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https://github.com/clearml/clearml-docs
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docs restructure - WIP
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@@ -22,7 +22,7 @@ Each layer provides distinct functionality to ensure an efficient and scalable A
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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.
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Utilizing the Infrastructure Control Plane, DevOps and IT teams can manage and optimize GPU resources to ensure high performance and cost efficiency.
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### Features
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#### Features
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- **Resource Management:** Automates the allocation and management of GPU resources.
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- **Workload Autoscaling:** Seamlessly scale GPU resources based on workload demands.
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- **Monitoring and Logging:** Provides comprehensive monitoring and logging for GPU utilization and performance.
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@@ -34,7 +34,7 @@ Utilizing the Infrastructure Control Plane, DevOps and IT teams can manage and o
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## AI Development Center
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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.
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### Features
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#### Features
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- **Integrated Development Environment:** A comprehensive IDE for training, testing, and debugging AI models.
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- **Model Training:** Scalable and distributed model training and hyperparameter optimization.
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- **Data Management:** Tools for data preprocessing, management, and versioning.
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@@ -46,7 +46,7 @@ The AI Development Center offers a robust environment for developing, training,
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## GenAI App Engine
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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.
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### Features
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#### Features
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- **LLM Deployment:** Seamlessly deploy LLMs into GPU clusters.
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- **RAG Workloads:** Efficiently manage and execute RAG workloads.
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- **Networking and Authentication:** Deploy GenAI through secure, authenticated network endpoints
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@@ -87,9 +87,15 @@ module.exports = {
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position: 'left',
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},
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{
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to: '/docs/getting_started/main',
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to: '/docs/deploying_clearml/clearml_server',
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label: 'Setup',
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position: 'left'},
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position: 'left'
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},
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{
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to: '/docs/getting_started/ds/ds_first_steps',
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label: 'Using ClearML',
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position: 'left'
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},
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{
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label: 'Developer Center',
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position: 'left', // or 'right'
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53
sidebars.js
53
sidebars.js
@@ -122,9 +122,43 @@ module.exports = {
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'user_management/identity_providers'
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]
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},
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//'Comments': ['Notes'],
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*/
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],
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usecaseSidebar: [
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'getting_started/main',
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{
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type: 'category',
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collapsible: true,
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label: 'Where do I start?',
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items: [
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{'Data Scientists': [
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'getting_started/ds/ds_first_steps',
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'getting_started/ds/ds_second_steps',
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]},
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{'MLOps and LLMOps': [
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'getting_started/mlops/mlops_first_steps',
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'getting_started/mlops/mlops_second_steps',
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]}
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],
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},
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{
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type: 'category',
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collapsible: true,
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label: 'Best Practices',
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items: [
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{
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type: 'doc',
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label: 'Data Scientists',
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id: 'getting_started/ds/best_practices'
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},
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{
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type: 'doc',
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label: 'MLOps and LLMOps',
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id: 'getting_started/mlops/mlops_best_practices'
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}
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],
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},
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],
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integrationsSidebar: [
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{
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type: 'doc',
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@@ -614,22 +648,9 @@ module.exports = {
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]},
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]
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},
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{'Getting Started': [
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'getting_started/main',
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{'Where do I start?': [
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{'Data Scientists': [
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'getting_started/ds/ds_first_steps',
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'getting_started/ds/ds_second_steps',
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'getting_started/ds/best_practices'
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]},
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{'MLOps and LLMOps': [
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'getting_started/mlops/mlops_first_steps',
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'getting_started/mlops/mlops_second_steps',
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'getting_started/mlops/mlops_best_practices'
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]}
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]},
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/* {'Getting Started': [
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'getting_started/architecture',
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]},
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]},*/
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{
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type: 'category',
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collapsible: true,
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