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Posted to commits@singa.apache.org by wa...@apache.org on 2016/10/06 07:46:16 UTC

incubator-singa git commit: update the docs of schedule for v1.1 and installation for a FAQ entry

Repository: incubator-singa
Updated Branches:
  refs/heads/master 3a64342d0 -> 8cf18e5b0


update the docs of schedule for v1.1 and installation for a FAQ entry


Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/8cf18e5b
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/8cf18e5b
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/8cf18e5b

Branch: refs/heads/master
Commit: 8cf18e5b069b1093026a583a0011af20225dc7ca
Parents: 3a64342
Author: Wei Wang <wa...@comp.nus.edu.sg>
Authored: Thu Oct 6 15:13:07 2016 +0800
Committer: Wei Wang <wa...@comp.nus.edu.sg>
Committed: Thu Oct 6 15:14:36 2016 +0800

----------------------------------------------------------------------
 doc/en/develop/schedule.rst | 74 ++++++++++++++++++++++------------------
 doc/en/docs/installation.md | 29 ++++++++++++++--
 2 files changed, 67 insertions(+), 36 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/8cf18e5b/doc/en/develop/schedule.rst
----------------------------------------------------------------------
diff --git a/doc/en/develop/schedule.rst b/doc/en/develop/schedule.rst
index ef51496..c097407 100644
--- a/doc/en/develop/schedule.rst
+++ b/doc/en/develop/schedule.rst
@@ -20,38 +20,44 @@ Development Schedule
 ====================
 
 .. csv-table::
-	:header: "Release", "Module", "Feature", "Status"
+  :header: "Release","Module","Feature"
 
-	" 0.1 Sep 2015     "," Neural Network          "," Feed forward neural network, including CNN, MLP                                                                 "," done  "
-	"                  ","                         "," RBM-like model, including RBM                                                                                   "," done   "
-	"                  ","                         "," Recurrent neural network, including standard RNN                                                                "," done   "
-	"                  ","  Architecture           "," One worker group on single node (with data partition)                                                           "," done   "
-	"                  ","                         "," Multi worker groups on single node using [Hogwild](http://www.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf)      ","done"
-	"                  ","                         "," Distributed Hogwild","done"
-	"                  ","                         "," Multi groups across nodes, like [Downpour](http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks) ","done"
-	"                  ","                         "," All-Reduce training architecture like [DeepImage](http://arxiv.org/abs/1501.02876) ","done"
-	"                  ","                         "," Load-balance among servers "," done"
-	"                  ","  Failure recovery       "," Checkpoint and restore ","done"
-	"                  ","  Tools                  "," Installation with GNU auto tools"," done"
-	"0.2 Jan 2016      "," Neural Network          "," Feed forward neural network, including AlexNet, cuDNN layers, etc."," done "
-	"                  ","                         "," Recurrent neural network, including GRULayer and BPTT","done "
-	"                  ","                         "," Model partition and hybrid partition","done"
-	"      		   "," Tools                   "," Integration with Mesos for resource management","done"
-	"         	   ","                         "," Prepare Docker images for deployment","done"
-	"              	   ","                         "," Visualization of neural net and debug information ","done"
-	"                  "," Binding                 "," Python binding for major components ","done"
-	"                  "," GPU                     "," Single node with multiple GPUs ","done"
-	"0.3 April 2016    "," GPU                     "," Multiple nodes, each with multiple GPUs","done"
-	"                  ","                         "," Heterogeneous training using both GPU and CPU [CcT](http://arxiv.org/abs/1504.04343)","done"
-	"                  ","                         "," Support cuDNN v4 "," done"
-	"                  "," Installation            "," Remove dependency on ZeroMQ, CZMQ, Zookeeper for single node training","done"
-	"                  "," Updater                 "," Add new SGD updaters including Adam, AdamMax and AdaDelta","done"
-	"                  "," Binding                 "," Enhance Python binding for training","done"
-	"1.0 Sep 2016     "," Programming abstraction ","Tensor with linear algebra, neural net and random operations "," "
-	"                  ","                         ","Updater for distributed parameter updating ",""
-	"                  "," Hardware                "," Use Cuda and Cudnn for Nvidia GPU",""
-	"                  ","                         "," Use OpenCL for AMD GPU or other devices",""
-	"                  "," Cross-platform          "," To extend from Linux to MacOS",""
-	"                  "," Examples                "," Speech recognition example",""
-	"                  ","                         ","Large image models, e.g., [VGG](https://arxiv.org/pdf/1409.1556.pdf) and [Residual Net](http://arxiv.org/abs/1512.03385)",""
-	"1.1 Dec 2016     "," ",""," "
+  "0.1 Sep 2015      ","Neural Network               ","Feed forward neural network, including CNN, MLP                                                                     "
+  "                  ","                             ","RBM-like model, including RBM                                                                                       "
+  "                  ","                             ","Recurrent neural network, including standard RNN                                                                    "
+  "                  ","Architecture                 ","One worker group on single node (with data partition)                                                               "
+  "                  ","                             ","Multi worker groups on single node using `Hogwild <http://www.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf>`_     "
+  "                  ","                             ","Distributed Hogwild"
+  "                  ","                             ","Multi groups across nodes, like `Downpour <http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks>`_"
+  "                  ","                             ","All-Reduce training architecture like `DeepImage <http://arxiv.org/abs/1501.02876>`_                                "
+  "                  ","                             ","Load-balance among servers                                                                                          "
+  "                  ","Failure recovery             ","Checkpoint and restore                                                                                              "
+  "                  ","Tools                        ","Installation with GNU auto Tools                                                                                    "
+  "0.2 Jan 2016      ","Neural Network               ","Feed forward neural network, including AlexNet, cuDNN layers,Tools                                                  "
+  "                  ","                             ","Recurrent neural network, including GRULayer and BPTT                                                               "
+  "                  ","                             ","Model partition and hybrid partition                                                                                "
+  "                  ","Tools                        ","Integration with Mesos for resource management                                                                      "
+  "                  ","                             ","Prepare Docker images for deployment"
+  "                  ","                             ","Visualization of neural net and debug information "
+  "                  ","Binding                      ","Python binding for major components "
+  "                  ","GPU                          ","Single node with multiple GPUs "
+  "0.3 April 2016    ","GPU                          ","Multiple nodes, each with multiple GPUs"
+  "                  ","                             ","Heterogeneous training using both GPU and CPU `CcT <http://arxiv.org/abs/1504.04343>`_"
+  "                  ","                             ","Support cuDNN v4 "
+  "                  ","Installation                 ","Remove dependency on ZeroMQ, CZMQ, Zookeeper for single node training"
+  "                  ","Updater                      ","Add new SGD updaters including Adam, AdamMax and AdaDelta"
+  "                  ","Binding                      ","Enhance Python binding for training"
+  "1.0 Sep 2016      ","Programming abstraction      ","Tensor with linear algebra, neural net and random operations "
+  "                  ","                             ","Updater for distributed parameter updating "
+  "                  ","Hardware                     ","Use Cuda and Cudnn for Nvidia GPU"
+  "                  ","                             ","Use OpenCL for AMD GPU or other devices"
+  "                  ","Cross-platform               ","To extend from Linux to MacOS"
+  "                  ","                             ","Large image models, e.g., `VGG <https://arxiv.org/pdf/1409.1556.pdf>`_ and `Residual Net <http://arxiv.org/abs/1512.03385>`_"
+  "1.1 Dec 2016      ","Model Zoo                    ","Health-care models and popular image models"
+  "                  ","Caffe converter              ","Use SINGA to train models configured in caffe proto files"
+  "                  ","Memory optimization          ","Replace CNMEM with new memory pool to reduce memory footprint"
+  "                  ","Distributed training         ","Migrate distributed training frameworks from V0.3"
+  "                  ","Compilation and installation ","Windows suppport"
+  "                  ","                             ","Simplify the installation by compiling protobuf and openblas together with SINGA"
+  "                  ","                             ","Build python wheel automatically using Jenkins"
+  "                  ","                             ","Deploy SINGA programs on Android phones for prediction tasks"

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/8cf18e5b/doc/en/docs/installation.md
----------------------------------------------------------------------
diff --git a/doc/en/docs/installation.md b/doc/en/docs/installation.md
index e18447b..e4b76c0 100755
--- a/doc/en/docs/installation.md
+++ b/doc/en/docs/installation.md
@@ -37,7 +37,7 @@ The following instructions are tested on Ubuntu 14.04 for installing dependent l
 
     # optional libraries
     $ sudo apt-get install python2.7-dev python-pip python-numpy
-    $ sudo apt-get install llibopencv-dev ibgoogle-glog-dev liblmdb-dev
+    $ sudo apt-get install libopencv-dev libgoogle-glog-dev liblmdb-dev
 
 Please note that PySINGA requires swig >=3.0, which could be installed via
 apt-get on Ubuntu 16.04; but it has to be installed from source for other Ubuntu versions including 14.04.
@@ -68,6 +68,7 @@ To let the runtime know the openblas path, please export
 
 ### pip and anaconda for PySINGA
 pip and anaconda could be used to install python packages, e.g. numpy.
+Python virtual environment is recommended to run PySINGA.
 To use pip with virtual environment,
 
     # install virtualenv
@@ -219,6 +220,30 @@ To be added.
 
 ## FAQ
 
+* Q: Error from 'import singa' using PySINGA installed from wheel.
+
+    A: Please check the detailed error from `python -c  "from singa import _singa_wrap"`. Sometimes it is
+    caused by the dependent libraries, e.g. there are multiple versions of protobuf or missing of cudnn. Following
+    steps show the solutions for different cases
+    1. check the cudnn and cuda and gcc versions, cudnn5 and cuda7.5 and gcc4.8/4.9 are preferred. if gcc is 5.0, then downgrade it.
+       if cudnn is missing or not match with the wheel version, you can download the correct version of cudnn into ~/local/cudnn/ and
+        ```
+        echo "export LD_LIBRARY_PATH=/home/<yourname>/local/cudnn/lib64:$LD_LIBRARY_PATH" >> ~/.bashrc
+        ```
+    2. if it is the problem related to protobuf, then better install protobuf from source into a local folder, say ~/local/;
+       Decompress the tar file, and then
+       ```
+       ./configure --prefix=/home/<yourname>local
+       make && make install
+       echo "export LD_LIBRARY_PATH=/home/<yourname>/local/lib:$LD_LIBRARY_PATH" >> ~/.bashrc
+       source ~/.bashrc
+    3. if it cannot find other libs including python, then please create virtual env using pip or conda;
+       and then install SINGA via
+       ```
+       pip install --upgrade <url of singa wheel>
+       ```
+
+
 * Q: Error from running `cmake ..`, which cannot find the dependent libraries.
 
     A: If you haven't installed the libraries, please install them. If you installed
@@ -276,7 +301,7 @@ To be added.
 
 * Q: When I build protocol buffer, it reports that GLIBC++_3.4.20 not found in /usr/lib64/libstdc++.so.6.
 
-    A9: This means the linker found libstdc++.so.6 but that library
+    A: This means the linker found libstdc++.so.6 but that library
     belongs to an older version of GCC than was used to compile and link the
     program. The program depends on code defined in
     the newer libstdc++ that belongs to the newer version of GCC, so the linker