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PrimeHub Deploy

PrimeHub provides a ready-to-use research and training environment for data scientists to focus on their true productivity in a collaborative environment. Don't be constrained by the tools, PrimeHub will adapt to your preferred workflow and work in conjunction with your technology stacks.

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Our benefits are getting 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.

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For Data Scientist Team

Model Containerization

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

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For Data Scientist Team

Rolling Updating Models

Reducing unnecessary downtime while deploying model by rolling update.

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For Data Scientist Team

Real-time Monitoring

Allocating computing resources through real-time data monitoring.

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For Data Scientist Team

Comprehensive Status

Getting comprehensive Model API deployment status of each project.

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For IT Administrator

Authority Management

Setting different authentication for different Model API deployments

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For IT Administrator

Dynamical Adjustment

Adjusting Model API deployment computing resources dynamically.

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For IT Administrator

Changelog Review

Checking every Model API deployment changelog.

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For IT Administrator

In Just 30 Minutes
you can get all these features

With just 1-click, PrimeHub will setup everything you need for your machine learning adventure on a cloud-native environment.

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Our features empower your data team

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

We support your model to run on anyway, anywhere 

Data Source
AWS S3
Azure
GCP
GitHub
GitLab
Grafana
Hadoop
Jenkins
Julia
Jupyter Notebook
Kubernetes
NVIDIA
OpenShift
Power Bi
PyCharm
Python
PyTorch
R
R Studio
Scikit-learn
Seldon
SQL
Tableau
TensorFlow
VS Code
AWS
IDE
AWS S3
Azure
GCP
GitHub
GitLab
Grafana
Hadoop
Jenkins
Julia
Jupyter Notebook
Kubernetes
NVIDIA
OpenShift
Power Bi
PyCharm
Python
PyTorch
R
R Studio
Scikit-learn
Seldon
SQL
Tableau
TensorFlow
VS Code
AWS
Workflow Intergration
AWS S3
Azure
GCP
GitHub
GitLab
Grafana
Hadoop
Jenkins
Julia
Jupyter Notebook
Kubernetes
NVIDIA
OpenShift
Power Bi
PyCharm
Python
PyTorch
R
R Studio
Scikit-learn
Seldon
SQL
Tableau
TensorFlow
VS Code
AWS
Programming Language
AWS S3
Azure
GCP
GitHub
GitLab
Grafana
Hadoop
Jenkins
Julia
Jupyter Notebook
Kubernetes
NVIDIA
OpenShift
Power Bi
PyCharm
Python
PyTorch
R
R Studio
Scikit-learn
Seldon
SQL
Tableau
TensorFlow
VS Code
AWS
Algorithm & Library
AWS S3
Azure
GCP
GitHub
GitLab
Grafana
Hadoop
Jenkins
Julia
Jupyter Notebook
Kubernetes
NVIDIA
OpenShift
Power Bi
PyCharm
Python
PyTorch
R
R Studio
Scikit-learn
Seldon
SQL
Tableau
TensorFlow
VS Code
AWS
Model Operations
AWS S3
Azure
GCP
GitHub
GitLab
Grafana
Hadoop
Jenkins
Julia
Jupyter Notebook
Kubernetes
NVIDIA
OpenShift
Power Bi
PyCharm
Python
PyTorch
R
R Studio
Scikit-learn
Seldon
SQL
Tableau
TensorFlow
VS Code
AWS
Infrastructure
AWS S3
Azure
GCP
GitHub
GitLab
Grafana
Hadoop
Jenkins
Julia
Jupyter Notebook
Kubernetes
NVIDIA
OpenShift
Power Bi
PyCharm
Python
PyTorch
R
R Studio
Scikit-learn
Seldon
SQL
Tableau
TensorFlow
VS Code
AWS

Not sure if PrimeHub works for you?
Try out our forever-free Community Edition

view code on github
Group 6
Group 30

FAQ

1. What is PrimeHub?

PrimeHub is an 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 community 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 datasets using the Admin dashboard.