As of this day, this is probably the only and fastest way of installing it.
Based from the GDAL and PROJ build requirements, here is the full list of required packages to install:
""" | |
Support for filtering for sensor values. | |
Example config: | |
sensor: | |
- platform: filter | |
entity_id: sensor.outside_temperature | |
For more details about this platform, please refer to the documentation at |
version: '3.1' | |
services: | |
unifi: | |
container_name: unifi | |
restart: unless-stopped | |
image: linuxserver/unifi | |
volumes: | |
- /srv/docker/unifi:/config | |
environment: | |
- PGID=0 |
from flask import Flask | |
from flask_restful import Api, Resource, reqparse | |
app = Flask(__name__) | |
api = Api(app) | |
users = [ | |
{ | |
"name": "Nicholas", | |
"age": 42, |
# Change this path to your config directory | |
CONFIG_DIR="/home/homeassistant/.homeassistant" | |
cd /tmp | |
# Clone the latest code from GitHub | |
git clone --depth 1 https://github.com/tensorflow/models.git tensorflow-models | |
# download protobuf 3.4 | |
curl -OL https://github.com/google/protobuf/releases/download/v3.4.0/protoc-3.4.0-linux-x86_64.zip |
"""Hack to add per-session state to Streamlit. | |
Usage | |
----- | |
>>> import SessionState | |
>>> | |
>>> session_state = SessionState.get(user_name='', favorite_color='black') | |
>>> session_state.user_name | |
'' |
from pytorch_lightning import LightningDataModule | |
from torch_geometric.datasets import TUDataset | |
from torch_geometric.data import DataLoader | |
from sklearn.model_selection import KFold | |
class ProteinsKFoldDataModule(LightningDataModule): | |
def __init__( | |
self, | |
data_dir: str = "data/", |
import pandas as pd | |
import hvplot.pandas | |
import panel as pn | |
pn.extension(sizing_mode="stretch_width") | |
PALETTE = [ | |
"#ff6f69", | |
"#ffcc5c", | |
"#88d8b0", |