docs restructure - WIP

This commit is contained in:
alnoam
2025-02-12 12:09:04 +02:00
parent c5be8a40d1
commit 5e0f7d1d78
3 changed files with 48 additions and 21 deletions

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@@ -22,7 +22,7 @@ Each layer provides distinct functionality to ensure an efficient and scalable A
The Infrastructure Control Plane serves as the foundation of the ClearML platform, offering compute resource provisioning and management, enabling administrators to make the compute available through GPUaaS capabilities and no-hassle configuration.
Utilizing the Infrastructure Control Plane, DevOps and IT teams can manage and optimize GPU resources to ensure high performance and cost efficiency.
### Features
#### Features
- **Resource Management:** Automates the allocation and management of GPU resources.
- **Workload Autoscaling:** Seamlessly scale GPU resources based on workload demands.
- **Monitoring and Logging:** Provides comprehensive monitoring and logging for GPU utilization and performance.
@@ -34,7 +34,7 @@ Utilizing the Infrastructure Control Plane, DevOps and IT teams can manage and o
## AI Development Center
The AI Development Center offers a robust environment for developing, training, and testing AI models. It is designed to be cloud and on-premises agnostic, providing flexibility in deployment.
### Features
#### Features
- **Integrated Development Environment:** A comprehensive IDE for training, testing, and debugging AI models.
- **Model Training:** Scalable and distributed model training and hyperparameter optimization.
- **Data Management:** Tools for data preprocessing, management, and versioning.
@@ -46,7 +46,7 @@ The AI Development Center offers a robust environment for developing, training,
## GenAI App Engine
The GenAI App Engine is designed to deploy large language models (LLM) into GPU clusters and manage various AI workloads, including Retrieval-Augmented Generation (RAG) tasks. This layer also handles networking, authentication, and role-based access control (RBAC) for deployed services.
### Features
#### Features
- **LLM Deployment:** Seamlessly deploy LLMs into GPU clusters.
- **RAG Workloads:** Efficiently manage and execute RAG workloads.
- **Networking and Authentication:** Deploy GenAI through secure, authenticated network endpoints

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@@ -87,9 +87,15 @@ module.exports = {
position: 'left',
},
{
to: '/docs/getting_started/main',
to: '/docs/deploying_clearml/clearml_server',
label: 'Setup',
position: 'left'},
position: 'left'
},
{
to: '/docs/getting_started/ds/ds_first_steps',
label: 'Using ClearML',
position: 'left'
},
{
label: 'Developer Center',
position: 'left', // or 'right'

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@@ -122,9 +122,43 @@ module.exports = {
'user_management/identity_providers'
]
},
//'Comments': ['Notes'],
*/
],
usecaseSidebar: [
'getting_started/main',
{
type: 'category',
collapsible: true,
label: 'Where do I start?',
items: [
{'Data Scientists': [
'getting_started/ds/ds_first_steps',
'getting_started/ds/ds_second_steps',
]},
{'MLOps and LLMOps': [
'getting_started/mlops/mlops_first_steps',
'getting_started/mlops/mlops_second_steps',
]}
],
},
{
type: 'category',
collapsible: true,
label: 'Best Practices',
items: [
{
type: 'doc',
label: 'Data Scientists',
id: 'getting_started/ds/best_practices'
},
{
type: 'doc',
label: 'MLOps and LLMOps',
id: 'getting_started/mlops/mlops_best_practices'
}
],
},
],
integrationsSidebar: [
{
type: 'doc',
@@ -614,22 +648,9 @@ module.exports = {
]},
]
},
{'Getting Started': [
'getting_started/main',
{'Where do I start?': [
{'Data Scientists': [
'getting_started/ds/ds_first_steps',
'getting_started/ds/ds_second_steps',
'getting_started/ds/best_practices'
]},
{'MLOps and LLMOps': [
'getting_started/mlops/mlops_first_steps',
'getting_started/mlops/mlops_second_steps',
'getting_started/mlops/mlops_best_practices'
]}
]},
/* {'Getting Started': [
'getting_started/architecture',
]},
]},*/
{
type: 'category',
collapsible: true,