jperl

-- original code: https://github.com/ericelliott/cuid

-- Add the "plv8" extension
create extension if not exists "plv8";

-- Add the "pgcrypto" extension
create extension if not exists "pgcrypto";
\dx

-- Connect a database

jperl

import keras.backend as K
import tensorflow as tf
from keras.layers import Layer

"""Not tested, I'll play around with GANs soon with it."""


class CoordConv2D(Layer):
    def __init__(self, channel, kernel, padding='valid', **kwargs):
        self.layer = Conv2D(channel, kernel, padding=padding)

jperl

#!/bin/bash

docker run -it --rm --privileged --net host \
    -v /tmp/.X11-unix:/tmp/.X11-unix \
    -v $PWD:$HOME/workspace \
    -v $HOME/.Xauthority:$HOME/.Xauthority \
    -e HOME=$HOME \
    -e DISPLAY=$DISPLAY \
    --entrypoint /usr/sbin/ffmpeg \
    kfei/ffmpeg $*

jperl

#!/bin/bash

docker run -it --rm --privileged --net host \
    -v /tmp/.X11-unix:/tmp/.X11-unix \
    -v $PWD:$HOME/workspace \
    -v $HOME/.Xauthority:$HOME/.Xauthority \
    -e HOME=$HOME \
    -e DISPLAY=$DISPLAY \
    --entrypoint /usr/sbin/ffmpeg \
    kfei/ffmpeg $*

jperl

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.contrib.distributions import Bernoulli


class VariationalDense:
    """Variational Dense Layer Class"""
    def __init__(self, n_in, n_out, model_prob, model_lam):
        self.model_prob = model_prob

jperl

FLAGS = flags.FLAGS

if FLAGS.log_dir:
  if not os.path.exists(FLAGS.log_dir):
    os.makedirs(FLAGS.log_dir)
  logging.get_absl_handler().use_absl_log_file()

jperl

"""
Clean and simple Keras implementation of network architectures described in:
    - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf).
    - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).
    
Python 3.
"""

from keras import layers
from keras import models

jperl

# cuda

export PATH=/usr/local/cuda-9.2/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-9.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH

# python

alias env='source ./env/bin/activate'

jperl

"nvidia-smi has failed because it couldn't communicate with the NVIDIA driver"

• Disable Secure Boot

jperl

import six
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.training import training_util
from tensorflow.python.training.session_run_hook import SessionRunArgs


class RayTuneReportingHook(tf.train.SessionRunHook):
    def __init__(self, params, reporter):
        self.reporter = reporter