A simple, high-performance, scalable model storage service.
Modelx contains three components
-
modelx
Cli tools for the user side. You can use it to
initialized、push、pullmodels，or management model repository locally. -
modelxd
modelx repository server. It follows the OCI protocol and provides an http server to receive the modelx cli push models.
-
modelxdl
model deployment tools. It is integrated into the kubegems model deployment flows.
More infomation see: How Modelx born
Download modelxd binary from latest release to your PATH.
Determine your version with modelxd --version.
Show all CLI options with modelxd --help. Common configurations can be seen below.
Usage:
modelxd [flags]
Flags:
--enable-redirect enable blob storage redirect
-h, --help help for modelxd
--listen string listen address (default ":8080")
--oidc-issuer string oidc issuer
--s3-access-key string s3 access key
--s3-bucket string s3 bucket (default "registry")
--s3-presign-expire duration s3 presign expire (default 1h0m0s)
--s3-region string s3 region
--s3-secret-key string s3 secret key
--s3-url string s3 url (default "https://s3.amazonaws.com")
--tls-ca string tls ca file
--tls-cert string tls cert file
--tls-key string tls key file
-v, --version version for modelxd
Using with Amazon S3 or Compatible services like Minio or DigitalOcean.
Make sure your environment is properly setup to access my-s3-bucket
modelxd --listen=:8080 \
--s3-url=http://<Minio_URL>:<Port> \
--s3-access-key=<AccessKey> \
--s3-secret-key=<SecretKey> \
--s3-bucket=<Bucket> \
--enable-redirect=true
Using HTTPS
If both of the following options are provided, the server will listen and serve HTTPS:
- --tls-cert= - path to tls certificate chain file
- --tls-key= - path to tls key file
HTTPS with Client Certificate Authentication
If the above HTTPS values are provided in addition to below, the server will listen and serve HTTPS and authenticate client requests against the CA certificate:
- --tls-ca-cert= - path to tls certificate file
Using OIDC with KubeGems
Make sure you KubeGems API is properly access
modelxd --listen=:8080 \
--s3-url=http(s)://<Minio_URL>:<Port> \
--s3-access-key=<AccessKey> \
--s3-secret-key=<SecretKey> \
--s3-bucket=<Bucket> \
--enable-redirect=true \
--oidc-issuer=http(s)://<Kubegems_URL>:<Port>
Clone this repository, and run this command below.
export ADVERTISED_IP=<Host_IP> //set your host ip
sed -i "s/__ADVERTISED_IP__/${ADVERTISED_IP}/g" docker-compose.yaml
docker compose up -d
Setup a temp S3 server using minio:
helm install --namespace minio --create-namespace --repo https://charts.min.io \
--set rootUser=root,rootPassword=password \
--set 'mode=standalone,replicas=1,persistence.enabled=false,buckets[0].name=modelx,buckets[0].policy=none' \
--set service.type=NodePort \
minio minioMake sure we can access S3 url out of cluster, modelx client pull/push from the address directly.
Setup modelx from helm:
export S3_URL="http://$(kubectl get node -o jsonpath='{.items[0].status.addresses[0].address}'):$(kubectl -n minio get svc minio -o jsonpath='{.spec.ports[0].nodePort}')"
echo ${S3_URL} # minio service node port address
helm install --namespace modelx --create-namespace --repo https://charts.kubegems.io/kubegems \
--set "storage.s3.url=${S3_URL},storage.s3.accessKey=root,storage.s3.secretKey=password,storage.s3.bucket=modelx" \
--set service.type=NodePort \
modelx modelxAccess modelx server fom node port:
export MODELX_URL="http://$(kubectl get node -o jsonpath='{.items[0].status.addresses[0].address}'):$(kubectl -n modelx get svc modelx -ojsonpath='{.spec.ports[0].nodePort}')"
echo ${MODELX_URL} # modelx service node port address
curl ${MODELX_URL}
# {"schemaVersion":0,"manifests":null} # OK, if see this outputFor more infomations see setup.
Download binary from latest release to your PATH.
Completions provided via modelx completions zsh|bash|fish|powershell.
First, add and login a model repository
# Add model repository
$ modelx repo add modelx http://<your_modelxd_url>
# Login repository, if you don't set oidc iusername, press "enter" to skip token authentication.
$ modelx login modelx
Token:
Login successful for modelxSecond, Init a model locally
$ modelx init class
Modelx model initialized in class
$ tree class
class
├── modelx.yaml
└── README.md
$ cd class
# add model files
$ echo "some script" > scripy.sh
$ echo -n "some binary" > binary.datFinally, push your models ! 💪🏻
# add modelx registry
$ modelx push modelx/library/class@v1
Pushing to http://modelx.kubegems.io/library/class@v1
17e682f0 [++++++++++++++++++++++++++++++++++++++++] done
17e682f0 [++++++++++++++++++++++++++++++++++++++++] done
17e682f0 [++++++++++++++++++++++++++++++++++++++++] done
b6f9dd31 [++++++++++++++++++++++++++++++++++++++++] done
test.img [++++++++++++++++++++++++++++++++++++++++] done
4c513e54 [++++++++++++++++++++++++++++++++++++++++] donelist repository models
$ modelx list modelx
+---------+-------+------------------------------------------+
| PROJECT | NAME | URL |
+---------+-------+------------------------------------------+
| library | class | http://modelx.kubegems.io/library/class |
+---------+-------+------------------------------------------+list model versions
$ modelx list test/class
+---------+--------------------------------------------+--------+
| VERSION | URL | SIZE |
+---------+--------------------------------------------+--------+
| v1 | http://modelx.kubegems.io/library/class@v1 | 4.29GB |
| v2 | http://modelx.kubegems.io/library/class@v2 | 4.29GB |
| v3 | http://modelx.kubegems.io/library/class@v3 | 4.29GB |
+---------+--------------------------------------------+--------+get model infomation
$ modelx info modelx/library/class@v1
config:
inputs: {}
outputs: {}
description: This is a modelx model
framework: pytorch
maintainers:
- [email protected]
modelFiles: []
tags:
- modelx
task: ""
modelx.yaml contains model's metadata, a full example is:
config:
inputs: {}
outputs: {}
description: This is a modelx model
framework: <some framework>
maintainers:
- maintainer
modelFiles: []
tags:
- modelx
- <other>
task: ""