Theano

_Intro _

_Theano_ is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Check gpu is working

Source: [http://deeplearning.net/software/theano/tutorial/using_gpu.html](http://deeplearning.net/software/theano/tutorial/using_gpu.html)

Test script:

:::: from theano import function, config, shared, sandbox import theano.tensor as T import numpy import time

<span class="n">vlen</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">30</span> <span class="o">*</span> <span class="mi">768</span>  <span class="c"># 10 x #cores x # threads per core</span>
<span class="n">iters</span> <span class="o">=</span> <span class="mi">1000</span>

<span class="n">rng</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(0</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">shared</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">vlen</span><span class="p">),</span> <span class="n">config</span><span class="o">.</span><span class="n">floatX</span><span class="p">))</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">function</span><span class="p">([],</span> <span class="n">T</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">print</span> <span class="n">f</span><span class="o">.</span><span class="n">maker</span><span class="o">.</span><span class="n">fgraph</span><span class="o">.</span><span class="n">toposort</span><span class="p">()</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">iters</span><span class="p">):</span>
    <span class="n">r</span> <span class="o">=</span> <span class="n">f</span><span class="p">()</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">print</span> <span class="s">'Looping </span><span class="si">%d</span><span class="s"> times took'</span> <span class="o">%</span> <span class="n">iters</span><span class="p">,</span> <span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">,</span> <span class="s">'seconds'</span>
<span class="k">print</span> <span class="s">'Result is'</span><span class="p">,</span> <span class="n">r</span>
<span class="k">if</span> <span class="n">numpy</span><span class="o">.</span><span class="n">any</span><span class="p">([</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">op</span><span class="p">,</span> <span class="n">T</span><span class="o">.</span><span class="n">Elemwise</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">maker</span><span class="o">.</span><span class="n">fgraph</span><span class="o">.</span><span class="n">toposort</span><span class="p">()]):</span>
    <span class="k">print</span> <span class="s">'Used the cpu'</span>
<span class="k">else</span><span class="p">:</span>
    <span class="k">print</span> <span class="s">'Used the gpu'</span>

Run with two configurations:

:::: $ THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python check1.py [Elemwise{exp,no_inplace}()] Looping 1000 times took 3.06635117531 seconds Result is [ 1.23178029 1.61879337 1.52278066 ..., 2.20771813 2.29967761 1.62323284] Used the cpu

$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python check1.py
Using gpu device 0: GeForce GTX 580
[GpuElemwise{exp,no_inplace}(<CudaNdarrayType(float32, vector)>), HostFromGpu(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.638810873032 seconds
Result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu