This configuration worked for me, hope it helps
It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b
and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support
| import requests | |
| import shutil | |
| import os | |
| def download_images(url_filename, out_directory, max_urls=None): | |
| """Download images from a file containing URLS. | |
| The URL file must contain a single URL per line. | |
| Args: |
| { | |
| "emojis": [ | |
| {"emoji": "👩👩👧👧", "name": "family: woman, woman, girl, girl", "shortname": ":woman_woman_girl_girl:", "unicode": "1F469 200D 1F469 200D 1F467 200D 1F467", "html": "👩‍👩‍👧‍👧", "category": "People & Body (family)", "order": ""}, | |
| {"emoji": "👩👩👧👦", "name": "family: woman, woman, girl, boy", "shortname": ":woman_woman_girl_boy:", "unicode": "1F469 200D 1F469 200D 1F467 200D 1F466", "html": "👩‍👩‍👧‍👦", "category": "People & Body (family)", "order": ""}, | |
| {"emoji": "👩👩👦👦", "name": "family: woman, woman, boy, boy", "shortname": ":woman_woman_boy_boy:", "unicode": "1F469 200D 1F469 200D 1F466 200D 1F466", "html": "👩‍👩‍👦‍👦", "category": "People & Body (family)", "order": ""}, | |
| {"emoji": "👨👩👧👧", "name": "family: man, woman, girl, girl", "shortname": ":man_woman_girl_girl:", "unicode": "1F468 200D 1F469 200D 1F467 200D 1F467", "html": "👨‍👩&z |
| import json, time, os.path | |
| import blinkt, requests | |
| status_colors = {'current':[1,1,1],'high':[1,0,0],'low':[0,0,1],'precip':[0,1,0]} | |
| blinkt.set_clear_on_exit() | |
| blinkt.set_brightness(.1) | |
| def getForecast(key,lat,lng): | |
| url = 'https://api.darksky.net/forecast/' + key + '/' + str(lat) + ',' + str(lng) |
| { | |
| "Version": "2012-10-17", | |
| "Statement": [ | |
| { | |
| "Sid": "VisualEditor0", | |
| "Effect": "Allow", | |
| "Action": [ | |
| "cloudformation:CreateUploadBucket", | |
| "cloudformation:CancelUpdateStack", | |
| "cloudformation:CreateStack", |
| // @flow | |
| import * as React from 'react'; | |
| import {asyncComponent} from 'react-async-component'; | |
| export const loaderMaker = (bgColor: string = 'transparent') => () => ( | |
| <div style={{position: 'absolute', width: '100%', height: '100%', backgroundColor: bgColor}} /> | |
| ); | |
| const MAX_RETRIES = 3; | |
| const STARTING_BACKOFF = 500; |
| #!/bin/bash -e | |
| export IGNORE_FILES=$(ls -p | grep -v /) | |
| detect_changed_folders() { | |
| if [[ "${DRONE_PULL_REQUEST}" ]]; then | |
| folders=$(git --no-pager diff --name-only FETCH_HEAD FETCH_HEAD~1 | sort -u | awk 'BEGIN {FS="/"} {print $1}' | uniq); | |
| else | |
| folders=$(git --no-pager diff --name-only HEAD~1 | sort -u | awk 'BEGIN {FS="/"} {print $1}' | uniq); | |
| fi | |
| export changed_components=$folders |
React recently introduced an experimental profiler API. After discussing this API with several teams at Facebook, one common piece of feedback was that the performance information would be more useful if it could be associated with the events that caused the application to render (e.g. button click, XHR response). Tracing these events (or "interactions") would enable more powerful tooling to be built around the timing information, capable of answering questions like "What caused this really slow commit?" or "How long does it typically take for this interaction to update the DOM?".
With version 16.4.3, React added experimental support for this tracing by way of a new NPM package, scheduler. However the public API for this package is not yet finalized and will likely change with upcoming minor releases, so it should be used with caution.
