soumith

import os
import threading
from flask import Flask
import socketio
from flask_cors import cross_origin, CORS
import dlevent
import logging
import json
import random
try:

soumith

version: 0.2.1
command: /home/bluebox/miniconda3/bin/ghstack
status: e01eae026 "use event dispatcher in dashboard and agents"

$ git remote get-url origin
Using selector: EpollSelector
git@github.com:fairinternal/minecraft.git

# POST https://api.github.com/graphql
Request GraphQL query:

soumith

Fatal Python error: Aborted

Thread 0x00007fdbce852700 (most recent call first):
  File "/usr/lib/python3.6/http/client.py", line 368 in begin
  File "/usr/lib/python3.6/http/client.py", line 1345 in getresponse
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1170 in single_request
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1154 in request
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1458 in __request
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1112 in __call__
  File "/home/soumith/pyrobot_catkin_ws/src/ros_comm/clients/rospy/src/rospy/impl/tcpros_base.py", line 89 in _is_use_tcp_keepalive

soumith

Fatal Python error: Aborted

Thread 0x00007fde70f24700 (most recent call first):
  File "/usr/lib/python3.6/http/client.py", line 1221 in putheader
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1309 in send_content
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1279 in send_request
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1166 in single_request
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1154 in request
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1452 in __request
  File "/usr/lib/python3.6/xmlrpc/client.py", line 1112 in __call__

soumith

# *****************************************************************************
#  Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
#  Redistribution and use in source and binary forms, with or without
#  modification, are permitted provided that the following conditions are met:
#      * Redistributions of source code must retain the above copyright
#        notice, this list of conditions and the following disclaimer.
#      * Redistributions in binary form must reproduce the above copyright
#        notice, this list of conditions and the following disclaimer in the
#        documentation and/or other materials provided with the distribution.

soumith

### First, tokenize the input
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-cased', do_basic_tokenize=False)

text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"

# Tokenized input
indexed_tokens = tokenizer.encode(text_1, text_2, add_special_tokens=True)
### Get the hidden states computed by `BertModel`

soumith

import numpy as np
import os
import time
import warnings
import pickle
# from accimage import Image
from PIL import Image
import io

try:

soumith

#include <torch/torch.h>
#include <iostream>
#include <ATen/Parallel.h>
#include <ATen/ATen.h>

// using namespace at;
using namespace torch;

void submodular_select(Tensor candidate_points, Tensor features_done, Tensor features)
{

soumith

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F

class simpnet_imgnet_drpall(nn.Module):
    """
    args: classes 
    scale 
    network_idx (0,1):simpnet5m, simpnet8m

soumith

op_version_set = 0
def forward(self,
    input_1: Tensor) -> Tensor:
  input_2 = torch._convolution(input_1, self.features.conv0.weight, None, [2, 2], [3, 3], [1, 1], False, [0, 0], 1, False, False, True)
  input_3 = torch.batch_norm(input_2, self.features.norm0.weight, self.features.norm0.bias, self.features.norm0.running_mean, self.features.norm0.running_var, False, 0., 1.0000000000000001e-05, True)
  input_4 = torch.threshold_(input_3, 0., 0.)
  input_5, _0 = torch.max_pool2d_with_indices(input_4, [3, 3], [2, 2], [1, 1], [1, 1], False)
  input_6 = torch.batch_norm(input_5, self.features.denseblock1.denselayer1.norm1.weight, self.features.denseblock1.denselayer1.norm1.bias, self.features.denseblock1.denselayer1.norm1.running_mean, self.features.denseblock1.denselayer1.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
  input_7 = torch.threshold_(input_6, 0., 0.)
  input_8 = torch._convolution(input_7, self.features.denseblock1.denselayer1.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0],