The Tensorleap settings page:
* Controls the limits of Tensorleap use of available compute
* Controls several global properties
## Adjusting Tensorleap compute limits
The tensorleap compute limits is usually set by modifying the [required and limit](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#requests-and-limits) threshold for the kubernetes pods that are running the different [Tensorleap processes](/user-interface/project/menu-bar/runs-and-processes.md#process-types-in-tensorleap).
### The main settings
This control the main engine settings:
This settings effects all processes except the [Population Exploration](/user-interface/project/menu-bar/runs-and-processes/process-types/population-exploration-process.md) and [Fetch Similar](/user-interface/project/menu-bar/runs-and-processes/process-types/fetch-similar-process.md) processes
* The required and limit of the CPU per process run
* The required and limit memory per process run
* The number of GPUS per run (default - 1)
* The number of visualizer processes workers. This would increase and decrease according to need dynamically after run starts
### The worker settings
* CPU limits
* Worker memory limits
* Number of worker processes. This would increase and decrease according to need dynamically after run starts
{% hint style="info" %}
This number serves as an initial estimate - once the platform is running a specific process - it would start with this number of worker processes and then scale it down or up as needed
{% endhint %}
### Pop Explorer settings
* CPU limits
* Memory limits
* time-to-live
{% hint style="warning" %}
In case OOM is encountered when running a process, this is usually caused by a memory limit that is too low, causing the pods that runs the process to be evacuated.
If you're encountering OOMs, it is recommended to increase either the main memory limit or the pop-explorer memory limit to a larger limit and re-run the process.
To verify that you're error is a result of an OOM - the [pod describe](/user-interface/project/menu-bar/runs-and-processes.md#the-process-inspection-view) within the relevant run logs should show an "OOM" status if the error reason is an OOM.
Another issue (for very large datasets > 3M samples) might be time-to-live in the population exploration. If you see an error starting roughly after time-to-live seconds, increase this limit.
{% endhint %}
## Global Tensorleap properties
This view allow you to control several global Tensorleap settings:
{% hint style="warning" %}
The dynamic dependencies flag is usually needed for when integrating a complete repository with specific dependencies into Tensorleap. This flag was previously located within the settings page, but is now located within the [code viewer](/user-interface/project/network/code-integration/code-viewing.md#the-top-controller).
{% endhint %}
* **internal pip server**: In case your organization has an internal pip server that you are using, you can fill its details within the pip index URL to cause the system to use it for dependency installation
* **Keep visualization resolution**: by default, some of the visualizer change image resolution to preserve space working memory when displaying these elements. If you want to prevent this, turn this flag on
* **Enable warmup**: Turning this flag would ensure a short pending time for new processes in the platform.
## Tensorleap Settings Video Tutorial
{% embed url="" %}
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