brbsix

"""Render HTML for scraping"""
# -*- coding: utf-8 -*-

import os
import sys
from contextlib import contextmanager
from multiprocessing import Pool

try:
    TimeoutError

mrdrozdov

from tf_logger import TFLogger


""" Example of using TFLogger to save train & dev statistics. To visualize
    in tensorboard simply do:
    
        tensorboard --logdir /path/to/summaries
        
    This code does depend on Tensorflow, but does not require that your model
    is built using Tensorflow. For instance, could build a model in Chainer, then

hollance

dpattison3

import commentjson
import os
import sys

import collections

import numpy as np
import scipy as scp
import scipy.misc
import tensorflow as tf

jubjamie

import tensorflow as tf
from tensorflow.python.platform import gfile
with tf.Session() as sess:
    model_filename ='PATH_TO_PB.pb'
    with gfile.FastGFile(model_filename, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        g_in = tf.import_graph_def(graph_def)
LOGDIR='/logs/tests/1/'
train_writer = tf.summary.FileWriter(LOGDIR)

AdilsonFuxe

If your pods are showing ErrImagePull, ErrImageNeverPull, or ImagePullBackOff errors after running kubectl apply, the simplest solution is to provide an imagePullPolicy to the pod.

First, run kubectl delete -f infra/k8s/

Then, update your pod manifest:

spec:
  containers:
    - name: posts
      image: cygnet/posts:0.0.1

Fweeb

Rendering in Blender on a machine with multiple GPUs

So here's the premise: For scenes that take around a minute or less to render, performance is actually worse if you render on all of the cards with a single instance of Blender. This is because (AFAIK) there's a bit of additional time necessary to collect the render results from each card and stitch them together. That time is a fixed short duration, so it's negligible on larger/longer render jobs. However, on shorter render jobs, the 'stitch time' has a much more significant impact.

I ran into this with a machine I render on that has 4 Quadro K6000s in it. To render animations, I ended up writing a few little scripts to facilitate launching 4 separate instances of Blender, each one tied to one GPU. Overall rendertime was much shorter with that setup than one instance of Blender using all 4 GPUs.

The setup works basically like this... I have a the following Python script (it can be anywhere on your hard drive, so long as you remember the path to it).

carlthome

omoindrot

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

iangreenleaf

Rails naming conventions

General Ruby conventions

Class names are CamelCase.

Methods and variables are snake_case.

Methods with a ? suffix will return a boolean.