docs/how_to/faq.md - mxnet-test - Git at Google

 # Frequently Asked Questions

 This topic provides answers to the frequently asked questions on [mxnet/issues](https://github.com/dmlc/mxnet/issues). Before posting an issue, please check this page. If you would like to contribute to this page, please make the questions and answers simple. If your answer is  extremely detailed, please post it elsewhere and link to it.

 ## Building and Installation

 You can find answers to most questions the [build page](http://mxnet.io/get_started/setup.html).

 ## Speed

 #### It took a long time to start running on a GPU

 Try to disable opencv to use a GPU: [build opencv from source with GPU module disabled](http://mxnet.io/get_started/setup.html#build-opencv-from-source-code).

 #### It's slow on a single GPU

 Check the following:

 1. Ensure that your CUDA/driver version is not too old.
 2. Build with `USE_CUDNN=1`. This often increases speed 50+%. Try to use the newest version.
 3. Set `export MXNET_CUDNN_AUTOTUNE_DEFAULT=1` before running. This often increases speed 10%-15%.
 4. If you are using Tesla GPUs by `nvidia-smi -e 0`, disable ECC. You might need root permission and have to reboot.
 5. For Tesla cards by `nvidia-smi -ac ??`, set to the maximal clock. For details, see [this blog](https://devblogs.nvidia.com/parallelforall/increase-performance-gpu-boost-k80-autoboost/).
 6. No throttle reason `nvidia-smi -q -d PERFORMANCE` is often caused by temperature.

 #### No increase in speed when using more than one GPU or computer

 Check the following:

 1. Does your neural network already run fast, such as >1000 example/sec or >10 batches/sec? If yes, it's unlikely to speed up any further by adding more resources because of the communication overhead.
 2. Are you using a small batch size? Try to increase it.
 3. Are you using more than 4 GPUs? Try using `--kv-store=device`.

 ## Memory Usage

 #### Abnormal CPU memory usage

 This might be due to the data pre-fetch. See [issue 2111](https://github.com/dmlc/mxnet/issues/2111).

 ## Pending Review
 The following topics need to be reviewed.

 #### How to Copy Part of Parameters to Another Model
 Most MXNet model consists two parts, the argument arrays and symbol. You can simply copy the argument array to the argument array of another model. For example, in the Python model API, you can do this:

 ```python
 copied_model =  mx.model.FeedForward(ctx=mx.gpu(), symbol=new_symbol,
                                      arg_params=old_arg_params, aux_params=old_aux_params,
                                      allow_extra_params=True);
 ```
 For information about copying model parameters from an existing ```old_arg_params```, see this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).

 #### How to Extract the Feature Map of a Certain Layer
 See this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).


 #### What Is the Relationship Between MXNet and CXXNet, Minerva, and Purine2?
 MXNet is created in collaboration by authors from the three projects.
 MXNet reflects what we have learned from these projects.
 It combines the important aspects of the existing projects: general efficiency, flexibility, and memory efficiency.

 MXNet also contains new approaches that allow you to combine different
 ways of programming and write CPU/GPU applications that are more
 memory efficient than CXXNet and Purine, and more flexible than Minerva.


 #### What Is the Relationship Between MXNet and TensorFlow?
 Both MXNet and [TensorFlow](https://www.tensorflow.org/) use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a [mixed approach](https://mxnet.io/architecture/program_model.html), with a dynamic dependency scheduler to combine symbolic and imperative programming.
 In short, MXNet is lightweight and *mixed* with flexibility from imperative programming, while using a computation graph to make it very fast and memory efficient.
	# Frequently Asked Questions

	This topic provides answers to the frequently asked questions on [mxnet/issues](https://github.com/dmlc/mxnet/issues). Before posting an issue, please check this page. If you would like to contribute to this page, please make the questions and answers simple. If your answer is extremely detailed, please post it elsewhere and link to it.

	## Building and Installation

	You can find answers to most questions the [build page](http://mxnet.io/get_started/setup.html).

	## Speed

	#### It took a long time to start running on a GPU

	Try to disable opencv to use a GPU: [build opencv from source with GPU module disabled](http://mxnet.io/get_started/setup.html#build-opencv-from-source-code).

	#### It's slow on a single GPU

	Check the following:

	1. Ensure that your CUDA/driver version is not too old.
	2. Build with `USE_CUDNN=1`. This often increases speed 50+%. Try to use the newest version.
	3. Set `export MXNET_CUDNN_AUTOTUNE_DEFAULT=1` before running. This often increases speed 10%-15%.
	4. If you are using Tesla GPUs by `nvidia-smi -e 0`, disable ECC. You might need root permission and have to reboot.
	5. For Tesla cards by `nvidia-smi -ac ??`, set to the maximal clock. For details, see [this blog](https://devblogs.nvidia.com/parallelforall/increase-performance-gpu-boost-k80-autoboost/).
	6. No throttle reason `nvidia-smi -q -d PERFORMANCE` is often caused by temperature.

	#### No increase in speed when using more than one GPU or computer

	Check the following:

	1. Does your neural network already run fast, such as >1000 example/sec or >10 batches/sec? If yes, it's unlikely to speed up any further by adding more resources because of the communication overhead.
	2. Are you using a small batch size? Try to increase it.
	3. Are you using more than 4 GPUs? Try using `--kv-store=device`.

	## Memory Usage

	#### Abnormal CPU memory usage

	This might be due to the data pre-fetch. See [issue 2111](https://github.com/dmlc/mxnet/issues/2111).

	## Pending Review
	The following topics need to be reviewed.

	#### How to Copy Part of Parameters to Another Model
	Most MXNet model consists two parts, the argument arrays and symbol. You can simply copy the argument array to the argument array of another model. For example, in the Python model API, you can do this:

	```python
	copied_model = mx.model.FeedForward(ctx=mx.gpu(), symbol=new_symbol,
	arg_params=old_arg_params, aux_params=old_aux_params,
	allow_extra_params=True);
	```
	For information about copying model parameters from an existing ```old_arg_params```, see this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).

	#### How to Extract the Feature Map of a Certain Layer
	See this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb). More notebooks please refer to [dmlc/mxnet-notebooks](https://github.com/dmlc/mxnet-notebooks).


	#### What Is the Relationship Between MXNet and CXXNet, Minerva, and Purine2?
	MXNet is created in collaboration by authors from the three projects.
	MXNet reflects what we have learned from these projects.
	It combines the important aspects of the existing projects: general efficiency, flexibility, and memory efficiency.

	MXNet also contains new approaches that allow you to combine different
	ways of programming and write CPU/GPU applications that are more
	memory efficient than CXXNet and Purine, and more flexible than Minerva.


	#### What Is the Relationship Between MXNet and TensorFlow?
	Both MXNet and [TensorFlow](https://www.tensorflow.org/) use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a [mixed approach](https://mxnet.io/architecture/program_model.html), with a dynamic dependency scheduler to combine symbolic and imperative programming.
	In short, MXNet is lightweight and mixed with flexibility from imperative programming, while using a computation graph to make it very fast and memory efficient.