Setup

Docker Images

Using the Docker images published for Raster Vision makes it easy to use a fully set up environment. We have tested this with Docker 19, although you may be able to use a lower version.

The images we publish include plugins and dependencies for using Raster Vision with PyTorch, and AWS S3 and Batch. These are published to quay.io/azavea/raster-vision. To run the container for the latest release, run:

> docker run --rm -it quay.io/azavea/raster-vision:pytorch-0.13 /bin/bash

There are also images with the -latest suffix for the latest commits on the master branch. You’ll likely need to mount volumes and expose ports to make this container fully useful; see the docker/run script for an example usage.

You can also base your own Dockerfiles off the Raster Vision image to use with your own codebase. See Bootstrap new projects with a template for more information.

Docker Scripts

There are several scripts under docker/ in the Raster Vision repo that make it easier to build the Docker images from scratch, and run the container in various ways. These are useful if you are experimenting with changes to the Raster Vision source code, or writing plugins.

After cloning the repo, you can build the Docker image using:

> docker/build

Before running the container, set an environment variable to a local directory in which to store data.

> export RASTER_VISION_DATA_DIR="/path/to/data"

To run a Bash console in the PyTorch Docker container use:

> docker/run

This will mount the $RASTER_VISION_DATA_DIR local directory to to /opt/data/ inside the container.

Warning

Users running under WSL2 in Windows will need to unset the NAME environment variable. For example, instead of docker/run, you would run NAME='' docker/run. By default, WSL2 sets a NAME variable that matches the network name of your computer. This environment variable collides with a variable in the docker/run script.

This script also has options for forwarding AWS credentials, and running Jupyter notebooks which can be seen below.

> docker/run --help

Usage: run <options> <command>

Run a console in a Raster Vision Docker image locally.
By default, the raster-vision-pytorch image is used in the CPU runtime.

Environment variables:
RASTER_VISION_DATA_DIR (directory for storing data; mounted to /opt/data)
RASTER_VISION_NOTEBOOK_DIR (optional directory for Jupyter notebooks; mounted to /opt/notebooks)
AWS_PROFILE (optional AWS profile)

Options:
--aws forwards AWS credentials (sets AWS_PROFILE env var and mounts ~/.aws to /root/.aws)
--tensorboard maps port 6006
--name sets the name of the running container
--jupyter forwards port 8888, mounts RASTER_VISION_NOTEBOOK_DIR to /opt/notebooks, and runs Jupyter
--docs runs the docs server and forwards port 8000
--debug forwards port 3000 for use with remote debugger
--gpu use nvidia runtime

All arguments after above options are passed to 'docker run'.

Installing via pip

Rather than running Raster Vision from inside a Docker container, you can directly install the library using pip. However, we recommend using the Docker images since it can be difficult to install some of the dependencies.

> pip install rastervision==0.13

Note

Raster Vision requires Python 3.7 or later. Use pip3 install rastervision==0.13.0 if you have more than one version of Python installed.

You will also need various dependencies that are not pip-installable. For an example of setting these up, see the Dockerfile.

Install individual pip packages

Raster Vision is comprised of a required rastervision.pipeline package, and a number of optional plugin packages, as described in Codebase Overview. Each of these packages have their own dependencies, and can be installed individually. Running the following command:

> pip install rastervision==0.13

is equivalent to running the following sequence of commands:

> pip install rastervision_pipeline==0.13
> pip install rastervision_aws_s3==0.13
> pip install rastervision_aws_batch==0.13
> pip install rastervision_core==0.13
> pip install rastervision_pytorch_learner==0.13
> pip install rastervision_pytorch_backend==0.13
> pip install rastervision_gdal_vsi==0.13

Troubleshooting macOS Installation

If you encounter problems running pip install rastervision==0.13 on macOS, you may have to manually install Cython and pyproj.

To circumvent a problem installing pyproj with Python 3.7, you may also have to install that library using git+https:

> pip install cython
> pip install git+https://github.com/jswhit/pyproj.git@e56e879438f0a1688b89b33228ebda0f0d885c19
> pip install rastervision==0.13.0

Raster Vision Configuration

Raster Vision is configured via the everett library, and will look for configuration in the following locations, in this order:

  • Environment Variables

  • A .env file in the working directory that holds environment variables.

  • Raster Vision INI configuration files

By default, Raster Vision looks for a configuration file named default in the ${HOME}/.rastervision folder.

Profiles

Profiles allow you to specify profile names from the command line or environment variables to determine which settings to use. The configuration file used will be named the same as the profile: if you had two profiles (the default and one named myprofile), your ${HOME}/.rastervision would look like this:

> ls ~/.rastervision
default    myprofile

Use the rastervision --profile option in the Command Line Interface to set the profile.

Configuration File Sections

AWS_S3

[AWS_S3]
requester_pays = False
  • requester_pays - Set to True if you would like to allow using requester pays S3 buckets. The default value is False.

Other

Other configurations are documented elsewhere:

Environment Variables

Any profile file option can also be stated in the environment. Just prepend the section name to the setting name, e.g. export AWS_S3_REQUESTER_PAYS="False".

In addition to those environment variables that match the INI file values, there are the following environment variable options:

  • TMPDIR - Setting this environment variable will cause all temporary directories to be created inside this folder. This is useful, for example, when you have a Docker container setup that mounts large network storage into a specific directory inside the Docker container. The tmp_dir can also be set on Command Line Interface as a root option.

  • RV_CONFIG - Optional path to the specific Raster Vision Configuration file. These configurations will override configurations that exist in configurations files in the default locations, but will not cause those configurations to be ignored.

  • RV_CONFIG_DIR - Optional path to the directory that contains Raster Vision configuration. Defaults to ${HOME}/.rastervision

Running on a machine with GPUs

If you would like to run Raster Vision in a Docker container with GPUs - e.g. if you have your own GPU machine or you spun up a GPU-enabled machine on a cloud provider like a p3.2xlarge on AWS - you’ll need to check some things so that the Docker container can utilize the GPUs.

Here are some severely outdated, but potentially still useful instructions written by a community member on setting up an AWS account and a GPU-enabled EC2 instance to run Raster Vision.

Install nvidia-docker

You’ll need to install the nvidia-docker runtime on your system. Follow their Quickstart and installation instructions. Make sure that your GPU is supported by NVIDIA Docker - if not you might need to find another way to have your Docker container communicate with the GPU. If you figure out how to support more GPUs, please let us know so we can add the steps to this documentation!

Use the nvidia-docker runtime

When running your Docker container, be sure to include the --runtime=nvidia option, e.g.

> docker run --runtime=nvidia --rm -it quay.io/azavea/raster-vision:pytorch-0.13 /bin/bash

or use the --gpu option with the docker/run script.

Ensure your setup sees the GPUS

We recommend you ensure that the GPUs are actually enabled. If you don’t, you may run a training job that you think is using the GPU and isn’t, and runs very slowly.

One way to check this is to make sure PyTorch can see the GPU(s). To do this, open up a python console and run the following:

import torch
torch.cuda.is_available()
torch.cuda.get_device_name(0)

This should print out something like:

True
Tesla K80

If you have nvidia-smi installed, you can also use this command to inspect GPU utilization while the training job is running:

> watch -d -n 0.5 nvidia-smi

Setting up AWS Batch

To run Raster Vision using AWS Batch, you’ll need to setup your AWS account with a specific set of Batch resources, which you can do using Setup AWS Batch using CloudFormation.

AWS Batch Configuration

After creating the resources on AWS, set the following configuration in your Raster Vision Configuration. Check the AWS Batch console to see the names of the resources that were created, as they vary depending on how CloudFormation was configured.

[BATCH]
gpu_job_queue=RasterVisionGpuJobQueue
gpu_job_def=RasterVisionHostedPyTorchGpuJobDefinition
cpu_job_queue=RasterVisionCpuJobQueue
cpu_job_def=RasterVisionHostedPyTorchCpuJobDefinition
attempts=5
  • gpu_job_queue - job queue for GPU jobs

  • gpu_job_def - job definition that defines the GPU Batch jobs

  • cpu_job_queue - job queue for CPU-only jobs

  • cpu_job_def - job definition that defines the CPU-only Batch jobs

  • attempts - Optional number of attempts to retry failed jobs. It is good to set this to > 1 since Batch often kills jobs for no apparent reason.

See also

For more information about how Raster Vision uses AWS Batch, see the section: Running remotely.