Frequently Asked Questions¶
Does Theano support Python 3?¶
We support both Python 2 >= 2.7 and Python 3 >= 3.4.
Output slight numerical difference¶
Sometimes when you compare the output of Theano using different Theano flags, Theano versions, CPU and GPU or with other software like NumPy, you will see small numerical differences.
This is normal. Floating point numbers are approximations of real numbers. This is why doing a+(b+c) vs (a+b)+c can give small differences of value. This is normal. For more details, see: What Every Computer Scientist Should Know About Floating-Point Arithmetic.
Faster gcc optimization¶
You can enable faster gcc optimization with the
This list of flags was suggested on the mailing list:
-O3 -ffast-math -ftree-loop-distribution -funroll-loops -ftracer
Use it at your own risk. Some people warned that the
-ftree-loop-distribution optimization resulted in wrong results in the past.
In the past we said that if the
compiledir was not shared by multiple
computers, you could add the
-march=native flag. Now we recommend
to remove this flag as Theano does it automatically and safely,
even if the
compiledir is shared by multiple computers with different
CPUs. In fact, Theano asks g++ what are the equivalent flags it uses, and re-uses
Faster Theano Function Compilation¶
Theano function compilation can be time consuming. It can be sped up by setting
mode=FAST_COMPILE which instructs Theano to skip most
optimizations and disables the generation of any c/cuda code. This is useful
for quickly testing a simple idea.
If c/cuda code is necessary, as when using a GPU, the flag
optimizer=fast_compile can be used instead. It instructs Theano to
skip time consuming optimizations but still generate c/cuda code.
Similarly using the flag
optimizer_excluding=inplace will speed up
compilation by preventing optimizations that replace operations with a
version that reuses memory where it will not negatively impact the
integrity of the operation. Such optimizations can be time
consuming. However using this flag will result in greater memory usage
because space must be allocated for the results which would be
unnecessary otherwise. In short, using this flag will speed up
compilation but it will also use more memory because
optimizer_excluding=inplace excludes inplace optimizations
resulting in a trade off between speed of compilation and memory
Alternatively, if the graph is big, using the flag
will speedup the computations by removing some of the inplace
optimizations. This would allow theano to skip a time consuming cycle
detection algorithm. If the graph is big enough,we suggest that you use
this flag instead of
optimizer_excluding=inplace. It will result in a
computation time that is in between fast compile and fast run.
Theano flag reoptimize_unpickled_function controls if an unpickled theano function should reoptimize its graph or not. Theano users can use the standard python pickle tools to save a compiled theano function. When pickling, both graph before and after the optimization are saved, including shared variables. When set to True, the graph is reoptimized when being unpickled. Otherwise, skip the graph optimization and use directly the optimized graph from the pickled file. The default is False.
Faster Theano function¶
You can set the Theano flag
False to get a speed-up by using
more memory. By default, Theano frees intermediate results when we don’t need
them anymore. Doing so prevents us from reusing this memory. So disabling the
garbage collection will keep all intermediate results’ memory space to allow to
reuse them during the next call to the same Theano function, if they are of the
correct shape. The shape could change if the shapes of the inputs change.
CNMeM, this isn’t very useful with GPU
Some Theano optimizations make the assumption that the user inputs are
valid. What this means is that if the user provides invalid values (like
incompatible shapes or indexing values that are out of bounds) and
the optimizations are applied, the user error will get lost. Most of the
time, the assumption is that the user inputs are valid. So it is good
to have the optimization being applied, but loosing the error is bad.
The newest optimization in Theano with such assumption will add an
assertion in the graph to keep the user error message. Computing
these assertions could take some time. If you are sure everything is valid
in your graph and want the fastest possible Theano, you can enable an
optimization that will remove those assertions with:
Faster Small Theano function¶
For Theano 0.6 and up.
For Theano functions that don’t do much work, like a regular logistic
regression, the overhead of checking the input can be significant. You
can disable it by setting
f.trust_input to True.
Make sure the types of arguments you provide match those defined when
the function was compiled.
For example, replace the following
import theano from theano import function x = theano.tensor.scalar('x') f = function([x], x + 1.) f(10.)
import numpy import theano from theano import function x = theano.tensor.scalar('x') f = function([x], x + 1.) f.trust_input = True f(numpy.array([10.], dtype=theano.config.floatX))
Also, for small Theano functions, you can remove more Python overhead by
making a Theano function that does not take any input. You can use shared
variables to achieve this. Then you can call it like this:
f.fn(n_calls=N) to speed it up. In the last case, only the last
function output (out of N calls) is returned.
You can also use the
C linker that will put all nodes in the same C
compilation unit. This removes some overhead between node in the graph,
but requires that all nodes in the graph have a C implementation:
x = theano.tensor.scalar('x') f = function([x], (x + 1.) * 2, mode=theano.Mode(linker='c')) f(10.)
New GPU backend using libgpuarray¶
The new theano GPU backend (GpuArray Backend) uses
config.gpuarray.preallocate for GPU memory allocation.
Likewise, the old back-end uses
config.lib.cnmem for GPU memory allocation.
“What are Theano’s Limitations?”¶
Theano offers a good amount of flexibility, but has some limitations too. You must answer for yourself the following question: How can my algorithm be cleverly written so as to make the most of what Theano can do?
Here is a list of some of the known limitations:
- While- or for-Loops within an expression graph are supported, but only via
theano.scan()op (which puts restrictions on how the loop body can interact with the rest of the graph).
- Neither goto nor recursion is supported or planned within expression graphs.