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Deep Learning Applications on Kubernetes

Many large corporations have begun exploring the advantages of deep learning to optimize their processes. For example, Netflix uses machine learning to recommend movies and shows to its users. Similarly, Facebook applies machine learning to classify, rank, and understand content. Google also uses machine learning in its Nest smart thermostats for analyzing the needs of the household and helping them cut down their energy bills. Many companies have been using deep learning techniques to recommend products to their customers.

To ensure that the machine learning programs perform efficiently, data scientists should be able to focus on the outcome instead of the underlying infrastructure.

Deep learning framework like PyTorch and TensorFlow and data processing engines, such as MapReduce, are supported by different libraries. Managing the dependencies can be complex that shifts the focus of the programmers from the product to the infrastructure. This is the main reason for the bugs that represent discrepancies between the intended and actual results.

How Kubernetes Can Help Overcome Deep Learning Programming Challenges

Kubernetes is an open source platform for container orchestration developed by Google in 2014. The platform has become the de facto choice for orchestration of containers due to support from all the major vendors of deep learning products. It has become popular because of the workload portability that is created between the public cloud and the on-premises environment.

Kubernetes has the required features that allow complete lifecycle management of the applications and services in a reliable and scalable manner. It can help optimize the process of data management.

Deep learning requires large data for testing and training. Some of the data can be fed manually but the transformation should be automated. It should also be carried out quickly to ensure reproducibility of data. The outcome may need to be saved that will double the storage requirements of the team.

Cloud-based data repository is more flexible and efficient in meeting the needs as compared to the on-premise hardware. It can also support high-throughput access that is required by the training workloads that prevents data replication.

Let’s explore the application of deep learning on Kubernetes and the benefits in terms of agile and effective product.

Data Management

Kubernetes supports a range of third party tools, such as Spark and Apache Hadoop. It allows automation of complex data transformations across multiple clusters.

Orchestrating a deep learning pipeline is a complex process. Managing the details such as how to move data from one step to another requires expert coding skills. Deep learning tools use libraries like Keras and TenserFlow to abstract this process so that the developers focus on the overall logic of the program. The libraries are also compatible with Kubernetes and there are guides on how to implement these libraries in a containerized workflow.

Efficient Resource Usage

Kubernetes cluster can be scaled as required by adding more virtual or physical servers. The tool has the ability to monitor different node attributes. It can track the number and type of GPUs and CPUs and other hardware resources. The attributes are evaluated when jobs are being scheduled to nodes to ensure that the resources are efficiently allocated.

Users face the issues of over and under allocation for resources intensive workloads. Kubernetes allows users to maintain a fine balance between running out of resources and using more resources than required.

Auto Scaling

Kubernetes supports auto scaling that allows the users to change the number of nodes for specific workload, depending on the requirements. The modes can be scaled up or down depending on the application requirements.

A physical cluster can be divided into multiple clusters using the namespaces feature. It allows the clusters to support different projects, teams, and functions. Each namespace can have its own resource quotas as well as security policies. It helps in specifying actions that a user can perform and the resources that they can access.

Namespace level configuration makes it possible to scale the resource up or down based on the team, project or function. The cluster can consist of virtual or physical servers that use specialized hardware. The containerized workload can be implemented of different platforms in multiple locations without changing the application code.

Hiding Complexity

Kubernetes provide an efficient way of specifying deep learning payloads independent of the framework or language. It offers a reliable abstraction layer for managing workloads. Moreover, it also offers the required APIs, configuration options and tools to manage the workloads declaratively.

Using the interfaces and integration with repositories of source code hides the complexity of creating deep learning applications. Users are provided with familiar tools to create deep learning applications within the framework of Kubernetes. It also introduces its set of tools to encapsulate or hide data science that helps in shielding the workloads from the underlying stacks.


The deep learning application is made of a varied workload that is managed by different teams. Dividing the workloads into different hardware environments causes wastage of precious resources. A simpler and more efficient method is to create shared environments that support concurrent workloads.

Kubernetes provides the required features that allow a single cluster to be divided into a number of virtual clusters. Each cluster can be configured with separate access control policies and quotes. It allows a single cluster to support multiple steps of the deep learning process.

Conclusion for Deep Learning Applications on Kubernetes

Kubernetes is a container orchestration solution that provides a central access point for different types of data. It helps manage the volume lifecycle and allows teams to access cloud-based storage for deep learning.

Machine learning developers constantly struggle to meet the needs of the organization. Deploying a deep learning-based application is a complex task using traditional tools. Kubernetes automates the deployment, management, and scaling of containerized deep learning applications.

The containerization orchestration process addresses the challenges of deploying machine learning applications. It provides flexibility to the team in applying the cloud native development to the machine learning applications. The scalability of Kubernetes ensures that the team efficiently manages the resources for deep learning application development.

Are you ready to make the move toDeep Learning Applications on Kubernetes? Contact us for a no-obligation consultation on cloud deployment for optimized workflow.

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