the SDSS API for delivering and accessing remote information.
This API is built using the FastAPI web server. Python depdendices are managed with poetry.
git clone https://github.com/sdss/valis valis
cd valis
poetry install
The default install does not install any of the Solara dependencies for Jdaviz and the SDSS DataView Explorer. To install these dependencies, run
poetry install -E solara
To update poetry itself, run
poetry self update
To update the package dependencies for valis
, run
poetry update [package]
This will update all the packages, or the specified [package]
, resolve all dependencies, and update the poetry.lock
file.
To install new packages and add them to the pyproject.toml
and poetry.lock
files, run
poetry install [package]
To run a local instance for development, run the following from the top level of the valis
repo.
uvicorn valis.wsgi:app --reload
This will start a local web server at http://localhost:8000/valis/
. The API documentation will be located at http://localhost:8000/valis/docs
. Or to see the alternate documentation, go to http://localhost:8000/valis/redoc/
Valis uses the sdssdb
package for all connections to databases. The most relevant database for the API is the sdss5db
on pipelines.sdss.org
. The easiest way to connect is through a local SSH tunnel. To set up a tunnel,
~/.ssh/config
. Replace unid
with your Utah unid.Host pipe
HostName pipelines.sdss.org
User [unid]
ForwardX11Trusted yes
ProxyCommand ssh -A [unid]@mwm.sdss.org nc %h %p
ssh -L 6000:localhost:5432 pipe
~/.pgass
file with the following lines. Replace port
, unid
, and password
, with your tunneled port, Utah unid, and db password, respectively. Alternatively, just set the VALIS_DB_PASS environment variable with your database password.
localhost:[port]:*:[unid]:[password]
host.docker.internal:[port]:*:[unid]:[password]
or optionally add them to the ~/.config/sdss/valis.yaml
configuration file.
allow_origin: ['http://localhost:3000']
db_remote: true
db_port: 6000
db_user: {unid}
Additionally, you can set the environment variable VALIS_DB_RESET=false
or add db_reset: false
to valis.yaml
. This will prevent the DB connection to be closed after a query completes and should speed up new queries. This setting should not be used in production.
This section describes a variety of deployment methods. Valis uses gunicorn as its wsgi http server. It binds the app both to port 8000, and a unix socket. The defaut mode is to start valis with an awsgi uvicorn server, with 4 workers.
See the SDSS Zora+Valis Docker repo page.
TBD
gunicorn -c python/valis/wsgi_conf.py valis.wsgi:app
This also exposes valis to port 8000, and should be available at http://localhost:8000
.
There are two dockerfiles, one for running in development mode and one for production. To connect valis to the sdss5db
database, you'll need to set several VALIS_DB_XXX environment variables during the docker run
command.
You will also need to volume mount the SDSS SAS to /root/sas
, e.g. -v $SAS_BASE_DIR:/root/sas
. You can also mount individual SAS directories, but you will need to explicitly set the SAS_BASE_DIR
environment variable to point the root location, e.g. -v local/sas/dr17:/data/sas/dr17 -e SAS_BASE_DIR=/data/sas
.
The following examples show how to connect the valis docker to a database running on the same machine, following the database setup instructions above.
Development
To build the docker image, run
docker build -t valis-dev -f Dockerfile.dev .
To start a container, run
docker run -p 8000:8000 -e VALIS_DB_REMOTE=True -e VALIS_DB_HOST=host.docker.internal -e VALIS_DB_USER=[user] -e VALIS_DB_PASS=[password] -e VALIS_DB_PORT=6000 -v $SAS_BASE_DIR:/root/sas valis-dev
Production
To build the docker image, run
docker build -t valis -f Dockerfile .
To start a container, run
docker run -p 8000:8000 -e VALIS_DB_REMOTE=True -e VALIS_DB_HOST=host.docker.internal -e VALIS_DB_USER=[user] -e VALIS_DB_PASS=[password] -e VALIS_DB_PORT=6000 -v $SAS_BASE_DIR:/root/sas valis
Note: If your docker vm has only a small resource allocation, the production container may crash on start, due to the number of workers allocated. You can adjust the number of workers with the VALIS_WORKERS
envvar. For example, add -e VALIS_WORKERS=2
to scale the number of workers down to 2.
All dockerfiles work with podman
, and the syntax is the same as above. Simply replace docker
with podman
.