Merge pull request #1023 from nouiz/doc_python_mem

NEWS.txt

@@ -23,8 +23,6 @@ Highlights:
 
 Known bugs:
  * A few crash cases that will be fixed by the final release.
- * CAReduce with NaN in inputs do not return the correct output. (reported by Pascal L.)
-     * This is used in tensor.{all,any,max,mean,prod,sum} and in the grad of PermuteRowElements.
 
 Bug fixes:
  * Outputs of Scan nodes could contain corrupted values: some parts of the
@@ -229,8 +227,8 @@ Speed up:
 Speed up GPU:
  * Convolution on the GPU now checks the generation of the card to make
    it faster in some cases (especially medium/big ouput image) (Frederic B.)
-   * We had hardcoded 512 as the maximum number of threads per block. Newer cards
-     support up to 1024 threads per block.
+     * We had hardcoded 512 as the maximum number of threads per block. Newer cards
+       support up to 1024 threads per block.
  * Faster GpuAdvancedSubtensor1, GpuSubtensor, GpuAlloc (Frederic B.)
  * We now pass the GPU architecture to nvcc when compiling (Frederic B.)
  * Now we use the GPU function async feature by default. (Frederic B.)
@@ -242,7 +240,7 @@ Speed up GPU:
 Sparse Sandbox graduate (moved from theano.sparse.sandbox.sp):
  * sparse.remove0 (Frederic B., Nicolas B.)
  * sparse.sp_sum(a, axis=None) (Nicolas B.)
-   * bugfix: the not structured grad was returning a structured grad.
+     * bugfix: the not structured grad was returning a structured grad.
  * sparse.{col_scale,row_scale,ensure_sorted_indices,clean} (Nicolas B.)
  * sparse.{diag,square_diagonal} (Nicolas B.)
 
@@ -257,8 +255,8 @@ Sparse:
      * Optimized op: StructuredAddSV, StrucutedAddSVCSR (inserted automatically)
  * New Op: sparse.mul_s_v multiplication of sparse matrix by broadcasted vector (Yann D.)
  * New Op: sparse.Cast() (Yann D., Nicolas B.)
-   * Add sparse_variable.astype() and theano.sparse.cast() and
-     theano.sparse.{b,w,i,l,f,d,c,z}cast() as their tensor equivalent (Nicolas B.)
+     * Add sparse_variable.astype() and theano.sparse.cast() and
+       theano.sparse.{b,w,i,l,f,d,c,z}cast() as their tensor equivalent (Nicolas B.)
  * Op class: SamplingDot (Yann D., Nicolas B.)
    * Optimized version: SamplingDotCsr, StructuredDotCSC
    * Optimizations to insert the optimized version: local_sampling_dot_csr, local_structured_add_s_v
@@ -268,9 +266,9 @@ Sparse:
 
 New flags:
  * `profile=True` flag now prints the sum of all printed profiles. (Frederic B.)
-   * It works with the linkers vm/cvm (default).
-   * Also print compile time, optimizer time and linker time.
-   * Also print a summary by op class.
+     * It works with the linkers vm/cvm (default).
+     * Also print compile time, optimizer time and linker time.
+     * Also print a summary by op class.
  * new flag "profile_optimizer" (Frederic B.)
    when profile=True, will also print the time spent in each optimizer.
    Useful to find optimization bottleneck.

doc/NEWS.txt

@@ -23,8 +23,6 @@ Highlights:
 
 Known bugs:
  * A few crash cases that will be fixed by the final release.
- * CAReduce with NaN in inputs do not return the correct output. (reported by Pascal L.)
-     * This is used in tensor.{all,any,max,mean,prod,sum} and in the grad of PermuteRowElements.
 
 Bug fixes:
  * Outputs of Scan nodes could contain corrupted values: some parts of the
@@ -229,8 +227,8 @@ Speed up:
 Speed up GPU:
  * Convolution on the GPU now checks the generation of the card to make
    it faster in some cases (especially medium/big ouput image) (Frederic B.)
-   * We had hardcoded 512 as the maximum number of threads per block. Newer cards
-     support up to 1024 threads per block.
+     * We had hardcoded 512 as the maximum number of threads per block. Newer cards
+       support up to 1024 threads per block.
  * Faster GpuAdvancedSubtensor1, GpuSubtensor, GpuAlloc (Frederic B.)
  * We now pass the GPU architecture to nvcc when compiling (Frederic B.)
  * Now we use the GPU function async feature by default. (Frederic B.)
@@ -242,7 +240,7 @@ Speed up GPU:
 Sparse Sandbox graduate (moved from theano.sparse.sandbox.sp):
  * sparse.remove0 (Frederic B., Nicolas B.)
  * sparse.sp_sum(a, axis=None) (Nicolas B.)
-   * bugfix: the not structured grad was returning a structured grad.
+     * bugfix: the not structured grad was returning a structured grad.
  * sparse.{col_scale,row_scale,ensure_sorted_indices,clean} (Nicolas B.)
  * sparse.{diag,square_diagonal} (Nicolas B.)
 
@@ -257,8 +255,8 @@ Sparse:
      * Optimized op: StructuredAddSV, StrucutedAddSVCSR (inserted automatically)
  * New Op: sparse.mul_s_v multiplication of sparse matrix by broadcasted vector (Yann D.)
  * New Op: sparse.Cast() (Yann D., Nicolas B.)
-   * Add sparse_variable.astype() and theano.sparse.cast() and
-     theano.sparse.{b,w,i,l,f,d,c,z}cast() as their tensor equivalent (Nicolas B.)
+     * Add sparse_variable.astype() and theano.sparse.cast() and
+       theano.sparse.{b,w,i,l,f,d,c,z}cast() as their tensor equivalent (Nicolas B.)
  * Op class: SamplingDot (Yann D., Nicolas B.)
    * Optimized version: SamplingDotCsr, StructuredDotCSC
    * Optimizations to insert the optimized version: local_sampling_dot_csr, local_structured_add_s_v
@@ -268,9 +266,9 @@ Sparse:
 
 New flags:
  * `profile=True` flag now prints the sum of all printed profiles. (Frederic B.)
-   * It works with the linkers vm/cvm (default).
-   * Also print compile time, optimizer time and linker time.
-   * Also print a summary by op class.
+     * It works with the linkers vm/cvm (default).
+     * Also print compile time, optimizer time and linker time.
+     * Also print a summary by op class.
  * new flag "profile_optimizer" (Frederic B.)
    when profile=True, will also print the time spent in each optimizer.
    Useful to find optimization bottleneck.

doc/index.txt

@@ -50,7 +50,7 @@ installation and configuration, see :ref:`installing Theano <install>`.
 
 Master Tests Status:
 
-.. image:: https://secure.travis-ci.org/Theano/Theano.png
+.. image:: https://secure.travis-ci.org/Theano/Theano.png?branch=master
     :target: http://travis-ci.org/Theano/Theano/builds
 
 .. _available on PyPI: http://pypi.python.org/pypi/Theano

doc/install.txt

@@ -206,7 +206,7 @@ Bleeding-edge install instructions
 
 Master Tests Status:
 
-.. image:: https://secure.travis-ci.org/Theano/Theano.png
+.. image:: https://secure.travis-ci.org/Theano/Theano.png?branch=master
     :target: http://travis-ci.org/Theano/Theano/builds
 
 If you are a developer of Theano, then check out the :ref:`dev_start_guide`.

doc/library/tensor/nnet/conv.txt

@@ -8,7 +8,7 @@
 
     Two similar implementation exists for conv2d:
     :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>` and
-    :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>. The former implements a traditional
+    :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`. The former implements a traditional
     2D convolution, while the latter implements the convolutional layers
     present in convolutional neural networks (where filters are 3D and pool
     over several input channels).

doc/tutorial/index.txt

@@ -43,3 +43,4 @@ you out.
     debug_faq
     extending_theano
     faq
+    python-memory-management

doc/tutorial/python-memory-management.rst → doc/tutorial/python-memory-management.txt

+.. _python-memory-management:
+
 Python Memory Management
 ========================
 
@@ -156,7 +158,7 @@ on a 32-bit platform and
       96 [4, 'toaster', 230.1]
 
 on a 64-bit platform. An empty list eats up 72 bytes. The size of an
-empty, 64-bit C++ ``std::list()``is only 16 bytes, 4-5 times less. What
+empty, 64-bit C++ ``std::list()`` is only 16 bytes, 4-5 times less. What
 about tuples? (and dictionaries?):
 
 ::

doc/tutorial/python.txt

@@ -11,3 +11,5 @@ tutorials/exercises if you need to learn it or only need a refresher:
   * `Python Challenge <http://www.pythonchallenge.com/>`__
   * `Dive into Python <http://diveintopython.net/>`__
   * `Google Python Class <http://code.google.com/edu/languages/google-python-class/index.html>`__
+
+We have a tutorial on how :ref:`python manage its memory <python-memory-management>`