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import airsimneurips
import threading
import time
import random
class ReproduceResetRaceCondition():
def __init__(self, drone_name = "drone_1"):
self.airsim_client = airsimneurips.MultirotorClient()
self.airsim_client_2 = airsimneurips.MultirotorClient()
self.airsim_client_3 = airsimneurips.MultirotorClient()
"ClockSpeed": 1,
"SeeDocsAt": "",
"SettingsVersion": 1.2,
"SimMode": "Multirotor",
"ViewMode": "NoDisplay",
"Vehicles": {
"drone_1": {
"Cameras": {
"fpv_cam": {
mkdir frames
ffmpeg -i video.mp4  -r 5 'frames/frame-%05d.png'
mogrify -resize 360x640 *.png
convert -delay 10 -loop 0 *.png myimage.gif
madratman@madratman-HP-Z4-G4-Workstation:~$ ./Downloads/Blocks/ -windowed
Increasing per-process limit of core file size to infinity.
LogPlatformFile: Using cached read wrapper
LogInit: Display: RandInit(754615442) SRandInit(754615442).
LogTaskGraph: Started task graph with 5 named threads and 17 total threads with 3 sets of task threads.
LogStats: Stats thread started at 0.022213
LogPluginManager: Mounting plugin AirSim
LogPluginManager: Mounting plugin PhysXVehicles
LogPluginManager: Mounting plugin Paper2D
LogPluginManager: Mounting plugin LightPropagationVolume
% Totally not inspired by LSD-SLAM
A 3D rigid body transform, $\mathbf{G} \in SE(3)$ :
&\mathbf{G} =
* Software License Agreement (BSD License)
* Copyright (c) 2010, Rice University
* All rights reserved.
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
madratman /
Created November 16, 2017 05:45
No BS cluster instructions
  • Login to cluster ssh

Request a node (use any one of these) srun -w clamps -t 5-00:00 -p gpu --gres=gpu:1 --pty /bin/bash srun -w roberto -t 5-00:00 -p gpu --gres=gpu:1 --pty /bin/bash

  • to run your docker image: nvidia-docker run -it --rm --ipc=host -e CUDA_VISIBLE_DEVICES=`echo $CUDA_VISIBLE_DEVICES` -v /data/datasets:/data/datasets -v /storage2/datasets:/storage2/datasets -v /local:/local -v /home/$USER:/home/$USER -v /storage1:/storage1 nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
import torch
import pytorch_autoencoder
list_of_numbers = get them from cpp
sum = sum
model = pytorch_autoencoder.ConvCVAENet(state_dim = 2,
condition_dim = (200, 200, 2),
latent_dim = 3,
encoder_hidden_dim = 512,

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