Prepare v0.3.0-RC1
Update readme file for the running instruction (compiled without any
enable list, i.e., `./configure`).
diff --git a/README.md b/README.md
index 3ae8fe0..4d124aa 100644
--- a/README.md
+++ b/README.md
@@ -19,8 +19,19 @@
* `google-protobuf` (New BSD)
* `openblas` (New BSD)
+###Optional dependencies
+For advanced features, the following libraries are needed:
+
+ * `zeromq` (LGPLv3 + static link exception),`czmq` (Mozilla Public License Version 2.0) and `zookeeper` (Apache 2.0), for distributed training with multiple processes. Compile SINGA with `--enable-dist`
+ * `cuda` (NVIDIA CUDA Toolkit EUL) for training using NVIDIA GPUs.
+ * `cudnn` (NVIDIA CuDNN EULA) for training using NVIDIA's CuDNN library.
+ * `Apache Mesos` (Apache 2.0)
+ * `Apache Hadoop` (Apache 2.0)
+ * `libhdfs3` (Apache 2.0)
+ * `swig` (GPL) for using Python Binding.
+
We have tested SINGA on Ubuntu 12.04, Ubuntu 14.01 and CentOS 6.
-You can install all dependencies into `$PREFIX` folder by
+You can install all dependencies (including optional dependencies) into `$PREFIX` folder by
./thirdparty/install.sh all $PREFIX
@@ -32,17 +43,6 @@
$ export LIBRARY_PATH=$PREFIX/lib:$LIBRARY_PATH
$ export PATH=$PREFIX/bin:$PATH
-###Optional dependencies
-For advanced features, the following libraries are needed:
-
- * `zeromq` (LGPLv3 + static link exception),`czmq` (Mozilla Public License Version 2.0) and `zookeeper` (Apache 2.0), for distributed training with multiple processes. Compile SINGA with `--enable-dist`
- * `cuda` (NVIDIA CUDA Toolkit EUL) for training using NVIDIA GPUs.
- * `cudnn` (NVIDIA CuDNN EULA) for training using NVIDIA's CUDNN library.
- * `Apache Mesos` (Apache 2.0)
- * `Apache Hadoop` (Apache 2.0)
- * `libhdfs3` (Apache 2.0)
- * `swig` (GPL) for using Python Binding.
-
##Documentation
@@ -76,7 +76,7 @@
$ ./tool/python/singa/generatepy.sh
$ ./configure --enable-python --with-python=/PATH/TO/Python.h
---with-python is optinal as by default the path is /usr/local/include.
+--with-python is optional as by default the path is /usr/local/include.
You can also run the following command for further configuration.
@@ -107,10 +107,9 @@
Next, start the training:
$ cd ../../
- $ ./bin/zk-service.sh start
- $ ./bin/singa-run.sh -conf examples/cifar10/job.conf
+ $ ./singa -conf examples/cifar10/job.conf
-Now we just need to wait until it is done!
+For GPU training or distributed training, please refer to the [online guide](http://singa.apache.org/docs).
##LICENSE
diff --git a/src/main.cc b/src/main.cc
index a07f86b..0ce7d19 100644
--- a/src/main.cc
+++ b/src/main.cc
@@ -46,8 +46,8 @@
* easily, e.g., MLP(layer1_size, layer2_size, tanh, loss);
*/
int main(int argc, char **argv) {
- if (argc < 4) {
- std::cout << "Args: -conf JOB_CONF -singa SINGA_CONF -job_id JOB_ID "
+ if (argc < 2) {
+ std::cout << "Args: -conf JOB_CONF [-singa SINGA_CONF] [-job_id JOB_ID] "
<< " [-resume|-test]\n"
<< "-resume\t resume training from latest checkpoint files\n"
<< "-test\t test performance or extract features\n";
diff --git a/src/neuralnet/input_layer/store.cc b/src/neuralnet/input_layer/store.cc
index a4754f4..32f1887 100644
--- a/src/neuralnet/input_layer/store.cc
+++ b/src/neuralnet/input_layer/store.cc
@@ -34,7 +34,6 @@
if (store_ != nullptr) {
delete store_;
}
-
}
void StoreInputLayer::Setup(const LayerProto& conf,
@@ -104,10 +103,8 @@
} else {
fetch_data();
}
- LOG(ERROR) << "batchsize << " << batchsize_;
for (int k = 0; k < batchsize_; k++)
Parse(k, flag, buf_keys_[k], buf_vals_[k]);
- LOG(ERROR) << "after parse ";
if (layer_conf_.store_conf().prefetching())
thread_ = new thread(&StoreInputLayer::fetch_data, this);
}
