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ElasticDL on Personal Computer

This document shows how to run ElasticDL jobs on your personal computer using Minikube.


  1. Install Minikube, preferably >= v1.11.0, following the installation guide. Minikube runs a single-node Kubernetes cluster in a virtual machine on your personal computer.

  2. Install Docker CE, preferably >= 18.x, following the guide for building Docker images containing user-defined models and the ElasticDL framework.

  3. Install Python, preferably >= 3.6, because the ElasticDL command-line tool is in Python.


Among all machine learning toolkits that ElasticDL can work with, TensorFlow is the most tested and used. In this tutorial, we use a model from the model zoo directory. This model is defined using TensorFlow Keras API. To write your models, please refer to this tutorial.


We use the MNIST dataset in this tutorial. The dynamic data partitioning mechanism of ElasticDL requires that the training data files are in the RecordIO format. To download the MNIST dataset and convert it into RecordIO files, please run the following command.

docker run --rm -it \
  -v $HOME/.keras:/root/.keras \
  -v $PWD:/work \
  -w /work \
  elasticdl/elasticdl:dev bash -c "/scripts/ data"

After the running of this command, we will see the generated dataset files in the directory ./data.

The Kubernetes Cluster

The following command starts a Kubernetes cluster locally using Minikube. It uses hyperkit, a hypervisor coming with macOS, to create the virtual machine cluster. If you want, please feel free to use other hypervisors including VirtualBox.

minikube start --vm-driver=hyperkit \
  --cpus 2 --memory 6144 --disk-size=50gb \
  --mount=true --mount-string="./data:/data"
eval $(minikube docker-env)

The command-line option --mount-string exposes the directory ./data on the host to Minikube as /data, which, we can later bind mount into containers running on the Kubernetes cluster.

The command minikube docker-env returns a set of Bash environment variable exports to configure your local environment to re-use the Docker daemon inside the Minikube instance.

The following command is necessary to enable RBAC of Kubernetes.

kubectl apply -f \

If you happen to live in a region where is banned, you might want to Git clone the above repository to get the YAML file.

Install ElasticDL Client Tool

The following command installs the command line tool elasticdl, which talks to the Kubernetes cluster and operates ElasticDL jobs.

pip install elasticdl_client

Build the Docker Image with Model Definition

Kubernetes runs Docker containers, so we need to put the training system, consisting of user-defined models, ElasticDL the trainer, and all dependencies, into a Docker image.

In this tutorial, we use a predefined model in the ElasticDL repository. To retrieve the source code, please run the following command.

git clone

Model definitions are in directory elasticdl/model_zoo.

Build the Docker Image for Parameter Server

The following commands build the Docker image elasticdl:mnist_ps

cd elasticdl
elasticdl zoo init --model_zoo=model_zoo
elasticdl zoo build --image=elasticdl:mnist_ps .

Build the Docker Image for AllReduce

We have not released ElasticDL packages with AllReduce yet. Thus, we need to manually build packages with AllReduce support.

We must build an image elasticdl:dev_allreduce first using the


Then we use this image to build packages with AllReduce support.


After this, we can build the AllReduce training image elasticdl:mnist_allreduce with model definitions in model_zoo.

elasticdl zoo init \
  --base_image=elasticdl:dev_allreduce \
  --model_zoo=model_zoo \
elasticdl zoo build --image=elasticdl:mnist_allreduce .

Submit the Training Job Using Parameter Server

The following command submits a training job:

elasticdl train \
  --image_name=elasticdl:mnist_ps \
  --model_zoo=model_zoo \
  --model_def=mnist.mnist_functional_api.custom_model \
  --training_data=/data/mnist/train \
  --validation_data=/data/mnist/test \
  --num_epochs=2 \
  --master_resource_request="cpu=0.2,memory=1024Mi" \
  --master_resource_limit="cpu=1,memory=2048Mi" \
  --worker_resource_request="cpu=0.4,memory=1024Mi" \
  --worker_resource_limit="cpu=1,memory=2048Mi" \
  --ps_resource_request="cpu=0.2,memory=1024Mi" \
  --ps_resource_limit="cpu=1,memory=2048Mi" \
  --minibatch_size=64 \
  --num_minibatches_per_task=2 \
  --num_ps_pods=1 \
  --num_workers=1 \
  --evaluation_steps=50 \
  --job_name=test-mnist \
  --image_pull_policy=Never \
  --volume="host_path=/data,mount_path=/data" \

We had exposed the directory ./data to Minikube in above sections. Here, the option --volume="host_path=/data,mount_path=/data" bind mount it into the containers/pods.

The above command starts a Kubernetes job with only one container, or pod, which are exchangeable in this document), the master container.

The option --num_workers=1 tells the master container to start a worker pod.

The option --distribution_strategy=ParameterServerStrategy chooses the parameter server for the distributed stochastic gradient descent (SGD) algorithm. The option --num_ps_pods=1 tells the master to start one parameter server pod. For more details about parameter server strategy, please refer to the design doc.

Check Job Status

After the job submission, we can run the command kubectl get pods to list related containers.

NAME                            READY   STATUS    RESTARTS   AGE
elasticdl-test-mnist-master     1/1     Running   0          33s
elasticdl-test-mnist-ps-0       1/1     Running   0          30s
elasticdl-test-mnist-worker-0   1/1     Running   0          30s

We can also trace the training progress by watching the log from the master container. The following command watches the evaluation metrics changing over iterations.

kubectl logs elasticdl-test-mnist-master | grep "Evaluation"

The output looks like the following.

[2020-04-14 02:46:21,836] [INFO] [] Evaluation service started
[2020-04-14 02:46:40,750] [INFO] [] Evaluation metrics[v=50]: {'accuracy': 0.21933334}
[2020-04-14 02:46:53,827] [INFO] [] Evaluation metrics[v=100]: {'accuracy': 0.5173333}
[2020-04-14 02:47:07,529] [INFO] [] Evaluation metrics[v=150]: {'accuracy': 0.6253333}
[2020-04-14 02:47:23,251] [INFO] [] Evaluation metrics[v=200]: {'accuracy': 0.752}
[2020-04-14 02:47:35,746] [INFO] [] Evaluation metrics[v=250]: {'accuracy': 0.77}
[2020-04-14 02:47:52,082] [INFO] [] Evaluation service stopped

The logs show that the accuracy reaches to 0.77 after 250 steps iteration.

Submit the Training Job Using AllReduce

elasticdl train \
  --image_name=elasticdl:mnist_allreduce \
  --model_zoo=model_zoo \
  --model_def=mnist.mnist_functional_api.custom_model \
  --training_data=/data/mnist/train \
  --num_epochs=1 \
  --master_resource_request="cpu=0.2,memory=1024Mi" \
  --master_resource_limit="cpu=1,memory=2048Mi" \
  --worker_resource_request="cpu=0.4,memory=1024Mi" \
  --worker_resource_limit="cpu=1,memory=2048Mi" \
  --minibatch_size=64 \
  --num_minibatches_per_task=2 \
  --num_workers=2 \
  --job_name=test-mnist-allreduce \
  --image_pull_policy=Never \
  --volume="host_path=/data,mount_path=/data" \

After the job submission, we can run the command kubectl get pods to list related containers.

NAME                                      READY   STATUS    RESTARTS   AGE
elasticdl-test-mnist-allreduce-master     1/1     Running   0          102s
elasticdl-test-mnist-allreduce-worker-0   1/1     Running   0          98s
elasticdl-test-mnist-allreduce-worker-1   1/1     Running   0          98s

Then, we can view the loss in the worker log using the following command

kubectl logs elasticdl-test-mnist-allreduce-worker-0 | grep Loss

The outputs look like.

[2020-08-27 13:22:47,930] [INFO] [] Loss = 2.686038017272949, steps = 2
[2020-08-27 13:23:17,254] [INFO] [] Loss = 0.08301685750484467, steps = 100
[2020-08-27 13:23:47,887] [INFO] [] Loss = 0.0823458805680275, steps = 200
[2020-08-27 13:24:19,067] [INFO] [] Loss = 0.14079990983009338, steps = 300