Blog

Scaling out Inspur’s TF2 Deep Learning Inference engine

In this case we show how InAccel orchestrator can be integrated with TF2 to allow multi-tenant scalable deployment of TF2 to a cluster of Intel-based FPGAs. We show how InAccel’s orchestrator allows easy deployment, scaling, resource management, and task scheduling for FPGAs making it easier than ever, the deployment and the utilization of FPGA for Deep Learning Inference.

Hybrid, Heterogeneous auto-scalable FPGA deployment

Enterprise customers now have the option to adopt a hybrid deployment for their FPGA-accelerated applications. That means, that companies can purchase a small number of FPGAs deployed on-prem and if these FPGAs are overloaded, they can automatically scale it using the cloud resources. InAccel, using the unique platform-agnostic FPGA orchestration allows auto-scaling hybrid FPGA deployment in the most efficient way.

Accelerated Deep Learning Inference using FPGAs from your browser

Any user can now enjoy the speedup of the FPGA accelerators from their browser. In the DL example we show how users can enjoy much faster ResNet50 inference from the same Keras python notebook with zero code changes.

Deploying FPGAs over OpenShift for accelerated Machine Learning

InAccel, a pioneer on the domain of application accelerations, has released today the easiest way to utilize FPGA-based accelerators over OpenShift. InAccel has developed a unique FPGA orchestrator that abstracts away the FPGA resources making much easier the deployment, scaling and automated resource management of the accelerators. Through the power of OpenShift, users can now deploy their applications on the cloud utilizing the power of FPGAs, easier than ever.

Accelerated Machine Learning from your browser

Data scientists and ML engineers can now speedup their applications from their browser using the power of FPGAs. ML developers can now run up to 15x faster their applications on Jupyter notebooks from their browser with zero code changes. ML developers are welcome to evaluate their applications for free on the new InAccel portal. InAccel, a pioneer in making FPGAs accessible and scalable, is providing the benefits of FPGA acceleration and utilization from your browser using the familiar Jupyter platform.

The need of an Accelerator Operating System

InAccel FPGA orchestrator abstracts away the available FPGA resources serving as an OS layer and “kubernetes-alike” layer for the applications that need to be deployed on FPGAs. The main advantages that InAccel’s orchestrator offers are easy deployment, automated resource management and instant scaling

Data scientists, ML engineers and HPC users can now speedup their applications using the power of FPGAs from their browser. InAccel has made available as a demo a framework that allow any user to instantly log in in the platform and start accessing the Xilinx Vitis™ libraries on a cluster of Alveo™ cards. The framework is built on top of Jupyter Hub.

InAccel FPGA Operator: Simplifying FPGA management in Kubernetes

Helm is a package manager for Kubernetes that allows developers and operators to more easily package, configure, and deploy applications and services onto Kubernetes clusters. The InAccel FPGA Operator is a Helm chart deployed, cloud-native method to standardize and automate the deployment of all necessary components for provisioning FPGA-enabled Kubernetes systems.

FPGA integration easier than ever

InAccel has developed a unique FPGA orchestrator that allows easy deployment, instant scaling and dynamic resource management of the FPGA clusters. InAccel’s FPGA orchestrator allows the deployment of FPGAs as a Platform ready to be used by the software developers just like any other computing system. InAccel FPGA orchestrator abstracts away the available FPGA resources serving as an OS layer and “kubernetes-alike” layer for the applications that need to be deployed on FPGAs allowing easy integration with any framework.

What FPGAs can learn from the CPU and the GPU world: Automatic deployment, scaling and dynamic resource management

Accelerate ML workflows with Kubeflow using InAccel FPGA orchestrator

In this article we show how Kubeflow can be used to perform a design space exploration and hyper parameter tuning faster using Kubeflow and utilizing the power of FPGAs.

FPGA deployment and scaling on distributed systems; the missing layer

, InAccel developed world-first FPGA orchestrator that allows easy deployment, instant scaling and seamless resource management of the FPGA clusters. InAccel’s FPGA orchestrator allows the deployment of FPGAs as a Platform ready to be used by the software developers just like any other computing system. InAccel FPGA orchestrator abstracts away the available FPGA resources serving as an OS layer and “kubernetes-alike” layer for the applications that need to be deployed on FPGAs.

Run 100x faster your Scikit-learn machine learning applications using FPGAs: A use case on Naive Bayes

Check how you can speedup your Scikit learn applications by more than 100x using FPGAs and with zero code changes.

How to make FPGA deployment in data centers as easy as any computing resource

, InAccel developed the unique FPGA orchestrator that allows easy deployment, instant scaling and seamless resource management of the FPGA clusters. InAccel’s FPGA orchestrator allows the deployment of FPGAs as a Platform ready to be used by the software developers just like any other computing system. At the same time, InAccel’s orchestrator integrated with InAccel’s FPGA repository allows the utilization of FPGAs as a service using off-the-shelf ready to use accelerators.

Multi-tenant, enterprise-grade FPGA cluster deployment using Kubesphere console

Kubernetes on FPGA using Kubesphere extends the industry standard container orchestration platform with FPGA acceleration capabilities. With first class support for FPGA resources scheduling, developers and DevOps engineers can now build, deploy, orchestrate and monitor FPGA-accelerated application deployments on heterogeneous, multi-cloud clusters.

How FPGAs can prevail as the next computing platform

One of the main issues raised by many participants and speakers was the need for an OS layers for the widespread adoption of FPGAs that will allow easy deployment, instant scaling and seamless sharing of the FPGA resources. An abstraction layer that will allow software developers to utilize FPGAs without prior knowledge of FPGA can accelerate the adoption of FPGAs in many applications.

Making FPGAs deployment as easy as processors

CPU, GPU or FPGA: Performance evaluation of cloud computing platforms for Machine Learning training

FPGAs on the cloud (f1.2xlarge on this case with InAccel ML suite) achieves the best combination in terms of performance-accuracy and cost.

Deploying FPGAs from Git repos instantly using BinderHub

Connecting 2 worlds: Bringing the benefits of accelerators to the software community, instantly