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:
import pandas as pd | |
import hvplot.pandas | |
import panel as pn | |
pn.extension(sizing_mode="stretch_width") | |
PALETTE = [ | |
"#ff6f69", | |
"#ffcc5c", | |
"#88d8b0", |
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/", |
"""Hack to add per-session state to Streamlit. | |
Usage | |
----- | |
>>> import SessionState | |
>>> | |
>>> session_state = SessionState.get(user_name='', favorite_color='black') | |
>>> session_state.user_name | |
'' |
# 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 |
from flask import Flask | |
from flask_restful import Api, Resource, reqparse | |
app = Flask(__name__) | |
api = Api(app) | |
users = [ | |
{ | |
"name": "Nicholas", | |
"age": 42, |
version: '3.1' | |
services: | |
unifi: | |
container_name: unifi | |
restart: unless-stopped | |
image: linuxserver/unifi | |
volumes: | |
- /srv/docker/unifi:/config | |
environment: | |
- PGID=0 |