dmlc-core | common bricks library for building

I want to install the XG-Boost which I want to use in the C++ in win10 .

And I use the command followed (in the git bash):

Im trying to set up a custom version of XGBoost from https://github.com/robjhyndman/M4metalearning in R.

When I run devtools::install_github("pmontman/customxgboost") I get this error:

I'm trying to add external project as a library to my project using ExternalProject_Add:

I have a block of code that is supposed to build a RNN model with 5 lag variables for an observation of time series data. Here is the code:

While trying to train a lenet model for multiclass classification using h2o deepwater using mxnet backed I am getting the following errors:

Loading H2O mxnet bindings. Found CUDA_HOME or CUDA_PATH environment variable, trying to connect to GPU devices. Loading CUDA library. Loading mxnet library. Loading H2O mxnet bindings. Done loading H2O mxnet bindings. Constructing model. Done constructing model. Building network. mxnet data input shape: (32,100) [10:40:16] /home/jenkins/slave_dir_from_mr-0xb1/workspace/deepwater-master/thirdparty/mxnet/dmlc-core/include/dmlc/logging.h:235: [10:40:16] src/operator/./convolution-inl.h:349: Check failed: (dshape.ndim()) == (4) Input data should be 4D in batch-num_filter-y-x [10:40:16] src/symbol.cxx:189: Check failed: (MXSymbolInferShape(GetHandle(), keys.size(), keys.data(), arg_ind_ptr.data(), arg_shape_data.data(), &in_shape_size, &in_shape_ndim, &in_shape_data, &out_shape_size, &out_shape_ndim, &out_shape_data, &aux_shape_size, &aux_shape_ndim, &aux_shape_data, &complete)) == (0)

The details of my setup :
* Ubuntu : 16.04
* Ram : 12gb
* Graphics card : Nvidia 920mx driver version : 384.90
* Cuda : 8.0.61
* cudnn : 6.0
* R version : 3.4.3
* H2o version : 3.15.0.393 & h2o-R package : 3.16.0.2
* mxnet : 0.11.0
* Train data size : 400mb (when converting to the h2o frame object it comes around 822mb)

Things I have done :
1.) Gave enough memory to java heap while running h2o cluster (java -Xmx9g -jar h2o.jar)
2.) Build the mxnet from source for gpu
3.) Monitored the gpu and system via nvidia-smi and system monitor. At no point do they eat up all the ram to show "out of memory" issue. I still will be having around 2-3gb free before the error shows up
4.) Have tried with tensorflow-gpu(build from source). Checking the pip list made sure that its installed but during model creation in R it gives the error :
Error: java.lang.RuntimeException: Unable to initialize the native Deep Learning backend: null
5.) The only method I got it the h2o deepwater to work with all the backend and w/wo GPU is through docker setup provided in the installation tutorials.

I wanted the same functionality on my laptop instead of using Docker. Also is there any way to run deepwater using just CPU? The link Is it possible to build Deep Water/TensorFlow model in H2O without CUDA doesn't provide any helpful answers. Any help or advice will be greatly appreciated!

I'm trying to install xgboost R package on my linux server using:

I am trying to install XGBoost on a EC2 instance and continually get the following error after trying "pip install xgboost":

I am having trouble installing mxnet GPU for R on Amazon deep learning linux AMI. The environment variables are such a mess that it’s a nightmare for any non-expert sys-admin to figure out.

Step 1: install the ridiculous amount of missing/broken programs and R packages

I would like to train a neural network whilst utilising all 4 GPU's on my g2.8xarge EC2 instance using MXNet. I am using the following AWS Deep Learning Linux community AMI:

Deep Learning AMI Amazon Linux - 3.3_Oct2017 - ami-999844e0)

As per these instructions, when I connect to the instance I switch to keras v1 with the MXNet backend by issuing this command: