Collin Kindrom colllin

These notes mostly follow a tutorial on installing NVIDIA and CUDA to avoid collisions with X servers.

https://medium.com/@DJVJallday/a-how-to-on-deep-reinforcement-learning-setup-aws-with-keras-tensorflow-openai-gym-and-jupyter-88bc0cc67e02

Backup Links:

https://davidsanwald.github.io/2016/11/13/building-tensorflow-with-gpu-support.html
forgets to discuss the --overide --no-opengl-libs when installing cuda: https://gist.github.com/wangruohui/df039f0dc434d6486f5d4d098aa52d07

Open questions:

Install NVIDIA drivers
1. Find NVIDIA driver download link for your system at http://www.nvidia.com/Download/index.aspx?lang=en-us
2. wget -P ~/Downloads/ http://us.download.nvidia.com/tesla/390.46/NVIDIA-Linux-x86_64-390.46.run
3. sudo rm /etc/X11/xorg.conf # It's ok if this doesn't exist
4. NVIDIA will clash with the nouveau driver so deactivate it:
```
$ sudo vim /etc/modprobe.d/blacklist-nouveau.conf
```

Development Setup

$ sudo add-apt-repository ppa:jonathonf/python-3.6
$ sudo apt update
$ sudo apt install python3.6 python3.6-dev
$ wget https://bootstrap.pypa.io/get-pip.py
$ python3.6 get-pip.py
$ rm get-pip.py
$ sudo pip3.6 install pipenv

Prerequisites

Write your training script so that it can be killed, and then automatically resumes from the beginning of the current epoch when restarted. (See train-example.py for an example training loop incorporating these recommendations.)
- Save checkpoints at every epoch... (See utils.py for save_training_state helper function.)
  - model(s)
  - optimizer(s)
  - any hyperparameter schedules — I usually write the epoch number to a JSON file and compute the hyperparameter schedules as a function of the epoch number.
- At the beginning of training, check for any saved training checkpoints and load all relevent info (models, optimizers, hyperparameter schedules). (See utils.py for load_training_state helper function.)
- Consider using smaller epochs by limiting the number of batches pulled from your (shuffled) dataloader during each epoch.
This will cause your trai

	# From https://github.com/vlimant/caltech-jupyter/blob/0b113c4aac26d41a35c82c8214351b89b6e80ae5/jupyter/jupyter_notebook_config.py

	import os
	import json
	# Configuration file for jupyter-notebook.

	#------------------------------------------------------------------------------
	# Configurable configuration
	#------------------------------------------------------------------------------

	$ gdalsrsinfo source.shp

	PROJ.4 : '+proj=utm +zone=34 +south +datum=WGS84 +units=m +no_defs '

	OGC WKT :
	PROJCS["WGS_1984_UTM_Zone_34S",
	GEOGCS["GCS_WGS_1984",
	DATUM["WGS_1984",
	SPHEROID["WGS_84",6378137,298.257223563]],
	PRIMEM["Greenwich",0],

	"""
	Dynamic Routing Between Capsules
	https://arxiv.org/abs/1710.09829
	"""

	import torch
	import torch.nn as nn
	import torch.optim as optim
	import torch.nn.functional as F
	import torchvision.transforms as transforms

	# From https://gist.github.com/colllin/c02319fe3202470cc4d0a0b73cdbd1a6

	#!/usr/bin/env python
	###############################################################################
	# $Id$
	#
	# Project: GDAL2Tiles, Google Summer of Code 2007 & 2008
	# Global Map Tiles Classes
	# Purpose: Convert a raster into TMS tiles, create KML SuperOverlay EPSG:4326,
	# generate a simple HTML viewers based on Google Maps and OpenLayers

	# Based on https://github.com/pytorch/pytorch/pull/3740
	import torch
	import math

	class AdamW(torch.optim.Optimizer):
	"""Implements AdamW algorithm.

	It has been proposed in `Fixing Weight Decay Regularization in Adam`_.

	Arguments:

	# xvdg1 and xvdh1 are 2 attached volumes; md0 will be the (virtual) raid volume
	mdadm -C /dev/md0 -l raid0 -c 64 -n 2 /dev/xvdg1 /dev/xvdh1
	mdadm -E /dev/xvd[g-h]1
	mdadm --detail /dev/md0
	mkfs.ext4 /dev/md0
	df -h
	mount /dev/md0 /mnt/