In Git you can add a submodule to a repository. This is basically a repository embedded in your main repository. This can be very useful. A couple of usecases of submodules:
- Separate big codebases into multiple repositories.
function condalist -d 'List conda environments.' | |
for dir in (ls $HOME/miniconda3/envs) | |
echo $dir | |
end | |
end | |
function condactivate -d 'Activate a conda environment' -a cenv | |
if test -z $cenv | |
echo 'Usage: condactivate <env name>' | |
return 1 |
# This program is free software: you can redistribute it and/or modify | |
# it under the terms of the GNU General Public License as published by | |
# the Free Software Foundation, either version 3 of the License, or | |
# (at your option) any later version. | |
# | |
# This program is distributed in the hope that it will be useful, | |
# but WITHOUT ANY WARRANTY; without even the implied warranty of | |
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
# GNU General Public License for more details. | |
# |
NOTE: This is a question I found on StackOverflow which I’ve archived here, because the answer is so effing phenomenal.
If you are not into long explanations, see [Paolo Bergantino’s answer][2].
import numpy as np | |
from keras.models import Sequential | |
from keras.layers.core import Activation, Dense | |
training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32") | |
target_data = np.array([[0],[1],[1],[0]], "float32") | |
model = Sequential() | |
model.add(Dense(32, input_dim=2, activation='relu')) | |
model.add(Dense(1, activation='sigmoid')) |
#!/bin/bash | |
## | |
## Creates explicit dirs on the bucket | |
## | |
function print_help() { | |
echo "Usage:" $(basename $0) bucket_mounted_dir | |
exit 1 | |
} |
"""Simple example on how to log scalars and images to tensorboard without tensor ops. | |
License: BSD License 2.0 | |
""" | |
__author__ = "Michael Gygli" | |
import tensorflow as tf | |
from StringIO import StringIO | |
import matplotlib.pyplot as plt | |
import numpy as np |
This is about documenting getting Linux running on the late 2016 and mid 2017 MPB's; the focus is mostly on the MacBookPro13,3 and MacBookPro14,3 (15inch models), but I try to make it relevant and provide information for MacBookPro13,1, MacBookPro13,2, MacBookPro14,1, and MacBookPro14,2 (13inch models) too. I'm currently using Fedora 27, but most the things should be valid for other recent distros even if the details differ. The kernel version is 4.14.x (after latest update).
The state of linux on the MBP (with particular focus on MacBookPro13,2) is also being tracked on https://github.com/Dunedan/mbp-2016-linux . And for Ubuntu users there are a couple tutorials (here and here) focused on that distro and the MacBook.
Note: For those who have followed these instructions ealier, and in particular for those who have had problems with the custom DSDT, modifying the DSDT is not necessary anymore - se
This configuration worked for me, hope it helps
It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b
and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support
Thanks everyone for commenting/contributing! I made this in college for a class and I no longer really use the technology. I encourage you all to help each other, but I probably won't be answering questions anymore.
This article is also on my blog: https://emilykauffman.com/blog/install-anaconda-on-wsl
Note: $
denotes the start of a command. Don't actually type this.
x86_64.sh
. If I had a 32-bit computer, I'd select the x86.sh
version. If you accidentally try to install the wrong one, you'll get a warning in the terminal. I chose `Anaconda3-5.2.0-Li