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# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

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package main

import (
	"bytes"
	"encoding/base64"
	"fmt"
	"io/ioutil"
	"mime/multipart"
	"net/smtp"
	"os"

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#!/usr/bin/python3

import asyncio
import time
import socket
import argparse

import aiohttp

class MyConnector(aiohttp.TCPConnector):

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import os
import sys
from functools import wraps

import gevent
import six

sys.stderr = open(os.devnull, 'w')

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package main

import (
	"fmt"
	"net/http"
	"os"

	"github.com/apex/log"
	"github.com/gorilla/mux"
)

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#!/usr/bin/env python
"""
Very simple HTTP server in python.

Usage::
    ./dummy-web-server.py [<port>]

Send a GET request::
    curl http://localhost

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So Hive in CDH is horribly, painfully slow. Cloudera ships Hive 1.1, which is actually moderately modern. It is, however, very badly configured out of the box and patched with custom code from Cloudera. With a bit of effort, we managed to improve hive performance considerably. We really shouldn't have to do this, but Cloudera is actively working against supporting a performant Hive.

First, building Tez was fairly straightforward. Using the instructions at https://github.com/apache/tez/blob/master/docs/src/site/markdown/install.md, the only change was to use the version string "2.6.0" for the build. I believe that was the default. Don't use the CDH string, it won't work.

At the bottom of the installation instructions, there's mention of the fact that to use the local hadoop jars (rather than those packaged with tez) you must unpack the jars in HDFS rather than using the tarball. In this case, unpack the tez-minimal tarball and upload the contents to /apps/tez-0.7.0 (or whatever you prefer). Don't fo

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## Configure eth0
#
# vi /etc/sysconfig/network-scripts/ifcfg-eth0

DEVICE="eth0"
NM_CONTROLLED="yes"
ONBOOT=yes
HWADDR=A4:BA:DB:37:F1:04
TYPE=Ethernet
BOOTPROTO=static

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#on cluster
thrift /spark/sbin/start-thriftserver.sh --master yarn-client
#ssh tunnel, direct 10000 to unused 8157
ssh -i ~/caserta-1.pem -N -L 8157:ec2-54-221-27-21.compute-1.amazonaws.com:10000 hadoop@ec2-54-221-27-21.compute-1.amazonaws.com
#see this for JDBC config on client http://blogs.aws.amazon.com/bigdata/post/TxT7CJ0E7CRX88/Using-Amazon-EMR-with-SQL-Workbench-and-other-BI-Tools

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package main

import (
  "fmt"
  "reflect"
)

// Name of the struct tag used in examples
const tagName = "validate"