There are two versions of operator: one for v1alpha1 and one for v1alpha2.
Create a symbolic link inside your GOPATH to the location you checked out the code
mkdir -p ${GOPATH}/src/github.com/kubeflow
ln -sf ${GIT_TRAINING} ${GOPATH}/src/github.com/kubeflow/tf-operator
- GIT_TRAINING should be the location where you checked out https://github.com/kubeflow/tf-operator
Resolve dependencies (if you don't have dep install, check how to do it here)
Install dependencies
dep ensure
Build it
go install github.com/kubeflow/tf-operator/cmd/tf-operator
If you want to build the operator for v1alpha2, please use the command here:
go install github.com/kubeflow/tf-operator/cmd/tf-operator.v2
pipenv is recommended to manage local Python environment. You can find setup information on their website.
To build the following artifacts:
- Docker image for the operator
- Helm chart for deploying it
You can run
# to setup pipenv you have to step into the directory where Pipfile is located
cd py
pipenv install
pipenv shell
cd ..
python -m py.release local --registry=${REGISTRY}
- The docker image will be tagged into your registry
- The helm chart will be created in ./bin
Running the operator locally (as opposed to deploying it on a K8s cluster) is convenient for debugging/development.
First, you need to run a Kubernetes cluster locally. There are lots of choices:
local-up-cluster.sh
runs a single-node Kubernetes cluster locally, but Minikube runs a single-node Kubernetes cluster inside a VM. It is all compilable with the controller, but the Kubernetes version should be 1.8
or above.
Notice: If you use local-up-cluster.sh
, please make sure that the kube-dns is up, see kubernetes/kubernetes#47739 for more details.
We can configure the operator to run locally using the configuration available in your kubeconfig to communicate with a K8s cluster. Set your environment:
export KUBECONFIG=$(echo ~/.kube/config)
export KUBEFLOW_NAMESPACE=$(your_namespace)
- KUBEFLOW_NAMESPACE is used when deployed on Kubernetes, we use this variable to create other resources (e.g. the resource lock) internal in the same namespace. It is optional, use
default
namespace if not set.
After the cluster is up, the TFJob CRD should be created on the cluster.
# If you are using v1alpha1
kubectl create -f ./examples/crd/crd.yml
Or
# If you are using v1alpha2
kubectl create -f ./examples/crd/crd-v1alpha2.yaml
Now we are ready to run operator locally:
tf-operator
To verify local operator is working, create an example job and you should see jobs created by it.
# If you are using v1alpha1
kubectl create -f ./examples/tf_job.yaml
Or
# If you are using v1alpha2
cd ./examples/v1alpha2/dist-mnist
docker build -f Dockerfile -t kubeflow/tf-dist-mnist-test:1.0 .
kubectl create -f ./tf-job-mnist.yaml
On ubuntu the default go package appears to be gccgo-go which has problems see issue golang-go package is also really old so install from golang tarballs instead.
-
Use yapf to format Python code
-
yapf
style is configured in.style.yapf
file -
To autoformat code
yapf -i py/**/*.py
-
To sort imports
isort path/to/module.py