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This document shows how to run an ElasticDL AllReduce job to train a PyTorch model using MNIST dataset on Minikube.
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.
Install Docker CE, preferably >= 18.x, following the guide for building Docker images containing user-defined models and the ElasticDL framework.
Install Python, preferably >= 3.6, because the ElasticDL command-line tool is in Python.
In this tutorial, we use the model defined in the model zoo directory. This model is defined using PyTorch API.
We use the MINST dataset in this tutorial. After downloading the dataset, we should unzip it into a local directory.
The following command starts a Kubernetes cluster locally using Minikube. It uses VirtualBox, a hypervisor coming with macOS, to create the virtual machine cluster.
minikube start --vm-driver=virtualbox \
--cpus 2 --memory 6144 --disk-size=50gb
eval $(minikube docker-env)
The command minikube docker-env returns a set of Bash environment variable
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 \
https://raw.githubusercontent.com/sql-machine-learning/elasticdl/develop/elasticdl/manifests/elasticdl-rbac.yaml
If you happen to live in a region where raw.githubusercontent.com is banned,
you might want to Git clone the above repository to get the YAML file.
The following command installs the command line tool elasticdl, which talks to
the Kubernetes cluster and operates ElasticDL jobs.
pip install elasticdl_client
Kubernetes runs Docker containers, so we need to put user-defined models, the ElasticDL api package 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 https://github.com/sql-machine-learning/elasticdl
The model definition is in directory elasticdl/model_zoo/mnist/mnist_pytorch.py.
We build the image based on horovod/horovod:0.20.0-tf2.3.0-torch1.6.0-mxnet1.5.0-py3.7-cpu
and the dockerfile is
FROM horovod/horovod:0.20.0-tf2.3.0-torch1.6.0-mxnet1.5.0-py3.7-cpu as base
RUN pip install opencv-python
RUN apt update
RUN apt install -y libgl1-mesa-glx libglib2.0-dev
RUN HOROVOD_WITHOUT_MPI=1 \
HOROVOD_WITH_GLOO=1 \
HOROVOD_WITHOUT_MXNET=1 \
HOROVOD_WITH_TENSORFLOW=1 \
HOROVOD_WITH_PYTORCH=1 \
pip install horovod==0.21.0
RUN pip install elasticdl_api
COPY ./model_zoo model_zoo
ENV PYTHONUNBUFFERED 0
Then, we use docker to build the image
docker build -t elasticdl:mnist_pytorch -f ${mnist_dockerfile} .
The following command submits a training job:
elasticdl train \
--image_name=elasticdl/elasticdl:v1.0.0 \
--worker_image=elasticdl:mnist_pytorch \
--job_command="python -m model_zoo.mnist.mnist_pytorch --batch_size 64 --num_epochs 1 --training_data=/data/mnist_png/training --validation_data=/data/mnist_png/testing" \
--num_minibatches_per_task=2 \
--num_workers=2 \
--master_resource_request="cpu=0.2,memory=1024Mi" \
--master_resource_limit="cpu=1,memory=2048Mi" \
--worker_resource_request="cpu=0.3,memory=1024Mi" \
--worker_resource_limit="cpu=1,memory=2048Mi" \
--envs="USE_TORCH=true,HOROVOD_GLOO_TIMEOUT_SECONDS=60,PYTHONUNBUFFERED=true" \
--job_name=test-mnist-allreduce \
--image_pull_policy=Never \
--volume="host_path={mnist_data_dir},mount_path=/data" \
--distribution_strategy=AllreduceStrategy
--image_name is the image to launch the ElasticDL master which
has nothing to do with the estimator model. The ElasticDL master is
responsible for launching pod and assigning data shards to workers with
elasticity and fault-tolerance.
{mnist_data_dir} is the absolute path of the ./data with the directory of
mnist_png. Here, the option --volume="host_path={mnist_data_dir},mount_path=/data"
bind mount it into the containers/pods.
The option --num_workers=2 tells the master to start 2 worker pods.
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 7s
test-mnist-allreduce-edljob-worker-0 1/1 Running 0 5s
test-mnist-allreduce-edljob-worker-1 1/1 Running 0 5s
In order to support fault-tolerance and elasticity with ElasticDL, you only need to create a custom dataset and wrap the function to perform forward and backward computation using ElasticDL APIs.
ElasticDL can split the total dataset into shards and assign those
shards to workers. If some workers fail, ElasticDL can re-assign
shards of failed workers to other running workers. We can get sample
indices in those shards by RecordIndexService. We can create a
dataset to read images by indices from the RecordIndexService.
class ElasticDataset(Dataset):
def __init__(self, images, data_shard_service=None):
"""The dataset supports elastic training.
Args:
images: A list with tuples like (image_path, label_index).
For example, we can use `torchvision.datasets.ImageFolder`
to get the list.
data_shard_service: If we want to use elastic training, we
need to use the `data_shard_service` of the elastic controller
in elasticai_api.
"""
self.data_shard_service = data_shard_service
self._images = images
def __len__(self):
if self.data_shard_service:
# Set the maxsize because the size of dataset is not fixed
# when using dynamic sharding
return sys.maxsize
else:
return len(self._images)
def __getitem__(self, index):
if self.data_shard_service:
index = self.data_shard_service.fetch_record_index()
return self.read_image(index)
else:
return self.read_image(index)
def read_image(self, index):
image_path, label = self._images[index]
image = cv2.imread(image_path)
image = np.array(image / 255.0, np.float32)
image = image.reshape(3, 28, 28)
return image, label
if __name__ == "__main__":
...
data_shard_service = RecordIndexService(
batch_size=args.batch_size,
dataset_size=len(train_data.imgs),
num_epochs=args.num_epochs,
shuffle=True,
dataset_name="mnist_training_data",
)
train_dataset = ElasticDataset(train_data.imgs, data_shard_service)
...
In ElasticDL AllReduce, we need to create a PyTorchAllReduceController
of ElasticDL. At the begining, The controller can broadcast the model and
optimizer. If some workers fail, the controller will re-initialize Horovod
using a new world. After creating the controller, we should wrap the function
to perform the forward and backward computation by elastic_run.
def train_one_batch(model, optimizer, data, target):
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
return loss
if __name__ == "__main__":
...
model = ...
optimizer = ...
allreduce_controller = PyTorchAllReduceController(data_shard_service)
allreduce_controller.set_broadcast_model(model)
allreduce_controller.set_broadcast_optimizer(optimizer)
# Use the elastic function to wrap the training function with a batch.
elastic_train_one_batch = allreduce_controller.elastic_run(train_one_batch)
with allreduce_controller.scope():
for batch_idx, (data, target) in enumerate(train_loader):
loss = elastic_train_one_batch(model, optimizer, data, target)
...
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