> For the complete documentation index, see [llms.txt](https://platformdocs.lattica.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://platformdocs.lattica.ai/architecture-overview/management-client.md).

# Management Client

The Management Client is designed for **Computation Providers** to manage all aspects of computation setup, integration, and resource management. Providers can perform these activities using either the **Lattica Web Console** or the **Python SDK**.

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Before using either option, you must **sign up** on the Lattica Console to create an account and receive your **Account ID**.
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### Installing the Management Package (Python SDK)

If you choose to use the Python SDK, [install](/how-to-guides/client-installation/how-to-install-management-client.md) the **Management Client package.** This package provides all functionalities needed for deploying computations, managing tokens, and controlling workers.
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### Model Compilation and Integration

The Management Client enables Workload Providers to deploy and manage their computations in Lattica’s system.

* **Deployment Options**:
  * Use the **Web Console** for an intuitive user interface.
  * Use the **Python SDK** for programmatic integration.
* **Process**:
  * [Deploy](/how-to-guides/workload-lifecycle/how-to-deploy-workload.md) workload to LatticaAI’s backend
  * Workloads can be [managed](/how-to-guides/workload-lifecycle/how-to-modify-workload-settings.md) (updated, activated, or deactivated) after deployment.

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**Note**: The model will be ready for processing only after LatticaAI builds the **homomorphic-ready model** in the backend. 

After LatticaAI confirms the model's successful creation, the provider can begin using it for encrypted data processing.
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### Access Token Management

The Management Client provides tools to manage **User Access Tokens**, which control secure access to deployed computations.

* **Token Functions**:
  * [Create ](/how-to-guides/access-control/how-to-create-user-access-token.md)new tokens.
  * [Activate or deactivate](/how-to-guides/access-control/how-to-modify-user-access-token-setting.md) tokens.
  * [Assign ](/how-to-guides/access-control/how-to-assign-token-to-workload.md)or [reassign ](/how-to-guides/access-control/how-to-remove-tokens-assignment.md)tokens to specific workload.

These activities can be performed via the Web Console or the Python SDK.
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### Worker (GPU) Management

After workloads and tokens are set up, Providers can control computational resources by managing **workers**.

* **Worker Functions**:
  * [Start](/how-to-guides/resource-management/how-to-start-worker.md) or [stop ](/how-to-guides/resource-management/how-to-stop-worker.md)**workers** as needed for active operations.
  * [Monitor ](/how-to-guides/resource-management/how-to-monitor-worker-performance.md)worker performance and optimize resource allocation.

Providers can perform these activities through the Web Console or Python SDK, giving full control over runtime and resources.
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