distributed ml system enables training and processing large language models and building such a system will give you an edge in this modern era of modern deep learning.
every tech company that uses ml wants to serve their customers at scale. to enable this train the models in parallel by dividing the workload on different machines as data and the size of the models are getting huge. keeping expensive GPUs idle is not at all recommended. utilizing multi-GPU training and optimizing inferene can save costs and we can serve customers at scale.
so recently i was involved in a project where we developed a distributed scalable ml service that hundreds of thousands of customers. tbh we faced a lot of challenges and building a distributed system is very tough. i started working on this project for a deeper understanding of the distributed ml systems.
distributed systems are a group of nodes that talk to each other to achieve a specific task, such as streaming movies across devices, search engines, etc.
how these complex models with millions or billions of parameters are trained and served? answer is distributed systems. they allow developers to handle massive datasets across multiple clusters, use automation tools, and benefit from hardware accelerations.
this repository includes code and references to implement a scalable and reliable machine learning system. im automating machine learning tasks with kubernetes, argo workflows, kubeflow, and tensorflow. my aim is to construct machine learning pipelines that do data ingestion, distributed training, model serving, managing, and monitoring these workloads.
in this project im building an image classification end-to-end machine learning system.
steps involved are:
- setup
- data ingestion
- distributed training
- evaluation
- serving
- end-to-end workflow
im using a macbook. in real-world people use cloud services like eks/gke. im installing tensorflow, docker desktop, kubectl, and k3d. im using conda as a package manager.
[1] im using tensorflow for data processing, model building and evaluation.
conda install tensorflow[2] im using docker to create single- or multi-node clusters.
[3] im installing a cli utility called kubectl
brew install kubectl[4] to use kubernetes on my machine, im installing k3d. its a lightweight wrapper to run k3s in docker. theres minikube which can use hyperkit instead of docker and which is recommended btw. i find k3d lean, memory efficient and simpler.
wget -q -O - https://raw.githubusercontent.com/rancher/k3d/main/install.sh | bashlets create a cluster. im not gonna create a multiserver cluster but you can.
k3d cluster create dist-ml --image rancher/k3s:v1.27.12-k3s1
see the cluster info
kubectl cluster-infosee the nodes info
kubectl get nodes
[5] im also installing kubectx to easily switch between clusters and kubens for namespaces. this is a very handy utility.
brew install kubectx[6] im installing kubeflow training operator.
to install the training operator, create a namespace. namespaces provide a mechanism for isolating groups of resources within a single cluster like a logical separation.
create a namespace called kubeflow.
kubectl create namespace kubeflowswitch the current context default to kubeflow.
kubens kubeflowNote
i was getting an error couldn't get resource list for metrics.k8s.io/v1beta1: the server is currently unable to handle the request. after googling a bit and updating the metrics server deployment yaml and add hostNetwork: true after dnsPolicy, it started working again.
finally install kubeflow training operator using custom resources.
kubectl apply -k "github.com/kubeflow/training-operator/manifests/overlays/standalone?ref=v1.7.0"
[7] im using argo workflows to construct an end-to-end workflows.
this is the most atomic part of kubernetes ecosystem.
we can create a simple kubernetes pod using a yaml manifest file(you can name it as per your choice).
apiVersion: v1
kind: Pod
metadata:
name: whalesay
spec:
containers:
- name: whalesay
image: docker/whalesay:latest
command: [cowsay]
args: ["Hello world"]next, submit the job to our cluster.
kubectl create -f hello-world.yamlwe can see the status of the pod.
kubectl get pods -o wideWe can see what is being printed out in the container.
kubectl logs whalesayif you want to get the details of a single pod with the raw yaml(there are other formats as well like json), then enter the following command.
kubectl get pod whalesay -o yamlthis system includes a model training pipeline and a inference service. there are different design patterns to design the system, you can choose the one which fits their needs.
im using the fashion_mnist dataset which contains 70,000 grayscale images in 10 categories. the images contain different types of clothes at a resolution of 28x28 px.
60,000 images are used for training and 10,000 images are in test set.
the tf.data api enables you to build complex input pipelines from simple, reusable pieces. its very efficient and enable handling large amounts of data, reading from different data formats, and performing complex transformations.
im loading the fashion_mnist dataset into a tf.data.Dataset object and cast the images to float32 dtype. im normalizing the image pixel values from the [0, 255] to the [0, 1] range. these are some standard practices. im keeping an in-memory cache to improve performance. lets also shuffle the training data to add some randomness.
import tensorflow_datasets as tfds
import tensorflow as tf
def mnist_dataset():
BUFFER_SIZE = 10000
def scale(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)
mnist_train = datasets['train']
return mnist_train.map(scale).cache().shuffle(BUFFER_SIZE)im using tensorflow_datasets module to load the dataset. the above piece of code gives a shuffled dataset where each element consists of images and labels.
to consume the dataset in a distributed fashion lets use the same function with some tweaks. to train a model on multiple GPUs and use the extra computing power effectively, just increase the batch size.
Tip
use the largest batch size that fits the GPU memory
theres a MirroredStrategy() that can be used to train on a single machine with multiple GPUs. but theres a way to distribute training on multiple machines in a cluster(recommended and my goal), then MultiWorkerMirroredStrategy() strategy is a way to go.
strategy = tf.distribute.MultiWorkerMirroredStrategy()
BATCH_SIZE_PER_REPLICA = 64
BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_syncthe num_replicas_in_sync equals the number of devices that are used in the all-reduce operation. use the tf.distribute.MultiWorkerMirroredStrategy api and with the help of this strategy, a keras model that was designed to run on a single worker can seamlessly work on multiple workers with minimal code changes.
what actually happens under the hood?
-
each GPU performs the forward pass on a different slice of the input data and computes the loss
-
next each GPU compute the gradients based on the loss
-
these gradients are then aggregated across all of the devices(using an all-reduce algorithm)
-
the optimizer updates the weights using the reduced gradients thereby keeping the devices in sync
im enabling automatic data sharding across workers by setting tf.data.experimental.AutoShardPolicy to AutoShardPolicy.DATA. this setting is needed to ensure convergence and performance. sharding means handing each worker a subset of the entire dataset.
now the final training workflow can be written as below
with strategy.scope():
dataset = mnist_dataset().batch(BATCH_SIZE).repeat()
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
dataset = dataset.with_options(options)
model = build_and_compile_cnn_model()
model.fit(dataset, epochs=3, steps_per_epoch=70)now i've a distributed data ingestion and training component. now im creating the neural net.
heres a simple nn
def build_and_compile_cnn_model():
print("Training a simple neural net")
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
return modelinstantiate the Adam optimizer. im using accuracy to evaluate the model and sparse categorical cross entropy as the loss function(remember we have 10 categories to predict).
lets define some necessary callbacks which will execute during model training.
tf.keras.callbacks.ModelCheckpointsaves the model at a certain frequency, for example, after every epoch.
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")im defining the checkpoint directory to store the checkpoints and the name for the files. checkpoints are important to restore the weights if the model training stops due to some issues.
tf.keras.callbacks.TensorBoardwrites a log for TensorBoard, which allows you to visualize the graphs.tf.keras.callbacks.LearningRateSchedulerschedules the learning rate to change after, for example, every epoch/batch.
def decay(epoch):
if epoch < 3:
return 1e-3
elif epoch >= 3 and epoch < 7:
return 1e-4
else:
return 1e-5- PrintLR prints the learning rate at the end of each epoch.
class PrintLR(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
print('\nLearning rate for epoch {} is {}'.format( epoch + 1, model.optimizer.lr.numpy()))now put all the components together.
callbacks = [
tf.keras.callbacks.TensorBoard(log_dir='./logs'),
tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix,
save_weights_only=True),
tf.keras.callbacks.LearningRateScheduler(decay),
PrintLR()
]now i can finally train the model.
single_worker_model = build_and_compile_cnn_model()
single_worker_model.fit(dataset, epochs=3, steps_per_epoch=70, callbacks=callbacks)im getting an accuracy of 94% on the training data. accuracy doesn't matter. remember im a building a system.
to insert the distributed training logic and train the model on multiple workers, use the MultiWorkerMirroredStrategy with keras(tf as backend).
there are different ways to do distributed training and data parallelism is the most common one. there are two common ways to do distributed training with data parallelism:
-
Synchronous training, where the steps of training are synced across the workers and replicas. all workers train over different slices of input data in sync, and aggregating gradients at each step.
-
Asynchronous training, where the training steps are not strictly synced. all workers are independently training over the input data and updating variables asynchronously. see parameter server training.
im using the MultiWorkerMirroredStrategy that does synchronous distributed training across multiple workers, each with potentially multiple GPUs. it replicates all variables and computations to each local device and uses distributed collective implementation (e.g. all-reduce) so that multiple workers can work together.
once its defined, i initiate the distributed input data pipeline and the model inside the strategy scope.
to save the model use model.save. the saving destinations(temporary dirs) need to be different for each worker.
- for workers nodes, save the model to a temporary directory
- for the master, save the model to the provided directory
the temporary directories of the workers need to be unique to prevent errors. the model saved in all the directories is identical, and only the model saved by the chief should be referenced for restoring or serving.
im not saving the model to temporary directories as doing this will waste my laptops computing resources and memory. im determining which worker node is the master and save its model only.
to determine if the worker node is the master or not use the environment variable TF_CONFIG
an example configuration
tf_config = {
'cluster': {
'worker': ['localhost:12345', 'localhost:23456']
},
'task': {'type': 'worker', 'index': 0}
}_is_worker_master is a function that inspects the cluster specs and current task type and returns True if the worker is the master and False otherwise.
def _is_worker_master():
return TASK_INDEX == 0
tf_config = json.loads(os.environ.get('TF_CONFIG') or '{}')
TASK_INDEX = tf_config['task']['index']
if _is_worker_master():
model_path = args.saved_model_dir
else:
model_path = args.saved_model_dir + '/worker_tmp_' + str(TASK_INDEX)
multi_worker_model.save(model_path)to containerize i've a python script called multi-worker-distributed-training.py that has all the models. now dockerize the app.
FROM python:3.9
RUN pip install tensorflow==2.12.0 tensorflow_datasets==4.9.2
COPY multi-worker-distributed-training.py /then build the docker image
docker build -f Dockerfile -t kubeflow/multi-worker-strategy:v0.1 .
next, import the image to the k3d cluster as it cannot access the image registry.
k3d image import kubeflow/multi-worker-strategy:v0.1 --cluster dist-ml
while training a model in the pod, once the ops are completed or failed, the files in the pod are recycled by the kubernetes garbage collecter. this means all the model checkpoints are lost. now we don't have a model for serving.
to avoid this we have to use PersistentVolume(PV) and PersistentVolumeClaim(PVC).
a PersistentVolume (PV) is a piece of storage in the cluster that has been provisioned by an administrator or dynamically provisioned. its a resource in the cluster just like a node is a cluster resource. PVs are volume plugins like volumes but have a lifecycle independent of any individual Pod that uses the PV. this means the storage will persist and live even when the pods are deleted.
a PersistentVolumeClaim (PVC) is a request for storage by a user. pods consume node resources and PVCs consume PV resources. pods can request specific levels of resources (CPU and Memory). claims can request specific size and access modes (e.g., they can be mounted ReadWriteOnce, ReadOnlyMany, or ReadWriteMany).
next, create a PVC to submit a request for storage that will be used in worker pods to store the trained model. im requesting 1GB storage with ReadWriteOnce mode.
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: strategy-volume
spec:
accessModes: [ "ReadWriteOnce" ]
volumeMode: Filesystem
resources:
requests:
storage: 1Gikubectl create -f multi-worker-pvc.yamlnext, define a TFJob(model training with tf) specification with the image we built before that contains the distributed training script.
theres a concept of deployments and the main difference between deployments and jobs is how they handle a pod that is terminated. a deployment is intended to be a "service", e.g. it should be up-and-running, so it will try to restart the pods its managing, to match the desired number of replicas. while a job is intended to execute and successfully terminate.
apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
name: multi-worker-training
spec:
runPolicy:
cleanPodPolicy: None
tfReplicaSpecs:
Worker:
replicas: 2
restartPolicy: Never
template:
spec:
containers:
- name: tensorflow
image: kubeflow/multi-worker-strategy:v0.1
imagePullPolicy: IfNotPresent
command: ["python", "/multi-worker-distributed-training.py", "--saved_model_dir", "/trained_model/saved_model_versions/2/", "--checkpoint_dir", "/trained_model/checkpoint", "--model_type", "cnn"]
volumeMounts:
- mountPath: /trained_model
name: training
resources:
limits:
cpu: 500m
volumes:
- name: training
persistentVolumeClaim:
claimName: strategy-volumepass saved_model_dir and checkpoint_dir to the container. the volumes field specifies the persistent volume claim and volumeMounts field specifies what folder to mount the files. the CleanPodPolicy in the TFJob spec controls the deletion of pods when a job terminates. the restartPolicy determines whether pods will be restarted when they exit.
submit the TFJob to our cluster and start our distributed model training.
kubectl create -f multi-worker-tfjob.yaml
we can see 2 pods running our distributed training as we've specified 2 workers.
- multi-worker-training-worker-0
- multi-worker-training-worker-1
we can also see the logs from the pod multi-worker-training-worker-0
kubectl logs multi-worker-training-worker-0
im training a model and stored it in the /saved_model_versions/1/ path. we can edit/update the code and resubmit the job. just delete the running job, rebuild the docker image, import it, and resubmit the job. these are the steps to remember everytime we change the code.
kubectl delete tfjob --all; docker build -f Dockerfile -t kubeflow/multi-worker-strategy:v0.1 .; k3d image import kubeflow/multi-worker-strategy:v0.1 --cluster dist-ml; kubectl create -f multi-worker-tfjob.yamlnext, evaluate the model's performance.
kubectl create -f predict-service.yamlwe finally have a trained model stored in the file path trained_model/saved_model_versions/2/.
kubectl exec --stdin --tty predict-service -- bin/bashto see the evals, enter into a running container predict-service
and execute predict-service.py which takes the trained model and evaluates it on the test dataset
ive implemented a distributed model training component. in reality we might need to train multiple models and pick the top performeing model for inference.
to implement this lets create two more models.
this nn has batch norm layer and have more conv2d layers.
def build_and_compile_cnn_model_with_batch_norm():
print("Training CNN model with batch normalization")
model = models.Sequential()
model.add(layers.Input(shape=(28, 28, 1), name='image_bytes'))
model.add(layers.Conv2D(32, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Activation('sigmoid'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Activation('sigmoid'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return modelim creating one more model with a dropout layer and some more conv2d layers.
def build_and_compile_cnn_model_with_dropout():
print("Training CNN model with dropout")
model = models.Sequential()
model.add(layers.Input(shape=(28, 28, 1), name='image_bytes'))
model.add(layers.Conv2D(32, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.5))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return modeltrain these models by submitting three different TFJobs with an argument --model_type and --saved_model_dir
kubectl delete tfjob --all
kubectl apply -f multi-worker-tfjob.yamlnext, load the testing data and the trained model to evaluate its performance. the model with the highest accuracy score can be moved to a different folder and used for model serving.
def scale(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
best_model_path = ""
best_accuracy = 0
for i in range(3):
model_path = "trained_models/saved_model_versions/" + str(i)
model = tf.keras.models.load_model(model_path)
datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)
mnist_test = datasets['test']
ds = mnist_test.map(scale).cache().shuffle(BUFFER_SIZE).batch(64)
loss, accuracy = model.evaluate(ds)
if accuracy > best_accuracy:
best_accuracy = accuracy
best_model_path = model_path
dst = "trained_model/saved_model_versions/3"
shutil.copytree(best_model_path, dst)add the above script to the Dockerfile, rebuild the image, and create a pod that runs the script for model selection.
apiVersion: v1
kind: Pod
metadata:
name: model-selection
spec:
containers:
- name: predict
image: kubeflow/multi-worker-strategy:v0.1
command: ["python", "/model-selection.py"]
volumeMounts:
- name: model
mountPath: /trained_model
volumes:
- name: model
persistentVolumeClaim:
claimName: strategy-volume
ive implemented distributed training and model selection compoenents. next im creating a model serving component. thin component takes the trained model from trained_model/saved_model_versions/3.
the inference service should be very high performant and robut. im not considering cost at this moment.
model_path = "trained_models/saved_model_versions/3"
model = tf.keras.models.load_model(model_path)
datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)
mnist_test = datasets['test']
ds = mnist_test.map(scale).cache().shuffle(BUFFER_SIZE).batch(64)
loss, accuracy = model.predict(ds)im using TFServing to expose the model as an endpoint service.
# Environment variable with the path to the model
os.environ["MODEL_PATH"] = f"{model_path}"
nohup tensorflow_model_server \
--port=8500 \
--rest_api_port=8501 \
--model_name=model \
--model_base_path=$MODEL_PATHNohup, short for no hang-up is a command in Linux systems that keeps processes running even after exiting the shell or terminal.
the above method works great if you're experimenting locally but there are more efficient ways for model serving.
tensorflow models contain a signature definition that defines the signature of a computation supported in a tensorflow graph. SignatureDefs aims to provide generic support to identify the inputs and outputs of a function. modify this input layer with a preprocessing function so that clients can use base64 encoded images, which is a standard way of sending images through a restful api. save a model with new serving signatures. the new signatures use python functions to handle preprocessing the image from a JPEG to a tensor. Refer
def _preprocess(bytes_inputs):
decoded = tf.io.decode_jpeg(bytes_inputs, channels=1)
resized = tf.image.resize(decoded, size=(28, 28))
return tf.cast(resized, dtype=tf.uint8)
def _get_serve_image_fn(model):
@tf.function(input_signature=[tf.TensorSpec([None], dtype=tf.string, name='image_bytes')])
def serve_image_fn(bytes_inputs):
decoded_images = tf.map_fn(_preprocess, bytes_inputs, dtype=tf.uint8)
return model(decoded_images)
return serve_image_fn
signatures = {
"serving_default": _get_serve_image_fn(model).get_concrete_function(
tf.TensorSpec(shape=[None], dtype=tf.string, name='image_bytes')
)
}
tf.saved_model.save(multi_worker_model, model_path, signatures=signatures)once theres an updated training script, rebuild the image and re-train the model.
use kserve for inference service. it enables serverless inferencing on k8s and provides performant, high-abstraction interfaces for common machine learning (ml) frameworks like tensorflow, pytorch, etc.
create an InferenceService yaml, which specifies the framework tensorflow and storageUri that is pointed to a saved tensorflow model.
apiVersion: "serving.kserve.io/v1beta1"
kind: InferenceService
metadata:
name: tf-mnist
spec:
predictor:
model:
modelFormat:
name: tensorflow
storageUri: "pvc://strategy-volume/saved_model_versions"install kserve
curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bashapply the inference-service.yaml to create the InferenceService. by default, it exposes an HTTP/REST endpoint.
kubectl apply -f inference-service.yamlWait for the InferenceService to be in a ready state.
kubectl get isvc tf-mnistrun the prediction.
but first, determine and set the INGRESS_HOST and INGRESS_PORT. an ingress gateway is similar to an api gateway that load balances requests.
test it locally using port-forward
INGRESS_GATEWAY_SERVICE=$(kubectl get svc --namespace istio-system --selector="app=istio-ingressgateway" --output jsonpath='{.items[0].metadata.name}')
kubectl port-forward --namespace istio-system svc/${INGRESS_GATEWAY_SERVICE} 8080:80in a different terminal window
export INGRESS_HOST=localhost
export INGRESS_PORT=8080send a sample request to our inference service.
lets use the curl command
MODEL_NAME=tf-mnist
INPUT_PATH=@./mnist-input.json
SERVICE_HOSTNAME=$(kubectl get inferenceservice $MODEL_NAME -n kubeflow -o jsonpath='{.status.url}' | cut -d "/" -f 3)
curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATHim using the requests library
input_path = "mnist-input.json"
with open(input_path) as json_file:
data = json.load(json_file)
response = requests.post(
url="http://localhost:8080/v1/models/tf-mnist:predict",
data=json.dumps(data),
headers={"Host": "tf-mnist.kubeflow.example.com"},
)
print(response.text)
inference service is working as expected
at scale, to handle large amounts of traffic systems should have multiple servers(replicas). the kserve can autoscale based on the requests. the autoscaler can scale down to zero if the application is receiving no traffic or we can specify a minimum number of replicas that need to be there. the autoscaling.knative.dev/target sets a soft limit. other specs that can be configured like minReplicas, containerConcurrency, and scaleMetric, etc.
apiVersion: "serving.kserve.io/v1beta1"
kind: InferenceService
metadata:
name: tf-mnist
annotations:
autoscaling.knative.dev/target: "1"
spec:
predictor:
model:
modelFormat:
name: tensorflow
storageUri: "pvc://strategy-volume/saved_model_versions"im installing Hey, a tiny program that sends some load to a web application. the Hey runs provided a number of requests in the provided concurrency level and prints stats.
# https://github.com/rakyll/hey
brew install hey
kubectl create -f inference-service.yaml
hey -z 30s -c 5 -m POST -host ${SERVICE_HOSTNAME} -D mnist-input.json "http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/tf-mnist:predict"
im sending traffic for 30 seconds with 5 concurrent requests. as the scaling target is set to 1 and we load the service with 5 concurrent requests, the autoscaler tries scaling up to 5 pods. theres always a cold start time initially to spin pods and may take longer(to pull the docker image) if is not cached on the node.
lets stitch all the components together as shown in the system architecture. im using argo workflow to orchestrate the jobs we executed before in an end-to-end fashion. build a cicd workflow using dag(e.g. gitlab cicd workflows) on k8s. argo is the defacto engine for orchestration on k8s.
im installing argo workflows
kubectl create namespace argo
kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.11/install.yamlim creating an end-to-end workflow with 4 steps
- data ingestion
- distributed training
- model selection
- model serving
apiVersion: argoproj.io/v1alpha1
kind: Workflow # new type of k8s spec
metadata:
generateName: tfjob-wf- # name of the workflow spec
spec:
entrypoint: tfjob-wf # invoke the tfjob template
templates:
- name: tfjob-wf
steps:
- - name: data-ingestion-step
template: data-ingestion-step
- - name: distributed-tf-training-steps
template: distributed-tf-training-steps
- - name: model-selection-step
template: model-selection-step
- - name: create-model-serving-service
template: create-model-serving-service
podGC:
strategy: OnPodSuccess
volumes:
- name: model
persistentVolumeClaim:
claimName: strategy-volumethis is a multi-step workflow where all the steps are executed sequentially(double dash). PodGC describes how to delete completed pods. deleting completed pods can free the resources. use persistent storage to store the dataset and the trained models.
the first step is the data ingestion. theres a memoize spec to cache the output of this step. memoization reduces cost and execution time. since we do not want to download the data every time, we can cache it using the configMap. specify the key and name for the config-map cache. set maxAge to 1h that defines how long should the cache be considered valid.
- name: data-ingestion-step
serviceAccountName: argo
memoize:
cache:
configMap:
name: data-ingestion-config
key: "data-ingestion-cache"
maxAge: "1h"
container:
image: kubeflow/multi-worker-strategy:v0.1
imagePullPolicy: IfNotPresent
command: ["python", "/data-ingestion.py"]execute the model training steps in parallel
- name: distributed-training-step
steps:
- - name: cnn-model
template: cnn-model
- name: cnn-model-with-dropout
template: cnn-model-with-dropout
- name: cnn-model-with-batch-norm
template: cnn-model-with-batch-normcreate a step to run distributed training with the model. create the TFJob using the manifest i created before. add the successCondition and failureCondition to indicate if the job is created. store the trained model in a different folder. create similar steps for the other two models configs.
- name: cnn-model
serviceAccountName: training-operator
resource:
action: create
setOwnerReference: true
successCondition: status.replicaStatuses.Worker.succeeded = 2
failureCondition: status.replicaStatuses.Worker.failed > 0
manifests: |
apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
generateName: multi-worker-training-
spec:
runPolicy:
cleanPodPolicy: None
tfReplicaSpecs:
Worker:
replicas: 2
restartPolicy: Never
template:
spec:
containers:
- name: tensorflow
image: kubeflow/multi-worker-strategy:v0.1
imagePullPolicy: IfNotPresent
command: ["python", "/multi-worker-distributed-training.py", "--saved_model_dir", "/trained_model/saved_model_versions/1/", "--checkpoint_dir", "/trained_model/checkpoint", "--model_type", "cnn"]
volumeMounts:
- mountPath: /trained_model
name: training
resources:
limits:
cpu: 500m
volumes:
- name: training
persistentVolumeClaim:
claimName: strategy-volumeadd the model selection step, similar to model-selection.yaml
- name: model-selection-step
serviceAccountName: argo
container:
image: kubeflow/multi-worker-strategy:v0.1
imagePullPolicy: IfNotPresent
command: ["python", "/model-selection.py"]
volumeMounts:
- name: model
mountPath: /trained_modellast step of the workflow is the model serving
- name: create-model-serving-service
serviceAccountName: training-operator
successCondition: status.modelStatus.states.transitionStatus = UpToDate
resource:
action: create
setOwnerReference: true
manifest: |
apiVersion: "serving.kserve.io/v1beta1"
kind: InferenceService
metadata:
name: tf-mnist
spec:
predictor:
model:
modelFormat:
name: tensorflow
storageUri: "pvc://strategy-volume/saved_model_versions"run the workflow.
kubectl create -f workflow.yamllogging is an essential component of the machine learning system. it helps debug issues, analyze performance, troubleshoot errors, gather insights, and implement a feedback loop. fortunately, kserve makes it easy to create a service called message-dumper. it logs the request and the response. it has a unique identifier for the request and the response.
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: message-dumper
spec:
template:
spec:
containers:
- image: gcr.io/knative-releases/knative.dev/eventing-contrib/cmd/event_displaykubectl create -f message-dumper.yamlinclude the logger which points to the message dumper URL in the InferenceService predictor.
logger:
mode: all
url: http://message-dumper.default/read about the inference logger here.
🚧 WIP
🚧 WIP
🚧 WIP
- distributed machine learning system is designed to train machine learning models on large datasets that cannot be processed on a single machine. there is a need to distribute the computation or training process to train complex models with millions or rather billions of parameters.
- k8s is a popular choice for building such complex distributed systems and thats the reason ive used it here.
- tensorflow provides several strategies for distributed training and have used
MultiWorkerMirroredStrategyhere. - used kserve for building an inference service which can be autoscaled based on the traffic.
- argo workflows help build cicd pipeline.
- monitoring the metrics with prometheus and grafana
- setup mlflow tracking server
- add gitOps
[1] Distributed Machine Learning Patterns by Yuan Tang
[2] Multi-worker training with Keras
[3] Custom training loop with Keras and MultiWorkerMirroredStrategy