https://github.com/seabbs/tidyverse-gpu

Adding in support for GPU acceleration via CUDA to rocker/tidyverse
https://github.com/seabbs/tidyverse-gpu

Last synced: about 2 months ago
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Adding in support for GPU acceleration via CUDA to rocker/tidyverse

• Host: GitHub
• URL: https://github.com/seabbs/tidyverse-gpu
• Owner: seabbs
• Created: 2019-01-25T17:23:18.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-01-29T10:00:57.000Z (over 5 years ago)
• Last Synced: 2024-05-02T12:01:32.667Z (5 months ago)
• Language: Dockerfile
• Size: 19.5 KB
• Stars: 5
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

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README

        
---

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>"

)

```

# tidyverse-gpu

A Docker image based on [rocker/tidyverse](https://github.com/rocker-org/rocker-versioned) including GPU support via CUDA. Based on [work](zhttps://github.com/ecohealthalliance/reservoir/blob/master/Dockerfile.gpu) done by [Noam Ross](https://github.com/noamross). The Docker image contains `xgboost` built for GPU's in both R and Python as well as the latest stable release of `h2o`. If you wanted to use a different version of CUDA to the one currently installed then change the relevant environment variables in the `Dockerfile` and rebuild the image.

## Usage

Use as [rocker/tidyverse](https://github.com/rocker-org/rocker-versioned) but replace all `docker` commands with [`nvidia-docker`](https://github.com/NVIDIA/nvidia-docker) commands (must have installed [`nvidia-docker`](https://github.com/NVIDIA/nvidia-docker) on the host system).

* Pull/Build

```{bash, eval = FALSE}

docker pull seabbs/tidyverse-gpu

## Or build 

## Clone repo and navigate into the repo in the terminal

docker build . -t tidyverse-gpu

```

* Run

```{bash, eval = FALSE}

nvidia-docker run -d -p 8787:8787 -e USER=tidyverse-gpu -e PASSWORD=tidyverse-gpu --name tidyverse-gpu seabbs/tidyverse-gpu

## Or build 

nvidia-docker run -d -p 8787:8787 -e USER=tidyverse-gpu -e PASSWORD=tidyverse-gpu --name tidyverse-gpu tidyverse-gpu

```

* Login: Go to `localhost:8787` and sign in using the password and username given with the `docker run` command

## Nvidia Test

Run `nvidia-smi` in a bash shell. If GPU support is working correctly it should return GPU usage and temperature information. 

```{bash, eval = FALSE}

nvidia-smi

```

## Xgboost GPU Test

If the following runs without errors `xgboost` is installed and using the GPU.

```{r, eval = FALSE}

library(xgboost)

# load data

data(agaricus.train, package = 'xgboost')

data(agaricus.test, package = 'xgboost')

train <- agaricus.train

test <- agaricus.test

# fit model

bst <- xgboost(data = train$data, label = train$label, max_depth = 5, eta = 0.001, nrounds = 100,

               nthread = 2, objective = "binary:logistic", tree_method = "gpu_hist")

# predict

pred <- predict(bst, test$data)

```

## Xgboost via H2O Test

`h2o` provides a nice interface to `xgboost`, along with some great tools for hyper-parameter tuning. (*Note: This is not an install of `h2o4gpu` so only `h2o.xgboost` supports GPU acceleration.*)

```{r, eval = FALSE}

# Init h2o

library(h2o)

h2o.init()

# Load test data

australia_path <- system.file("extdata", "australia.csv", package = "h2o")

australia <- h2o.uploadFile(path = australia_path)

independent <- c("premax", "salmax","minairtemp", "maxairtemp", "maxsst",

                 "maxsoilmoist", "Max_czcs")

dependent <- "runoffnew"

# Run xgboost without GPU

h2o.xgboost(y = dependent, x = independent, training_frame = australia,

        ntrees = 1000, backend = "cpu")

# Run xgboost with GPU

h2o.xgboost(y = dependent, x = independent, training_frame = australia,

            ntrees = 1000, backend = "gpu")

```