From 3a98ce4b2059d8f8a2e736606f0d510af4b38d0c Mon Sep 17 00:00:00 2001 From: Sean Sall Date: Mon, 15 Apr 2019 15:31:19 +0000 Subject: [PATCH] Update README --- README.md | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index da27dc0..f95851e 100644 --- a/README.md +++ b/README.md @@ -22,13 +22,13 @@ Please create an [issue](https://github.com/typicalTYLER/SolarPanelDataWrangler/ One of the requirements is rtree, which requires you to install libspatialindex, instructions [here](http://toblerity.org/rtree/install.html). -To install the environment, use the `setup/environment_cpu.yml` file, e.g. +To install the environment, choose either the `setup/environment_cpu.yml` and `setup/environment_gpu.yml`. If you have GPU that you intend to use, you'll need to setup the relevant packages / drivers (e.g. cuDNN / CUDA, nvidia diver, etc.) before installing the conda environment. Once you've chosen a YML file, you can create the environment via something like the following: ``` conda create --name spdw --file setup/environment_cpu.yml ``` -*Note*: This was created using `conda=4.6.11` and `python=3.6.8`. +*Note*: These environments are built on `conda=4.6.11`, `python=3.6.8`, and `tensorflow=1.12.0`. ### DeepSolar repository @@ -40,13 +40,27 @@ Your Mapbox API key must be in your environment variables as MAPBOX_ACCESS_TOKEN ## Docker Setup -Within the `setup` directory is a `build_docker_images.sh` file that can be used to automatically setup a docker container that works out of the box with only a single additional step (adding your Mapbox API key). Once docker is set up, you simply need to specify a username that will be used inside the docker container, e.g. +Within the `setup` directory is a `build_docker_images.sh` script that can be used to automatically setup a docker container to develop out of. + +### No GPU Build + +To build a docker image that uses the no GPU conda environment, first install docker. Next, choose a username that you would like to use inside the container, and from **within the setup directory**, run: + +``` +bash build_docker_images.sh --docker_user [specify username here] +``` + +Once the image is built, you should be able to work inside a container created from the image just as if you were logged into a remote instance. + +### GPU Build + +To build a docker image that uses the GPU conda environment, first install nvidia-docker (version 2.0). Next, make sure that you have the appropriate GPU driver installed for your system and for the version of tensorflow that will be used (`1.12.0`) as well as teh versions of CUDA and cuDNN (CUDA 9.0 and cuDNN7. Once that is done, choose a username that you would like to use inside the container, and from **within the setup directory**, run: ``` -bash setup/build_docker_images.sh --docker_user [specify username here] +bash build_docker_images.sh --docker_user [specify username here] --gpu_build ``` -After they're created, you should be able to work inside the container using the conda `spdw` environment that is set up, inside the the `repos` directory for the username you specified. +Once the image is built, you should be able to work inside a container created from the image just as if you were logged into a remote instance. # Overview