From 708643b60c77df37bd74881c45090907efd1fe83 Mon Sep 17 00:00:00 2001
From: Christoph Schranz <christoph.schranz@salzburgresearch.at>
Date: Tue, 14 Jul 2020 17:11:06 +0200
Subject: [PATCH] updated to non-docker-compose build and run

---
 .build/Dockerfile |  1 +
 README.md         | 26 +++++++++++++++++++-------
 2 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/.build/Dockerfile b/.build/Dockerfile
index 596360e..fa03214 100755
--- a/.build/Dockerfile
+++ b/.build/Dockerfile
@@ -5,6 +5,7 @@
 FROM nvidia/cuda:10.1-base-ubuntu18.04
 LABEL maintainer="Christoph Schranz <christoph.schranz@salzburgresearch.at>"
 # This is a concatenated Dockerfile, the maintainers of subsequent sections may vary.
+RUN chmod 1777 /tmp && chmod 1777 /var/tmp
 
 ############################################################################
 #################### Dependency: jupyter/base-image ########################
diff --git a/README.md b/README.md
index b1ed4c8..0662083 100644
--- a/README.md
+++ b/README.md
@@ -29,7 +29,7 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
     The CUDA toolkit is not required on the host system, as it will be deployed 
     in [NVIDIA-docker](https://github.com/NVIDIA/nvidia-docker). 
     You can be sure that you can access your GPU within Docker, 
-    if the command `docker run --runtime nvidia nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi`
+    if the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi`
     returns a result similar to this one:
     ```bash
     Mon Jun 22 09:06:28 2020       
@@ -58,19 +58,31 @@ The image of this repository is available on [Dockerhub](https://hub.docker.com/
 
 ## Quickstart
 
-First of all, it is necessary to generate the `Dockerfile` based on the
+First of all, it is necessary to generate the `Dockerfile` based on the NIVIDA base image and the 
 [docker-stacks](https://github.com/jupyter/docker-stacks).
 As soon as you have access to your GPU within Docker containers 
-(make sure the command `docker run --runtime nvidia nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi` shows your
-GPU statistics), you can generate a Dockerfile and build it via docker-compose.
-The two commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) with the default 
+(make sure the command `docker run --gpus all nvidia/cuda:10.1-base-ubuntu18.04 nvidia-smi` shows your
+GPU statistics), you can generate a Dockerfile, build and run it.
+The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) with the default 
 password `asdf`.
 
   ```bash
-  ./generate-Dockerfile.sh
-  ./start-local.sh -p 8848  # where -p stands for the port, default 8888
+  ./generate_Dockerfile.sh
+  docker build -t gpu-jupyter .build/  # will take a while
+  docker run -d -p [port]:8888 gpu-jupyter  # starts gpu-jupyter WITHOUT GPU support
   ``` 
 
+To run the container with GPU support, a local data volume and , run:
+  ```bash
+docker run -d -it --rm --gpus all -p [JUPYTER_PORT]:8888 -v ./data:/home/jovyan/work -e GRANT_SUDO="yes" -e JUPYTER_ENABLE_LAB="yes" gpu-jupyter
+  ``` 
+  
+Or on windows:
+  ```bash
+docker run -d -it --rm --gpus all -p [JUPYTER_PORT]:8888 -v /${PWD}/data:/home/jovyan/work -e GRANT_SUDO="yes" -e JUPYTER_ENABLE_LAB="yes" gpu-jupyter
+  ``` 
+
+
 ## Parameter
 
 The script `generate-Dockerfile.sh` has multiple parameters: