Rocketknight1

# This is a new feature, so make sure to update to the latest version of transformers!
# You will also need to pip install tensorflow_text

import tensorflow as tf
from transformers import TFAutoModel, TFBertTokenizer


class EndToEndModel(tf.keras.Model):
    def __init__(self, checkpoint):
        super().__init__()

stefan-it

TPU VM Cheetsheat

This TPU VM cheatsheet uses and was tested with the following library versions:

Library	Version
JAX	0.3.25
FLAX	0.6.4
Datasets	2.10.1
Transformers	4.27.1

sumanthratna

# adapted from https://web.archive.org/save/https://randomwalk.in/python/2019/10/27/Install-Python-copy.html
wget https://www.python.org/ftp/python/3.6.10/Python-3.6.10.tgz
tar zxfv Python-3.6.10.tgz
rm Python-3.6.10.tgz
find ./Python-3.6.10/Python -type d | xargs chmod 0755
cd Python-3.6.10
./configure --prefix=$PWD/Python-3.6.10/Python
make
make install
export PATH=$CWD:$PATH  # adds python3.6 to PATH

kmhofmann

Installing the NVIDIA driver, CUDA and cuDNN on Linux (Ubuntu 20.04)

This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software

• NVIDIA graphics card driver (v450.57)
• CUDA (v11.0.2)
• cuDNN (v8.0.2.39)

on an Ubuntu Linux system, in particular Ubuntu 20.04.

wchargin

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


import csv
import errno
import os
import re

rspeer

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

jihunchoi

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

taylorpaul

Environment:

Tensorflow: v0.11.0rc2 OS: CENTOS 6.8 (No root access)

Description:

1. The tensorboardSLURM.sh can be run with the following command to start a tensorboard server on a SLURM cluster:

sbatch --array=0-0 tensorboardSLURM.sh 

NTag

Installation

1. Install Atom on your computer (https://atom.io)

2. Launch Atom, go in Preferences, then chose Install, search for "remote-atom", and install it

3. On the distant machine:

   cd ~
   wget https://raw.githubusercontent.com/aurora/rmate/master/rmate
   chmod +x rmate