This tutorial shows how to make backups to Google Cloud Storage. The backups are:
- automatic
- stored off site
- incremental
import tensorflow as tf | |
def validate_dataset(filenames, reader_opts=None): | |
""" | |
Attempt to iterate over every record in the supplied iterable of TFRecord filenames | |
:param filenames: iterable of filenames to read | |
:param reader_opts: (optional) tf.python_io.TFRecordOptions to use when constructing the record iterator | |
""" | |
i = 0 |
import logging | |
import numpy as np | |
import tensorflow as tf | |
from tensorflow.contrib import layers | |
GO_TOKEN = 0 | |
END_TOKEN = 1 | |
UNK_TOKEN = 2 |
# Implementation of a rotating buffer on the GPU of size 2. | |
import threading | |
import tensorflow as tf | |
from tensorflow.python.client import timeline | |
import numpy as np | |
import time | |
params = { | |
'batch_size': 128, | |
'seg_len': 4000, |
This method avoids merge conflicts if you have periodically pulled master into your branch. It also gives you the opportunity to squash into more than 1 commit, or to re-arrange your code into completely different commits (e.g. if you ended up working on three different features but the commits were not consecutive).
Note: You cannot use this method if you intend to open a pull request to merge your feature branch. This method requires committing directly to master.
Switch to the master branch and make sure you are up to date:
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
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