Aaltonen's Offset Allocator:
	// https://github.com/sebbbi/OffsetAllocator/blob/main/offsetAllocator.cpp

	import "core:fmt"
	import "core:math/bits"
	import "core:math/rand"

	assert_allocator_layout_good :: proc(allocator: ^Block_Allocator) {

	#pragma once

	#include "util/types.hpp"
	#include "util/std.hpp"
	#include "util/ndarray.hpp"
	#include "util/collections.hpp"
	#include "util/rand.hpp"
	#include "util/hash.hpp"
	#include "util/assert.hpp"
	#include "util/bitset.hpp"

	version: "3"

	services:
	janusgraph:
	image: janusgraph/janusgraph:0.6.2
	container_name: jce-janusgraph
	env_file:
	- janusgraph.env
	ports:
	- "8182:8182"

	{
	"info": {
	"_postman_id": "07c117b0-7200-41b9-94fc-5836a6b08d7b",
	"name": "Kafka Rest Proxy API",
	"description": "Adapted from https://docs.confluent.io/current/kafka-rest/api.html",
	"schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
	},
	"item": [
	{
	"name": "v2",

	// Sample for reading a file asynchronously using libuv
	// taken from https://www.snip2code.com/Snippet/247423/Sample-for-reading-a-file-asynchronously

	#include <stdlib.h>
	#include <stdio.h>
	#include <string.h>
	#include <uv.h>

	static uv_fs_t openReq;
	static uv_fs_t readReq;

	// Doing the operation:
	//
	// \| a a a a \| \| y \|
	// x * A * y = [ x x x x ] \| a a a a \| \| y \|
	// \| a a a a \| \| y \|
	// \| a a a a \| \| y \|
	//
	// with SIMD intrinics (specifically AVX).
	//
	// adapted from https://gist.github.com/rygorous/4172889