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Troubleshooting

Import TensorFlow failed during installation

  1. Is TensorFlow installed?

If you see the error message below, it means that TensorFlow is not installed. Please install TensorFlow before installing Horovod.

error: import tensorflow failed, is it installed?

Traceback (most recent call last):
  File "/tmp/pip-OfE_YX-build/setup.py", line 29, in fully_define_extension
    import tensorflow as tf
ImportError: No module named tensorflow
  1. Are the CUDA libraries available?

If you see the error message below, it means that TensorFlow cannot be loaded. If you're installing Horovod into a container on a machine without GPUs, you may use CUDA stub drivers to work around the issue.

error: import tensorflow failed, is it installed?

Traceback (most recent call last):
  File "/tmp/pip-41aCq9-build/setup.py", line 29, in fully_define_extension
    import tensorflow as tf
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/__init__.py", line 24, in <module>
    from tensorflow.python import *
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/__init__.py", line 49, in <module>
    from tensorflow.python import pywrap_tensorflow
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 52, in <module>
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
    _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
ImportError: libcuda.so.1: cannot open shared object file: No such file or directory

To use CUDA stub drivers:

# temporary add stub drivers to ld.so.cache
$ ldconfig /usr/local/cuda/lib64/stubs

# install Horovod, add other HOROVOD_* environment variables as necessary
$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl pip install --no-cache-dir horovod

# revert to standard libraries
$ ldconfig

MPI is not found during installation

  1. Is MPI in PATH?

If you see the error message below, it means mpicxx was not found in PATH. Typically mpicxx is located in the same directory as mpirun. Add a directory containing mpicxx to PATH before installing Horovod.

error: mpicxx -show failed, is mpicxx in $PATH?

Traceback (most recent call last):
  File "/tmp/pip-dQ6A7a-build/setup.py", line 70, in get_mpi_flags
    ['mpicxx', '-show'], universal_newlines=True).strip()
  File "/usr/lib/python2.7/subprocess.py", line 566, in check_output
    process = Popen(stdout=PIPE, *popenargs, **kwargs)
  File "/usr/lib/python2.7/subprocess.py", line 710, in __init__
    errread, errwrite)
  File "/usr/lib/python2.7/subprocess.py", line 1335, in _execute_child
    raise child_exception
OSError: [Errno 2] No such file or directory

To use custom MPI directory:

$ export PATH=$PATH:/path/to/mpi/bin
$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl pip install --no-cache-dir horovod
  1. Are MPI libraries added to $LD_LIBRARY_PATH or ld.so.conf?

If you see the error message below, it means mpicxx was not able to load some of the MPI libraries. If you recently installed MPI, make sure that the path to MPI libraries is present the $LD_LIBRARY_PATH environment variable, or in the /etc/ld.so.conf file.

mpicxx: error while loading shared libraries: libopen-pal.so.40: cannot open shared object file: No such file or directory
error: mpicxx -show failed (see error below), is MPI in $PATH?
Note: If your version of MPI has a custom command to show compilation flags, please specify it with the HOROVOD_MPICXX_SHOW environment variable.

Traceback (most recent call last):
File "/tmp/pip-build-wrtVwH/horovod/setup.py", line 107, in get_mpi_flags
shlex.split(show_command), universal_newlines=True).strip()
File "/usr/lib/python2.7/subprocess.py", line 574, in check_output
raise CalledProcessError(retcode, cmd, output=output)
CalledProcessError: Command '['mpicxx', '-show']' returned non-zero exit status 127

If you have installed MPI in a user directory, you can add the MPI library directory to $LD_LIBRARY_PATH:

$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/mpi/lib

If you have installed MPI in a non-standard system location (i.e. not /usr or /usr/local), you should add it to the /etc/ld.so.conf file:

$ echo /path/to/mpi/lib | sudo tee -a /etc/ld.so.conf

Additionally, if you have installed MPI in a system location, you should run sudo ldconfig after installation to register libraries in the cache:

$ sudo ldconfig

Error during installation: invalid conversion from ‘const void*’ to ‘void*’ [-fpermissive]

If you see the error message below, it means that your MPI is likely outdated. We recommend installing Open MPI >=4.0.0.

Note: Prior to installing a new version of Open MPI, don't forget to remove your existing MPI installation.

horovod/tensorflow/mpi_ops.cc: In function ‘void horovod::tensorflow::{anonymous}::PerformOperation(horovod::tensorflow::{anonymous}::TensorTable&, horovod::tensorflow::MPIResponse)’:
horovod/tensorflow/mpi_ops.cc:802:79: # error: invalid conversion from ‘const void*’ to ‘void*’ [-fpermissive]
                                  recvcounts, displcmnts, dtype, MPI_COMM_WORLD);
                                                                               ^
In file included from horovod/tensorflow/mpi_ops.cc:38:0:
/usr/anaconda2/include/mpi.h:633:5: error:   initializing argument 1 of ‘int MPI_Allgatherv(void*, int, MPI_Datatype, void*, int*, int*, MPI_Datatype, MPI_Comm)’ [-fpermissive]
 int MPI_Allgatherv(void* , int, MPI_Datatype, void*, int *, int *, MPI_Datatype, MPI_Comm);
     ^
horovod/tensorflow/mpi_ops.cc:1102:45: error: invalid conversion from ‘const void*’ to ‘void*’ [-fpermissive]
                               MPI_COMM_WORLD))
                                             ^

Error during installation: fatal error: pyconfig.h: No such file or directory

If you see the error message below, it means that you need to install Python headers.

build/horovod/torch/mpi_lib/_mpi_lib.c:22:24: fatal error: pyconfig.h: No such file or directory
 #  include <pyconfig.h>
                        ^
compilation terminated.

You can do this by installing a python-dev or python3-dev package. For example, on a Debian or Ubuntu system:

$ sudo apt-get install python-dev

NCCL 2 is not found during installation

If you see the error message below, it means NCCL 2 was not found in the standard libraries location. If you have a directory where you installed NCCL 2 which has both include and lib directories containing nccl.h and libnccl.so respectively, you can pass it via HOROVOD_NCCL_HOME environment variable. Otherwise you can specify them separately via HOROVOD_NCCL_INCLUDE and HOROVOD_NCCL_LIB environment variables.

build/temp.linux-x86_64-2.7/test_compile/test_nccl.cc:1:18: fatal error: nccl.h: No such file or directory
 #include <nccl.h>
                  ^
compilation terminated.
error: NCCL 2.0 library or its later version was not found (see error above).
Please specify correct NCCL location via HOROVOD_NCCL_HOME environment variable or combination of HOROVOD_NCCL_INCLUDE and HOROVOD_NCCL_LIB environment variables.

HOROVOD_NCCL_HOME - path where NCCL include and lib directories can be found
HOROVOD_NCCL_INCLUDE - path to NCCL include directory
HOROVOD_NCCL_LIB - path to NCCL lib directory

For example:

$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl pip install --no-cache-dir horovod

Or:

$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_INCLUDE=/path/to/nccl/include HOROVOD_NCCL_LIB=/path/to/nccl/lib pip install --no-cache-dir horovod

Pip install: no such option: --no-cache-dir

If you see the error message below, it means that your version of pip is out of date. You can remove the --no-cache-dir flag since your version of pip does not do caching. The --no-cache-dir flag is added to all examples to ensure that when you change Horovod compilation flags, it will be rebuilt from source and not just reinstalled from the pip cache, which is modern pip's default behavior.

$ pip install --no-cache-dir horovod

Usage:
  pip install [options] <requirement specifier> ...
  pip install [options] -r <requirements file> ...
  pip install [options] [-e] <vcs project url> ...
  pip install [options] [-e] <local project path> ...
  pip install [options] <archive url/path> ...

no such option: --no-cache-dir

For example:

$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl pip install --no-cache-dir horovod

ncclAllReduce failed: invalid data type

If you see the error message below during the training, it means that Horovod was linked to the wrong version of NCCL library.

UnknownError (see above for traceback): ncclAllReduce failed: invalid data type
         [[Node: DistributedMomentumOptimizer_Allreduce/HorovodAllreduce_gradients_AddN_2_0 = HorovodAllreduce[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](gradients/AddN_2)]]
         [[Node: train_op/_653 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1601_train_op", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:
0"]()]]

If you're using Anaconda or Miniconda, you most likely have the nccl package installed. The solution is to remove the package and reinstall Horovod:

$ conda remove nccl
$ pip uninstall -y horovod
$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl pip install --no-cache-dir horovod

transport/p2p.cu:431 WARN failed to open CUDA IPC handle : 30 unknown error

If you see the error message below during the training with -x NCCL_DEBUG=INFO, it likely means that multiple servers share the same hostname.

node1:22671:22795 [1] transport/p2p.cu:431 WARN failed to open CUDA IPC handle : 30 unknown error

MPI and NCCL rely on hostnames to distinguish between servers, so you should make sure that every server has a unique hostname.

Running out of memory

If you notice that your program is running out of GPU memory and multiple processes are being placed on the same GPU, it's likely that your program (or its dependencies) create a tf.Session that does not use the config that pins specific GPU.

If possible, track down the part of program that uses these additional tf.Sessions and pass the same configuration.

Alternatively, you can place following snippet in the beginning of your program to ask TensorFlow to minimize the amount of memory it will pre-allocate on each GPU:

small_cfg = tf.ConfigProto()
small_cfg.gpu_options.allow_growth = True
with tf.Session(config=small_cfg):
    pass

As a last resort, you can replace setting config.gpu_options.visible_device_list with different code:

# Pin GPU to be used
import os
os.environ['CUDA_VISIBLE_DEVICES'] = str(hvd.local_rank())

Note: Setting CUDA_VISIBLE_DEVICES is incompatible with config.gpu_options.visible_device_list.

Setting CUDA_VISIBLE_DEVICES has additional disadvantage for GPU version - CUDA will not be able to use IPC, which will likely cause NCCL and MPI to fail. In order to disable IPC in NCCL and MPI and allow it to fallback to shared memory, use: * export NCCL_P2P_DISABLE=1 for NCCL. * --mca btl_smcuda_use_cuda_ipc 0 flag for OpenMPI and similar flags for other vendors.

libcudart.so.X.Y: cannot open shared object file: No such file or directory

If you notice that your program crashes with a libcudart.so.X.Y: cannot open shared object file: No such file or directory error, it's likely that your framework and Horovod were build with different versions of CUDA.

To build Horovod with a specific CUDA version, use the HOROVOD_CUDA_HOME environment variable during installation:

$ pip uninstall -y horovod
$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl HOROVOD_CUDA_HOME=/path/to/cuda pip install --no-cache-dir horovod

Alternatively, you can use the HOROVOD_CUDA_INCLUDE and HOROVOD_CUDA_LIB environment variables to specify the CUDA library to use:

$ pip uninstall -y horovod
$ HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl HOROVOD_CUDA_INCLUDE=/path/to/cuda/include HOROVOD_CUDA_LIB=/path/to/cuda/lib64 pip install --no-cache-dir horovod

FORCE-TERMINATE AT Data unpack would read past end of buffer

If you see the error message below during the training, it's likely that you have a wrong version of hwloc installed in your system.

--------------------------------------------------------------------------
An internal error has occurred in ORTE:

[[25215,0],1] FORCE-TERMINATE AT Data unpack would read past end of buffer:-26 - error grpcomm_direct.c(359)

This is something that should be reported to the developers.
--------------------------------------------------------------------------
[future5.stanford.edu:12508] [[25215,0],1] ORTE_ERROR_LOG: Data unpack would read past end of buffer in file grpcomm_direct.c at line 355

Purge hwloc from your system:

$ apt purge hwloc-nox libhwloc-dev libhwloc-plugins libhwloc5

After hwloc is purged, re-install Open MPI.

See this issue for more details.

segmentation fault with tensorflow 1.14 or higher mentioning hwloc

If you are using TensorFlow 1.14 or 1.15 and are getting a segmentation fault, check whether it mentions hwloc:

... Signal: Segmentation fault (11) Signal code: Address not mapped (1) Failing at address: 0x99 [ 0] /lib/x86_64-linux-gnu/libc.so.6(+0x3ef20)[0x7f309d34ff20] [ 1] /usr/lib/x86_64-linux-gnu/libopen-pal.so.20(opal_hwloc_base_free_topology+0x76)[0x7f3042871ca6] ...

If it does, this could be a conflict with the hwloc symbols explorted from TensorFlow.

To fix this, locate your hwloc library with ldconfig -p | grep libhwloc.so, and then set LD_PRELOAD. For example:

LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libhwloc.so python -c 'import horovod.tensorflow as hvd; hvd.init()'

See [this issue](horovod#1123) for more information.

bash: orted: command not found

If you see the error message below during the training, it's likely that Open MPI cannot find one of its components in PATH.

bash: orted: command not found
--------------------------------------------------------------------------
ORTE was unable to reliably start one or more daemons.
This usually is caused by:

* not finding the required libraries and/or binaries on
  one or more nodes. Please check your PATH and LD_LIBRARY_PATH
  settings, or configure OMPI with --enable-orterun-prefix-by-default

* lack of authority to execute on one or more specified nodes.
  Please verify your allocation and authorities.

* the inability to write startup files into /tmp (--tmpdir/orte_tmpdir_base).
  Please check with your sys admin to determine the correct location to use.

*  compilation of the orted with dynamic libraries when static are required
  (e.g., on Cray). Please check your configure cmd line and consider using
  one of the contrib/platform definitions for your system type.

* an inability to create a connection back to mpirun due to a
  lack of common network interfaces and/or no route found between
  them. Please check network connectivity (including firewalls
  and network routing requirements).
--------------------------------------------------------------------------

We recommended reinstalling Open MPI with the --enable-orterun-prefix-by-default flag, like so:

$ wget https://www.open-mpi.org/software/ompi/v4.0/downloads/openmpi-4.0.0.tar.gz
$ tar zxf openmpi-4.0.0.tar.gz
$ cd openmpi-4.0.0
$ ./configure --enable-orterun-prefix-by-default
$ make -j $(nproc) all
$ make install
$ ldconfig