Andrew Luo aluo-x

## resample.py
"""Good differentiable image resampling for PyTorch."""

from functools import update_wrapper
import math

import torch
from torch.nn import functional as F


def sinc(x):

## ddp_notes.md

      
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                Multi-node-training on slurm with PyTorch
              
          
    Multi-node-training on slurm with PyTorch

What's this?


A simple note for how to start multi-node-training on slurm scheduler with PyTorch.
Useful especially when scheduler is too busy that you cannot get multiple GPUs allocated,
or you need more than 4 GPUs for a single job.
Requirement: Have to use PyTorch DistributedDataParallel(DDP) for this purpose.
Warning: might need to re-factor your own code.
Warning: might be secretly condemned by your colleagues because using too many GPUs.


## tensorboard_logging.py
"""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

## convert-aax-to-m4a.cmd
@echo off
setlocal enableextensions
chcp 65001
set input=%1
set device=%2
set loglevel=quiet
for %%i in ("%~f1") do set dirname=%%~dpi
for %%i in ("%~f1") do set extension=%%~xi

set tempfile=%dirname%%RANDOM%-temp.m4a

## load_jpeg_with_tensorflow.py
# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed

## tmux_local_install.sh
#!/bin/bash

# Script for installing tmux on systems where you don't have root access.
# tmux will be installed in $HOME/local/bin.
# It's assumed that wget and a C/C++ compiler are installed.

# exit on error
set -e

TMUX_VERSION=1.8
	"""Good differentiable image resampling for PyTorch."""

	from functools import update_wrapper
	import math

	import torch
	from torch.nn import functional as F


	def sinc(x):
	"""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
	@echo off
	setlocal enableextensions
	chcp 65001
	set input=%1
	set device=%2
	set loglevel=quiet
	for %%i in ("%~f1") do set dirname=%%~dpi
	for %%i in ("%~f1") do set extension=%%~xi

	set tempfile=%dirname%%RANDOM%-temp.m4a
	# Typical setup to include TensorFlow.
	import tensorflow as tf

	# Make a queue of file names including all the JPEG images files in the relative
	# image directory.
	filename_queue = tf.train.string_input_producer(
	tf.train.match_filenames_once("./images/*.jpg"))

	# Read an entire image file which is required since they're JPEGs, if the images
	# are too large they could be split in advance to smaller files or use the Fixed
	#!/bin/bash

	# Script for installing tmux on systems where you don't have root access.
	# tmux will be installed in $HOME/local/bin.
	# It's assumed that wget and a C/C++ compiler are installed.

	# exit on error
	set -e

	TMUX_VERSION=1.8