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Engine shutdown #7958

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Harold-Zhang opened this issue Sep 20, 2017 · 13 comments
Closed

Engine shutdown #7958

Harold-Zhang opened this issue Sep 20, 2017 · 13 comments
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Pending Requester Info Unclear Error/Doc

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@Harold-Zhang
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Harold-Zhang commented Sep 20, 2017 •
edited

Environment info

Operating System:
Ubuntu 14.04
Compiler:
gcc 4.8.4
Package used (Python/R/Scala/Julia):
Python 2.7
MXNet version:
The latest version
GPU:
Tesla K40m

Error Message:

[19:43:24] src/nnvm/legacy_json_util.cc:198: Symbol successfully upgraded!
2017-09-19 19:43:45,834 - Epoch[0] Batch [20]	Speed: 4.56 samples/sec	accuracy=0.964286
2017-09-19 19:44:03,379 - Epoch[0] Batch [40]	Speed: 4.56 samples/sec	accuracy=1.000000
2017-09-19 19:44:20,942 - Epoch[0] Batch [60]	Speed: 4.56 samples/sec	accuracy=1.000000
2017-09-19 19:44:38,747 - Epoch[0] Batch [80]	Speed: 4.49 samples/sec	accuracy=1.000000
2017-09-19 19:44:56,319 - Epoch[0] Batch [100]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:45:13,862 - Epoch[0] Batch [120]	Speed: 4.56 samples/sec	accuracy=1.000000
2017-09-19 19:45:31,494 - Epoch[0] Batch [140]	Speed: 4.54 samples/sec	accuracy=1.000000
2017-09-19 19:45:49,110 - Epoch[0] Batch [160]	Speed: 4.54 samples/sec	accuracy=1.000000
2017-09-19 19:46:06,677 - Epoch[0] Batch [180]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:46:24,257 - Epoch[0] Batch [200]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:46:41,886 - Epoch[0] Batch [220]	Speed: 4.54 samples/sec	accuracy=1.000000
2017-09-19 19:46:59,501 - Epoch[0] Batch [240]	Speed: 4.54 samples/sec	accuracy=1.000000
2017-09-19 19:47:17,085 - Epoch[0] Batch [260]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:47:34,667 - Epoch[0] Batch [280]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:47:52,273 - Epoch[0] Batch [300]	Speed: 4.54 samples/sec	accuracy=1.000000
2017-09-19 19:48:09,861 - Epoch[0] Batch [320]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:48:27,503 - Epoch[0] Batch [340]	Speed: 4.53 samples/sec	accuracy=1.000000
2017-09-19 19:48:45,085 - Epoch[0] Batch [360]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:49:02,700 - Epoch[0] Batch [380]	Speed: 4.54 samples/sec	accuracy=1.000000
2017-09-19 19:49:20,358 - Epoch[0] Batch [400]	Speed: 4.53 samples/sec	accuracy=1.000000
2017-09-19 19:49:37,943 - Epoch[0] Batch [420]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:49:55,530 - Epoch[0] Batch [440]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:50:13,105 - Epoch[0] Batch [460]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:50:30,683 - Epoch[0] Batch [480]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:50:48,265 - Epoch[0] Batch [500]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:51:05,903 - Epoch[0] Batch [520]	Speed: 4.54 samples/sec	accuracy=1.000000
2017-09-19 19:51:23,492 - Epoch[0] Batch [540]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:51:41,176 - Epoch[0] Batch [560]	Speed: 4.52 samples/sec	accuracy=1.000000
2017-09-19 19:51:58,766 - Epoch[0] Batch [580]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:52:16,347 - Epoch[0] Batch [600]	Speed: 4.55 samples/sec	accuracy=1.000000
2017-09-19 19:52:33,933 - Epoch[0] Batch [620]	Speed: 4.55 samples/sec	accuracy=1.000000
[19:52:38] /home/harold/mxnet/dmlc-core/include/dmlc/logging.h:308: [19:52:38] src/io/image_io.cc:165: Check failed: !dst.empty() 

Stack trace returned 10 entries:
[bt] (0) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x3c) [0x7f9450196c8c]
[bt] (1) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet2io12ImdecodeImplEibPvmPNS_7NDArrayE+0x67a) [0x7f9451a3eefa]
[bt] (2) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZNSt17_Function_handlerIFvN5mxnet10RunContextENS0_6engine18CallbackOnCompleteEEZNS0_6Engine8PushSyncESt8functionIFvS1_EENS0_7ContextERKSt6vectorIPNS2_3VarESaISC_EESG_NS0_10FnPropertyEiPKcEUlS1_S3_E_E9_M_invokeERKSt9_Any_dataS1_S3_+0x23) [0x7f9450284963]
[bt] (3) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet6engine11NaiveEngine9PushAsyncESt8functionIFvNS_10RunContextENS0_18CallbackOnCompleteEEENS_7ContextERKSt6vectorIPNS0_3VarESaISA_EESE_NS_10FnPropertyEiPKc+0x8b) [0x7f94519daf4b]
[bt] (4) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet6Engine8PushSyncESt8functionIFvNS_10RunContextEEENS_7ContextERKSt6vectorIPNS_6engine3VarESaIS9_EESD_NS_10FnPropertyEiPKc+0x124) [0x7f9450285814]
[bt] (5) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet2io8ImdecodeERKN4nnvm9NodeAttrsERKSt6vectorINS_7NDArrayESaIS6_EEPS8_+0xc90) [0x7f9451a40a10]
[bt] (6) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_Z20ImperativeInvokeImplRKN5mxnet7ContextEON4nnvm9NodeAttrsEPSt6vectorINS_7NDArrayESaIS7_EESA_PS6_IbSaIbEESD_+0x3cf) [0x7f94519a91ff]
[bt] (7) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_Z22MXImperativeInvokeImplPviPS_PiPS0_iPPKcS5_+0x25b) [0x7f94519bb43b]
[bt] (8) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(MXImperativeInvokeEx+0x2f) [0x7f94519a982f]
[bt] (9) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7f9468629adc]

Traceback (most recent call last):
  File "train.py", line 142, in <module>
    image_shape='3,224,224', epoch=0, num_epoch=args.num_epoch, kv=kv)
  File "train.py", line 106, in train_model
    epoch_end_callback=checkpoint)
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/module/base_module.py", line 491, in fit
    next_data_batch = next(data_iter)
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/image/image.py", line 1151, in next
    data = self.imdecode(s)
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/image/image.py", line 1183, in imdecode
    return imdecode(s)
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/image/image.py", line 136, in imdecode
    return _internal._cvimdecode(buf, *args, **kwargs)
  File "<string>", line 16, in _cvimdecode
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/_ctypes/ndarray.py", line 92, in _imperative_invoke
    ctypes.byref(out_stypes)))
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/base.py", line 143, in check_call
    raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [19:52:38] src/io/image_io.cc:165: Check failed: !dst.empty() 

Stack trace returned 10 entries:
[bt] (0) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x3c) [0x7f9450196c8c]
[bt] (1) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet2io12ImdecodeImplEibPvmPNS_7NDArrayE+0x67a) [0x7f9451a3eefa]
[bt] (2) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZNSt17_Function_handlerIFvN5mxnet10RunContextENS0_6engine18CallbackOnCompleteEEZNS0_6Engine8PushSyncESt8functionIFvS1_EENS0_7ContextERKSt6vectorIPNS2_3VarESaISC_EESG_NS0_10FnPropertyEiPKcEUlS1_S3_E_E9_M_invokeERKSt9_Any_dataS1_S3_+0x23) [0x7f9450284963]
[bt] (3) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet6engine11NaiveEngine9PushAsyncESt8functionIFvNS_10RunContextENS0_18CallbackOnCompleteEEENS_7ContextERKSt6vectorIPNS0_3VarESaISA_EESE_NS_10FnPropertyEiPKc+0x8b) [0x7f94519daf4b]
[bt] (4) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet6Engine8PushSyncESt8functionIFvNS_10RunContextEEENS_7ContextERKSt6vectorIPNS_6engine3VarESaIS9_EESD_NS_10FnPropertyEiPKc+0x124) [0x7f9450285814]
[bt] (5) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet2io8ImdecodeERKN4nnvm9NodeAttrsERKSt6vectorINS_7NDArrayESaIS6_EEPS8_+0xc90) [0x7f9451a40a10]
[bt] (6) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_Z20ImperativeInvokeImplRKN5mxnet7ContextEON4nnvm9NodeAttrsEPSt6vectorINS_7NDArrayESaIS7_EESA_PS6_IbSaIbEESD_+0x3cf) [0x7f94519a91ff]
[bt] (7) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(_Z22MXImperativeInvokeImplPviPS_PiPS0_iPPKcS5_+0x25b) [0x7f94519bb43b]
[bt] (8) /usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/libmxnet.so(MXImperativeInvokeEx+0x2f) [0x7f94519a982f]
[bt] (9) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7f9468629adc]

[19:52:38] src/engine/naive_engine.cc:53: Engine shutdown

please provide the commands you have run that lead to the error.

I used the pretrained model from https://github.com/cypw/DPNs

commands:
python train.py --epoch 0 --model ./models/dpn92-extra --batch-size 4 --num-classes 2 --data-train ./lst_train.lst --image-train ./data/ --data-val ./lst_val.lst --image-val ./data/ --num-examples 2000 --lr 0.001 --gpus 0 --num-epoch 20 --save-result ./output

I have tried --batch-size 16/32, and I got the same result.

What have you tried to solve it?

At first, I got a result: An fatal error occurred in asynchronous engine operation.
According to a guide, I set environment MXNET_CUDNN_AUTOTUNE_DEFAULT=0 and MXNET_ENGINE_TYPE=NaiveEngine, then I got the above result.
@Harold-Zhang
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@piiswrong @szha

@ZiyueHuang
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It seems not relevant to the engine. src/io/image_io.cc:165: Check failed: !dst.empty()

@zhreshold
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Possibly failed in imdecode to the dst
Image not exists or image broken?

@piiswrong
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@zhreshold Could you change imdecode to imread and improve the error message to output which image is broken?

@zhreshold zhreshold mentioned this issue Sep 21, 2017
Merged
@Harold-Zhang
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Harold-Zhang commented Sep 25, 2017 •
edited by szha

@zhreshold I used a smaller test, and then I got the following result>>>

$ sh run_train.sh 
2017-09-24 20:20:16,154 - Namespace(batch_size=32, data_train='./test_train.lst', data_val='./test_val.lst', epoch=0, gpus='0', image_shape='3,224,224', image_train='./test/', image_val='./test/', kv_store='device', lr=0.001, model='./models/dpn92-extra', mom=0.9, num_classes=2, num_epoch=20, num_examples=1000, save_result='./output', wd=0.0001)
2017-09-24 20:20:16,156 - Using 1 threads for decoding...
2017-09-24 20:20:16,156 - Set enviroment variable MXNET_CPU_WORKER_NTHREADS to a larger number to use more threads.
2017-09-24 20:20:16,156 - ImageIter: loading image list ./test_train.lst...
2017-09-24 20:20:16,157 - Using 1 threads for decoding...
2017-09-24 20:20:16,157 - Set enviroment variable MXNET_CPU_WORKER_NTHREADS to a larger number to use more threads.
2017-09-24 20:20:16,157 - ImageIter: loading image list ./test_val.lst...
[20:20:16] src/nnvm/legacy_json_util.cc:190: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
[20:20:16] src/nnvm/legacy_json_util.cc:198: Symbol successfully upgraded!
**Traceback (most recent call last):
  File "train.py", line 142, in <module>
    image_shape='3,224,224', epoch=0, num_epoch=args.num_epoch, kv=kv)
  File "train.py", line 106, in train_model
    epoch_end_callback=checkpoint)
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/module/base_module.py", line 482, in fit
    next_data_batch = next(data_iter)
  File "/usr/local/lib/python2.7/dist-packages/mxnet-0.11.1-py2.7.egg/mxnet/image/image.py", line 1165, in next
    raise StopIteration
StopIteration**

I used the pretrained model(https://goo.gl/1sbov7), and I never modified any code. What should I do to debug? Thanks for your experience ~

@zhreshold
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@Harold-Zhang Can you provide the train.py ?

@Harold-Zhang
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Harold-Zhang commented Sep 26, 2017 •
edited

@zhreshold
Here is the train.py.

import argparse
import find_mxnet
import mxnet as mx
import time
import os, sys
import logging
import importlib
sys.path.insert(0, "./settings")
sys.path.insert(0, "../")

import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)

formatter = logging.Formatter('%(asctime)s - %(message)s')
console = logging.StreamHandler()
console.setFormatter(formatter)
logger.addHandler(console)

def get_fine_tune_model(sym, arg_params, num_classes, layer_name, batchsize):
    
    all_layers = sym.get_internals()
    net = all_layers[layer_name+'_output']
    net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes, name='fc')
    net = mx.symbol.SoftmaxOutput(data=net, name='softmax')

    new_args = dict({k:arg_params[k] for k in arg_params if 'fc' not in k})
    return (net, new_args)

def multi_factor_scheduler(begin_epoch, epoch_size, step=[7,14], factor=0.1):
    step_ = [epoch_size * (x-begin_epoch) for x in step if x-begin_epoch > 0]
    return mx.lr_scheduler.MultiFactorScheduler(step=step_, factor=factor) if len(step_) else None

def train_model(model, gpus, batch_size, image_shape, epoch=0, num_epoch=20, kv='device'):
    train = mx.image.ImageIter(
        batch_size          = args.batch_size,
        data_shape          = (3,224,224),        
        label_width         = 1,
        path_imglist        = args.data_train,
        path_root           = args.image_train,
        part_index          = kv.rank,
        num_parts           = kv.num_workers,
        shuffle             = True,        
        data_name           = 'data',
        label_name          = 'softmax_label',
        aug_list            = mx.image.CreateAugmenter((3,224,224),resize=224,rand_crop=True,rand_mirror=True,mean=True,std=True))

    val = mx.image.ImageIter(
        batch_size          = args.batch_size,
        data_shape          = (3,224,224),
        label_width         = 1,
        path_imglist        = args.data_val,
        path_root           = args.image_val,
        part_index          = kv.rank,
        num_parts           = kv.num_workers,       
        data_name           = 'data',
        label_name          = 'softmax_label',
        aug_list            = mx.image.CreateAugmenter((3,224,224),resize=224,mean=True,std=True))

    kv = mx.kvstore.create(args.kv_store)

    prefix = model
    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)

    (new_sym, new_args) = get_fine_tune_model(
        sym, arg_params, args.num_classes, 'flatten', args.batch_size)

    epoch_size = max(int(args.num_examples / args.batch_size / kv.num_workers), 1)
    lr_scheduler=multi_factor_scheduler(args.epoch, epoch_size)

    optimizer_params = {
            'learning_rate': args.lr,
            'momentum' : args.mom,
            'wd' : args.wd,
            'lr_scheduler': lr_scheduler}
    initializer = mx.init.Xavier(
            rnd_type='gaussian', factor_type="in", magnitude=2)

    if gpus == '':
        devs = mx.cpu()
    else:
        devs = [mx.gpu(int(i)) for i in gpus.split(',')]
        
    model = mx.mod.Module(
        context       = devs,
        symbol        = new_sym
    )

    checkpoint = mx.callback.do_checkpoint(args.save_result)

    eval_metric = ['accuracy']

    model.fit(train,
              begin_epoch=epoch,
              num_epoch=num_epoch,
              eval_data=val,
              eval_metric=eval_metric,
              kvstore=kv,
              optimizer='sgd',
              optimizer_params=optimizer_params,
              arg_params=new_args,
              aux_params=aux_params,
              initializer=initializer,
              allow_missing=True,
              batch_end_callback=mx.callback.Speedometer(args.batch_size, 20),
              epoch_end_callback=checkpoint)
    
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='score a model on a dataset')
    parser.add_argument('--model',         type=str, required=True,)
    parser.add_argument('--gpus',          type=str, default='0')
    parser.add_argument('--batch-size',    type=int, default=200)
    parser.add_argument('--epoch',         type=int, default=0)
    parser.add_argument('--image-shape',   type=str, default='3,224,224')
    parser.add_argument('--data-train',    type=str)
    parser.add_argument('--image-train',   type=str)
    parser.add_argument('--data-val',      type=str)
    parser.add_argument('--image-val',     type=str)
    parser.add_argument('--num-classes',   type=int, default=6)
    parser.add_argument('--lr',            type=float, default=0.001)
    parser.add_argument('--num-epoch',     type=int, default=20)
    parser.add_argument('--kv-store',      type=str, default='device', help='the kvstore type')
    parser.add_argument('--save-result',   type=str, help='the save path')
    parser.add_argument('--num-examples',  type=int)
    parser.add_argument('--mom',           type=float, default=0.9, help='momentum for sgd')
    parser.add_argument('--wd',            type=float, default=0.0001, help='weight decay for sgd')
    args = parser.parse_args()

    logger = logging.getLogger()
    logger.setLevel(logging.DEBUG)

    kv = mx.kvstore.create(args.kv_store)

    if not os.path.exists(args.save_result):
        os.mkdir(args.save_result)
    hdlr = logging.FileHandler(args.save_result+ '/train.log')
    hdlr.setFormatter(formatter)
    logger.addHandler(hdlr)
    logging.info(args)

    train_model(model=args.model, gpus=args.gpus, batch_size=args.batch_size,
          image_shape='3,224,224', epoch=0, num_epoch=args.num_epoch, kv=kv)

I downloaded it from here.

@Harold-Zhang
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Harold-Zhang commented Sep 30, 2017 •
edited

@zhreshold I am sorry about that I found I just @ a wrong person. I updated my mxnet code after you modified it, and then I got an error. The following code is where the error happened.

#if` (CV_MAJOR_VERSION > 2 || (CV_MAJOR_VERSION == 2 && CV_MINOR_VERSION >=4))
      cv::imdecode(buf, flag, &dst);
    + CHECK(!dst.empty()) << "Decoding failed. Invalid image file.";

Here is the error report:
/home/deeplearning/github/incubator-mxnet/dmlc-core/include/dmlc/./logging.h:308: [11:13:06] src/io/image_io.cc:160: Check failed: !dst.empty() Decoding failed. Invalid image file.
What should I do to correct this error?

@szha
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szha commented Dec 30, 2017

@apache/mxnet-committers: This issue has been inactive for the past 90 days. It has no label and needs triage.

For general "how-to" questions, our user forum (and Chinese version) is a good place to get help.

@Paul0M
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Paul0M commented Apr 26, 2018

Hi, I've got the same error.
using PIL, this can be avoid as:

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

But using mxnet.image.imread(img_path), python cannot catch the error to avoid this specific image(core dumped pops)

I wonder how to skip this image using mxnet.image.imread
core dumped pops even using PIL to skip a specific image

def somefun(net, img_path):
    img = Image.open(img_path)
    try:
        img.load()
    except Exception as e:
        print(f'[Error]:{e}\t{img_path}')
        return None 
    return(net(mx.image.imread(img_path)))  
terminate called recursively
terminate called after throwing an instance of 'dmlc::Error'
  what():  driver shutting down
Aborted (core dumped)

@Ishitori
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@Paul0M can you provide a minimal reproducible example with how to run it?

@lanking520
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@Paul0M I know it's kind of late, but I still want to reach out and see if you are still facing this issue?

@ankkhedia
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@sandeep-krishnamurthy @nswamy @anirudh2290 COuld you please close this issue due to lack of activity.

@Paul0M Please feel free to re-open in case closed in error

@szha szha closed this as completed Nov 1, 2018
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