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-----
2023-06-30 23:25:38.764871: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-30 23:25:38.925571: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
--------------------------------------------------------------------------
WARNING: No preset parameters were found for the device that Open MPI
detected:

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#!/bin/bash
# -----
# WARNING: this does NOT work on Lambda Labs H100 instances... yet
# -----

# From: https://huggingface.co/tiiuae/falcon-40b/discussions/38#6479de427c18dca75e9a0903
pip install git+https://www.github.com/huggingface/transformers@2e2088f24b60d8817c74c32a0ac6bb1c5d39544d
pip install huggingface-hub==0.15.1
pip install tokenizers==0.13.3
pip install safetensors==0.3.1

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SSH Reverse Proxy

Some simple notes to setup SSH reverse proxy

1. on source machine (behind the network), run: ssh -N -R 2222:localhost:22 YOURUSER@YOURSERVER

2. on YOURSERVER, run: ssh -l SOURCEUSER -p 2222 localhost

3. If you want to automated it, do something like...

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from transformers import AutoTokenizer, AutoModelForCausalLM
import time

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")

print("Model Loaded..!")

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#!/usr/bin/env python
import os
import docx2txt

from absl import app
from absl import flags

FLAGS = flags.FLAGS
flags.DEFINE_string("output", "output.txt", "The path of final output.")
flags.DEFINE_string("source", None, "The source path to process.")

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number of messages 2:
fingerprint 388: 4, 1024: 8
number of messages 4:
fingerprint 388: 4, 448: 6, 1024: 8, 1025: 8
number of messages 9:
fingerprint 274: 2, 388: 4, 448: 6, 752: 2, 817: 8, 906: 8, 924: 8, 1024: 8, 1025: 8
number of messages 14:
fingerprint 274: 2, 388: 4, 448: 6, 520: 8, 532: 8, 564: 8, 752: 2, 817: 8, 906: 8, 924: 8, 937: 8, 996: 8, 1024: 8, 1025: 8
number of messages 18:
fingerprint 274: 2, 388: 4, 448: 6, 520: 8, 532: 8, 564: 8, 752: 2, 817: 8, 820: 8, 906: 8, 924: 8, 926: 3, 937: 8, 995: 8, 996: 8, 1000: 8, 1024: 8, 1025: 8

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#!/usr/bin/env python
#!pip install sklearn xgboost
#!wget https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv

from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

import pprint

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#!/bin/sh
ulimit -v unlimited
wget http://dl.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm
rpm -Uvh epel-release-6-8.noarch.rpm
yum -w install bison gettext glib2 freetype fontconfig libpng libpng-devel libX11 libX11-devel glib2-devel libgdi* libexif glibc-devel urw-fonts java unzip gcc gcc-c++ automake autoconf libtool make bzip2 wget
cd /usr/local/src
wget http://download.mono-project.com/sources/mono/mono-2.10.8.tar.gz
# http://download.mono-project.com/sources/mono/mono-3.6.0.tar.bz2
tar zxvf mono-2.10.8.tar.gz
cd mono-2.10.8

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# Modified from: https://github.com/xeb/fastText-docker/blob/master/Dockerfile
# To build & run, do:
#  docker build -t fasttext-py3.5 -f Dockerfile.py35 .
#  docker run --rm -it fasttext-py3.5 ./eval.py
# 
FROM python:3.5
RUN apt-get update && apt-get install -y \
        build-essential \
        wget \
        git \