PrimeHub Deploy

Deploy, Manage and Monitor AI Models Effortlessly At Once

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BenefitsFeaturesEco System

The simple and easy-to-understand management interface greatly reduces the burden of deployment, operation and real-time management.

Our benefits are geting work done with more quality, but less effort

Setting Up Model API in a minute

Training and deploying model without writing extra code. The system currently supports common models such as TensorFlow, PyTorch, and SKLearn.

Model Containerlize

Data Scientist Team can write Dockefile and deploy through PrimeHub Deploy.

Rolling Updating Models

Reducing unnecessary downtime while deploying model by rolling update.

Real-time Monitoring

Allocating computing resources through real-time data monitoring.

Comprehensive Status

Getting comprehensive Model API deployment status of each project.

Authority Management

Setting different authentication for different Model API deployments

Dynamical Adjustment

Adjusting Model API deployment computing resources dynamically.

Chanelog Review

Checking every Model API deployment changelog.

Our features empower your data team

Cluster Computing
Rapid construction of research environments
Expansion to hundreds of nodes
Container orchestration with Kubernetes
Supports to on-premises and cloud installations
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One-click Research Environments
Develop interactively with Jupyter
Support various deep learning frameworks
Visualize training progress
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Easy Dataset Loading
Supports multiple forms of dataset loading
Automatic training data mounting according to group settings
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Management of Resource & Quota Privileges 
Personal and shared group folders
Fine-grained quota allocation for members and groups
Resource access privileges for groups
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Custom Deep Learning Environments 
Custom hardware specs of virtual machines
Supports multiple deep learning frameworks
Co-existence of multiple versions
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Enterprise-Class Account Management
2-factor authentication user account protection
Single sign-on support
Internal auditing tools 
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We support your model to run on anyway, anywhere 

Data Source
SQL
AWS S3
Hadoop
IDE
PyCharm
Jupyter Notebook
VS Code
R Studio
Workflow Intergration
GitHub
GitLab
Jenkins
Tableau
Power Bi
Programming Language
Python
Julia
R
Algorithm & Library
Scikit-learn
TensorFlow
PyTorch
Model Operations
Seldon
Grafana
Infrastructure
AWS
Google Cloud
Kubernetes
Azure
Openshift
NVIDIA
PrimeHub Deploy greatly improves efficiency, and shortens the model deployment time from several days to within one hour.
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FAQ

1. What is PrimeHub? 

PrimeHub is a on-premise machine learning platform that enables AI/ML teams to focus on their true productivity in a collaborative environment. PrimeHub helps administrators manage hardware resources, access control, group quota, datasets and more.

2. Who is PrimeHub primarily for?

Administrators/IT Leaders and Data Scientists. Administrators set up environments for the data scientists to use. They can create custom images and allocate resources to each user, then set up each users’ data access and group permissions. Data scientists are given a seamless ML experience with their own customized Jupyter notebook environment. 

3. Is PrimeHub Community Edition free? 

PrimeHub CE is our single node version of PrimeHub with basic features and  is available for anyone to use and contribute to. Visit our GitHub and ensure that you have the prerequisites you need to get started.  You can also visit our documentation site for more info. 

4. Can PrimeHub schedule jobs?

Yes. With our Job Submission feature, users are able to submit time-consuming jobs to run in the background, set a scheduled recurrence when creating a new job, or choose to run a job manually at a later time

5. How does group management and sharing data work? 

Administrators have the ability to add users to groups, and to allow groups access to specific data sets using the Admin dashboard.