Boost your productivity with AI Workspaces

Table of Contents

1. Introduction
2. Overview of AI Workspaces
3. Benefits of AI Workspaces
4. Getting Started with AI Workspaces
   • Creating a Workspace
   • Choosing Tools and Frameworks
   • Selecting Compute Resources
   • Adding Data Sources
5. Using AI Workspaces
   • Connecting to a Workspace
   • Using Jupyter Notebook
   • Utilizing MLflow
   • Managing Workspace Resources
6. GPU Utilization in AI Workspaces
7. Creating Custom Workspaces
   • Using Existing Templates
   • Creating from Scratch
8. Conclusion

Introduction

Welcome to this demo video of the new AI Workspaces. In this video, we will explore the features and capabilities of AI Workspaces, a platform that provides data scientists with an easy and efficient way to create and connect to their work environments.

Overview of AI Workspaces

AI Workspaces is a powerful tool for data scientists, enabling them to run model training workloads and distributed training workloads. With AI Workspaces, data scientists can focus on their research without the need to worry about building container images, networking configurations, or storage configurations.

Benefits of AI Workspaces

AI Workspaces offers several benefits to data scientists, including:

• Simplicity in creating and managing work environments
• Freedom to choose the desired tools and frameworks
• Seamless connectivity to workspaces
• Efficient utilization of compute resources
• Easy on/off control of workspaces

Getting Started with AI Workspaces

To begin using AI Workspaces, follow these steps:

Creating a Workspace

1. Navigate to your Run AI account and access the Workspaces page.
2. From the list of assigned workspaces, choose the workspace you want to use.
3. Click on the "Connect" button to access the selected workspace.

Choosing Tools and Frameworks

1. Within the workspace, you will see one or more tools assigned to it.
2. To use a specific tool, such as Jupyter Notebook or MLflow, click on the corresponding tool icon.
3. The selected tool will open, allowing you to start working on your models or projects.

Selecting Compute Resources

1. Each workspace can be configured with different amounts of compute resources.
2. Choose the desired resource allocation based on your specific requirements.
3. For GPU utilization, select the appropriate number of GPUs to optimize performance.

Adding Data Sources

1. In your workspace, you can easily include data sources for your projects.
2. Select the desired data sources, such as code from a Git repository or a dataset from NFS.
3. By adding data sources, you can efficiently integrate external resources into your workspace.

Using AI Workspaces

Once connected to your workspace, you can leverage various features and functionalities:

Connecting to a Workspace

• After selecting a workspace, click on the "Connect" button to establish a connection.
• This allows you to access your workspace and start working on your research projects.

Using Jupyter Notebook

• Within your workspace, you can utilize Jupyter Notebook for interactive programming and data analysis.
• Jupyter Notebook provides a user-friendly interface for data scientists to develop and execute code efficiently.

Utilizing MLflow

• For managing machine learning workflows, AI Workspaces offers integration with MLflow.
• MLflow provides tools for tracking experiments, packaging code, and sharing models, enhancing collaboration and reproducibility.

Managing Workspace Resources

• AI Workspaces allows you to control the usage of compute resources by starting and stopping workspaces as needed.
• By stopping a workspace, you free up compute resources for others to utilize, promoting resource sharing and efficiency.

GPU Utilization in AI Workspaces

AI Workspaces provide flexibility in utilizing GPUs for enhanced performance. Workspaces can be configured to run on whole GPUs or fractions of GPUs, based on the specific requirements of your projects.

Creating Custom Workspaces

Apart from pre-existing workspaces, AI Workspaces also offers the flexibility to create custom workspaces tailored to your needs.

Using Existing Templates

• AI Workspaces provides a range of existing templates that define the environment, data sources, and compute resources.
• Choose an existing template that aligns with your requirements, saving time and effort in configuring workspaces.

Creating from Scratch

• Alternatively, you can create a new workspace from scratch, starting with a blank slate.
• Customize the environment, select tools and frameworks, allocate compute resources, and add data sources as needed.

Conclusion

AI Workspaces revolutionizes the way data scientists create and connect to their work environments. With its user-friendly interface and extensive features, AI Workspaces empowers researchers to focus on their research rather than the technical complexities of setting up and managing workspaces.

🔗 Resources:

• Run AI

Article Title: AI Workspaces - Enhancing Data Scientists' productivity and Efficiency

Introduction:

Welcome to this demo video of the new AI Workspaces. In this article, we will explore the features and capabilities of AI Workspaces, a platform that provides data scientists with an easy and efficient way to create and connect to their work environments.

Overview of AI Workspaces:

AI Workspaces is a powerful tool for data scientists, enabling them to run model training workloads and distributed training workloads. With AI Workspaces, data scientists can focus on their research without the need to worry about building container images, networking configurations, or storage configurations.

Benefits of AI Workspaces:

AI Workspaces offers several benefits to data scientists, including simplicity in creating and managing work environments, the freedom to choose desired tools and frameworks, seamless connectivity to workspaces, efficient utilization of compute resources, and easy on/off control of workspaces.

Getting Started with AI Workspaces:

To begin using AI Workspaces, you can follow these steps:

1. Creating a Workspace

   • Navigate to your Run AI account and access the Workspaces page.
   • From the list of assigned workspaces, choose the workspace you want to use.
   • Click on the "Connect" button to access the selected workspace.

2. Choosing Tools and Frameworks

   • Within the workspace, you will see one or more tools assigned to it.
   • To use a specific tool, such as Jupyter Notebook or MLflow, click on the corresponding tool icon.
   • The selected tool will open, allowing you to start working on your models or projects.

3. Selecting Compute Resources

   • Each workspace can be configured with different amounts of compute resources.
   • Choose the desired resource allocation based on your specific requirements.
   • For GPU utilization, select the appropriate number of GPUs to optimize performance.

4. Adding Data Sources

   • In your workspace, you can easily include data sources for your projects.
   • Select the desired data sources, such as code from a Git repository or a dataset from NFS.
   • By adding data sources, you can efficiently integrate external resources into your workspace.

Using AI Workspaces:

Once connected to your workspace, you can leverage various features and functionalities:

Connecting to a Workspace

• After selecting a workspace, click on the "Connect" button to establish a connection.
• This allows you to access your workspace and start working on your research projects.

Using Jupyter Notebook

• Within your workspace, you can utilize Jupyter Notebook for interactive programming and data analysis.
• Jupyter Notebook provides a user-friendly interface for data scientists to develop and execute code efficiently.

Utilizing MLflow

• For managing machine learning workflows, AI Workspaces offers integration with MLflow.
• MLflow provides tools for tracking experiments, packaging code, and sharing models, enhancing collaboration and reproducibility.

Managing Workspace Resources

• AI Workspaces allows you to control the usage of compute resources by starting and stopping workspaces as needed.
• By stopping a workspace, you free up compute resources for others to utilize, promoting resource sharing and efficiency.

GPU Utilization in AI Workspaces:

AI Workspaces provide flexibility in utilizing GPUs for enhanced performance. Workspaces can be configured to run on whole GPUs or fractions of GPUs, based on the specific requirements of your projects.

Creating Custom Workspaces:

Apart from pre-existing workspaces, AI Workspaces also offers the flexibility to create custom workspaces tailored to your needs.

Using Existing Templates

• AI Workspaces provides a range of existing templates that define the environment, data sources, and compute resources.
• Choose an existing template that aligns with your requirements, saving time and effort in configuring workspaces.

Creating from Scratch

• Alternatively, you can create a new workspace from scratch, starting with a blank slate.
• Customize the environment, select tools and frameworks, allocate compute resources, and add data sources as needed.

Conclusion:

AI Workspaces revolutionizes the way data scientists create and connect to their work environments. With its user-friendly interface and extensive features, AI Workspaces empowers researchers to focus on their research rather than the technical complexities of setting up and managing workspaces.

🔗 Resources:

• Run AI

Highlights:

• AI Workspaces provides data scientists with a simple and easy way to create and connect to their work environments.
• The platform eliminates the need to worry about container images, networking configurations, and storage configurations.
• Data scientists can choose their preferred tools and frameworks, allowing them to focus on their research.
• AI Workspaces offers efficient GPU utilization options for enhanced performance.
• Custom workspaces can be created either using existing templates or from scratch.
• The platform allows data scientists to easily connect and disconnect from their workspaces, optimizing resource allocation.

🔗 Resources:

• Run AI

FAQ:

Q: Can I customize the tools and frameworks in my AI Workspace? A: Yes, AI Workspaces allows you to choose the tools and frameworks that best suit your needs. You can select from a variety of pre-configured options or specify your own.

Q: How does AI Workspaces manage compute resources? A: AI Workspaces provides resource allocation options, allowing you to choose the amount of compute resources required for your workspace. This ensures efficient utilization and optimal performance.

Q: Can multiple researchers work on the same workspace simultaneously? A: No, each workspace is assigned to a specific researcher. However, workspaces can be stopped and resources can be freed up for others to use when not in active use.

Q: Are there any limitations on the number of data sources that can be added to a workspace? A: AI Workspaces allows you to add multiple data sources to your workspace, facilitating easy integration of external resources into your projects.

Q: Can AI Workspaces be integrated with other machine learning tools or platforms? A: AI Workspaces offers integration with MLflow, a platform for managing machine learning workflows. MLflow provides tools for experiment tracking, code packaging, and model sharing, enhancing collaboration and reproducibility.

Q: Is GPU utilization available in all AI Workspaces? A: Yes, AI Workspaces supports GPU utilization. Workspaces can be configured to run on whole GPUs or fractions of GPUs based on your specific requirements.

🔗 Resources:

• Run AI