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 === Use conda environment to run a python script ===
SHELL=/bin/bash
CONDA_PREFIX=/home/code/packages/miniconda3
CONDA_INIT="/home/code/packages/miniconda3/etc/conda/activate.d/proj4-activate.sh"
PYTHON=/home/code/packages/miniconda3/bin/python
12 13 * * * . $CONDA_INIT ; $PYTHON <python file>

 === Use date utility for naming a log file ===
DATEVAR="date +%Y%m%d_%H%M"
17 * * * * echo "logs_$($DATEVAR).txt"

jainxy

=======================
FLASK module ->
from flask import Flask, g, request, redirect, url_for
=======================

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## Save tf model under graph-model

saver = tf.train.Saver() # general saver object
#saves a model every 2 hours and maximum 4 latest models are saved.
saver = tf.train.Saver(max_to_keep=4, keep_checkpoint_every_n_hours=2)
saver = tf.train.Saver([w1,w2]) # save a list of variables

saver.save(sess, 'my-test-model') / saver.save(sess, 'my_test_model',global_step=1000)
saver.save(sess, 'my-model', global_step=step,write_meta_graph=False) # dont save the model graph

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$ sudo mkdir -p /var/www/test.com/public_html
$ sudo mkdir -p /var/www/example.com/public_html

$ sudo chown -R $USER:$USER /var/www/example.com/public_html
$ sudo chown -R $USER:$USER /var/www/test.com/public_html

$ sudo chmod -R 755 /var/www

Create the virtual hosts configuration files for our two sites
$ sudo cp /etc/apache2/sites-available/000-default.conf /etc/apache2/sites-available/test.com.conf

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jainxy

import cv2  # still used to save images out
import os
import numpy as np
from decord import VideoReader
from decord import cpu, gpu


def extract_frames(video_path, frames_dir, overwrite=False, start=-1, end=-1, every=1):
    """
    Extract frames from a video using decord's VideoReader

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from concurrent.futures import ProcessPoolExecutor, as_completed
import cv2
import multiprocessing
import os
import sys


def print_progress(iteration, total, prefix='', suffix='', decimals=3, bar_length=100):
    """
    Call in a loop to create standard out progress bar

jainxy

Let machine M be the Main machine with the repo, and A the Auxiliary machine which wants to help out.

First time setup

1. Machine M creates bundle with complete repo:

git bundle create repo.bundle HEAD master

2. M sends repo.bundle to A.
3. A clones repo from bundle:

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# Which attributes/features to choose?
# Which model to use?
# Tune/optimize the chosen model for the best performance
# Ensuring the trained model will generalize to unseen data
# Estimate performance of the trained model on unseen data

# imports
import sklearn
import IPython.display
import matplotlib.pyplot as plt

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"""
    Training
    Validation on a holdout set generated from the original training data
    Evaluation on the test data
 - correct and test batch generation
 - Normalize input by 255?
 - add batchnorm layers? use model(x, training=False) then
 - tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size) ? dataset = dataset.cache()?
 - get_compiled_model()
 - test last batch having non-dividing batch-size aka residual batch issue