miladfa7

from simpletransformers.language_modeling import LanguageModelingModel
import logging


logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)


train_args = {

benkehoe

# MIT No Attribution
#
# Copyright 2022 Ben Kehoe
#
# 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.
#

sebsto

drop table store;

drop table sales;

drop materialized view city_sales;


CREATE TABLE "sales" (
  "id" int PRIMARY KEY,
  "item" varchar,

psychemedia

mkdir -p binder binder_1 binder_2

cat > binder_1/requirements.txt << EOF
pandas
scipy
EOF

cat > binder_2/requirements.txt << EOF
ipython-sql
pandas

jsomers

psychemedia

Alex-Wauters

FROM tensorflow/tensorflow:1.13.1-py3
RUN apt-get update && apt-get install -y \
        build-essential \
        cmake \
        git \
        wget \
        unzip \
        yasm \
        pkg-config \
        libswscale-dev \

nl5887

#!/usr/bin/env bash
AWS_CLI="aws --region $AWS_REGION"

PRICE=0.2
USER_NAME=#USERNAME#
KEY_NAME=#KEY NAME#
SECURITY_GROUP_ID=#SECURITY GROUP#
SUBNET_ID=#SUBNET#
VOLUME_SIZE=40
INSTANCE_TYPE=t2.2xlarge

nvbn

treuille

import streamlit as st
import pandas as pd

# Reuse this data across runs!
read_and_cache_csv = st.cache(pd.read_csv)

BUCKET = "https://streamlit-self-driving.s3-us-west-2.amazonaws.com/"
data = read_and_cache_csv(BUCKET + "labels.csv.gz", nrows=1000)
desired_label = st.selectbox('Filter to:', ['car', 'truck'])
st.write(data[data.label == desired_label])