part of mamba-org | ||
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Package Manager mamba | Package Server quetz | Package Builder boa |
The quetz project is an open source server for conda packages. It is built upon FastAPI with an API-first approach. A quetz server can have many users, channels and packages. With quetz, fine-grained permissions on channel and package-name level will be possible.
Quetz also comes with the quetz-client
that can be used to upload packages to a quetz server instance.
You should have mamba or conda installed.
Then create an environment:
mamba env create -f environment.yml
conda activate quetz
Get Quetz
sources:
git clone https://github.com/mamba-org/quetz.git
Install Quetz
:
Use the editable mode
-e
if you are developer and want to take advantage of thereload
option ofQuetz
pip install -e quetz
Use the CLI to create a Quetz
instance:
quetz run test_quetz --copy-conf ./dev_config.toml --dev --reload
Links:
- http://localhost:8000/ - Login with your github account
- http://localhost:8000/api/dummylogin/alice - Login with test user, one of [alice | bob | carol | dave]
- http://localhost:8000/docs - Swagger UI for this REST service
Download xtensor
as test package:
./download-test-package.sh
Run test upload using quetz-client. (For testing purposes, an API key is created for the test user "alice" at server launch and is printed to the terminal, so use that for this example):
export QUETZ_API_KEY=E_KaBFstCKI9hTdPM7DQq56GglRHf2HW7tQtq6si370
quetz-client http://localhost:8000/api/channels/channel0 xtensor/linux-64/xtensor-0.16.1-0.tar.bz2 xtensor/osx-64/xtensor-0.16.1-0.tar.bz2
Install the test package with conda:
mamba install --strict-channel-priority -c http://localhost:8000/get/channel0 -c conda-forge xtensor
Output:
...
Package Version Build Channel Size
─────────────────────────────────────────────────────────────────────────────────────────────────────
Install:
─────────────────────────────────────────────────────────────────────────────────────────────────────
xtensor 0.16.1 0 http://localhost:8000/get/channel0/osx-64 109 KB
xtl 0.4.16 h04f5b5a_1000 conda-forge/osx-64 47 KB
Summary:
Install: 2 packages
Total download: 156 KB
─────────────────────────────────────────────────────────────────────────────────────────────────────
...
Browse channels:
http://localhost:8000/get/channel0/
To enable the S3 backend, you will first require the s3fs library:
mamba install -c conda-forge s3fs
Then add your access and secret keys to the s3
section with your
config.toml
, like so:
[s3]
access_key = "AKIAIOSFODNN7EXAMPLE"
secret_key = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"
url = "https://..."
region = ""
bucket_prefix="..."
bucket_suffix="..."
Be sure to set the url and region field if not using AWS.
Channels are created with the following semantics:
{bucket_prefix}{channel_name}{bucket_suffix}
The s3 backend is currently designed for one bucket per channel. It may be possible to put all channels in one bucket, but that will require some code tweaks
If you're using IAM roles, dont set access_key
and secret_key
or set them to empty strings ""
.
To enable auth via Google, you will need to register an application at: https://console.developers.google.com/apis/credentials
Then add the client secret & ID to a google
section of your config.toml
:
[google]
client_id = "EXAMPLEID420127570681-6rbcgdj683l3odc3nqearn2dr3pnaisq.apps.googleusercontent.com"
client_secret = "EXAMPLESECRETmD-7UXVCMZV3C7b-kZ9yf70"
By default, quetz will run with sqlite database, which works well for local tests and small local instances. However, if you plan to run quetz in production, we recommend to configure it with the PostgreSQL database. There are several options to install PostgreSQL server on your local machine or production server, one of them being the official PostgreSQL docker image.
You can the PostgresSQL image from the docker hub and start the server with the commands:
docker pull postgres
docker run --name some-postgres -p 5432:5432 -e POSTGRES_PASSWORD=mysecretpassword -d postgres
This will start the server with the user postgres
and the password mysecretpassword
that will be listening for connection on the port 5432 of localhost.
You can then create a database in PostgreSQL for quetz tables:
sudo -u postgres psql -h localhost -c 'CREATE DATABASE quetz OWNER postgres;'
Then in your configuration file (such as dev_config.toml
) replace the [sqlalchemy]
section with:
[sqlalchemy]
database_url = "postgresql://postgres:mysecretpassword@localhost:5432/quetz"
Finally, you can create and run a new quetz deployment based on this configuration (we assume that you saved it in file config_postgres.toml
):
quetz run postgres_quetz --copy-conf config_postgres.toml
Note that this recipe will create an ephemeral PostgreSQL database and it will delete all data after the some-postgres
container is stopped and removed. To make the data persistent, please check the documentation of the postgres
image or your container orchestration system (Kubernetes or similar).
To run the tests with the PostgreSQL database instead of the default SQLite, follow the steps above to start the PG server. Then create an new database:
psql -U postgres -h localhost -c 'CREATE DATABASE test_quetz OWNER postgres;'
You will be asked to type the password to the DB, which you defined when starting your PG server. In the docker-based instructions above, we set it to mysecretpassword
.
To run the tests with this database you need to configure the QUETZ_TEST_DATABASE
environment variable:
QUETZ_TEST_DATABASE="postgresql://postgres:mysecretpassword@localhost:5432/test_quetz" pytest -v ./quetz/tests
Quetz comes with a initial frontend implementation. It can be found in quetz_frontend. To build it, one needs to install:
mamba install 'nodejs>=14'
cd quetz_frontend
npm install
npm run build
# for development
npm run watch
This will build the javascript files and place them in /quetz_frontend/dist/
from where they are automatically picked up by the quetz server.
We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.
This software is licensed under the BSD-3-Clause license. See the LICENSE file for details.
First, make sure you're logged in to the web app.
Then, using the swagger docs at <deployment url>:<port>/docs
, POST to /api/channels
with the name and description of your new channel:
{
"name": "my-channel",
"description": "Description for my-channel",
"private": false
}
This will create a new channel called my-channel
and your user will be the Owner of that channel.
API keys are scoped per channel, per user and optionally per package. In order to generate an API key the following must be true:
- First, make sure you're logged in to the web app.
- The user must be part of the target channel (you might need to create a channel first, see the previous section on how to create a channel via the swagger docs)
- Go to the swagger docs at
<deployment url>:<port>/docs
and POST to/api/api-keys
:
{
"description": "my-test-token",
"roles": [
{
"role": "owner",
"channel": "my-channel"
}
]
}
- Then, GET on
/api/api-keys
to retrieve your token - Finally, set this value to QUETZ_API_KEY so you can use quetz-client to interact with the server.
A proxy channel "mirrors" another channel usually from a different server, so that the packages can be installed from the proxy as if they were installed directly from that server. All downloaded packages are cached locally and the cache is always up to date (there is no risk of serving stale packages). The reason to use the proxy channel is to limit traffic to the server of origin or to serve a channel that could be inaccessible from behind the corporate firewall.
To create a proxy channel use the properties mirror_channel_url=URL_TO_SOURCE_CHANNEL
and mirror_mode='proxy'
in the POST method of /api/channels
endpoint. For example, to proxy the channel named btel
from anaconda cloud server, you might use the following request data:
{
"name": "proxy-channel",
"private": false,
"mirror_channel_url": "https://conda.anaconda.org/btel",
"mirror_mode": "proxy"
}
You may copy the data directly to the Swagger web interface under the heading POST /api/channels
or use the cURL tool from command line. Assuming that you deployed a quetz server on port 8000 (the default) on your local machine, you could make the request with the following cURL command:
export QUETZ_API_KEY=...
curl -X POST "http://localhost:8000/api/channels" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-H "X-API-Key: ${QUETZ_API_KEY}" \
-d '{"name":"proxy-channel",
"private":false,
"mirror_channel_url":"https://conda.anaconda.org/btel",
"mirror_mode":"proxy"}'
where the value of QUETZ_API_KEY
variable should be the API key that was printed when you created the quetz deployment or retrieved using the API as described in the section Generate an API key.
Then you can install packages from the channel the standard way using conda
or mamba
:
mamba install --strict-channel-priority -c http://localhost:8000/get/proxy-channel nrnpython
A mirror channel is an exact copy of another channel usually from a different (anaconda or quetz) server. The packages are downloaded from that server and added to the mirror channel. The mirror channel supports the standard Quetz API except requests that would add or modify the packages (POST /api/channels/{name}/files
, for example). Mirror channels can be used to off load traffic from the primary server, or to create a channel clone on the company Intranet.
Creating a mirror channel is similar to creating proxy channels except that you need to change the value of mirror_mode
attribute from proxy
to mirror
(and choose a more suitable channel name obviously):
{
"name": "mirror-channel",
"private": false,
"mirror_channel_url": "https://conda.anaconda.org/btel",
"mirror_mode": "mirror"
}
or with cURL:
export QUETZ_API_KEY=...
curl -X POST "http://localhost:8000/api/channels" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-H "X-API-Key: ${QUETZ_API_KEY}" \
-d '{"name":"mirror-channel",
"private":false,
"mirror_channel_url":"https://conda.anaconda.org/btel",
"mirror_mode":"mirror"}'
Mirror channels are read only (you can not add or change packages in these channels), but otherwise they are fully functional Quetz channels and support all standard read (GET) operations. For example, you may list all packages using GET /api/channels/{channel_name}/packages
endpoint:
curl http://localhost:8000/api/channels/mirror-channel/packages
If packages are added or modified on the primary server from which they were pulled initially, they won't be updated automatically in the mirror channel. However, you can trigger such synchronisation manually using the PUT /api/channels/{channel_name}/actions
endpoint:
curl -X PUT localhost:8000/api/channels/mirror-channel/actions \
-H "X-API-Key: ${QUETZ_API_KEY}" \
-d '{"action": "synchronize"}'
Only channel owners or maintainers are allowed to trigger synchronisation, therefore you have to provide a valid API key of a privileged user.