Troubleshooting

Here are Linux troubleshooting instructions. There is a specific MacOS section.

Why do I get a network error when I install Theano

If you are behind a proxy, you must do some extra configuration stepsbefore starting the installation. You must set the environmentvariable http_proxy to the proxy address. Using bash this isaccomplished with the commandexport http_proxy="http://user:pass@my.site:port/"You can also provide the —proxy=[user:pass@]url:port parameterto pip. The [user:pass@] portion is optional.

How to solve TypeError: object of type ‘TensorVariable’ has no len()

If you receive the following error, it is because the Python function len cannotbe implemented on Theano variables:

  1. TypeError: object of type 'TensorVariable' has no len()

Python requires that len returns an integer, yet it cannot be done as Theano’s variables are symbolic. However, var.shape[0] can be used as a workaround.

This error message cannot be made more explicit because the relevant aspects of Python’sinternals cannot be modified.

How to solve Out of memory Error

Occasionally Theano may fail to allocate memory when there appears to be morethan enough reporting:

Error allocating X bytes of device memory (out of memory). Driver report Y bytes free and Z total.

where X is far less than Y and Z (i.e. X << Y < Z).

This scenario arises when an operation requires allocation of a large contiguousblock of memory but no blocks of sufficient size are available.

GPUs do not have virtual memory and as such all allocations must be assigned toa continuous memory region. CPUs do not have this limitation because or theirsupport for virtual memory. Multiple allocations on a GPU can result in memoryfragmentation which can makes it more difficult to find contiguous regionsof memory of sufficient size during subsequent memory allocations.

A known example is related to writing data to shared variables. When updating ashared variable Theano will allocate new space if the size of the data does notmatch the size of the space already assigned to the variable. This can lead tomemory fragmentation which means that a continugous block of memory ofsufficient capacity may not be available even if the free memory overall islarge enough.

theano.function returns a float64 when the inputs are float32 and int{32, 64}

It should be noted that using float32 and int{32, 64} togetherinside a function would provide float64 as output.

Since the GPU can’t compute this kind of output, it would bepreferable not to use those dtypes together.

To help you find where float64 are created, see thewarn_float64 Theano flag.

How to test that Theano works properly

An easy way to check something that could be wrong is by making sure THEANO_FLAGShave the desired values as well as the ~/.theanorc

Also, check the following outputs :

  1. ipython
  1. import theano
  2. theano.__file__
  3. theano.__version__

Once you have installed Theano, you should run the test suite.

  1. python -c "import numpy; numpy.test()"
  2. python -c "import scipy; scipy.test()"
  3. THEANO_FLAGS=''; python -c "import theano; theano.test()"

All Theano tests should pass (skipped tests and known failures are normal). Ifsome test fails on your machine, you are encouraged to tell us what wentwrong on the theano-users@googlegroups.com mailing list.

Warning

Theano’s test should NOT be run with device=cuda or device=gpuor they will fail. The tests automatically use the gpu, if any, whenneeded. If you don’t want Theano to ever use the gpu when running tests,you can set config.device to cpu andconfig.force_device to True.

Why is my code so slow/uses so much memory

There is a few things you can easily do to change the trade-offbetween speed and memory usage. It nothing is said, this affect theCPU and GPU memory usage.

Could speed up and lower memory usage:

    • cuDNN default cuDNN convolution use less
    • memory then Theano version. But some flags allow it to use morememory. GPU only.

  • Shortly avail, multi-GPU.

Could raise memory usage but speed up computation:

  • config.gpuarray.preallocate =1 # Preallocates the GPU memory for the new backend(GpuArray Backend)and then manages it in a smart way. Does not raise much the memory usage, but ifyou are at the limit of GPU memory available you might need to specify alower value. GPU only.
  • config.lib.cnmem =1 # Equivalent on the old backend (CUDA backend). GPU only.
  • config.allow_gc =False
  • config.optimizer_excluding =low_memory , GPU only for now.

Could lower the memory usage, but raise computation time:

  • config.scan.allow_gc =True # Probably not significant slowdown if config.lib.cnmem is used.

  • config.scan.allow_output_prealloc =False

  • Use batch_normalization(). It use less memorythen building a corresponding Theano graph.

    • Disable one or scan more optimizations:
      • optimizer_excluding=scanOp_pushout_seqs_ops
      • optimizer_excluding=scan_pushout_dot1
      • optimizer_excluding=scanOp_pushout_output

  • Disable all optimization tagged as raising memory usage:optimizer_excluding=more_mem (currently only the 3 scan optimizations above)

  • float16.

If you want to analyze the memory usage during computation, thesimplest is to let the memory error happen during Theano execution anduse the Theano flags exception_verbosity=high.

How do I configure/test my BLAS library

There are many ways to configure BLAS for Theano. This is done with the Theanoflags blas.ldflags (config – Theano Configuration). The default is to use the BLASinstallation information in NumPy, accessible vianumpy.distutils.config.show(). You can tell theano to use a differentversion of BLAS, in case you did not compile NumPy with a fast BLAS or if NumPywas compiled with a static library of BLAS (the latter is not supported inTheano).

The short way to configure the Theano flags blas.ldflags is by setting theenvironment variable THEANO_FLAGS to blas.ldflags=XXX (in bashexport THEANO_FLAGS=blas.ldflags=XXX)

The ${HOME}/.theanorc file is the simplest way to set a relativelypermanent option like this one. Add a [blas] section with an ldflagsentry like this:

  1. # other stuff can go here
  2. [blas]
  3. ldflags = -lf77blas -latlas -lgfortran #put your flags here
  4.  
  5. # other stuff can go here

For more information on the formatting of ~/.theanorc and theconfiguration options that you can put there, see config – Theano Configuration.

Here are some different way to configure BLAS:

0) Do nothing and use the default config, which is to link against the sameBLAS against which NumPy was built. This does not work in the case NumPy wascompiled with a static library (e.g. ATLAS is compiled by default only as astatic library).

1) Disable the usage of BLAS and fall back on NumPy for dot products. To dothis, set the value of blas.ldflags as the empty string (ex: export THEANO_FLAGS=blas.ldflags=). Depending on the kind of matrix operations yourTheano code performs, this might slow some things down (vs. linking with BLASdirectly).

2) You can install the default (reference) version of BLAS if the NumPy version(against which Theano links) does not work. If you have root or sudo access infedora you can do sudo yum install blas blas-devel. Under Ubuntu/Debiansudo apt-get install libblas-dev. Then use the Theano flagsblas.ldflags=-lblas. Note that the default version of blas is not optimized.Using an optimized version can give up to 10x speedups in the BLAS functionsthat we use.

3) Install the ATLAS library. ATLAS is an open source optimized version ofBLAS. You can install a precompiled version on most OSes, but if you’re willingto invest the time, you can compile it to have a faster version (we have seenspeed-ups of up to 3x, especially on more recent computers, against theprecompiled one). On Fedora, sudo yum install atlas-devel. Under Ubuntu,sudo apt-get install libatlas-base-dev libatlas-base orlibatlas3gf-sse2 if your CPU supports SSE2 instructions. Then set theTheano flags blas.ldflags to -lf77blas -latlas -lgfortran. Note thatthese flags are sometimes OS-dependent.

4) Use a faster version like MKL, GOTO, … You are on your own to install it.See the doc of that software and set the Theano flags blas.ldflagscorrectly (for example, for MKL this might be -lmkl -lguide -lpthread or-lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -lguide -liomp5 -lmkl_mc -lpthread).

Note

Make sure your BLASlibraries are available as dynamically-loadable libraries.ATLAS is often installed only as a static library. Theano is not able touse this static library. Your ATLAS installation might need to be modifiedto provide dynamically loadable libraries. (On Linux thistypically means a library whose name ends with .so. On Windows this will bea .dll, and on OS-X it might be either a .dylib or a .so.)

This might be just a problem with the way Theano passes compilationarguments to g++, but the problem is not fixed yet.

Note

If you have problems linking with MKL, Intel Line Advisorand the MKL User Guidecan help you find the correct flags to use.

Note

If you have error that contain “gfortran” in it, like this one:

ImportError: (‘/home/Nick/.theano/compiledir_Linux-2.6.35-31-generic-x86_64-with-Ubuntu-10.10-maverick–2.6.6/tmpIhWJaI/0c99c52c82f7ddc775109a06ca04b360.so: undefined symbol: _gfortran_st_write_done’

The problem is probably that NumPy is linked with a different blasthen then one currently available (probably ATLAS). There is 2possible fixes:

  • Uninstall ATLAS and install OpenBLAS.
  • Use the Theano flag “blas.ldflags=-lblas -lgfortran” 1) is better as OpenBLAS is faster then ATLAS and NumPy isprobably already linked with it. So you won’t need any otherchange in Theano files or Theano configuration.

Testing BLAS

It is recommended to test your Theano/BLAS integration. There are many versionsof BLAS that exist and there can be up to 10x speed difference between them.Also, having Theano link directly against BLAS instead of using NumPy/SciPy asan intermediate layer reduces the computational overhead. This isimportant for BLAS calls to ger, gemv and small gemm operations(automatically called when needed when you use dot()). To run theTheano/BLAS speed test:

  1. python `python -c "import os, theano; print os.path.dirname(theano.__file__)"`/misc/check_blas.py

This will print a table with different versions of BLAS/numbers ofthreads on multiple CPUs and GPUs. It will also print some Theano/NumPyconfiguration information. Then, it will print the running time of the samebenchmarks for your installation. Try to find a CPU similar to yours inthe table, and check that the single-threaded timings are roughly the same.

Theano should link to a parallel version of Blas and use all coreswhen possible. By default it should use all cores. Set the environmentvariable “OMP_NUM_THREADS=N” to specify to use N threads.

Mac OS

Although the above steps should be enough, running Theano on a Mac maysometimes cause unexpected crashes, typically due to multiple versions ofPython or other system libraries. If you encounter such problems, you maytry the following.

  • You can ensure MacPorts shared libraries are given priority at run-timewith export LD_LIBRARY_PATH=/opt/local/lib:$LD_LIBRARY_PATH. In orderto do the same at compile time, you can add to your ~/.theanorc:
  1. [gcc]
  2. cxxflags = -L/opt/local/lib
  • More generally, to investigate libraries issues, you can use the otool -Lcommand on .so files found under your ~/.theano directory. This willlist shared libraries dependencies, and may help identify incompatibilities.

Please inform us if you have trouble installing and running Theano on your Mac.We would be especially interested in dependencies that we missed listing,alternate installation steps, GPU instructions, as well as tests that fail onyour platform (use the theano-users@googlegroups.com mailing list, butnote that you must first register to it, by going to theano-users).