JaySon-Huang

I want to debug with CodeLLDB on CentOS 7, but get an error like this:

/home/xxx/.vscode/extensions/vadimcn.vscode-lldb-1.6.1/adapter/codelldb: /lib64/libc.so.6: version `GLIBC_2.18' not found (required by /home/xxx/.vscode/extensions/vadimcn.vscode-lldb-1.6.1/adapter/codelldb)

LLDB requires a higher version of glibc.

I get it work by the following steps:

AmericanEnglish

How to compile TensorFlow 2.0 from source in an HPC environment that uses EasyBuild for CentOS7

Some background about why another Gist on compiling tensorflow without root

If you're like me and you don't have root access to a HPC system and your system adminstrators use EasyBuild combined with Lmod you are going to be in for a bad time. Our CentOS7 did not ship out of the box capable of compiling tensorflow 2.0 from source with no problems. I scoured several closed and recently opened issues to write a script that enables the creation of a fully functional .whl for tensorflow 2.0. Note that on our cluster we have only two different sets of GPUs. One set has compute capability 7.0 (V100) and compute capability 3.5 (K20m). So many issues came up

1. because Bazel can't handle symlinks for gcc leading to errors like

tkoolen

julia> solve(m)

******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
 Ipopt is released as open source code under the Eclipse Public License (EPL).
         For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************

  Internal error 2 in QAMD : Schur size expected:           0 Real:           1
 ** MPI_ABORT called

fguevaravas

## Better way for creating standalone EXE files using Julia,
## Taken from: https://github.com/JuliaComputing/static-julia
## Assumptions:
## 1. g++ / x86_64-w64-mingw32-gcc is available and is in path
## 2. patchelf is in the path
module MinimalBuild 

# Assumption: the application main module is in MyApplication.jl
# this module must export a function called julia_main that is ccallable
appname = "MyApplication"

sminot

#!/usr/local/bin/ipython

import pandas as pd
from collections import defaultdict
from random import choice

alph = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']

# Function to make some test data
def make_dat(nrows=1000, ncols=1000, nvals=1000):

maartenbreddels

ggggggggg

using HDF5

"HDF5 appears to be inefficent for small writes, so this a simple buffer that
allows me to write to HDF5 only once per unit time (typically one second) to
limit the number of small writes."
mutable struct BufferedHDF5Dataset{T}
  ds::HDF5Dataset
  v::Vector{T}
  lasti::Int64 # last index in hdf5 dataset
  timeout_s::Float64 # interval in seconds at which to transfer data from v to ds

wassname

"""Another way, note this one will load the whole array into memory ."""
from keras.preprocessing.image import ImageDataGenerator
import h5py
from keras.utils.io_utils import HDF5Matrix
seed=0
batch_size=32

# we create two instances with the same arguments
data_gen_args = dict( 
    rotation_range=90.,

cdpath

{
  "schema_version": 1,
  "name": "WeChat",
  "description": "微信公众号全文 RSS",
  "source_url": false,
  "guid": "dd67102f09869c2228f8ed903a32d063",
  "tag_fg_color": "#333333",
  "tag_bg_color": "#66ff66",
  "icon": "leaf",
  "exported_at": "2019-01-12T10:56:41Z",

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown: