hamedhsn/distributed-tensorflow-on-k8s

Distributed Tensorflow on Kubernetes

Build a fully integrated pipeline to train your machine learning models with Tensorflow and Kubernetes.

This repo will guide you through:

1. setting up a local environment with python, pip and tensorflow
2. packaging up your models as Docker containers
3. creating and configuring a Kubernetes cluster
4. deploying models in your cluster
5. scaling your model using Distributed Tensorflow
6. serving your model
7. tuning your model using hyperparameter optimisation

Prerequisites

You should have the following tools installed:

• minikube
• kubectl
• ksonnet
• python 2.7
• pip
• sed
• an account on Docker Hub
• an account on GCP
• Gcloud

Recognising handwritten digits

MNIST is a simple computer vision dataset. It consists of images of handwritten digits like these:

It also includes labels for each image, telling us which digit it is. For example, the labels for the above images are 5, 0, 4, and 1.

In this tutorial, you're going to train a model to look at images and predict what digits they are.

Writing scalable Tensorflow

If you plan to train your model using distributed Tensorflow you should be aware of:

• you should use the Estimator API where possible.
• distribute Tensorflow works only with tf.estimator.train_and_evaluate. If you use the method train and evaluate it won't work.
• you should save your model with export_savedmodel so that Tensorflow serving can serve them
• you should use use tf.estimator.RunConfig to read the configuration from the environment. The Tensorflow operator in Kubedflow automatically populated the environment variables that are consumed by that class.

Setting up a local environment

You can create a virtual environment for python with:

virtualenv --system-site-packages --python /usr/bin/python src

Please note that you may have to customise the path for your python binary.

You can activate the virtual environment with:

cd src
source bin/activate

You should install the dependencies with:

pip install -r requirements.txt

You can test that the script works as expected with:

python main.py

Packaging up your models as Docker containers

You can package your application in a Docker image with:

cd src
docker build -t learnk8s/mnist:1.0.0 .

Please note that you may want to customise the image to have the username of your Docker Hub account instead of learnk8s

You can test the Docker image with:

docker run -ti learnk8s/mnist:1.0.0

You can upload the Docker image to the Docker Hub registry with:

docker push learnk8s/mnist:1.0.0

Creating and configuring a Kubernetes cluster

You can train your models in the cloud or locally.

Minikube

You can create a local Kubernetes cluster with minikube:

minikube start --cpus 4 --memory 8096 --disk-size=40g

Once your cluster is ready, you can install kubeflow.

Kubeflow

You can download the packages with:

ks init my-kubeflow
cd my-kubeflow
ks registry add kubeflow github.com/kubeflow/kubeflow/tree/v0.1.2/kubeflow
ks pkg install kubeflow/core@v0.1.2
ks pkg install kubeflow/tf-serving@v0.1.2
ks pkg install kubeflow/tf-job@v0.1.2

You can generate a component from a Ksonnet prototype with:

ks generate core kubeflow-core --name=kubeflow-core

Create a separate namespace for kubeflow:

kubectl create namespace kubeflow

Make the environment the default environment for ksonnet with:

ks env set default --namespace kubeflow

Deploy kubeflow with:

ks apply default -c kubeflow-core

NFS

To use distributed Tensorflow, you have to share a filesystem between the master node and the parameter servers.

You can create an NFS server with:

kubectl create -f kube/nfs-minikube.yaml

Make a note of the IP of the service for the NFS server with:

kubectl get svc nfs-server

Replace nfs-server.default.svc.cluster.local with the ip address of the service in kube/pvc-minikube.yaml.

The change is necessary since kube-dns is not configured correctly in the VM and the kubelet can't resolve the domain name.

Create the volume with:

kubectl create -f kube/pvc-minikube.yaml

GKE

Create a cluster on GKE with:

gcloud container clusters create distributed-tf --machine-type=n1-standard-8 --num-nodes=3

You can obtain the credentials for kubectl with:

gcloud container clusters get-credentials distributed-tf

Give yourself admin permission to install kubeflow:

kubectl create clusterrolebinding default-admin --clusterrole=cluster-admin --user=daniele.polencic@gmail.com

Kubeflow

You can download the packages with:

ks init my-kubeflow
cd my-kubeflow
ks registry add kubeflow github.com/kubeflow/kubeflow/tree/v0.1.2/kubeflow
ks pkg install kubeflow/core@v0.1.2
ks pkg install kubeflow/tf-serving@v0.1.2
ks pkg install kubeflow/tf-job@v0.1.2

You can generate a component from a Ksonnet prototype with:

ks generate core kubeflow-core --name=kubeflow-core

Create a separate namespace for kubeflow:

kubectl create namespace kubeflow

Make the environment the default environment for ksonnet with:

ks env set default --namespace kubeflow

Configure kubeflow to run in the Google Cloud Platform:

ks param set kubeflow-core cloud gcp

Deploy kubeflow with:

ks apply default -c kubeflow-core

NFS

To use distributed Tensorflow, you have to share a filesystem between the master node and the parameter servers.

Create a Google Compute Engine persistent disk:

gcloud compute disks create --size=10GB gce-nfs-disk

You can create an NFS server with:

kubectl create -f kube/nfs-gke.yaml

Create an NFS volume with:

kubectl create -f kube/pvc-gke.yaml

Deploying models in your cluster

You can submit a job to Kubernetes to run your Docker container with:

kubectl create -f kube/job.yaml

Please note that you may want to customise the image for your container.

The job runs a single container and doesn't scale.

However, it is still more convenient than running it on your computer.

Scaling your model using Distributed Tensorflow

You can run a distributed Tensorflow job on your NFS filesystem with:

kubectl create -f kube/tfjob.yaml

The results are stored in the NFS volume.

You can visualise the detail of your distributed tensorflow job with Tensorboard.

You can deploy Tensorboard with:

kubectl create -f kube/tensorboard.yaml

Retrieve the name of the Tensorboard's Pod with:

kubectl get pods -l app=tensorboard

You can forward the traffic from the Pod on your cluster to your computer with:

kubectl port-forward tensorboard-XX-ID-XX 8080:6006

Please note that you should probably use an Ingress manifest to expose your service to the public permanently.

You can visit the dashboard at http://localhost:8080.

Serving your model

You can serve your model with Tensorflow Serving.

You can create a Tensorflow Serving server with:

kubectl create -f kube/serving.yaml

Retrieve the name of the Tensorflow Serving's Pod with:

kubectl get pods -l app=tf-serving

You can forward the traffic from the Tensorboard's Pod on your cluster to your computer with:

kubectl port-forward tf-serving-XX-ID-XX 8080:9000

Please note that you should probably use an Ingress manifest to expose your service to the public permanently.

You can query the model using the client:

cd src
python client.py --host localhost --port 8080 --image ../data/4.png --signature_name predict --model test

Please make sure your virtualenv is still active.

The model should recognise the digit 4.

Tuning your model using hyperparameter optimisation

The model can be tuned with the following parameters:

• the learning rate
• the number of hidden layers in the neural network

You could submit a set of jobs to investigate the different combinations of parameters.

The templated folder contains a tf-templated.yaml file with placeholders for the variables.

The run.sh script interpolated the values and submit the TFJobs to the cluster.

Before you run the jobs, make sure you have your Tensorboard running locally:

kubectl port-forward tensorboard-XX-ID-XX 8080:6006

You can run the test with:

cd templated
./run.sh

You can follow the progress of the training in real-time at http://localhost:8080.

Final notes

You should probably expose your services such as Tensorboard and Tensorflow Serving with an ingress manifest rather than using the port forwarding functionality in kube-proxy.

The NFS volume is running on a single instance and isn't highly available. Having a single node for your storage may work if you run small workloads, but you should probably investigate Ceph, GlusterFS or rook.io as a way to manage distributed storage.

You should consider using Helm instead of crafting your own scripts to interpolate yaml files.