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 steps before starting the installation. You must set the environment variable http_proxy to the proxy address. Using bash this is accomplished with the command export http_proxy="" You can also provide the --proxy=[user:pass@]url:port parameter to 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__ cannot be implemented on Theano variables:

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’s internals cannot be modified.

How to solve Out of memory Error

Occasionally Theano may fail to allocate memory when there appears to be more than 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 contiguous block of memory but no blocks of sufficient size are available.

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

A known example is related to writing data to shared variables. When updating a shared variable Theano will allocate new space if the size of the data does not match the size of the space already assigned to the variable. This can lead to memory fragmentation which means that a continugous block of memory of sufficient capacity may not be available even if the free memory overall is large 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} together inside a function would provide float64 as output.

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

To help you find where float64 are created, see the warn_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_FLAGS have the desired values as well as the ~/.theanorc

Also, check the following outputs :

import theano

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

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

All Theano tests should pass (skipped tests and known failures are normal). If some test fails on your machine, you are encouraged to tell us what went wrong on the mailing list.


Theano’s test should NOT be run with device=cuda or device=gpu or they will fail. The tests automatically use the gpu, if any, when needed. If you don’t want Theano to ever use the gpu when running tests, you can set config.device to cpu and config.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-off between speed and memory usage. It nothing is said, this affect the CPU 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 more memory. 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 if you are at the limit of GPU memory available you might need to specify a lower 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 memory then 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, the simplest is to let the memory error happen during Theano execution and use 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 Theano flags blas.ldflags (config – Theano Configuration). The default is to use the BLAS installation information in NumPy, accessible via You can tell theano to use a different version of BLAS, in case you did not compile NumPy with a fast BLAS or if NumPy was compiled with a static library of BLAS (the latter is not supported in Theano).

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

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

# other stuff can go here
ldflags = -lf77blas -latlas -lgfortran #put your flags here

# other stuff can go here

For more information on the formatting of ~/.theanorc and the configuration 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 same BLAS against which NumPy was built. This does not work in the case NumPy was compiled with a static library (e.g. ATLAS is compiled by default only as a static library).

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

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 in fedora you can do sudo yum install blas blas-devel. Under Ubuntu/Debian sudo apt-get install libblas-dev. Then use the Theano flags blas.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 functions that we use.

3) Install the ATLAS library. ATLAS is an open source optimized version of BLAS. You can install a precompiled version on most OSes, but if you’re willing to invest the time, you can compile it to have a faster version (we have seen speed-ups of up to 3x, especially on more recent computers, against the precompiled one). On Fedora, sudo yum install atlas-devel. Under Ubuntu, sudo apt-get install libatlas-base-dev libatlas-base or libatlas3gf-sse2 if your CPU supports SSE2 instructions. Then set the Theano flags blas.ldflags to -lf77blas -latlas -lgfortran. Note that these 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.ldflags correctly (for example, for MKL this might be -lmkl -lguide -lpthread or -lmkl_intel_lp64 -lmkl_intel_thread -lmkl_core -lguide -liomp5 -lmkl_mc -lpthread).


Make sure your BLAS libraries are available as dynamically-loadable libraries. ATLAS is often installed only as a static library. Theano is not able to use this static library. Your ATLAS installation might need to be modified to provide dynamically loadable libraries. (On Linux this typically means a library whose name ends with .so. On Windows this will be a .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 compilation arguments to g++, but the problem is not fixed yet.


If you have problems linking with MKL, Intel Line Advisor and the MKL User Guide can help you find the correct flags to use.


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/ undefined symbol: _gfortran_st_write_done’

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

  1. Uninstall ATLAS and install OpenBLAS.
  2. Use the Theano flag “blas.ldflags=-lblas -lgfortran”

1) is better as OpenBLAS is faster then ATLAS and NumPy is probably already linked with it. So you won’t need any other change in Theano files or Theano configuration.

Testing BLAS

It is recommended to test your Theano/BLAS integration. There are many versions of 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 as an intermediate layer reduces the computational overhead. This is important for BLAS calls to ger, gemv and small gemm operations (automatically called when needed when you use dot()). To run the Theano/BLAS speed test:

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

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

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

Mac OS

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

  • You can ensure MacPorts shared libraries are given priority at run-time with export LD_LIBRARY_PATH=/opt/local/lib:$LD_LIBRARY_PATH. In order to do the same at compile time, you can add to your ~/.theanorc:

    cxxflags = -L/opt/local/lib
  • More generally, to investigate libraries issues, you can use the otool -L command on .so files found under your ~/.theano directory. This will list 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 on your platform (use the mailing list, but note that you must first register to it, by going to theano-users).