From aad795a8a419bf7d2e6313421dedf3f9d211a533 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Wed, 24 Sep 2025 13:23:21 -0400 Subject: [PATCH 01/59] Adds new directory for service --- content/en/guides/training/_index.md | 7 +++++++ content/en/guides/training/serverless-rl.md | 0 2 files changed, 7 insertions(+) create mode 100644 content/en/guides/training/_index.md create mode 100644 content/en/guides/training/serverless-rl.md diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md new file mode 100644 index 0000000000..71f97f3407 --- /dev/null +++ b/content/en/guides/training/_index.md @@ -0,0 +1,7 @@ +--- +menu: + default: + identifier: training +title: W&B Training +weight: 4 +--- \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl.md b/content/en/guides/training/serverless-rl.md new file mode 100644 index 0000000000..e69de29bb2 From a665ddeaff9f2dff0ac093de2bae44f337ae7207 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 26 Sep 2025 16:05:59 -0400 Subject: [PATCH 02/59] Adds WB training and serverless RL section --- content/en/guides/training/_index.md | 2 +- content/en/guides/training/serverless-rl.md | 23 +++++++++++++++++++++ 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index 71f97f3407..51ffa18328 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -3,5 +3,5 @@ menu: default: identifier: training title: W&B Training -weight: 4 +weight: 5 --- \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl.md b/content/en/guides/training/serverless-rl.md index e69de29bb2..dd4c92f0e0 100644 --- a/content/en/guides/training/serverless-rl.md +++ b/content/en/guides/training/serverless-rl.md @@ -0,0 +1,23 @@ +--- +description: Learn about Serverless RL and how to start using it. +title: Use Serverless RL +weight: 100 +--- + +W&B Training's Serverless RL (reinforncement learning) allows you to post-train LLMs to extend their knowledge and improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. For example, instead of building your own model or retraining an entire model to use HIPAA compliant medical terminology or legal citation formats, you can use Serverless RL to train a small module about these subjects and then integrate it into your existing LLM workflows to extend the existing model's capabilities. + +Serverless RL trains [low-rank adaptors (LoRAs)](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)#Low-rank_adaptation) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model's training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adaptors as artifacts stored in your W&B account. + +Serverless RL uses a combination of the following W&B services to operate: + +* [Inference]({{< relref "guides/inference" >}}): To access your models +* [Models]({{< relref "guides/models" >}}): To train the LoRA adaptors +* [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adaptors + +We charge for the use of inference and the storage of artifacts. We do not currently charge to train the adaptors. See the [Serverless RL pricing page](#update-link) for more details. + +## Use Serverless RL + +Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about). See the [OpenPipe's Serverless RL quickstart](#update-link) for code examples and workflows. + +See the [Serverless RL API]({{< relref "ref/training" >}}) reference for more information on the service's endpoints. \ No newline at end of file From 83423132dd91c8821412bafd5e1c7c472c410f80 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 26 Sep 2025 16:06:37 -0400 Subject: [PATCH 03/59] Adds Serverless RL reference section with redoc integration --- archetypes/api.md | 25 +++++ content/en/ref/training.md | 8 ++ layouts/partials/redoc.html | 58 ++++++++++ layouts/redoc.html | 211 ++++++++++++++++++++++++++++++++++++ 4 files changed, 302 insertions(+) create mode 100644 archetypes/api.md create mode 100644 content/en/ref/training.md create mode 100644 layouts/partials/redoc.html create mode 100644 layouts/redoc.html diff --git a/archetypes/api.md b/archetypes/api.md new file mode 100644 index 0000000000..9816565c99 --- /dev/null +++ b/archetypes/api.md @@ -0,0 +1,25 @@ +--- +title: "{{ replace .Name "-" " " | title }}" +date: {{ .Date }} +draft: false +layout: "api" +description: "API documentation for {{ replace .Name "-" " " | title }}" +weight: 100 + +# API Specification Configuration +# Option 1: External URL +# api_spec_url: "https://example.com/openapi.yaml" + +# Option 2: Local file (place in static/api-specs/) +# api_spec_file: "api-specs/your-api-spec.yaml" + +# Optional: Redoc configuration +# redoc_options: +# hideDownloadButton: false +# disableSearch: false +# expandResponses: "200,201" +# requiredPropsFirst: true +--- + + + diff --git a/content/en/ref/training.md b/content/en/ref/training.md new file mode 100644 index 0000000000..3c3464482f --- /dev/null +++ b/content/en/ref/training.md @@ -0,0 +1,8 @@ +--- +title: Serverless RL API Reference +description: Generated documentation for W&B APIs +menu: + reference: + identifier: serverless-rl-api-reference +layout: redoc +--- \ No newline at end of file diff --git a/layouts/partials/redoc.html b/layouts/partials/redoc.html new file mode 100644 index 0000000000..60666bebae --- /dev/null +++ b/layouts/partials/redoc.html @@ -0,0 +1,58 @@ +{{/* Redoc partial - handles the core Redoc initialization logic */}} +{{/* Parameters: spec_url or spec_content, container_id, options */}} + +{{ $spec_url := .spec_url }} +{{ $spec_content := .spec_content }} +{{ $container_id := .container_id | default "redoc-container" }} +{{ $options := .options | default (dict "theme" (dict "colors" (dict "primary" (dict "main" "#FF7557")))) }} + + +
+ + + + + diff --git a/layouts/redoc.html b/layouts/redoc.html new file mode 100644 index 0000000000..6d898615aa --- /dev/null +++ b/layouts/redoc.html @@ -0,0 +1,211 @@ + + + + + + {{ .Title }} | {{ .Site.Title }} + + + {{ hugo.Generator }} + {{ range .AlternativeOutputFormats -}} + + {{ end -}} + + + {{ if templates.Exists "partials/favicons.html" }} + {{ partialCached "favicons.html" . }} + {{ else }} + + {{ end }} + + + + + + + + + + + +
+ {{ partial "navbar.html" . }} +
+
+ +
+ {{ partial "footer.html" . }} + {{ partial "scripts.html" . }} + + + + + + + \ No newline at end of file From 5a5558a13156ae99d0ac58f710dbe06f4edcb8db Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 26 Sep 2025 16:15:16 -0400 Subject: [PATCH 04/59] Remove unnecessary files --- archetypes/api.md | 25 ---------------- layouts/partials/redoc.html | 58 ------------------------------------- 2 files changed, 83 deletions(-) delete mode 100644 archetypes/api.md delete mode 100644 layouts/partials/redoc.html diff --git a/archetypes/api.md b/archetypes/api.md deleted file mode 100644 index 9816565c99..0000000000 --- a/archetypes/api.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -title: "{{ replace .Name "-" " " | title }}" -date: {{ .Date }} -draft: false -layout: "api" -description: "API documentation for {{ replace .Name "-" " " | title }}" -weight: 100 - -# API Specification Configuration -# Option 1: External URL -# api_spec_url: "https://example.com/openapi.yaml" - -# Option 2: Local file (place in static/api-specs/) -# api_spec_file: "api-specs/your-api-spec.yaml" - -# Optional: Redoc configuration -# redoc_options: -# hideDownloadButton: false -# disableSearch: false -# expandResponses: "200,201" -# requiredPropsFirst: true ---- - - - diff --git a/layouts/partials/redoc.html b/layouts/partials/redoc.html deleted file mode 100644 index 60666bebae..0000000000 --- a/layouts/partials/redoc.html +++ /dev/null @@ -1,58 +0,0 @@ -{{/* Redoc partial - handles the core Redoc initialization logic */}} -{{/* Parameters: spec_url or spec_content, container_id, options */}} - -{{ $spec_url := .spec_url }} -{{ $spec_content := .spec_content }} -{{ $container_id := .container_id | default "redoc-container" }} -{{ $options := .options | default (dict "theme" (dict "colors" (dict "primary" (dict "main" "#FF7557")))) }} - - -
- - - - - From 1365cee52866f043b8330db50464d6b0bfab6d85 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Thu, 2 Oct 2025 10:02:40 -0400 Subject: [PATCH 05/59] Feedback via D. Corbitt --- content/en/guides/training/_index.md | 31 +++++++++++++++++++-- content/en/guides/training/serverless-rl.md | 23 --------------- 2 files changed, 28 insertions(+), 26 deletions(-) delete mode 100644 content/en/guides/training/serverless-rl.md diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index 51ffa18328..111ca46cec 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -1,7 +1,32 @@ --- menu: default: - identifier: training -title: W&B Training + identifier: serverless-rl +title: W&B Serverless RL weight: 5 ---- \ No newline at end of file +--- + +W&B Serverless RL (reinforcement learning) allows you to post-train LLMs to learn new behavior and improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. Serverless RL runs your reinforcement learning (RL) jobs on a large, managed cluster of GPUs that give you the freedom to scale your training run up to dozens of GPUs or down to zero instantly. + +Serverless RL is ideal for: + +* + + + +Serverless RL trains [low-rank adapters (LoRAs)](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)#Low-rank_adaptation) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model's training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adapters as artifacts stored in your W&B account. + +Serverless RL uses a combination of the following W&B services to operate: + +* [Inference]({{< relref "guides/inference" >}}): To run your models +* [Models]({{< relref "guides/models" >}}): To track performance metrics during the LoRA adapter's training +* [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters +* [Weave]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop + +We charge for the use of inference and the storage of artifacts. We do not currently charge to train the adapters. See the [Serverless RL pricing page](#update-link) for more details. + +## Use Serverless RL + +Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and a [W&B API](#UPDATE-LINK). See the [OpenPipe's Serverless RL quickstart](#UPDATE-LINK) for code examples and workflows. + +See the [Serverless RL API]({{< relref "ref/training" >}}) reference for more information on the service's endpoints. \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl.md b/content/en/guides/training/serverless-rl.md deleted file mode 100644 index dd4c92f0e0..0000000000 --- a/content/en/guides/training/serverless-rl.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -description: Learn about Serverless RL and how to start using it. -title: Use Serverless RL -weight: 100 ---- - -W&B Training's Serverless RL (reinforncement learning) allows you to post-train LLMs to extend their knowledge and improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. For example, instead of building your own model or retraining an entire model to use HIPAA compliant medical terminology or legal citation formats, you can use Serverless RL to train a small module about these subjects and then integrate it into your existing LLM workflows to extend the existing model's capabilities. - -Serverless RL trains [low-rank adaptors (LoRAs)](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)#Low-rank_adaptation) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model's training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adaptors as artifacts stored in your W&B account. - -Serverless RL uses a combination of the following W&B services to operate: - -* [Inference]({{< relref "guides/inference" >}}): To access your models -* [Models]({{< relref "guides/models" >}}): To train the LoRA adaptors -* [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adaptors - -We charge for the use of inference and the storage of artifacts. We do not currently charge to train the adaptors. See the [Serverless RL pricing page](#update-link) for more details. - -## Use Serverless RL - -Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about). See the [OpenPipe's Serverless RL quickstart](#update-link) for code examples and workflows. - -See the [Serverless RL API]({{< relref "ref/training" >}}) reference for more information on the service's endpoints. \ No newline at end of file From cda4165be16b3d4612fb7886d010f90441318bf5 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 10:17:17 -0400 Subject: [PATCH 06/59] Updates Serverless RL API page with proper spec --- layouts/redoc.html | 2 +- static/api-specs/serverlessRL.json | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 static/api-specs/serverlessRL.json diff --git a/layouts/redoc.html b/layouts/redoc.html index 6d898615aa..7e6e27232b 100644 --- a/layouts/redoc.html +++ b/layouts/redoc.html @@ -91,7 +91,7 @@ {{ partial "navbar.html" . }}
- Date: Fri, 3 Oct 2025 10:21:36 -0400 Subject: [PATCH 07/59] Adds back the Training branding --- content/en/guides/training/_index.md | 25 +----------------- content/en/guides/training/serverless-rl.md | 28 +++++++++++++++++++++ 2 files changed, 29 insertions(+), 24 deletions(-) create mode 100644 content/en/guides/training/serverless-rl.md diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index 111ca46cec..3ecf304e7f 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -6,27 +6,4 @@ title: W&B Serverless RL weight: 5 --- -W&B Serverless RL (reinforcement learning) allows you to post-train LLMs to learn new behavior and improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. Serverless RL runs your reinforcement learning (RL) jobs on a large, managed cluster of GPUs that give you the freedom to scale your training run up to dozens of GPUs or down to zero instantly. - -Serverless RL is ideal for: - -* - - - -Serverless RL trains [low-rank adapters (LoRAs)](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)#Low-rank_adaptation) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model's training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adapters as artifacts stored in your W&B account. - -Serverless RL uses a combination of the following W&B services to operate: - -* [Inference]({{< relref "guides/inference" >}}): To run your models -* [Models]({{< relref "guides/models" >}}): To track performance metrics during the LoRA adapter's training -* [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters -* [Weave]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop - -We charge for the use of inference and the storage of artifacts. We do not currently charge to train the adapters. See the [Serverless RL pricing page](#update-link) for more details. - -## Use Serverless RL - -Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and a [W&B API](#UPDATE-LINK). See the [OpenPipe's Serverless RL quickstart](#UPDATE-LINK) for code examples and workflows. - -See the [Serverless RL API]({{< relref "ref/training" >}}) reference for more information on the service's endpoints. \ No newline at end of file +An introduction to W&B Training suite of products. \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl.md b/content/en/guides/training/serverless-rl.md new file mode 100644 index 0000000000..199c84ef31 --- /dev/null +++ b/content/en/guides/training/serverless-rl.md @@ -0,0 +1,28 @@ +--- +description: Learn about Serverless RL and how to start using it. +title: Use Serverless RL +weight: 100 +--- + +W&B Serverless RL (reinforcement learning) allows you to post-train LLMs to learn new behavior and improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. Serverless RL runs your reinforcement learning (RL) jobs on a large, managed cluster of GPUs that give you the freedom to scale your training run up to dozens of GPUs or down to zero instantly. + +Serverless RL is ideal for: + +* + +Serverless RL trains [low-rank adapters (LoRAs)](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)#Low-rank_adaptation) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model's training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adapters as artifacts stored in your W&B account. + +Serverless RL uses a combination of the following W&B services to operate: + +* [Inference]({{< relref "guides/inference" >}}): To run your models +* [Models]({{< relref "guides/models" >}}): To track performance metrics during the LoRA adapter's training +* [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters +* [Weave]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop + +We charge for the use of inference and the storage of artifacts. We do not currently charge to train the adapters. See the [Serverless RL pricing page](#update-link) for more details. + +## Use Serverless RL + +Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and a [W&B API](#UPDATE-LINK). See the [OpenPipe's Serverless RL quickstart](#UPDATE-LINK) for code examples and workflows. + +See the [Serverless RL API]({{< relref "ref/training" >}}) reference for more information on the service's endpoints. \ No newline at end of file From eac7606f9ee45ecc828bb63f864bad8e2a25e9df Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 10:31:02 -0400 Subject: [PATCH 08/59] Updates front matter with training branding --- content/en/guides/training/_index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index 3ecf304e7f..9b759b5086 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -2,7 +2,7 @@ menu: default: identifier: serverless-rl -title: W&B Serverless RL +title: W&B Training weight: 5 --- From 9d4dfeb22210bf8170d68a019b7d00587e81b906 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 10:33:30 -0400 Subject: [PATCH 09/59] new identifier --- content/en/guides/training/_index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index 9b759b5086..fac0898fc1 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -1,7 +1,7 @@ --- menu: default: - identifier: serverless-rl + identifier: training title: W&B Training weight: 5 --- From cca7cb65faccdfc2a72cd255a4eb5b159dc4ca56 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 10:52:29 -0400 Subject: [PATCH 10/59] use cases --- content/en/guides/training/serverless-rl.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/en/guides/training/serverless-rl.md b/content/en/guides/training/serverless-rl.md index 199c84ef31..35e20c69ee 100644 --- a/content/en/guides/training/serverless-rl.md +++ b/content/en/guides/training/serverless-rl.md @@ -6,9 +6,12 @@ weight: 100 W&B Serverless RL (reinforcement learning) allows you to post-train LLMs to learn new behavior and improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. Serverless RL runs your reinforcement learning (RL) jobs on a large, managed cluster of GPUs that give you the freedom to scale your training run up to dozens of GPUs or down to zero instantly. -Serverless RL is ideal for: +Serverless RL is ideal for training: -* +* Voice agents +* Deep research assistants +* On-prem models +* Content marketing analysis agents Serverless RL trains [low-rank adapters (LoRAs)](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)#Low-rank_adaptation) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model's training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adapters as artifacts stored in your W&B account. From 2d40548f6253062cc5d80cb13dbc2c2b12f27980 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 11:35:15 -0400 Subject: [PATCH 11/59] fixes top nav rendering in cloudflare preview --- layouts/partials/navbar.html | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/layouts/partials/navbar.html b/layouts/partials/navbar.html index 0477e0db25..ab1db87991 100644 --- a/layouts/partials/navbar.html +++ b/layouts/partials/navbar.html @@ -9,11 +9,21 @@
From 2f908da8e0834a1d513c41577b70ad7680d5c6e8 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 11:40:50 -0400 Subject: [PATCH 12/59] Adds static light and dark logos --- static/icons/logo_dark.svg | 34 ++++++++++++++++++++++++++++++++++ static/icons/logo_light.svg | 34 ++++++++++++++++++++++++++++++++++ 2 files changed, 68 insertions(+) create mode 100644 static/icons/logo_dark.svg create mode 100644 static/icons/logo_light.svg diff --git a/static/icons/logo_dark.svg b/static/icons/logo_dark.svg new file mode 100644 index 0000000000..c82541476d --- /dev/null +++ b/static/icons/logo_dark.svg @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/static/icons/logo_light.svg b/static/icons/logo_light.svg new file mode 100644 index 0000000000..9eb4ed3f9a --- /dev/null +++ b/static/icons/logo_light.svg @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 5a8ba8e0493ad2c775ff82b7fad28f38778069f3 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 11:54:23 -0400 Subject: [PATCH 13/59] fixes top nav --- layouts/redoc.html | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/layouts/redoc.html b/layouts/redoc.html index 7e6e27232b..353c4e36a6 100644 --- a/layouts/redoc.html +++ b/layouts/redoc.html @@ -22,7 +22,13 @@ {{ end }} - + + + + From f9fb079aad30cc58f69cf2d90226e80081e4606a Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 13:04:27 -0400 Subject: [PATCH 14/59] CSS references assets directory now --- layouts/redoc.html | 8 +- static/css/main.css | 1493 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 1498 insertions(+), 3 deletions(-) create mode 100644 static/css/main.css diff --git a/layouts/redoc.html b/layouts/redoc.html index 353c4e36a6..08b0389671 100644 --- a/layouts/redoc.html +++ b/layouts/redoc.html @@ -22,7 +22,9 @@ {{ end }} - + {{ $scss := resources.Get "scss/_variables_project.scss" | toCSS (dict "targetPath" "css/_variables_project.scss") | minify | fingerprint }} + + - --> \ No newline at end of file diff --git a/static/css/main.css b/static/css/main.css new file mode 100644 index 0000000000..edaf1036fe --- /dev/null +++ b/static/css/main.css @@ -0,0 +1,1493 @@ +/* + +Add styles or override variables from the theme here. + +*/ + +@import url('https://fonts.googleapis.com/css2?family=Source+Serif+4:ital,opsz,wght@0,8..60,200..900;1,8..60,200..900&display=swap'); + +// First define the base color tokens +// Moon (Grayscale) +$moon-white: #ffffff; +$moon-50: #fafafa; +$moon-100: #f5f6f7; +$moon-200: #e4e6eb; +$moon-250: #dfe0e2; +$moon-300: #d3d7de; +$moon-350: #c1c6cf; +$moon-400: #aeb3bd; +$moon-500: #79808a; +$moon-600: #565c66; +$moon-650: #4a4f58; +$moon-700: #3f464f; +$moon-750: #363c44; +$moon-800: #2b3038; +$moon-900: #1a1d24; +$moon-950: #171a1f; + +// Teal colors +$teal-300: #a9edf2; +$teal-400: #58d3db; +$teal-450: #10bfcc; +$teal-500: #13a9ba; +$teal-550: #0e97a7; +$teal-600: #038194; + +// Border colors +$border-default: $moon-200; // Using moon-200 instead of moon-250 +$border-hover: $moon-350; // color/moon/350 +$border-active: $teal-500; // color/teal/500 +$border-subtle: $moon-100; // color/moon/100 +$dark-border-default: $moon-750; // color/moon/750 +$dark-border-hover: $moon-650; // color/moon/650 +$dark-border-subtle: $moon-800; // color/moon/800 + +// Border radius +$border-radius-medium: 8px; // Used for most components +$border-radius-large: 12px; // Used for modals and larger components + +// Then define the semantic color tokens +// Text colors +$text-primary: $moon-800; // color/moon/800 +$text-secondary: $moon-600; // color/moon/600 +$text-tertiary: $moon-500; // color/moon/500 +$text-disabled: $moon-350; // color/moon/350 + +// Link colors +$link-primary: $teal-600; // color/teal/600 +$link-hover: $teal-550; // color/teal/550 +$link-secondary: $moon-600; // color/moon/600 +$link-secondary-hover: $moon-500; // color/moon/500 + +// Background colors +$bg-primary: $moon-white; // White background +$bg-secondary: $moon-50; // color/moon/50 +$bg-tertiary: $moon-100; // color/moon/100 +$bg-disabled: $moon-350; // color/moon/350 + +// Dark mode colors +$dark-text-primary: $moon-200; // color/moon/200 +$dark-text-secondary: $moon-400; // color/moon/400 +$dark-text-tertiary: $moon-500; // color/moon/500 +$dark-text-disabled: $moon-650; // color/moon/650 + +$dark-link-primary: $teal-500; // color/teal/500 +$dark-link-hover: $teal-450; // color/teal/450 +$dark-link-secondary: $moon-600; // color/moon/600 +$dark-link-secondary-hover: $moon-500; // color/moon/500 + +$dark-bg-primary: $moon-900; // color/moon/900 +$dark-bg-secondary: $moon-800; // color/moon/800 +$dark-bg-tertiary: $moon-700; // color/moon/700 +$dark-bg-disabled: $moon-650; // color/moon/650 + +// Other brand colors +$nav-bg: rgb(26, 28, 31); +$wandb-gold: #FFCC33; +$wandb-link-blue: rgb(7, 177, 196); +$hover-bg: #A9EDF260; + +// Gradients +$sunset-gradient: linear-gradient(98deg, #e180ff 0%, #ffcf4d 100%); +$wandb-gradient: linear-gradient(180deg, #ffcc33 39%, #ffad33 72%); + +// Font weights +$font-weight-light: 300; +$font-weight-regular: 400; +$font-weight-medium: 500; +$font-weight-semibold: 600; +$font-weight-bold: 700; + +// Bootstrap overrides +:root { + --bs-nav-link-font-size: 18px; +} + +// Typography Component +// Base body styles +body { + font-family: "Source Sans Pro", sans-serif; + font-size: 16px; + font-weight: $font-weight-regular; + color: $text-primary; + background-color: $bg-primary; + + [data-bs-theme=dark] & { + color: $dark-text-primary; + background-color: $dark-bg-primary; + } +} +p, td, div +{ + font-family: "Source Sans Pro", sans-serif; +} +.td-sidebar-nav__section-title, .td-sidebar-nav__section without-child +{ + font-size: 16px; + font-weight: $font-weight-bold; +} + +// Headings +h1, h2, h3, h4, h5, h6 { + font-weight: $font-weight-regular !important; + font-family: "Source Serif 4", sans-serif; + color: $text-primary; + + [data-bs-theme=dark] & { + color: $dark-text-primary; + } +} + +// Heading sizes +h1, .td-content h1 { font-size: 32px !important; } +h2, .td-content h2 { font-size: 24px !important; } +h3, .td-content h3 { font-size: 20px !important; } +h4, .td-content h4 { font-size: 18px !important; } +h5, .td-content h5 { font-size: 17px !important; } +h6, .td-content h6 { font-size: 16px !important; } + +// Lead paragraph +.td-content .lead { + font-size: 16px !important; + font-weight: $font-weight-regular !important; +} + +// Reading time +.reading-time { + font-size: 14px; + color: $text-secondary; + + [data-bs-theme=dark] & { + color: $dark-text-secondary; + } +} + +// Links +a { + color: $link-primary !important; + text-decoration: none !important; + + &:hover { + color: $link-hover !important; + text-decoration: none !important; + } + + [data-bs-theme=dark] & { + color: $dark-link-primary !important; + + &:hover { + color: $dark-link-hover !important; + } + } +} + +// hide "create child" and "create project issue" links +.td-page-meta__child, .td-page-meta__project-issue { display: none !important; } + +// shrink the right nav a bit +.td-page-meta { + display: none !important; + font-size: 16px !important; + a { + padding-bottom: 15px; + line-height: 22px; + } +} + +// hide left nav label +.tree-root { display: none !important;} + +// First define breakpoints +$breakpoint-sm: 576px; // Phone +$breakpoint-md: 768px; // Tablet +$breakpoint-lg: 992px; // Desktop +$breakpoint-xl: 1200px; // Large desktop + +// Consolidated navbar styles with mobile-first approach +.td-navbar { + background-color: $bg-primary !important; + padding: 0 !important; + border-bottom: 1px solid $border-default !important; + + // Base styles (mobile first) + .container-fluid { + padding: 8px !important; + gap: 16px !important; + + .td-navbar-nav-scroll { + align-items: center !important; + height: auto !important; + margin: 0 !important; + padding: 0 !important; + gap: 16px !important; + + .navbar-nav { + display: flex !important; + align-items: center !important; + padding-left: 0 !important; + margin: 0 !important; + gap: 8px !important; + + .nav-item { + padding-inline-end: 0 !important; + } + + &.main-nav { + padding: 0 !important; + margin: 0 !important; + + // Nav links base styles + .nav-link { + color: $text-secondary !important; + font-weight: $font-weight-regular !important; + transition: all 0.2s ease-in-out !important; + border-bottom: none !important; + border-radius: $border-radius-medium !important; + padding: 8px 10px !important; + font-size: 16px !important; + + &:hover { + text-decoration: none !important; + background-color: rgba($moon-300, 0.2) !important; + } + + &.active { + color: $link-primary !important; + background-color: rgba($moon-300, 0.2) !important; + font-weight: $font-weight-regular !important; + } + } + + } + + &.main-nav-right { + padding: 0 !important; + margin: 0 !important; + align-items: center !important; + gap: 0 !important; + + // Update mobile light/dark toggle styles + .td-light-dark-menu { + display: flex !important; + align-items: center !important; + margin: 0 !important; + gap: 0 !important; + min-width: 0 !important; + } + + // Dropdown styles + .nav-item.dropdown { + margin: 0 !important; + + .nav-link { + font-size: 16px !important; + font-weight: $font-weight-regular !important; + color: $text-secondary !important; + + &:hover { + background-color: transparent !important; + } + + &::after { + display: none !important; + } + + [data-bs-theme=dark] & { + color: $dark-text-secondary !important; + } + } + } + } + } + } + } + + // Brand/logo styles + .navbar-brand { + display: flex !important; + align-items: center !important; + padding: 0 !important; + margin-right: 0 !important; + + .navbar-logo { + display: block; + width: auto; + height: auto; + margin: 0 !important; + + svg { + margin: 0 !important; + width: 100%; + } + } + } + + // Hide the doc site name + .navbar-brand__name { + display: none !important; + } + + + // Tablet styles (577px and up) + @media (min-width: $breakpoint-sm + 1) { + .container-fluid { + padding: 8px 12px !important; + } + + .navbar-brand { + margin-right: 1rem !important; + } + } + + // Desktop styles (992px and up) + @media (min-width: $breakpoint-lg) { + .container-fluid { + padding: 8px 24px !important; + flex-direction: row !important; + align-items: center !important; + justify-content: flex-start !important; + gap: 32px !important; + + .td-navbar-nav-scroll { + flex-grow: 1 !important; + + .navbar-nav { + + &.main-nav { + flex-grow: 1 !important; + gap: 16px !important; + + .nav-link { + padding: 6px 12px !important; + font-size: 18px !important; + } + } + + &.main-nav-right { + gap: 16px !important; + + .nav-link{ + font-size: 16px !important; + color: $text-secondary !important; + } + } + } + .main-nav-search { + min-width: 300px !important; + + .DocSearch.DocSearch-Button { + margin: 0 !important; + min-width: 100% !important; + } + } + } + } + + + .nav-item.dropdown { + &.d-none.d-lg-block, + &.td-light-dark-menu { + margin: 0 4px !important; + + .nav-link { + font-size: 16px !important; + } + } + } + } + + // Dark mode styles + [data-bs-theme=dark] & { + background-color: $dark-bg-primary !important; + border-bottom: 1px solid $dark-border-default !important; + + &.js-navbar-scroll { + background-color: $dark-bg-primary !important; + } + + .td-navbar-nav-scroll { + + .navbar-nav { + + &.main-nav { + .nav-link { + color: $moon-white !important; + + &:hover { + color: $teal-500 !important; + } + + &.active { + color: $teal-500 !important; + } + } + } + + .nav-item.dropdown { + &.d-none.d-lg-block, + &.td-light-dark-menu { + .nav-link { + color: $dark-text-primary !important; + + &:hover { + color: $dark-link-primary !important; + } + } + } + } + } + } + } + + // Update the light/dark menu styles + .td-light-dark-menu { + &.nav-item.dropdown { + margin: 0 2px !important; + display: flex !important; + align-items: center !important; + } + + // Hide from navbar-right on mobile + &.d-none { + @media (max-width: #{$breakpoint-lg - 1}) { + display: none !important; + } + } + } +} + +// headings use WandB serif font +h1, h2, h3, h4, h5, h6 { + font-weight: $font-weight-regular !important; + font-family: "Source Serif 4", sans-serif; + color: $text-primary; +} + +.td-sidebar-nav-active-item { + text-decoration: none; +} + +ol.breadcrumb li { + font-size: 13px; +} +ol.breadcrumb li:not(:first-child):before { + content: ">" !important; +} + +.td-sidebar-nav__section-title, .td-sidebar-nav__section { + margin-bottom: 10px !important; + .nav-link.dropdown-toggle { + margin-left: -5px !important; + } +} + +.td-content .lead { + font-size: 16px !important; + font-weight: $font-weight-regular !important; +} + +// Table of Contents Component +#TableOfContents { + + font-size: 13px !important; + font-family: "Source Sans Pro", sans-serif; + a{ + margin: 0px !important; + padding: 8px !important; + font-weight: 500 !important; + } + // List styles + ul { + padding: 0px !important; + li { + padding: 0px !important; + line-height: 14px; + } + } + + // Link styles + a { + display: block; + color: $text-secondary !important; + + &:hover { + color: $link-primary !important; + text-decoration: none !important; + } + + [data-bs-theme=dark] & { + color: $dark-text-secondary !important; + + &:hover { + color: $dark-link-primary !important; + } + } + } + + // Dark mode + [data-bs-theme=dark] & { + color: $dark-text-primary; + } +} + +// tabs +ul { + &.nav-tabs { + border-bottom: none !important; + + li { + &.nav-item { + margin: 0 !important; + + button { + color: $text-primary !important; + + &.active { + color: $link-primary !important; + background-color: var(--td-pre-bg) !important; + border-bottom-color: var(--td-pre-bg) !important; + } + + // Add dark mode styles + [data-bs-theme=dark] & { + color: $dark-text-primary !important; + + &.active { + color: $dark-link-primary !important; + } + } + } + } + } + } +} + +.tab-content { + > .tab-pane { + border-radius: 0 $border-radius-medium $border-radius-medium $border-radius-medium !important; + border: 1px solid $border-default !important; + + // Add dark mode styles + [data-bs-theme=dark] & { + border-color: $dark-border-default !important; + } + } +} + +// Buttons Component +// Base button styles +.button-base { + display: inline-flex; + align-items: center; + justify-content: center; + font-size: 16px; + line-height: 20px; + font-weight: $font-weight-semibold; + padding: 0 12px; + min-height: 40px; + border-radius: $border-radius-medium; + transition: background-color 0.3s, color 0.3s; + text-decoration: none !important; + border: none; + + &:hover { + text-decoration: none !important; + } +} + +// CTA Container styles +.cta-container { + display: flex; + align-items: center; // Vertically center the buttons + gap: 16px; // Consistent spacing between buttons + margin: 24px 0; + flex-wrap: wrap; // Allow wrapping on smaller screens +} + +// CTA Button +.cta-button { + @extend .button-base; + color: $moon-800 !important; + background-color: $moon-100; + height: 40px; // Fixed height for consistency + padding: 0 16px; + display: inline-flex; + align-items: center; // Center button contents vertically + white-space: nowrap; // Prevent text wrapping inside button + + &:hover { + background-color: $hover-bg; + } + + // Icon styles + svg, img { + width: 24px; + height: 24px; + margin-right: 8px; + } + + [data-bs-theme=dark] & { + color: $dark-text-primary !important; + background-color: $dark-bg-secondary; + + &:hover { + background-color: $dark-bg-tertiary; + } + } +} + +// Contact Us Button +.contact-us-button { + @extend .button-base; + background-color: $teal-500; + color: $moon-white !important; + gap: 8px; + + &:hover { + background-color: $teal-450; + } + + img { + width: 112px; + height: 24px; + } + + p { + margin: 0; + color: $moon-white; + } + + [data-bs-theme=dark] & { + background-color: $teal-450; + + &:hover { + background-color: $teal-400; + } + } +} + +// Banners Component +// Base banner styles +.banner-base { + display: flex; + padding: 32px 40px; + flex-direction: column; + align-items: flex-start; + gap: 24px; + border-radius: $border-radius-large; + margin-bottom: 30px; + + h1, h2, p, div { + filter: drop-shadow(5px 5px 5px rgba(0, 0, 0, 0.5)); + } +} + +// Standard Banner +.banner { + @extend .banner-base; + background-position: center; + background-repeat: no-repeat; + background-size: cover; + min-height: 200px; + color: $moon-white; + overflow: hidden; +} + +// Help Banner +.help-banner { + @extend .banner-base; + padding: 56px 40px 72px 40px; + background: $moon-900; + background-image: url('/images/support/support_banner.png'); + background-size: cover; + background-position: center; + box-shadow: none; + + &.card:hover { + box-shadow: none; + transform: none; + } + + h2 { + color: $moon-50; + font-size: 40px; + font-weight: $font-weight-semibold; + font-family: "Source Sans Pro"; + line-height: 140%; + } + + p { + color: $moon-250; + font-size: 20px; + font-weight: $font-weight-regular; + font-family: "Source Sans Pro"; + line-height: 140%; + } +} + +// Card Banner +.card-banner { + @extend .banner-base; + align-items: center; + background: $moon-50; + color: $moon-800; + + h2 { + font-size: 24px; + font-weight: $font-weight-semibold; + color: $moon-900; + margin: 0; + } + + p { + font-size: 16px; + font-weight: $font-weight-regular; + margin: 0; + } + + &.card-banner-gray { + &:hover { + box-shadow: none; + transform: none; + } + } + + // Icon container + &-icon { + display: flex; + width: 64px; + height: 64px; + justify-content: center; + align-items: center; + border-radius: 100px; + background: rgba($teal-300, 0.48); + + img { + width: 32px; + height: 32px; + } + } + + [data-bs-theme=dark] & { + background: $dark-bg-secondary; + color: $dark-text-primary; + + h2 { + color: $dark-text-primary; + } + + p { + color: $dark-text-secondary; + } + } +} + +// Add border radius to all images, code blocks and alerts +img, pre, .highlight, .code-toolbar, .alert { + border-radius: $border-radius-medium !important; +} + +// Style images and iframes consistently +img, iframe { + margin-top: 1rem; + margin-bottom: 2rem; + display: block; +} + +.section-index { + max-width: 80%; +} + +.section-index .entry p { + margin-top: 5px; +} + +@media (min-width: $breakpoint-lg) { + img, iframe { + max-width: 80% !important; + } +} + +// Override section index heading font weight +.section-index h5 a, .section-index .h5 a { + font-size: 20px !important; + font-weight: $font-weight-regular !important; +} + +// Add dark mode styles using data-bs-theme attribute +[data-bs-theme=dark] { + body { + color: $dark-text-primary; + background-color: $dark-bg-primary; + } + + // Update sidebar nav colors for dark mode + .td-sidebar-nav { + a { + color: $dark-text-primary !important; + &:hover { + color: $dark-link-primary !important; + } + .td-sidebar-nav-active-item { + color: $dark-link-primary !important; + } + } + } + + // Update headings color for dark mode + h1, h2, h3, h4, h5, h6 { + color: $dark-text-primary; + } + + // Update reading time color for dark mode + .reading-time { + color: $dark-text-secondary; + } + + // Update table of contents color + #TableOfContents { + color: $dark-text-primary; + } + + .td-footer { + background-color: transparent !important; + color: $dark-text-primary !important; + border-top: 1px solid $moon-750 !important; + } + + // Update border colors for dark mode + .border, + .border-top, + .border-right, + .border-bottom, + .border-left { + border-color: $dark-border-default !important; + + &:hover { + border-color: $dark-border-hover !important; + } + } +} + +// For light mode (default) borders +.border, +.border-top, +.border-right, +.border-bottom, +.border-left { + border-color: $border-default !important; + + &:hover { + border-color: $border-hover !important; + } +} + +// Footer Component +.td-footer { + background-color: transparent !important; + color: $text-primary !important; + border-top: 1px solid $moon-200 !important; + padding: 1.5rem 0 !important; + min-height: auto !important; + margin: 0 !important; + + // Container styles + .container-fluid { + padding: 0 2rem !important; + } + + // Row alignment + .row { + align-items: center !important; + } + + // Footer links + &__links-item { + margin-bottom: 0 !important; + } + + // Links and icons + a, i { + color: inherit !important; + transition: color 0.3s !important; + + &:hover { + color: $link-hover !important; + } + } + + // Dark mode styles + [data-bs-theme=dark] & { + background-color: transparent !important; + color: $dark-text-primary !important; + border-top: 1px solid $moon-750 !important; + + a:hover, + i:hover { + color: $wandb-gold !important; + } + } +} + +// Feedback Component +.td-content > .d-print-none { // Target only the feedback section at bottom of content + margin-top: 4rem; + display: flex; + align-items: center; // Change back to center for vertical alignment + flex-wrap: wrap; // Allow wrapping on smaller screens + gap: 8px; // Consistent spacing between elements + + // Hide feedback title + .feedback--title { + display: none; + } + + // Question and buttons container + .feedback--question { + margin: 0; + display: flex; + align-items: center; + gap: 16px; + } + + // Buttons container + .btn { + min-width: 50px; + display: flex; + align-items: center; + justify-content: center; + + &.feedback--answer-no { + margin: 0 !important; + } + } + + // Response styling + .feedback--response { + width: 100%; + margin-top: 1rem; + } +} + +// Last modified date with feedback links - mobile styles +div.td-page-meta__lastmod { + display: flex !important; + flex-direction: column !important; // Stack elements vertically on mobile + gap: 8px !important; + color: $text-secondary !important; + font-size: 14px !important; + margin-top: 0 !important; + + // Container for last modified text and commit link + &:first-child { + display: flex !important; + align-items: center !important; + gap: 4px !important; + } + + // Style the commit link + a { + color: $text-secondary !important; + &:hover { + color: $link-primary !important; + } + } + + .feedback--links { + display: flex !important; + margin-top: 8px !important; + align-items: center !important; + gap: 16px !important; + + // Style the edit/issue links + a { + color: $text-secondary !important; + &:hover { + color: $link-primary !important; + } + } + } + + // Tablet and desktop styles + @media (min-width: $breakpoint-lg) { + flex-direction: row !important; // Switch to horizontal layout + align-items: center !important; + justify-content: space-between !important; + + .feedback--links { + margin-top: 0 !important; + margin-left: auto !important; + } + } + + // Dark mode styles + [data-bs-theme=dark] & { + color: $dark-text-secondary !important; + + a { + color: $dark-text-secondary !important; + &:hover { + color: $dark-link-primary !important; + } + } + } +} + +// Feedback buttons +.feedback--answer { + @extend .button-base; + color: $moon-800 !important; + background-color: $moon-100 !important; + background-image: none !important; + margin-right: 8px !important; + box-shadow: none !important; + display: inline-block !important; + margin-bottom: 0 !important; + border: none !important; + + &:hover { + background-color: $hover-bg !important; + text-decoration: none !important; + border: none !important; + box-shadow: none !important; + } + + // Remove unwanted margin + &.mb-4 { + margin-bottom: 0 !important; + } + + // Hide disabled buttons + &:disabled { + display: none !important; + } + + // Dark mode styles + [data-bs-theme=dark] & { + color: $dark-text-primary !important; + background-color: $dark-bg-secondary !important; + border: none !important; + + &:hover { + background-color: $dark-bg-tertiary !important; + border: none !important; + box-shadow: none !important; + } + } +} + +// Remove feedback links from original location +.td-content > .d-print-none .feedback--links { + display: none !important; +} + +// Update the sidebar navigation styles +.td-sidebar-nav { + font-family: "Source Sans Pro", sans-serif; + padding-left: 15px !important; + + // Base link styles + a { + color: $text-secondary !important; + font-weight: $font-weight-regular; + text-decoration: none !important; + + &:hover { + color: $link-primary !important; + text-decoration: none !important; + } + } + + // Active item styles + .td-sidebar-nav-active-item { + color: $link-primary !important; + font-weight: $font-weight-medium !important; + text-decoration: none !important; + + &:hover { + text-decoration: none !important; + } + } + + // Section title styles + &__section-title { + display: block; + font-weight: $font-weight-regular !important; + line-height: 19px; + margin-bottom: 10px !important; + margin-top: 5px !important; + + .active { + font-weight: $font-weight-regular !important; + } + + a { + color: var(--bs-secondary-color); + font-weight: $font-weight-regular !important; + } + } + + // Dark mode overrides + [data-bs-theme=dark] & { + a { + color: $dark-text-secondary !important; + + &:hover { + color: $dark-link-primary !important; + } + } + + .td-sidebar-nav-active-item { + color: $dark-link-primary !important; + } + } +} + +// Foldable Nav +nav.foldable-nav .with-child, nav.foldable-nav .without-child { + padding-left: 10px !important; + } + // Update existing chevron styles + nav.foldable-nav .ul-1 .with-child { + & > label { + display: flex; + justify-content: space-between; + align-items: center; + width: 100%; + position: relative; + padding-right: 20px; // Space for chevron + + // Remove default chevrons + &:before { + display: none !important; + } + } + } + + // Add chevrons to all expandable items + nav.foldable-nav .ul-1 .with-child > label:after { + content: "\f105" !important; // fa-angle-right (thinner chevron) + font-family: "Font Awesome 6 Free", "Font Awesome 5 Free"; + font-weight: 900; + color: $moon-500; + transition: color 0.3s; + font-size: 14px; + position: absolute; + right: 0px; + top: 35%; + transform: translateY(-50%); + } + + // Change chevron for expanded items + nav.foldable-nav .ul-1 .with-child > input:checked + label:after { + content: "\f107" !important; // fa-angle-down (thinner chevron) + } + + // Hover states + nav.foldable-nav .ul-1 .with-child > label:hover:after { + color: $teal-600 !important; + } + + // Dark mode hover state + [data-bs-theme=dark] nav.foldable-nav .ul-1 .with-child > label:hover:after { + color: $wandb-gold !important; + } + + // Dark mode hover state + [data-bs-theme=dark] nav.foldable-nav .ul-1 .with-child > label:hover:after { + color: $wandb-gold !important; + } + + +// Theme-based logo visibility +[data-bs-theme="dark"] { + .theme-light-only { + display: none; + } + .theme-dark-only { + display: block; + } +} + +[data-bs-theme="light"] { + .theme-light-only { + display: block; + } + .theme-dark-only { + display: none; + } +} + + +// Add consistent padding for main content +.td-main { + main { + &.ps-md-5 { + padding-left: 2rem !important; + padding-right: 2rem !important; + + // Tablet (768px and up) + @media (min-width: $breakpoint-md) { + padding-left: 3rem !important; + padding-right: 3rem !important; + } + } + } +} + +// Google Search style overrides +.gsc-control-cse, .gsc-webResult.gsc-result, .gsc-cursor-page, .gsc-refinementHeader, .gs-no-results-result div { + background-color: transparent !important; + border: 0px !important; +} +.gs-title { + font-family: "Source Serif 4", sans-serif !important; +} +.gs-title b, .gs-title a, .gsc-cursor-page, .gsc-above-wrapper-area, .gcsc-find-more-on-google-query, .gcsc-find-more-on-google-magnifier { + color: $link-primary !important; + text-decoration: none !important; + font-size: 18px !important; + &:hover { + color: $link-hover !important; + text-decoration: none !important; + } + [data-bs-theme=dark] & { + color: $dark-link-primary !important; + + &:hover { + color: $dark-link-hover !important; + } + } +} +.gsc-above-wrapper-area a:hover{ + background-color: $bg-secondary !important; +} +.gsc-result-info, .gs-per-result-labels { + font-family: "Source Sans Pro", sans-serif !important; + font-size: 14px !important; + color: $text-secondary !important; + [data-bs-theme=dark] & { + color: $dark-text-secondary !important; + } +} +.gsc-refinementBlock span { + margin-left: 10px !important; + margin-right: 10px !important; +} +.gs-visibleUrl-breadcrumb { + font-family: "Source Sans Pro", sans-serif !important; + font-size: 14px !important; + color: $text-tertiary !important; + [data-bs-theme=dark] & { + color: $wandb-gold !important; + } +} + +.gs-snippet, .gsc-refinementHeader { + font-family: "Source Sans Pro", sans-serif !important; + font-size: 16px !important; + color: $text-primary !important; + [data-bs-theme=dark] & { + color: $dark-text-primary !important; + } +} +.gsc-above-wrapper-area { + display: none !important; +} +body > div.container-fluid.td-default.td-outer > main > section > div > h2 { + margin-left: 1.0rem !important; +} +.gs-per-result-labels { + padding: 5px !important; + border-radius: $border-radius-medium; + font-size: 12px; + max-width: fit-content; +} +.gs-per-result-labels span { + display: none; +} +.gsc-inline-block:hover, .gsc-refinementhActive, .gs-per-result-labels { + background-color: $bg-tertiary !important; + [data-bs-theme=dark] & { + background-color: $dark-bg-tertiary !important; + } +} +.td-search__input, .form-control, .td-search-input +{ + color: $text-tertiary !important; + border: $text-tertiary !important; + background-color: transparent !important; + [data-bs-theme=dark] & { + color: $dark-text-tertiary !important; + border: $dark-text-tertiary !important; + } +} +.gcsc-find-more-on-google { + display: none !important; +} + +// DocSearch Component +.DocSearch { + // Button styles + &-Button { + font-family: "Source Sans Pro", sans-serif; + font-size: 16px; + background: transparent !important; + border: 1px solid $border-default !important; + + &:active, + &:focus, + &:hover { + background: var(--docsearch-searchbox-focus-background); + box-shadow: inset 0 0 0 2px $teal-500 !important; + color: var(--docsearch-text-color); + outline: none; + border-color: $teal-500 !important; + } + + // Dark mode styles + [data-bs-theme=dark] & { + border: 1px solid $dark-border-default !important; + + &:hover, &:focus, &:active { + border-color: $teal-500 !important; + box-shadow: inset 0 0 0 2px $teal-450 !important; + } + } + } + + // Placeholder text color + &-Button-Placeholder { + color: $text-secondary !important; + + [data-bs-theme=dark] & { + color: $dark-text-secondary !important; + } + } + + // Search icon color + &-Search-Icon { + color: $text-secondary !important; + + [data-bs-theme=dark] & { + color: $dark-text-secondary !important; + } + } + + // Modal styles + &-Modal { + border-radius: $border-radius-large !important; + } + + // Hide footer + &-Footer { + display: none !important; + } +} + +// DocSearch Container with CSS Variables +.DocSearch-Container { + font-family: "Source Sans Pro", sans-serif; + font-size: 16px; + + // Light mode variables + --docsearch-primary-color: #{$teal-500} !important; + --docsearch-highlight-color: #{$teal-500} !important; + --docsearch-text-color: #{$moon-800} !important; + --docsearch-muted-color: #{$moon-500} !important; + --docsearch-container-background: rgba(101, 108, 133, 0.8) !important; + --docsearch-modal-background: #{$moon-white} !important; + --docsearch-searchbox-background: #{$moon-100} !important; + --docsearch-searchbox-focus-background: #{$moon-white} !important; + --docsearch-hit-color: #{$moon-800} !important; + --docsearch-hit-background: #{$moon-white} !important; + --docsearch-hit-active-background: #{$teal-500} !important; + --docsearch-footer-background: #{$moon-white} !important; + --docsearch-logo-color: #{$teal-500} !important; + --docsearch-key-gradient: linear-gradient(-26.5deg, #{$moon-200}, #{$moon-100}) !important; + --docsearch-key-shadow: inset 0 -2px 0 0 #{$moon-300} !important; + --docsearch-searchbox-shadow: inset 0 0 0 2px #{$teal-500} !important; + + .DocSearch-Modal { + border-radius: 0 !important; + max-width: 800px !important; + } + + // Search form styles + .DocSearch-Form { + border-radius: 30px !important; + } + + // Search results styles + .DocSearch-Hit { + border-radius: $border-radius-medium; + + > a { + box-shadow: none !important; + border-radius: $border-radius-medium; + border: 1px solid $border-default !important; + + } + } + + // Dark mode styles + [data-bs-theme=dark] & { + --docsearch-primary-color: #{$teal-400} !important; + --docsearch-highlight-color: #{$teal-400} !important; + --docsearch-text-color: #{$moon-200} !important; + --docsearch-muted-color: #{$moon-400} !important; + --docsearch-container-background: rgba(21, 23, 31, 0.8) !important; + --docsearch-modal-background: #{$moon-800} !important; + --docsearch-searchbox-background: #{$moon-900} !important; + --docsearch-searchbox-focus-background: #{$moon-900} !important; + --docsearch-hit-color: #{$moon-200} !important; + --docsearch-hit-background: #{$moon-800} !important; + --docsearch-hit-active-background: #{$teal-400} !important; + --docsearch-footer-background: #{$moon-800} !important; + --docsearch-logo-color: #{$teal-400} !important; + --docsearch-key-gradient: linear-gradient(-26.5deg, #{$moon-800}, #{$moon-750}) !important; + --docsearch-key-shadow: inset 0 -2px 0 0 #{$moon-700} !important; + --docsearch-searchbox-shadow: inset 0 0 0 2px #{$teal-400} !important; + } + + .DocSearch-Hit-source { + color: $text-secondary !important; + font-size: 14px !important; + } + + .DocSearch-Hit-title { + font-size: 1rem !important; + } + + // Dark mode styles + [data-bs-theme=dark] & { + + .DocSearch-Modal { + box-shadow: inset 1px 1px 0 0 hsla(0, 0%, 18%, 0.5), 0 3px 8px 0 $moon-900; + } + + .DocSearch-Hit { + > a { + border: 1px solid $dark-border-default !important; + } + } + + .DocSearch-Hit-source { + color: $dark-text-secondary !important; + font-size: 14px !important; + } + + .DocSearch-Hit-title { + font-size: 1rem !important; + } + } +} + +@media (min-width: $breakpoint-lg) { + .DocSearch { + + .DocSearch-Modal { + border-radius: $border-radius-large !important; + } + } +} From da4f30bddd32c13fcbb20cc5a835f703775f2c85 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 13:44:50 -0400 Subject: [PATCH 15/59] new test implementation of redoc using redoc2 --- content/en/ref/training.md | 2 +- layouts/redoc2.html | 92 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 93 insertions(+), 1 deletion(-) create mode 100644 layouts/redoc2.html diff --git a/content/en/ref/training.md b/content/en/ref/training.md index 3c3464482f..5c6863be20 100644 --- a/content/en/ref/training.md +++ b/content/en/ref/training.md @@ -4,5 +4,5 @@ description: Generated documentation for W&B APIs menu: reference: identifier: serverless-rl-api-reference -layout: redoc +layout: redoc2 --- \ No newline at end of file diff --git a/layouts/redoc2.html b/layouts/redoc2.html new file mode 100644 index 0000000000..1ae35f8a94 --- /dev/null +++ b/layouts/redoc2.html @@ -0,0 +1,92 @@ + + + + {{ partial "head.html" . }} + + + + +
+ {{ partial "navbar.html" . }} +
+
+
+
+
+ {{ partial "version-banner.html" . }} + {{ if not (.Param "ui.breadcrumb_disable") -}} + {{ partial "breadcrumb.html" . -}} + {{ end -}} +
+
+ +
+
+
+
+
+ {{ partial "footer.html" . }} +
+ {{ partial "scripts.html" . }} + + + \ No newline at end of file From 326b5c0532637c24f8ac84f5f2de6036327c73ff Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 13:58:42 -0400 Subject: [PATCH 16/59] force light mode on redoc page --- layouts/redoc2.html | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/layouts/redoc2.html b/layouts/redoc2.html index 1ae35f8a94..775f643300 100644 --- a/layouts/redoc2.html +++ b/layouts/redoc2.html @@ -2,7 +2,7 @@ + class="no-js" data-bs-theme="light"> {{ partial "head.html" . }} @@ -88,5 +88,25 @@
{{ partial "scripts.html" . }} + \ No newline at end of file From 9cc3fed924bbde4ea7f996d8187b9f5f92a679a2 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 14:10:33 -0400 Subject: [PATCH 17/59] fix scrolling issue in redoc2 --- layouts/redoc2.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/layouts/redoc2.html b/layouts/redoc2.html index 775f643300..f742a16a97 100644 --- a/layouts/redoc2.html +++ b/layouts/redoc2.html @@ -78,7 +78,7 @@ {{ end -}}
- +
From c92099da1cc421c8eab39c0937b151849a7a241f Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 14:14:14 -0400 Subject: [PATCH 18/59] Replaces old layout with new one --- content/en/ref/training.md | 4 +- layouts/redoc.html | 194 ++++++++----------------------------- layouts/redoc2.html | 112 --------------------- 3 files changed, 42 insertions(+), 268 deletions(-) delete mode 100644 layouts/redoc2.html diff --git a/content/en/ref/training.md b/content/en/ref/training.md index 5c6863be20..3ebed9a2c5 100644 --- a/content/en/ref/training.md +++ b/content/en/ref/training.md @@ -1,8 +1,8 @@ --- -title: Serverless RL API Reference +title: W&B Training API Reference description: Generated documentation for W&B APIs menu: reference: identifier: serverless-rl-api-reference -layout: redoc2 +layout: redoc --- \ No newline at end of file diff --git a/layouts/redoc.html b/layouts/redoc.html index 08b0389671..89de17e80d 100644 --- a/layouts/redoc.html +++ b/layouts/redoc.html @@ -2,43 +2,13 @@ + class="no-js" data-bs-theme="light"> - - - {{ .Title }} | {{ .Site.Title }} - - - {{ hugo.Generator }} - {{ range .AlternativeOutputFormats -}} - - {{ end -}} - - - {{ if templates.Exists "partials/favicons.html" }} - {{ partialCached "favicons.html" . }} - {{ else }} - - {{ end }} - - - {{ $scss := resources.Get "scss/_variables_project.scss" | toCSS (dict "targetPath" "css/_variables_project.scss") | minify | fingerprint }} - - - - - - - + {{ partial "head.html" . }} - - - - -
- {{ partial "navbar.html" . }} -
-
-
-
-
- {{ partial "version-banner.html" . }} - {{ if not (.Param "ui.breadcrumb_disable") -}} - {{ partial "breadcrumb.html" . -}} - {{ end -}} -
-
- -
-
-
-
-
- {{ partial "footer.html" . }} -
- {{ partial "scripts.html" . }} - - - - \ No newline at end of file From 61aa8a22f8c97815bc503712b44e9e36550096b0 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 14:36:30 -0400 Subject: [PATCH 19/59] Fixes width of redoc page --- layouts/redoc.html | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/layouts/redoc.html b/layouts/redoc.html index 89de17e80d..d567849f55 100644 --- a/layouts/redoc.html +++ b/layouts/redoc.html @@ -20,6 +20,14 @@ height: auto !important; } + /* Make the main content area full-width without breaking Bootstrap grid */ + main.redoc-page { + padding-left: 0 !important; + padding-right: 0 !important; + margin-left: 0 !important; + margin-right: 0 !important; + } + /* Override any conflicting styles from the theme */ .redoc-container redoc { display: block; @@ -64,7 +72,7 @@
-
+
{{ partial "version-banner.html" . }} {{ if not (.Param "ui.breadcrumb_disable") -}} {{ partial "breadcrumb.html" . -}} From e13cc4742f9a27fe579796432da377b8a0e4224d Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 17:01:02 -0400 Subject: [PATCH 20/59] Updates API spec URL for Redoc --- content/en/guides/training/serverless-rl.md | 31 --------------------- content/en/ref/training.md | 2 +- layouts/redoc.html | 2 +- 3 files changed, 2 insertions(+), 33 deletions(-) delete mode 100644 content/en/guides/training/serverless-rl.md diff --git a/content/en/guides/training/serverless-rl.md b/content/en/guides/training/serverless-rl.md deleted file mode 100644 index 35e20c69ee..0000000000 --- a/content/en/guides/training/serverless-rl.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -description: Learn about Serverless RL and how to start using it. -title: Use Serverless RL -weight: 100 ---- - -W&B Serverless RL (reinforcement learning) allows you to post-train LLMs to learn new behavior and improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. Serverless RL runs your reinforcement learning (RL) jobs on a large, managed cluster of GPUs that give you the freedom to scale your training run up to dozens of GPUs or down to zero instantly. - -Serverless RL is ideal for training: - -* Voice agents -* Deep research assistants -* On-prem models -* Content marketing analysis agents - -Serverless RL trains [low-rank adapters (LoRAs)](https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)#Low-rank_adaptation) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model's training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adapters as artifacts stored in your W&B account. - -Serverless RL uses a combination of the following W&B services to operate: - -* [Inference]({{< relref "guides/inference" >}}): To run your models -* [Models]({{< relref "guides/models" >}}): To track performance metrics during the LoRA adapter's training -* [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters -* [Weave]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop - -We charge for the use of inference and the storage of artifacts. We do not currently charge to train the adapters. See the [Serverless RL pricing page](#update-link) for more details. - -## Use Serverless RL - -Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and a [W&B API](#UPDATE-LINK). See the [OpenPipe's Serverless RL quickstart](#UPDATE-LINK) for code examples and workflows. - -See the [Serverless RL API]({{< relref "ref/training" >}}) reference for more information on the service's endpoints. \ No newline at end of file diff --git a/content/en/ref/training.md b/content/en/ref/training.md index 3ebed9a2c5..63d5a367fb 100644 --- a/content/en/ref/training.md +++ b/content/en/ref/training.md @@ -1,5 +1,5 @@ --- -title: W&B Training API Reference +title: W&B Training API description: Generated documentation for W&B APIs menu: reference: diff --git a/layouts/redoc.html b/layouts/redoc.html index d567849f55..5cd9efb2bf 100644 --- a/layouts/redoc.html +++ b/layouts/redoc.html @@ -79,7 +79,7 @@ {{ end -}}
- +
From be1ce2f62e1189586d3001540a9d16f038075d21 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 17:01:45 -0400 Subject: [PATCH 21/59] adds prereqs page --- content/en/guides/training/prerequisites.md | 33 +++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 content/en/guides/training/prerequisites.md diff --git a/content/en/guides/training/prerequisites.md b/content/en/guides/training/prerequisites.md new file mode 100644 index 0000000000..6fcdac7c51 --- /dev/null +++ b/content/en/guides/training/prerequisites.md @@ -0,0 +1,33 @@ +--- +title: "Prerequisites" +linkTitle: "Prerequisites" +weight: 1 +description: > + Set up your environment to use W&B Training +--- + +Complete these steps before using W&B Training features through the OpenPipe ART framework or API. + +{{< alert title="Tip" >}} +Before starting, review the [usage information and limits]({{< relref "guides/training/serverless-rl/usage-limits" >}}) to understand costs and restrictions. +{{< /alert >}} + +## Set up your W&B account and project + +You need these items to access W&B Training: + +1. **A W&B account** + Sign up at [W&B](https://app.wandb.ai/login?signup=true) + +2. **A W&B API key** + Get your API key at [https://wandb.ai/authorize](https://wandb.ai/authorize) + +3. **A W&B project** + Create a project in your W&B account to track usage + +## Next steps + +After completing the prerequisites: + +* Check the [API reference]({{< relref "/ref/training" >}}) to learn about available endpoints +* Try the [ART quickstart](https://art.openpipe.ai/getting-started/quick-start) From 93b8a6dde9eca146bdf5aad0c081f2b9aa769b13 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 17:02:17 -0400 Subject: [PATCH 22/59] adds Serverless RL sub-section --- .../guides/training/serverless-rl/_index.md | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 content/en/guides/training/serverless-rl/_index.md diff --git a/content/en/guides/training/serverless-rl/_index.md b/content/en/guides/training/serverless-rl/_index.md new file mode 100644 index 0000000000..c26c81f97c --- /dev/null +++ b/content/en/guides/training/serverless-rl/_index.md @@ -0,0 +1,27 @@ +--- +menu: + default: + identifier: serverless-rl +title: Serverless RL +description: Learn about how to more efficiently post-train your models using reinforcement learning. +weight: 5 +--- + +Serverless RL lets you post-train LLMs to learn new behaviors and improve reliability, speed, and costs when performing multi-turn, agentic tasks while we handle the GPUs and infrastructure for you. Serverless RL gives you instant access to GPUs on a fully managed, distributed training cluster that elastically auto-scales to dozens of GPUs or down to zero instantly, matching your reinforcement learning (RL) job at every moment. By automatically splitting RL workflows into inference and training phases and multiplexing them across jobs, Serverless RL maximizes GPU utilization and reduces your training time and costs. + +Serverless RL is ideal for: +* Voice agents +* Deep research assistants +* On-prem models +* Content marketing analysis agents + +Serverless RL trains low-rank adapters (LoRAs) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model’s training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adapters as artifacts stored in your W&B account. + +Serverless RL uses a combination of the following W&B services to operate: + +* [Inference]({{< relref "guides/inference" >}}): To run your models +* [Models]({{< relref "guides/models" >}}): To track performance metrics during the LoRA adapter's training +* [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters +* [Weave]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop + +Serverless RL is currently in public preview. We only charge for the use of inference and the storage of artifacts. We do not charge for adapter training during the preview period. We will make training pricing details available when the service is released for general availability. From 8cf9d02a805627ff21a1e162bbb9a0895bf418ac Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 17:02:40 -0400 Subject: [PATCH 23/59] Adds available models section --- .../training/serverless-rl/available-models.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 content/en/guides/training/serverless-rl/available-models.md diff --git a/content/en/guides/training/serverless-rl/available-models.md b/content/en/guides/training/serverless-rl/available-models.md new file mode 100644 index 0000000000..056a5e498c --- /dev/null +++ b/content/en/guides/training/serverless-rl/available-models.md @@ -0,0 +1,15 @@ +--- +title: "Available Models" +linkTitle: "Available Models" +weight: 20 +description: > + See what models you can use with Serverless RL. +--- + +Serverless RL uses one open-source foundation model for training. + +## Model catalog + +| Model | Model ID (for API usage) | Type | Context Window | Parameters | Description | +|-------|--------------------------|------|----------------|------------|-------------| +| Qwen2.5 14B | Qwen/Qwen2.5-14B-Instruct | Text | 32K | 14B (Active-Total) | High-performance Mixture-of-Experts model optimized for structured reasoning, math, and long-form generation | From 0b9aa3a2d36200e5687822c99d6c5f592669e751 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 17:03:09 -0400 Subject: [PATCH 24/59] Adds use serverless RL section --- .../en/guides/training/serverless-rl/serverless-rl.md | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 content/en/guides/training/serverless-rl/serverless-rl.md diff --git a/content/en/guides/training/serverless-rl/serverless-rl.md b/content/en/guides/training/serverless-rl/serverless-rl.md new file mode 100644 index 0000000000..4f5aff6cde --- /dev/null +++ b/content/en/guides/training/serverless-rl/serverless-rl.md @@ -0,0 +1,9 @@ +--- +description: Learn how to start using it. +title: Use Serverless RL +weight: 10 +--- + +Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and the W&B Training API. + +See [OpenPipe's Serverless RL quickstart](https://art.openpipe.ai/getting-started/quick-start) for code examples and workflows. See the [W&B Training API]({{< relref "ref/training" >}}) reference for more information on the Serverless RL's endpoints. \ No newline at end of file From 82e017d338751f500651e0acd8751e37789e471a Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 17:03:31 -0400 Subject: [PATCH 25/59] Adds usage and limits section --- .../training/serverless-rl/usage-limits.md | 42 +++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 content/en/guides/training/serverless-rl/usage-limits.md diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md new file mode 100644 index 0000000000..5a216e5cc4 --- /dev/null +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -0,0 +1,42 @@ +--- +title: "Usage Information and Limits" +linkTitle: "Usage & Limits" +weight: 30 +description: > + Understand pricing, usage limits, and account restrictions for W&B Serverless RL. +--- + +## Pricing + +Pricing has three components: inference, training, and storage. Learn more: https://wandb.ai/site/pricing/reinforcement-learning + +### Inference + +Trajectories are batched at each training step and, together with rewards from your reward function (often an LLM-as-judge), are used to update a training copy of the LoRA. The training workload runs on a separate GPU cluster within Serverless RL. During the public preview, training is free. Pricing for training will be announced at general availability (GA). + +* Check the [model-specific costs](https://wandb.ai/site/pricing/inference) for more details. Learn more about purchasing credits, account tiers, and usage caps in the [W&B Inference docs]({{< relref "/guides/inference/usage-limits/#purchase-more-credits" >}}). + +### Training + +At each training step, Serverless RL batches trajectories and combines them with rewards from your reward function (typically an LLM-as-judge). These batched trajectories and rewards are then used to update the LoRA adapter weights on a training copy of the base model. All training workloads run on a dedicated GPU cluster managed by Serverless RL. + +During the public preview, training is free. Pricing for training will be announced at general availability (GA). + +### Model Storage + +Serverless RL stores checkpoints of your trained LoRA heads so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes free storage. + + +## Concurrency limits + +If you exceed the rate limit, the API returns a `429 Concurrency limit reached for requests` response. To fix this error, reduce the number of concurrent requests. +W&B applies rate limits per project. For example, if you have three projects in a team, each project has its own rate limit quota. The default rate limit is 2000 concurrent requests. + +## Personal entities unsupported + +Serverless RL and W&B Inference don't support personal entities (personal accounts). To access Serverless RL, switch to a non-personal account by [creating a Team]({{< relref "/guides/hosting/iam/access-management/manage-organization/#add-and-manage-teams" >}}). Personal entities (personal accounts) were deprecated in May 2024, so this only applies to legacy accounts. + +## Geographic restrictions + +Serverless RL is only available from supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). + From 69a0b7ff2294b906d66303d3f26d1e20ac5fde3b Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Fri, 3 Oct 2025 17:20:15 -0400 Subject: [PATCH 26/59] Adds API ref placeholder section --- content/en/guides/training/api-reference.md | 16 ++++++++++++++++ content/en/ref/training.md | 5 +---- 2 files changed, 17 insertions(+), 4 deletions(-) create mode 100644 content/en/guides/training/api-reference.md diff --git a/content/en/guides/training/api-reference.md b/content/en/guides/training/api-reference.md new file mode 100644 index 0000000000..c06888e05d --- /dev/null +++ b/content/en/guides/training/api-reference.md @@ -0,0 +1,16 @@ +--- +title: "API Reference" +linkTitle: "API Reference" +weight: 100 +description: > + Complete API documentation for W&B Training. +--- + +View the complete [W&B Training API Reference]({{< relref "/ref/training" >}}). + +The API reference includes: +* All available endpoints +* Request and response schemas +* Authentication details +* Example requests and responses + diff --git a/content/en/ref/training.md b/content/en/ref/training.md index 63d5a367fb..c9cf6b31cb 100644 --- a/content/en/ref/training.md +++ b/content/en/ref/training.md @@ -1,8 +1,5 @@ --- title: W&B Training API description: Generated documentation for W&B APIs -menu: - reference: - identifier: serverless-rl-api-reference layout: redoc ---- \ No newline at end of file +--- From fc4b33fb2a9c407a69e7c73aeb30b1068d7cf3f8 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 12:40:37 -0400 Subject: [PATCH 27/59] Updates marketecture diagram --- assets/images/general/architecture.png | Bin 188609 -> 65156 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/assets/images/general/architecture.png b/assets/images/general/architecture.png index 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1562 deletions(-) delete mode 100644 static/api-specs/serverlessRL.json delete mode 100644 static/css/main.css delete mode 100644 static/icons/logo_dark.svg delete mode 100644 static/icons/logo_light.svg diff --git a/static/api-specs/serverlessRL.json b/static/api-specs/serverlessRL.json deleted file mode 100644 index 434f0507a3..0000000000 --- a/static/api-specs/serverlessRL.json +++ /dev/null @@ -1 +0,0 @@ -{"openapi":"3.1.0","info":{"title":"Art Training Backend","version":"1.0.0"},"paths":{"/v1/chat/completions/":{"post":{"tags":["chat-completions"],"summary":"Create Chat Completion","description":"Create a new chat completion.","operationId":"create_chat_completion_v1_chat_completions__post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ChatCompletionRequest"}}},"required":true},"responses":{"200":{"description":"Successful 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-$bg-secondary: $moon-50; // color/moon/50 -$bg-tertiary: $moon-100; // color/moon/100 -$bg-disabled: $moon-350; // color/moon/350 - -// Dark mode colors -$dark-text-primary: $moon-200; // color/moon/200 -$dark-text-secondary: $moon-400; // color/moon/400 -$dark-text-tertiary: $moon-500; // color/moon/500 -$dark-text-disabled: $moon-650; // color/moon/650 - -$dark-link-primary: $teal-500; // color/teal/500 -$dark-link-hover: $teal-450; // color/teal/450 -$dark-link-secondary: $moon-600; // color/moon/600 -$dark-link-secondary-hover: $moon-500; // color/moon/500 - -$dark-bg-primary: $moon-900; // color/moon/900 -$dark-bg-secondary: $moon-800; // color/moon/800 -$dark-bg-tertiary: $moon-700; // color/moon/700 -$dark-bg-disabled: $moon-650; // color/moon/650 - -// Other brand colors -$nav-bg: rgb(26, 28, 31); -$wandb-gold: #FFCC33; -$wandb-link-blue: rgb(7, 177, 196); -$hover-bg: #A9EDF260; - -// Gradients -$sunset-gradient: linear-gradient(98deg, #e180ff 0%, #ffcf4d 100%); -$wandb-gradient: linear-gradient(180deg, #ffcc33 39%, #ffad33 72%); - -// Font weights -$font-weight-light: 300; -$font-weight-regular: 400; -$font-weight-medium: 500; -$font-weight-semibold: 600; -$font-weight-bold: 700; - -// Bootstrap overrides -:root { - --bs-nav-link-font-size: 18px; -} - -// Typography Component -// Base body styles -body { - font-family: "Source Sans Pro", sans-serif; - font-size: 16px; - font-weight: $font-weight-regular; - color: $text-primary; - background-color: $bg-primary; - - [data-bs-theme=dark] & { - color: $dark-text-primary; - background-color: $dark-bg-primary; - } -} -p, td, div -{ - font-family: "Source Sans Pro", sans-serif; -} -.td-sidebar-nav__section-title, .td-sidebar-nav__section without-child -{ - font-size: 16px; - font-weight: $font-weight-bold; -} - -// Headings -h1, h2, h3, h4, h5, h6 { - font-weight: $font-weight-regular !important; - font-family: "Source Serif 4", sans-serif; - color: $text-primary; - - [data-bs-theme=dark] & { - color: $dark-text-primary; - } -} - -// Heading sizes -h1, .td-content h1 { font-size: 32px !important; } -h2, .td-content h2 { font-size: 24px !important; } -h3, .td-content h3 { font-size: 20px !important; } -h4, .td-content h4 { font-size: 18px !important; } -h5, .td-content h5 { font-size: 17px !important; } -h6, .td-content h6 { font-size: 16px !important; } - -// Lead paragraph -.td-content .lead { - font-size: 16px !important; - font-weight: $font-weight-regular !important; -} - -// Reading time -.reading-time { - font-size: 14px; - color: $text-secondary; - - [data-bs-theme=dark] & { - color: $dark-text-secondary; - } -} - -// Links -a { - color: $link-primary !important; - text-decoration: none !important; - - &:hover { - color: $link-hover !important; - text-decoration: none !important; - } - - [data-bs-theme=dark] & { - color: $dark-link-primary !important; - - &:hover { - color: $dark-link-hover !important; - } - } -} - -// hide "create child" and "create project issue" links -.td-page-meta__child, .td-page-meta__project-issue { display: none !important; } - -// shrink the right nav a bit -.td-page-meta { - display: none !important; - font-size: 16px !important; - a { - padding-bottom: 15px; - line-height: 22px; - } -} - -// hide left nav label -.tree-root { display: none !important;} - -// First define breakpoints -$breakpoint-sm: 576px; // Phone -$breakpoint-md: 768px; // Tablet -$breakpoint-lg: 992px; // Desktop -$breakpoint-xl: 1200px; // Large desktop - -// Consolidated navbar styles with mobile-first approach -.td-navbar { - background-color: $bg-primary !important; - padding: 0 !important; - border-bottom: 1px solid $border-default !important; - - // Base styles (mobile first) - .container-fluid { - padding: 8px !important; - gap: 16px !important; - - .td-navbar-nav-scroll { - align-items: center !important; - height: auto !important; - margin: 0 !important; - padding: 0 !important; - gap: 16px !important; - - .navbar-nav { - display: flex !important; - align-items: center !important; - padding-left: 0 !important; - margin: 0 !important; - gap: 8px !important; - - .nav-item { - padding-inline-end: 0 !important; - } - - &.main-nav { - padding: 0 !important; - margin: 0 !important; - - // Nav links base styles - .nav-link { - color: $text-secondary !important; - font-weight: $font-weight-regular !important; - transition: all 0.2s ease-in-out !important; - border-bottom: none !important; - border-radius: $border-radius-medium !important; - padding: 8px 10px !important; - font-size: 16px !important; - - &:hover { - text-decoration: none !important; - background-color: rgba($moon-300, 0.2) !important; - } - - &.active { - color: $link-primary !important; - background-color: rgba($moon-300, 0.2) !important; - font-weight: $font-weight-regular !important; - } - } - - } - - &.main-nav-right { - padding: 0 !important; - margin: 0 !important; - align-items: center !important; - gap: 0 !important; - - // Update mobile light/dark toggle styles - .td-light-dark-menu { - display: flex !important; - align-items: center !important; - margin: 0 !important; - gap: 0 !important; - min-width: 0 !important; - } - - // Dropdown styles - .nav-item.dropdown { - margin: 0 !important; - - .nav-link { - font-size: 16px !important; - font-weight: $font-weight-regular !important; - color: $text-secondary !important; - - &:hover { - background-color: transparent !important; - } - - &::after { - display: none !important; - } - - [data-bs-theme=dark] & { - color: $dark-text-secondary !important; - } - } - } - } - } - } - } - - // Brand/logo styles - .navbar-brand { - display: flex !important; - align-items: center !important; - padding: 0 !important; - margin-right: 0 !important; - - .navbar-logo { - display: block; - width: auto; - height: auto; - margin: 0 !important; - - svg { - margin: 0 !important; - width: 100%; - } - } - } - - // Hide the doc site name - .navbar-brand__name { - display: none !important; - } - - - // Tablet styles (577px and up) - @media (min-width: $breakpoint-sm + 1) { - .container-fluid { - padding: 8px 12px !important; - } - - .navbar-brand { - margin-right: 1rem !important; - } - } - - // Desktop styles (992px and up) - @media (min-width: $breakpoint-lg) { - .container-fluid { - padding: 8px 24px !important; - flex-direction: row !important; - align-items: center !important; - justify-content: flex-start !important; - gap: 32px !important; - - .td-navbar-nav-scroll { - flex-grow: 1 !important; - - .navbar-nav { - - &.main-nav { - flex-grow: 1 !important; - gap: 16px !important; - - .nav-link { - padding: 6px 12px !important; - font-size: 18px !important; - } - } - - &.main-nav-right { - gap: 16px !important; - - .nav-link{ - font-size: 16px !important; - color: $text-secondary !important; - } - } - } - .main-nav-search { - min-width: 300px !important; - - .DocSearch.DocSearch-Button { - margin: 0 !important; - min-width: 100% !important; - } - } - } - } - - - .nav-item.dropdown { - &.d-none.d-lg-block, - &.td-light-dark-menu { - margin: 0 4px !important; - - .nav-link { - font-size: 16px !important; - } - } - } - } - - // Dark mode styles - [data-bs-theme=dark] & { - background-color: $dark-bg-primary !important; - border-bottom: 1px solid $dark-border-default !important; - - &.js-navbar-scroll { - background-color: $dark-bg-primary !important; - } - - .td-navbar-nav-scroll { - - .navbar-nav { - - &.main-nav { - .nav-link { - color: $moon-white !important; - - &:hover { - color: $teal-500 !important; - } - - &.active { - color: $teal-500 !important; - } - } - } - - .nav-item.dropdown { - &.d-none.d-lg-block, - &.td-light-dark-menu { - .nav-link { - color: $dark-text-primary !important; - - &:hover { - color: $dark-link-primary !important; - } - } - } - } - } - } - } - - // Update the light/dark menu styles - .td-light-dark-menu { - &.nav-item.dropdown { - margin: 0 2px !important; - display: flex !important; - align-items: center !important; - } - - // Hide from navbar-right on mobile - &.d-none { - @media (max-width: #{$breakpoint-lg - 1}) { - display: none !important; - } - } - } -} - -// headings use WandB serif font -h1, h2, h3, h4, h5, h6 { - font-weight: $font-weight-regular !important; - font-family: "Source Serif 4", sans-serif; - color: $text-primary; -} - -.td-sidebar-nav-active-item { - text-decoration: none; -} - -ol.breadcrumb li { - font-size: 13px; -} -ol.breadcrumb li:not(:first-child):before { - content: ">" !important; -} - -.td-sidebar-nav__section-title, .td-sidebar-nav__section { - margin-bottom: 10px !important; - .nav-link.dropdown-toggle { - margin-left: -5px !important; - } -} - -.td-content .lead { - font-size: 16px !important; - font-weight: $font-weight-regular !important; -} - -// Table of Contents Component -#TableOfContents { - - font-size: 13px !important; - font-family: "Source Sans Pro", sans-serif; - a{ - margin: 0px !important; - padding: 8px !important; - font-weight: 500 !important; - } - // List styles - ul { - padding: 0px !important; - li { - padding: 0px !important; - line-height: 14px; - } - } - - // Link styles - a { - display: block; - color: $text-secondary !important; - - &:hover { - color: $link-primary !important; - text-decoration: none !important; - } - - [data-bs-theme=dark] & { - color: $dark-text-secondary !important; - - &:hover { - color: $dark-link-primary !important; - } - } - } - - // Dark mode - [data-bs-theme=dark] & { - color: $dark-text-primary; - } -} - -// tabs -ul { - &.nav-tabs { - border-bottom: none !important; - - li { - &.nav-item { - margin: 0 !important; - - button { - color: $text-primary !important; - - &.active { - color: $link-primary !important; - background-color: var(--td-pre-bg) !important; - border-bottom-color: var(--td-pre-bg) !important; - } - - // Add dark mode styles - [data-bs-theme=dark] & { - color: $dark-text-primary !important; - - &.active { - color: $dark-link-primary !important; - } - } - } - } - } - } -} - -.tab-content { - > .tab-pane { - border-radius: 0 $border-radius-medium $border-radius-medium $border-radius-medium !important; - border: 1px solid $border-default !important; - - // Add dark mode styles - [data-bs-theme=dark] & { - border-color: $dark-border-default !important; - } - } -} - -// Buttons Component -// Base button styles -.button-base { - display: inline-flex; - align-items: center; - justify-content: center; - font-size: 16px; - line-height: 20px; - font-weight: $font-weight-semibold; - padding: 0 12px; - min-height: 40px; - border-radius: $border-radius-medium; - transition: background-color 0.3s, color 0.3s; - text-decoration: none !important; - border: none; - - &:hover { - text-decoration: none !important; - } -} - -// CTA Container styles -.cta-container { - display: flex; - align-items: center; // Vertically center the buttons - gap: 16px; // Consistent spacing between buttons - margin: 24px 0; - flex-wrap: wrap; // Allow wrapping on smaller screens -} - -// CTA Button -.cta-button { - @extend .button-base; - color: $moon-800 !important; - background-color: $moon-100; - height: 40px; // Fixed height for consistency - padding: 0 16px; - display: inline-flex; - align-items: center; // Center button contents vertically - white-space: nowrap; // Prevent text wrapping inside button - - &:hover { - background-color: $hover-bg; - } - - // Icon styles - svg, img { - width: 24px; - height: 24px; - margin-right: 8px; - } - - [data-bs-theme=dark] & { - color: $dark-text-primary !important; - background-color: $dark-bg-secondary; - - &:hover { - background-color: $dark-bg-tertiary; - } - } -} - -// Contact Us Button -.contact-us-button { - @extend .button-base; - background-color: $teal-500; - color: $moon-white !important; - gap: 8px; - - &:hover { - background-color: $teal-450; - } - - img { - width: 112px; - height: 24px; - } - - p { - margin: 0; - color: $moon-white; - } - - [data-bs-theme=dark] & { - background-color: $teal-450; - - &:hover { - background-color: $teal-400; - } - } -} - -// Banners Component -// Base banner styles -.banner-base { - display: flex; - padding: 32px 40px; - flex-direction: column; - align-items: flex-start; - gap: 24px; - border-radius: $border-radius-large; - margin-bottom: 30px; - - h1, h2, p, div { - filter: drop-shadow(5px 5px 5px rgba(0, 0, 0, 0.5)); - } -} - -// Standard Banner -.banner { - @extend .banner-base; - background-position: center; - background-repeat: no-repeat; - background-size: cover; - min-height: 200px; - color: $moon-white; - overflow: hidden; -} - -// Help Banner -.help-banner { - @extend .banner-base; - padding: 56px 40px 72px 40px; - background: $moon-900; - background-image: url('/images/support/support_banner.png'); - background-size: cover; - background-position: center; - box-shadow: none; - - &.card:hover { - box-shadow: none; - transform: none; - } - - h2 { - color: $moon-50; - font-size: 40px; - font-weight: $font-weight-semibold; - font-family: "Source Sans Pro"; - line-height: 140%; - } - - p { - color: $moon-250; - font-size: 20px; - font-weight: $font-weight-regular; - font-family: "Source Sans Pro"; - line-height: 140%; - } -} - -// Card Banner -.card-banner { - @extend .banner-base; - align-items: center; - background: $moon-50; - color: $moon-800; - - h2 { - font-size: 24px; - font-weight: $font-weight-semibold; - color: $moon-900; - margin: 0; - } - - p { - font-size: 16px; - font-weight: $font-weight-regular; - margin: 0; - } - - &.card-banner-gray { - &:hover { - box-shadow: none; - transform: none; - } - } - - // Icon container - &-icon { - display: flex; - width: 64px; - height: 64px; - justify-content: center; - align-items: center; - border-radius: 100px; - background: rgba($teal-300, 0.48); - - img { - width: 32px; - height: 32px; - } - } - - [data-bs-theme=dark] & { - background: $dark-bg-secondary; - color: $dark-text-primary; - - h2 { - color: $dark-text-primary; - } - - p { - color: $dark-text-secondary; - } - } -} - -// Add border radius to all images, code blocks and alerts -img, pre, .highlight, .code-toolbar, .alert { - border-radius: $border-radius-medium !important; -} - -// Style images and iframes consistently -img, iframe { - margin-top: 1rem; - margin-bottom: 2rem; - display: block; -} - -.section-index { - max-width: 80%; -} - -.section-index .entry p { - margin-top: 5px; -} - -@media (min-width: $breakpoint-lg) { - img, iframe { - max-width: 80% !important; - } -} - -// Override section index heading font weight -.section-index h5 a, .section-index .h5 a { - font-size: 20px !important; - font-weight: $font-weight-regular !important; -} - -// Add dark mode styles using data-bs-theme attribute -[data-bs-theme=dark] { - body { - color: $dark-text-primary; - background-color: $dark-bg-primary; - } - - // Update sidebar nav colors for dark mode - .td-sidebar-nav { - a { - color: $dark-text-primary !important; - &:hover { - color: $dark-link-primary !important; - } - .td-sidebar-nav-active-item { - color: $dark-link-primary !important; - } - } - } - - // Update headings color for dark mode - h1, h2, h3, h4, h5, h6 { - color: $dark-text-primary; - } - - // Update reading time color for dark mode - .reading-time { - color: $dark-text-secondary; - } - - // Update table of contents color - #TableOfContents { - color: $dark-text-primary; - } - - .td-footer { - background-color: transparent !important; - color: $dark-text-primary !important; - border-top: 1px solid $moon-750 !important; - } - - // Update border colors for dark mode - .border, - .border-top, - .border-right, - .border-bottom, - .border-left { - border-color: $dark-border-default !important; - - &:hover { - border-color: $dark-border-hover !important; - } - } -} - -// For light mode (default) borders -.border, -.border-top, -.border-right, -.border-bottom, -.border-left { - border-color: $border-default !important; - - &:hover { - border-color: $border-hover !important; - } -} - -// Footer Component -.td-footer { - background-color: transparent !important; - color: $text-primary !important; - border-top: 1px solid $moon-200 !important; - padding: 1.5rem 0 !important; - min-height: auto !important; - margin: 0 !important; - - // Container styles - .container-fluid { - padding: 0 2rem !important; - } - - // Row alignment - .row { - align-items: center !important; - } - - // Footer links - &__links-item { - margin-bottom: 0 !important; - } - - // Links and icons - a, i { - color: inherit !important; - transition: color 0.3s !important; - - &:hover { - color: $link-hover !important; - } - } - - // Dark mode styles - [data-bs-theme=dark] & { - background-color: transparent !important; - color: $dark-text-primary !important; - border-top: 1px solid $moon-750 !important; - - a:hover, - i:hover { - color: $wandb-gold !important; - } - } -} - -// Feedback Component -.td-content > .d-print-none { // Target only the feedback section at bottom of content - margin-top: 4rem; - display: flex; - align-items: center; // Change back to center for vertical alignment - flex-wrap: wrap; // Allow wrapping on smaller screens - gap: 8px; // Consistent spacing between elements - - // Hide feedback title - .feedback--title { - display: none; - } - - // Question and buttons container - .feedback--question { - margin: 0; - display: flex; - align-items: center; - gap: 16px; - } - - // Buttons container - .btn { - min-width: 50px; - display: flex; - align-items: center; - justify-content: center; - - &.feedback--answer-no { - margin: 0 !important; - } - } - - // Response styling - .feedback--response { - width: 100%; - margin-top: 1rem; - } -} - -// Last modified date with feedback links - mobile styles -div.td-page-meta__lastmod { - display: flex !important; - flex-direction: column !important; // Stack elements vertically on mobile - gap: 8px !important; - color: $text-secondary !important; - font-size: 14px !important; - margin-top: 0 !important; - - // Container for last modified text and commit link - &:first-child { - display: flex !important; - align-items: center !important; - gap: 4px !important; - } - - // Style the commit link - a { - color: $text-secondary !important; - &:hover { - color: $link-primary !important; - } - } - - .feedback--links { - display: flex !important; - margin-top: 8px !important; - align-items: center !important; - gap: 16px !important; - - // Style the edit/issue links - a { - color: $text-secondary !important; - &:hover { - color: $link-primary !important; - } - } - } - - // Tablet and desktop styles - @media (min-width: $breakpoint-lg) { - flex-direction: row !important; // Switch to horizontal layout - align-items: center !important; - justify-content: space-between !important; - - .feedback--links { - margin-top: 0 !important; - margin-left: auto !important; - } - } - - // Dark mode styles - [data-bs-theme=dark] & { - color: $dark-text-secondary !important; - - a { - color: $dark-text-secondary !important; - &:hover { - color: $dark-link-primary !important; - } - } - } -} - -// Feedback buttons -.feedback--answer { - @extend .button-base; - color: $moon-800 !important; - background-color: $moon-100 !important; - background-image: none !important; - margin-right: 8px !important; - box-shadow: none !important; - display: inline-block !important; - margin-bottom: 0 !important; - border: none !important; - - &:hover { - background-color: $hover-bg !important; - text-decoration: none !important; - border: none !important; - box-shadow: none !important; - } - - // Remove unwanted margin - &.mb-4 { - margin-bottom: 0 !important; - } - - // Hide disabled buttons - &:disabled { - display: none !important; - } - - // Dark mode styles - [data-bs-theme=dark] & { - color: $dark-text-primary !important; - background-color: $dark-bg-secondary !important; - border: none !important; - - &:hover { - background-color: $dark-bg-tertiary !important; - border: none !important; - box-shadow: none !important; - } - } -} - -// Remove feedback links from original location -.td-content > .d-print-none .feedback--links { - display: none !important; -} - -// Update the sidebar navigation styles -.td-sidebar-nav { - font-family: "Source Sans Pro", sans-serif; - padding-left: 15px !important; - - // Base link styles - a { - color: $text-secondary !important; - font-weight: $font-weight-regular; - text-decoration: none !important; - - &:hover { - color: $link-primary !important; - text-decoration: none !important; - } - } - - // Active item styles - .td-sidebar-nav-active-item { - color: $link-primary !important; - font-weight: $font-weight-medium !important; - text-decoration: none !important; - - &:hover { - text-decoration: none !important; - } - } - - // Section title styles - &__section-title { - display: block; - font-weight: $font-weight-regular !important; - line-height: 19px; - margin-bottom: 10px !important; - margin-top: 5px !important; - - .active { - font-weight: $font-weight-regular !important; - } - - a { - color: var(--bs-secondary-color); - font-weight: $font-weight-regular !important; - } - } - - // Dark mode overrides - [data-bs-theme=dark] & { - a { - color: $dark-text-secondary !important; - - &:hover { - color: $dark-link-primary !important; - } - } - - .td-sidebar-nav-active-item { - color: $dark-link-primary !important; - } - } -} - -// Foldable Nav -nav.foldable-nav .with-child, nav.foldable-nav .without-child { - padding-left: 10px !important; - } - // Update existing chevron styles - nav.foldable-nav .ul-1 .with-child { - & > label { - display: flex; - justify-content: space-between; - align-items: center; - width: 100%; - position: relative; - padding-right: 20px; // Space for chevron - - // Remove default chevrons - &:before { - display: none !important; - } - } - } - - // Add chevrons to all expandable items - nav.foldable-nav .ul-1 .with-child > label:after { - content: "\f105" !important; // fa-angle-right (thinner chevron) - font-family: "Font Awesome 6 Free", "Font Awesome 5 Free"; - font-weight: 900; - color: $moon-500; - transition: color 0.3s; - font-size: 14px; - position: absolute; - right: 0px; - top: 35%; - transform: translateY(-50%); - } - - // Change chevron for expanded items - nav.foldable-nav .ul-1 .with-child > input:checked + label:after { - content: "\f107" !important; // fa-angle-down (thinner chevron) - } - - // Hover states - nav.foldable-nav .ul-1 .with-child > label:hover:after { - color: $teal-600 !important; - } - - // Dark mode hover state - [data-bs-theme=dark] nav.foldable-nav .ul-1 .with-child > label:hover:after { - color: $wandb-gold !important; - } - - // Dark mode hover state - [data-bs-theme=dark] nav.foldable-nav .ul-1 .with-child > label:hover:after { - color: $wandb-gold !important; - } - - -// Theme-based logo visibility -[data-bs-theme="dark"] { - .theme-light-only { - display: none; - } - .theme-dark-only { - display: block; - } -} - -[data-bs-theme="light"] { - .theme-light-only { - display: block; - } - .theme-dark-only { - display: none; - } -} - - -// Add consistent padding for main content -.td-main { - main { - &.ps-md-5 { - padding-left: 2rem !important; - padding-right: 2rem !important; - - // Tablet (768px and up) - @media (min-width: $breakpoint-md) { - padding-left: 3rem !important; - padding-right: 3rem !important; - } - } - } -} - -// Google Search style overrides -.gsc-control-cse, .gsc-webResult.gsc-result, .gsc-cursor-page, .gsc-refinementHeader, .gs-no-results-result div { - background-color: transparent !important; - border: 0px !important; -} -.gs-title { - font-family: "Source Serif 4", sans-serif !important; -} -.gs-title b, .gs-title a, .gsc-cursor-page, .gsc-above-wrapper-area, .gcsc-find-more-on-google-query, .gcsc-find-more-on-google-magnifier { - color: $link-primary !important; - text-decoration: none !important; - font-size: 18px !important; - &:hover { - color: $link-hover !important; - text-decoration: none !important; - } - [data-bs-theme=dark] & { - color: $dark-link-primary !important; - - &:hover { - color: $dark-link-hover !important; - } - } -} -.gsc-above-wrapper-area a:hover{ - background-color: $bg-secondary !important; -} -.gsc-result-info, .gs-per-result-labels { - font-family: "Source Sans Pro", sans-serif !important; - font-size: 14px !important; - color: $text-secondary !important; - [data-bs-theme=dark] & { - color: $dark-text-secondary !important; - } -} -.gsc-refinementBlock span { - margin-left: 10px !important; - margin-right: 10px !important; -} -.gs-visibleUrl-breadcrumb { - font-family: "Source Sans Pro", sans-serif !important; - font-size: 14px !important; - color: $text-tertiary !important; - [data-bs-theme=dark] & { - color: $wandb-gold !important; - } -} - -.gs-snippet, .gsc-refinementHeader { - font-family: "Source Sans Pro", sans-serif !important; - font-size: 16px !important; - color: $text-primary !important; - [data-bs-theme=dark] & { - color: $dark-text-primary !important; - } -} -.gsc-above-wrapper-area { - display: none !important; -} -body > div.container-fluid.td-default.td-outer > main > section > div > h2 { - margin-left: 1.0rem !important; -} -.gs-per-result-labels { - padding: 5px !important; - border-radius: $border-radius-medium; - font-size: 12px; - max-width: fit-content; -} -.gs-per-result-labels span { - display: none; -} -.gsc-inline-block:hover, .gsc-refinementhActive, .gs-per-result-labels { - background-color: $bg-tertiary !important; - [data-bs-theme=dark] & { - background-color: $dark-bg-tertiary !important; - } -} -.td-search__input, .form-control, .td-search-input -{ - color: $text-tertiary !important; - border: $text-tertiary !important; - background-color: transparent !important; - [data-bs-theme=dark] & { - color: $dark-text-tertiary !important; - border: $dark-text-tertiary !important; - } -} -.gcsc-find-more-on-google { - display: none !important; -} - -// DocSearch Component -.DocSearch { - // Button styles - &-Button { - font-family: "Source Sans Pro", sans-serif; - font-size: 16px; - background: transparent !important; - border: 1px solid $border-default !important; - - &:active, - &:focus, - &:hover { - background: var(--docsearch-searchbox-focus-background); - box-shadow: inset 0 0 0 2px $teal-500 !important; - color: var(--docsearch-text-color); - outline: none; - border-color: $teal-500 !important; - } - - // Dark mode styles - [data-bs-theme=dark] & { - border: 1px solid $dark-border-default !important; - - &:hover, &:focus, &:active { - border-color: $teal-500 !important; - box-shadow: inset 0 0 0 2px $teal-450 !important; - } - } - } - - // Placeholder text color - &-Button-Placeholder { - color: $text-secondary !important; - - [data-bs-theme=dark] & { - color: $dark-text-secondary !important; - } - } - - // Search icon color - &-Search-Icon { - color: $text-secondary !important; - - [data-bs-theme=dark] & { - color: $dark-text-secondary !important; - } - } - - // Modal styles - &-Modal { - border-radius: $border-radius-large !important; - } - - // Hide footer - &-Footer { - display: none !important; - } -} - -// DocSearch Container with CSS Variables -.DocSearch-Container { - font-family: "Source Sans Pro", sans-serif; - font-size: 16px; - - // Light mode variables - --docsearch-primary-color: #{$teal-500} !important; - --docsearch-highlight-color: #{$teal-500} !important; - --docsearch-text-color: #{$moon-800} !important; - --docsearch-muted-color: #{$moon-500} !important; - --docsearch-container-background: rgba(101, 108, 133, 0.8) !important; - --docsearch-modal-background: #{$moon-white} !important; - --docsearch-searchbox-background: #{$moon-100} !important; - --docsearch-searchbox-focus-background: #{$moon-white} !important; - --docsearch-hit-color: #{$moon-800} !important; - --docsearch-hit-background: #{$moon-white} !important; - --docsearch-hit-active-background: #{$teal-500} !important; - --docsearch-footer-background: #{$moon-white} !important; - --docsearch-logo-color: #{$teal-500} !important; - --docsearch-key-gradient: linear-gradient(-26.5deg, #{$moon-200}, #{$moon-100}) !important; - --docsearch-key-shadow: inset 0 -2px 0 0 #{$moon-300} !important; - --docsearch-searchbox-shadow: inset 0 0 0 2px #{$teal-500} !important; - - .DocSearch-Modal { - border-radius: 0 !important; - max-width: 800px !important; - } - - // Search form styles - .DocSearch-Form { - border-radius: 30px !important; - } - - // Search results styles - .DocSearch-Hit { - border-radius: $border-radius-medium; - - > a { - box-shadow: none !important; - border-radius: $border-radius-medium; - border: 1px solid $border-default !important; - - } - } - - // Dark mode styles - [data-bs-theme=dark] & { - --docsearch-primary-color: #{$teal-400} !important; - --docsearch-highlight-color: #{$teal-400} !important; - --docsearch-text-color: #{$moon-200} !important; - --docsearch-muted-color: #{$moon-400} !important; - --docsearch-container-background: rgba(21, 23, 31, 0.8) !important; - --docsearch-modal-background: #{$moon-800} !important; - --docsearch-searchbox-background: #{$moon-900} !important; - --docsearch-searchbox-focus-background: #{$moon-900} !important; - --docsearch-hit-color: #{$moon-200} !important; - --docsearch-hit-background: #{$moon-800} !important; - --docsearch-hit-active-background: #{$teal-400} !important; - --docsearch-footer-background: #{$moon-800} !important; - --docsearch-logo-color: #{$teal-400} !important; - --docsearch-key-gradient: linear-gradient(-26.5deg, #{$moon-800}, #{$moon-750}) !important; - --docsearch-key-shadow: inset 0 -2px 0 0 #{$moon-700} !important; - --docsearch-searchbox-shadow: inset 0 0 0 2px #{$teal-400} !important; - } - - .DocSearch-Hit-source { - color: $text-secondary !important; - font-size: 14px !important; - } - - .DocSearch-Hit-title { - font-size: 1rem !important; - } - - // Dark mode styles - [data-bs-theme=dark] & { - - .DocSearch-Modal { - box-shadow: inset 1px 1px 0 0 hsla(0, 0%, 18%, 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@@ -75,4 +65,4 @@
- + \ No newline at end of file From 1af1329b3b1efbea2c8b3265a39497faaae3d356 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 13:23:19 -0400 Subject: [PATCH 32/59] adds back whitespace to navbar partial --- layouts/partials/navbar.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/layouts/partials/navbar.html b/layouts/partials/navbar.html index 18cddb26fa..0477e0db25 100644 --- a/layouts/partials/navbar.html +++ b/layouts/partials/navbar.html @@ -65,4 +65,4 @@
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--- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -3,7 +3,12 @@ menu: default: identifier: training title: W&B Training +description: Post-train your models using reinforcement learning. weight: 5 --- -An introduction to W&B Training suite of products. \ No newline at end of file +W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. + +W&B Training includes integration with [ART](https://art.openpipe.ai/getting-started/about), a flexible RL fine-tuning framework; [RULER](https://openpipe.ai/blog/ruler), a universal verifier; and a fully managed serverless RL backend on CoreWeave cloud, so you can run RL training loops without provisioning, configuring, or managing GPUs and infrastructure. + +Learn how to use W&B Training's [Serverless RL]({{< relref "/guides/training/serverless-rl" >}}) to post-train your models. \ No newline at end of file diff --git a/content/en/guides/training/prerequisites.md b/content/en/guides/training/prerequisites.md index 6fcdac7c51..4b25ed9998 100644 --- a/content/en/guides/training/prerequisites.md +++ b/content/en/guides/training/prerequisites.md @@ -3,7 +3,7 @@ title: "Prerequisites" linkTitle: "Prerequisites" weight: 1 description: > - Set up your environment to use W&B Training + Set up your environment to use W&B Training. --- Complete these steps before using W&B Training features through the OpenPipe ART framework or API. From d9b2c13ef183b0a0c30fe3e2028532d4faa52e6f Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 16:22:12 -0400 Subject: [PATCH 35/59] Adds Training to homepage --- content/en/_index.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/content/en/_index.md b/content/en/_index.md index e8fcd94430..c2a4e75419 100644 --- a/content/en/_index.md +++ b/content/en/_index.md @@ -65,6 +65,30 @@ Use [W&B Inference]({{< relref "/guides/inference/" >}}) to access leading open-

{{% /card %}} {{< /cardpane >}} + + + +
+{{< cardpane >}} +{{% card %}}
+ +
+{{< img src="/icons/Name=Agents, Mode=Dark.svg" width="60" height="60" >}} +
+

W&B Training

+ +### Post-train your models + +Use [W&B Training]({{< relref "/guides/training/" >}}) to post-train large language models using serverless reinforcement learning (RL). Features include fully managed GPU infrastructure, integration with ART and RULER, and automatic scaling for multi-turn agentic tasks. + +- [Introduction]({{< relref "/guides/training/" >}}) +- [Serverless RL]({{< relref "/guides/training/serverless-rl/" >}}) +- [Prerequisites]({{< relref "/guides/training/prerequisites/" >}}) +- [API Reference]({{< relref "/guides/training/api-reference/" >}}) + +
{{% /card %}} +{{< /cardpane >}} +
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-{{< img src="/icons/Name=Agents, Mode=Dark.svg" width="60" height="60" >}} +{{< img src="/icons/Name=Training, Mode=Dark.svg" width="60" height="60" >}}

W&B Training

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deletions(-) diff --git a/content/en/_index.md b/content/en/_index.md index 9d8751690f..769f959ec4 100644 --- a/content/en/_index.md +++ b/content/en/_index.md @@ -79,7 +79,7 @@ Use [W&B Inference]({{< relref "/guides/inference/" >}}) to access leading open- ### Post-train your models -Use [W&B Training]({{< relref "/guides/training/" >}}) to post-train large language models using serverless reinforcement learning (RL). Features include fully managed GPU infrastructure, integration with ART and RULER, and automatic scaling for multi-turn agentic tasks. +Now in public preview, use [W&B Training]({{< relref "/guides/training/" >}}) to post-train large language models using serverless reinforcement learning (RL). Features include fully managed GPU infrastructure, integration with ART and RULER, and automatic scaling for multi-turn agentic tasks. - [Introduction]({{< relref "/guides/training/" >}}) - [Serverless RL]({{< relref "/guides/training/serverless-rl/" >}}) diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index e6e8cf583a..dbb75aff7e 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -7,7 +7,7 @@ description: Post-train your models using reinforcement learning. weight: 5 --- -W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. +Now win public preview, W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. W&B Training includes integration with [ART](https://art.openpipe.ai/getting-started/about), a flexible RL fine-tuning framework; [RULER](https://openpipe.ai/blog/ruler), a universal verifier; and a fully managed serverless RL backend on CoreWeave cloud, so you can run RL training loops without provisioning, configuring, or managing GPUs and infrastructure. From 6532356beda143b3253712b70c4623daf84b514e Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 17:19:24 -0400 Subject: [PATCH 40/59] Feedback --- content/en/guides/training/prerequisites.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/guides/training/prerequisites.md b/content/en/guides/training/prerequisites.md index 4b25ed9998..cbd93a6f76 100644 --- a/content/en/guides/training/prerequisites.md +++ b/content/en/guides/training/prerequisites.md @@ -23,7 +23,7 @@ You need these items to access W&B Training: Get your API key at [https://wandb.ai/authorize](https://wandb.ai/authorize) 3. **A W&B project** - Create a project in your W&B account to track usage + Create a project in your W&B account to track usage, record training metrics, and save trained models. ## Next steps From c747dabff48c6c727386ee69563669497d8d4800 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 6 Oct 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pages](https://github.com/wandb/docs/pull/1693#issuecomment-3374283089)** --- content/en/guides/training/prerequisites.md | 2 +- .../guides/training/serverless-rl/_index.md | 10 +-- .../serverless-rl/available-models.md | 8 ++- .../training/serverless-rl/serverless-rl.md | 17 ++++- .../training/serverless-rl/usage-limits.md | 30 ++++---- .../serverless-rl/using-trained-models.md | 69 +++++++++++++++++++ 6 files changed, 112 insertions(+), 24 deletions(-) create mode 100644 content/en/guides/training/serverless-rl/using-trained-models.md diff --git a/content/en/guides/training/prerequisites.md b/content/en/guides/training/prerequisites.md index cbd93a6f76..6113b2eb5e 100644 --- a/content/en/guides/training/prerequisites.md +++ b/content/en/guides/training/prerequisites.md @@ -23,7 +23,7 @@ You need these items to access W&B Training: Get your API key at [https://wandb.ai/authorize](https://wandb.ai/authorize) 3. **A W&B project** - Create a project in your W&B account to track usage, record training metrics, and save trained models. + Create a project in your W&B account to track usage, record training metrics, and save trained models ## Next steps diff --git a/content/en/guides/training/serverless-rl/_index.md b/content/en/guides/training/serverless-rl/_index.md index c26c81f97c..9f9a06c372 100644 --- a/content/en/guides/training/serverless-rl/_index.md +++ b/content/en/guides/training/serverless-rl/_index.md @@ -7,21 +7,21 @@ description: Learn about how to more efficiently post-train your models using re weight: 5 --- -Serverless RL lets you post-train LLMs to learn new behaviors and improve reliability, speed, and costs when performing multi-turn, agentic tasks while we handle the GPUs and infrastructure for you. Serverless RL gives you instant access to GPUs on a fully managed, distributed training cluster that elastically auto-scales to dozens of GPUs or down to zero instantly, matching your reinforcement learning (RL) job at every moment. By automatically splitting RL workflows into inference and training phases and multiplexing them across jobs, Serverless RL maximizes GPU utilization and reduces your training time and costs. +Serverless RL helps developers post-train LLMs to learn new behaviors and improve reliability, speed, and costs when performing multi-turn agentic tasks. We provision the training infrastructure for you while allowing full flexibility in your environment's setup. Serverless RL gives you instant access to a managed training cluster that elastically auto-scales to dozens of GPUs. By splitting RL workflows into inference and training phases and multiplexing them across jobs, Serverless RL increases GPU utilization and reduces your training time and costs. -Serverless RL is ideal for: +Serverless RL is ideal for tasks like: * Voice agents * Deep research assistants * On-prem models * Content marketing analysis agents -Serverless RL trains low-rank adapters (LoRAs) and then provides you the ability to integrate them into calls to your existing LLM models, such as GPT-4.1. This extends the existing model’s training with data and hyper-parameters that you provide and gives you the ability to access the adapted model from a singular endpoint. You can also store versions of your adapters as artifacts stored in your W&B account. +Serverless RL trains low-rank adapters (LoRAs) to specialize a model for your agent's specific task. This extends the original model’s capabilities with on-the-job experience. The LoRAs you train will automatically be stored as artifacts in your W&B account, and can be saved locally or to a third party for backup. Models that you train through Serverless RL will also be automatically hosted on W&B Inference. Serverless RL uses a combination of the following W&B services to operate: * [Inference]({{< relref "guides/inference" >}}): To run your models * [Models]({{< relref "guides/models" >}}): To track performance metrics during the LoRA adapter's training * [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters -* [Weave]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop +* [Weave (optional)]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop -Serverless RL is currently in public preview. We only charge for the use of inference and the storage of artifacts. We do not charge for adapter training during the preview period. We will make training pricing details available when the service is released for general availability. +Serverless RL is currently in public preview. We only charge for the use of inference and the storage of artifacts. We do not charge for adapter training during the preview period. We will make training pricing details available in the next few weeks. diff --git a/content/en/guides/training/serverless-rl/available-models.md b/content/en/guides/training/serverless-rl/available-models.md index 056a5e498c..6dcba1f521 100644 --- a/content/en/guides/training/serverless-rl/available-models.md +++ b/content/en/guides/training/serverless-rl/available-models.md @@ -3,13 +3,15 @@ title: "Available Models" linkTitle: "Available Models" weight: 20 description: > - See what models you can use with Serverless RL. + See the models you can train with Serverless RL. --- -Serverless RL uses one open-source foundation model for training. +Serverless RL currently only supports a single open-source foundation model for training - more to come soon! + +If there's a particular model you're interested in getting support for, please message us at support@wandb.com. ## Model catalog | Model | Model ID (for API usage) | Type | Context Window | Parameters | Description | |-------|--------------------------|------|----------------|------------|-------------| -| Qwen2.5 14B | Qwen/Qwen2.5-14B-Instruct | Text | 32K | 14B (Active-Total) | High-performance Mixture-of-Experts model optimized for structured reasoning, math, and long-form generation | +| Qwen2.5 14B | Qwen/Qwen2.5-14B-Instruct | Text | 32K | 14B (Active-Total) | Dense model optimized for throughput and quality | diff --git a/content/en/guides/training/serverless-rl/serverless-rl.md b/content/en/guides/training/serverless-rl/serverless-rl.md index 4f5aff6cde..35fc69789d 100644 --- a/content/en/guides/training/serverless-rl/serverless-rl.md +++ b/content/en/guides/training/serverless-rl/serverless-rl.md @@ -4,6 +4,19 @@ title: Use Serverless RL weight: 10 --- -Serverless RL is currently only supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and the W&B Training API. +Serverless RL is supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and the W&B Training API. -See [OpenPipe's Serverless RL quickstart](https://art.openpipe.ai/getting-started/quick-start) for code examples and workflows. See the [W&B Training API]({{< relref "ref/training" >}}) reference for more information on the Serverless RL's endpoints. \ No newline at end of file +### Why Serverless RL? + +RL is a set of powerful training techniques that can be utilized in many kinds of training setups, including on GPUs that you own or rent directly. In fact, much of the code that makes Serverless RL so effective is already open source in the [ART SDK](https://github.com/openpipe/art). So why use Serverless RL? + +* Lower training costs + * By multiplexing shared infra across many users, skipping the setup process for each job, and scaling your GPU costs down to 0 when you're not actively training, Serverless RL reduces training costs significantly. +* Faster training time + * By splitting inference requests across many GPUs and immediately provisioning training infra when you need it, Serverless RL speeds up your training jobs and lets you iterate faster. Shorter feedback cycles means more chances to find and fix bugs, quicker results, and happier developers. +* Automatic deployment + * Once you train your model, you need to somehow host it on a public-facing service that serves traffic at a high concurrency. Serverless RL solves that problem for you by **automatically deploying every checkpoint you train**. That means you can go right from training your model in a sandboxed environment to deploying testing it locally, in staging, or even production. + +### Get Started + + To start using Serverless RL, check out the ART [quickstart](https://art.openpipe.ai/getting-started/quick-start) for code examples and workflows. To learn about Serverless RL's API endpoints, see the [W&B Training API]({{< relref "ref/training" >}}). \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index 5a216e5cc4..b4e93372a6 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -8,35 +8,39 @@ description: > ## Pricing -Pricing has three components: inference, training, and storage. Learn more: https://wandb.ai/site/pricing/reinforcement-learning +Pricing has three components: inference, training, and storage. For specific billing rates, visit our [pricing page](https://wandb.ai/site/pricing/reinforcement-learning). ### Inference -Trajectories are batched at each training step and, together with rewards from your reward function (often an LLM-as-judge), are used to update a training copy of the LoRA. The training workload runs on a separate GPU cluster within Serverless RL. During the public preview, training is free. Pricing for training will be announced at general availability (GA). - -* Check the [model-specific costs](https://wandb.ai/site/pricing/inference) for more details. Learn more about purchasing credits, account tiers, and usage caps in the [W&B Inference docs]({{< relref "/guides/inference/usage-limits/#purchase-more-credits" >}}). +Pricing for Serverless RL inference requests matches W&B Inference pricing. See [model-specific costs](https://wandb.ai/site/pricing/inference) for more details. Learn more about purchasing credits, account tiers, and usage caps in the [W&B Inference docs]({{< relref "/guides/inference/usage-limits/#purchase-more-credits" >}}). ### Training -At each training step, Serverless RL batches trajectories and combines them with rewards from your reward function (typically an LLM-as-judge). These batched trajectories and rewards are then used to update the LoRA adapter weights on a training copy of the base model. All training workloads run on a dedicated GPU cluster managed by Serverless RL. +At each training step, Serverless RL collects batches of trajectories that include your agent's outputs and associated rewards (calculated by your reward function). The batched trajectories are then used to update the weights of a LoRA adapter that specializes a base model for your task. The training jobs to update these LoRAs run on dedicated GPU clusters managed by Serverless RL. -During the public preview, training is free. Pricing for training will be announced at general availability (GA). +During the public preview, training is free. Pricing for training will be announced in the next few weeks. -### Model Storage +### Model storage -Serverless RL stores checkpoints of your trained LoRA heads so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes free storage. +Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB free storage, which is enough for roughly 30 LoRAs. Deletion of low-performing LoRAs is encouraged and made easy through the [ART SDK](https://art.openpipe.ai). -## Concurrency limits +## Inference Concurrency limits -If you exceed the rate limit, the API returns a `429 Concurrency limit reached for requests` response. To fix this error, reduce the number of concurrent requests. -W&B applies rate limits per project. For example, if you have three projects in a team, each project has its own rate limit quota. The default rate limit is 2000 concurrent requests. +By default, Serverless RL currently supports up to 2000 concurrent requests per user and 6000 per project. If you exceed your rate limit, the Inference API returns a `429 Concurrency limit reached for requests` response. To avoid this error, reduce the number of concurrent requests your training job or production workload makes at once. If you need a higher rate limit, you can request one at support@wandb.com. +{{< alert title="Note" >}} ## Personal entities unsupported -Serverless RL and W&B Inference don't support personal entities (personal accounts). To access Serverless RL, switch to a non-personal account by [creating a Team]({{< relref "/guides/hosting/iam/access-management/manage-organization/#add-and-manage-teams" >}}). Personal entities (personal accounts) were deprecated in May 2024, so this only applies to legacy accounts. +Serverless RL and W&B Inference don't support personal entities (personal accounts). To access Serverless RL, switch to a non-personal account by [creating a Team]({{< relref "/guides/hosting/iam/access-management/manage-organization/#add-and-manage-teams" >}}). Personal entities (personal accounts) were deprecated in May 2024, so this advisory only applies to legacy accounts. ## Geographic restrictions -Serverless RL is only available from supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). +Serverless RL is only available in supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). +{{< /alert >}} + + + + + diff --git a/content/en/guides/training/serverless-rl/using-trained-models.md b/content/en/guides/training/serverless-rl/using-trained-models.md new file mode 100644 index 0000000000..c64745b985 --- /dev/null +++ b/content/en/guides/training/serverless-rl/using-trained-models.md @@ -0,0 +1,69 @@ +--- +title: "Using Trained Models" +linkTitle: "Using Trained Models" +weight: 20 +description: > + Make inference requests to the models you've trained. +--- + +After training a model with Serverless RL, it is automatically deployed and ready to be used in production. To make a request to the OpenAI-compatible endpoint, follow the instructions below. + +### Overview + +When making inference requests, set your base url to `https://api.training.wandb.ai/v1/`. As you did during training, use your W&B API key for authentication. Finally, set your model name to match the following schema: + +`wandb-artifact://///:` + +As an example, let's assume that your W&B team is named `email-specialists`, and your project was called `mail-search`. If you named your model `agent-001` and wanted to deploy it on step 25 (presumably because that step did the best on your evals), you would set the model name in your chat completion requests to the following: + +`wandb-artifact:///email-specialists/mail-search/agent-001:step25` + +This tells W&B Inference exactly where to find your model when initially loading it for inference. + + +### CuRL + +``` +curl https://api.training.wandb.ai/v1/chat/completions \ + -H "Authorization: Bearer $WANDB_API_KEY" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "wandb-artifact:///my-entity/my-project/my-model:step100", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Summarize our training run."} + ], + "temperature": 0.7, + "top_p": 0.95 + }' +``` + +### OpenAI SDK + +``` +from openai import OpenAI + +WANDB_API_KEY = "your-wandb-api-key" +ENTITY = "my-entity" +PROJECT = "my-project" + +client = OpenAI( + base_url="https://api.inference.coreweave.com/v1", + api_key=WANDB_API_KEY, + default_headers={"OpenAI-Project": f"{ENTITY}/{PROJECT}"}, +) + +response = client.chat.completions.create( + model=f"wandb-artifact:///{ENTITY}/{PROJECT}/my-model:step100", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Summarize our training run."}, + ], + temperature=0.7, + top_p=0.95, +) + +print(response.choices[0].message.content) +``` + +If you have any trouble getting started, reach out to support@wandb.com! \ No newline at end of file From cf5db6505bcc65aa9eaa22480f970713a3d03bb9 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 18:54:07 -0400 Subject: [PATCH 43/59] updates to David's copy and shuffles some stuff around --- content/en/guides/training/_index.md | 2 +- .../guides/training/serverless-rl/_index.md | 17 +++++++++++++-- .../serverless-rl/available-models.md | 6 +++--- .../training/serverless-rl/serverless-rl.md | 17 ++------------- .../training/serverless-rl/usage-limits.md | 4 ++-- .../serverless-rl/using-trained-models.md | 21 +++++++++---------- 6 files changed, 33 insertions(+), 34 deletions(-) diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index dbb75aff7e..e68c98c90f 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -4,7 +4,7 @@ menu: identifier: training title: W&B Training description: Post-train your models using reinforcement learning. -weight: 5 +weight: 6 --- Now win public preview, W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. diff --git a/content/en/guides/training/serverless-rl/_index.md b/content/en/guides/training/serverless-rl/_index.md index 9f9a06c372..5ba835f893 100644 --- a/content/en/guides/training/serverless-rl/_index.md +++ b/content/en/guides/training/serverless-rl/_index.md @@ -7,7 +7,7 @@ description: Learn about how to more efficiently post-train your models using re weight: 5 --- -Serverless RL helps developers post-train LLMs to learn new behaviors and improve reliability, speed, and costs when performing multi-turn agentic tasks. We provision the training infrastructure for you while allowing full flexibility in your environment's setup. Serverless RL gives you instant access to a managed training cluster that elastically auto-scales to dozens of GPUs. By splitting RL workflows into inference and training phases and multiplexing them across jobs, Serverless RL increases GPU utilization and reduces your training time and costs. +Now in public preview, Serverless RL helps developers post-train LLMs to learn new behaviors and improve reliability, speed, and costs when performing multi-turn agentic tasks. We provision the training infrastructure for you while allowing full flexibility in your environment's setup. Serverless RL gives you instant access to a managed training cluster that elastically auto-scales to dozens of GPUs. By splitting RL workflows into inference and training phases and multiplexing them across jobs, Serverless RL increases GPU utilization and reduces your training time and costs. Serverless RL is ideal for tasks like: * Voice agents @@ -15,7 +15,20 @@ Serverless RL is ideal for tasks like: * On-prem models * Content marketing analysis agents -Serverless RL trains low-rank adapters (LoRAs) to specialize a model for your agent's specific task. This extends the original model’s capabilities with on-the-job experience. The LoRAs you train will automatically be stored as artifacts in your W&B account, and can be saved locally or to a third party for backup. Models that you train through Serverless RL will also be automatically hosted on W&B Inference. +Serverless RL trains low-rank adapters (LoRAs) to specialize a model for your agent's specific task. This extends the original model’s capabilities with on-the-job experience. The LoRAs you train are automatically stored as artifacts in your W&B account, and can be saved locally or to a third party for backup. Models that you train through Serverless RL are also automatically hosted on W&B Inference. + +## Why Serverless RL? + +Reinforcement learning (RL) is a set of powerful training techniques that you can use in many kinds of training setups, including on GPUs that you own or rent directly. Serverless RL can provide the following advantages in your RL post-training: + +* Lower training costs + * By multiplexing shared infrastructure across many users, skipping the setup process for each job, and scaling your GPU costs down to 0 when you're not actively training, Serverless RL reduces training costs significantly. +* Faster training time + * By splitting inference requests across many GPUs and immediately provisioning training infrstructure when you need it, Serverless RL speeds up your training jobs and lets you iterate faster. +* Automatic deployment + * Serverless RL automatically deploys every checkpoint you train, eliminating the need to manually set up hosting infrastructure. Trained models can be accessed and tested immediately in local, staging, or production environments. + +## How Serverless RL uses W&B services Serverless RL uses a combination of the following W&B services to operate: diff --git a/content/en/guides/training/serverless-rl/available-models.md b/content/en/guides/training/serverless-rl/available-models.md index 6dcba1f521..a5604f652d 100644 --- a/content/en/guides/training/serverless-rl/available-models.md +++ b/content/en/guides/training/serverless-rl/available-models.md @@ -1,14 +1,14 @@ --- title: "Available Models" linkTitle: "Available Models" -weight: 20 +weight: 40 description: > See the models you can train with Serverless RL. --- -Serverless RL currently only supports a single open-source foundation model for training - more to come soon! +Serverless RL currently only supports a single open-source foundation model for training. We will add more models soon. -If there's a particular model you're interested in getting support for, please message us at support@wandb.com. +If there's a particular model you're interested in getting support for, email use at support@wandb.com. ## Model catalog diff --git a/content/en/guides/training/serverless-rl/serverless-rl.md b/content/en/guides/training/serverless-rl/serverless-rl.md index 35fc69789d..c141c03a23 100644 --- a/content/en/guides/training/serverless-rl/serverless-rl.md +++ b/content/en/guides/training/serverless-rl/serverless-rl.md @@ -1,22 +1,9 @@ --- -description: Learn how to start using it. +description: Directions on how to get started using Serverless RL. title: Use Serverless RL weight: 10 --- Serverless RL is supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and the W&B Training API. -### Why Serverless RL? - -RL is a set of powerful training techniques that can be utilized in many kinds of training setups, including on GPUs that you own or rent directly. In fact, much of the code that makes Serverless RL so effective is already open source in the [ART SDK](https://github.com/openpipe/art). So why use Serverless RL? - -* Lower training costs - * By multiplexing shared infra across many users, skipping the setup process for each job, and scaling your GPU costs down to 0 when you're not actively training, Serverless RL reduces training costs significantly. -* Faster training time - * By splitting inference requests across many GPUs and immediately provisioning training infra when you need it, Serverless RL speeds up your training jobs and lets you iterate faster. Shorter feedback cycles means more chances to find and fix bugs, quicker results, and happier developers. -* Automatic deployment - * Once you train your model, you need to somehow host it on a public-facing service that serves traffic at a high concurrency. Serverless RL solves that problem for you by **automatically deploying every checkpoint you train**. That means you can go right from training your model in a sandboxed environment to deploying testing it locally, in staging, or even production. - -### Get Started - - To start using Serverless RL, check out the ART [quickstart](https://art.openpipe.ai/getting-started/quick-start) for code examples and workflows. To learn about Serverless RL's API endpoints, see the [W&B Training API]({{< relref "ref/training" >}}). \ No newline at end of file +To start using Serverless RL, see the ART [quickstart](https://art.openpipe.ai/getting-started/quick-start) for code examples and workflows. To learn about Serverless RL's API endpoints, see the [W&B Training API]({{< relref "ref/training" >}}). \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index b4e93372a6..b48ac9dd27 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -18,11 +18,11 @@ Pricing for Serverless RL inference requests matches W&B Inference pricing. See At each training step, Serverless RL collects batches of trajectories that include your agent's outputs and associated rewards (calculated by your reward function). The batched trajectories are then used to update the weights of a LoRA adapter that specializes a base model for your task. The training jobs to update these LoRAs run on dedicated GPU clusters managed by Serverless RL. -During the public preview, training is free. Pricing for training will be announced in the next few weeks. +During the public preview, training is free. We will announce pricing for training soon. ### Model storage -Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB free storage, which is enough for roughly 30 LoRAs. Deletion of low-performing LoRAs is encouraged and made easy through the [ART SDK](https://art.openpipe.ai). +Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB of free storage, which is enough for roughly 30 LoRAs. We recommend deleteing low-performing LoRAs to save space. See the [ART SDK](https://art.openpipe.ai) for instructions on how to do this. ## Inference Concurrency limits diff --git a/content/en/guides/training/serverless-rl/using-trained-models.md b/content/en/guides/training/serverless-rl/using-trained-models.md index c64745b985..3deedf986d 100644 --- a/content/en/guides/training/serverless-rl/using-trained-models.md +++ b/content/en/guides/training/serverless-rl/using-trained-models.md @@ -1,34 +1,35 @@ --- -title: "Using Trained Models" -linkTitle: "Using Trained Models" +title: "Use Trained Models" +linkTitle: "Use Trained Models" weight: 20 description: > Make inference requests to the models you've trained. --- -After training a model with Serverless RL, it is automatically deployed and ready to be used in production. To make a request to the OpenAI-compatible endpoint, follow the instructions below. +After training a model with Serverless RL, it is automatically deployed and ready to use in production. To make a request to the OpenAI-compatible endpoint, follow the instructions below. ### Overview -When making inference requests, set your base url to `https://api.training.wandb.ai/v1/`. As you did during training, use your W&B API key for authentication. Finally, set your model name to match the following schema: +When making inference requests, set your base URL to `https://api.training.wandb.ai/v1/` and use your W&B API key for authentication. Finally, set your model name to match the following schema: `wandb-artifact://///:` -As an example, let's assume that your W&B team is named `email-specialists`, and your project was called `mail-search`. If you named your model `agent-001` and wanted to deploy it on step 25 (presumably because that step did the best on your evals), you would set the model name in your chat completion requests to the following: +For example, if your W&B team is named `email-specialists`, your project is called `mail-search`, your trained model is named `agent-001`, and you wanted to deploy it on step 25 (presumably because that step did the best on your evals), you would set the model name in your chat completion requests to the following: `wandb-artifact:///email-specialists/mail-search/agent-001:step25` This tells W&B Inference exactly where to find your model when initially loading it for inference. +The following examples show you how to make inference requests to your trained model using a CuRL request or the [Python OpenAI SDK](https://openai.github.io/openai-agents-python/). ### CuRL -``` +```shell curl https://api.training.wandb.ai/v1/chat/completions \ -H "Authorization: Bearer $WANDB_API_KEY" \ -H "Content-Type: application/json" \ -d '{ - "model": "wandb-artifact:///my-entity/my-project/my-model:step100", + "model": "wandb-artifact://///:", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Summarize our training run."} @@ -40,7 +41,7 @@ curl https://api.training.wandb.ai/v1/chat/completions \ ### OpenAI SDK -``` +```python from openai import OpenAI WANDB_API_KEY = "your-wandb-api-key" @@ -64,6 +65,4 @@ response = client.chat.completions.create( ) print(response.choices[0].message.content) -``` - -If you have any trouble getting started, reach out to support@wandb.com! \ No newline at end of file +``` \ No newline at end of file From 63c0a2f70a343f91d2224df7e7ff5319625218cb Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 18:56:15 -0400 Subject: [PATCH 44/59] spell check --- content/en/guides/training/serverless-rl/usage-limits.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index b48ac9dd27..dbb4ce836a 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -22,7 +22,7 @@ During the public preview, training is free. We will announce pricing for traini ### Model storage -Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB of free storage, which is enough for roughly 30 LoRAs. We recommend deleteing low-performing LoRAs to save space. See the [ART SDK](https://art.openpipe.ai) for instructions on how to do this. +Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB of free storage, which is enough for roughly 30 LoRAs. We recommend deleting low-performing LoRAs to save space. See the [ART SDK](https://art.openpipe.ai) for instructions on how to do this. ## Inference Concurrency limits From 3c5d387a44a262e138adbe40dada8fb15633ecb8 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 19:00:53 -0400 Subject: [PATCH 45/59] spelling error --- content/en/guides/training/_index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index e68c98c90f..f48a9ba636 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -7,7 +7,7 @@ description: Post-train your models using reinforcement learning. weight: 6 --- -Now win public preview, W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. +Now in public preview, W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. W&B Training includes integration with [ART](https://art.openpipe.ai/getting-started/about), a flexible RL fine-tuning framework; [RULER](https://openpipe.ai/blog/ruler), a universal verifier; and a fully managed serverless RL backend on CoreWeave cloud, so you can run RL training loops without provisioning, configuring, or managing GPUs and infrastructure. From 1d8c8727b942f6285cf1a8ce6906ea6337f2d950 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 19:51:02 -0400 Subject: [PATCH 46/59] Reweights top level items in left-nav --- content/en/guides/core/_index.md | 2 +- content/en/guides/hosting/_index.md | 2 +- content/en/guides/inference/_index.md | 2 +- content/en/guides/integrations/_index.md | 2 +- content/en/guides/models/_index.md | 2 +- content/en/guides/models_quickstart.md | 2 +- content/en/guides/quickstart.md | 2 +- content/en/guides/training/_index.md | 2 +- .../en/guides/training/serverless-rl/using-trained-models.md | 4 ++-- content/en/guides/weave/_index.md | 2 +- 10 files changed, 11 insertions(+), 11 deletions(-) diff --git a/content/en/guides/core/_index.md b/content/en/guides/core/_index.md index 4a26ba3e19..da95cd63f0 100644 --- a/content/en/guides/core/_index.md +++ b/content/en/guides/core/_index.md @@ -3,7 +3,7 @@ menu: default: identifier: core title: W&B Core -weight: 6 +weight: 70 no_list: true --- diff --git a/content/en/guides/hosting/_index.md b/content/en/guides/hosting/_index.md index 525ec9b574..9ffc064abd 100644 --- a/content/en/guides/hosting/_index.md +++ b/content/en/guides/hosting/_index.md @@ -3,7 +3,7 @@ menu: default: identifier: w-b-platform title: W&B Platform -weight: 7 +weight: 80 no_list: true --- W&B Platform is the foundational infrastructure, tooling and governance scaffolding which supports the W&B products like [Core]({{< relref "/guides/core" >}}), [Models]({{< relref "/guides/models/" >}}) and [Weave]({{< relref "/guides/weave/" >}}). diff --git a/content/en/guides/inference/_index.md b/content/en/guides/inference/_index.md index a6b2df3751..df096d81a3 100644 --- a/content/en/guides/inference/_index.md +++ b/content/en/guides/inference/_index.md @@ -1,6 +1,6 @@ --- title: "W&B Inference" -weight: 5 +weight: 50 description: > Access open-source foundation models through W&B Weave and an OpenAI-compatible API --- diff --git a/content/en/guides/integrations/_index.md b/content/en/guides/integrations/_index.md index 4aa0973507..21a86f0487 100644 --- a/content/en/guides/integrations/_index.md +++ b/content/en/guides/integrations/_index.md @@ -3,7 +3,7 @@ menu: default: identifier: integrations title: Integrations -weight: 8 +weight: 90 url: guides/integrations cascade: - url: guides/integrations/:filename diff --git a/content/en/guides/models/_index.md b/content/en/guides/models/_index.md index b7605d6cf8..806dcd90fb 100644 --- a/content/en/guides/models/_index.md +++ b/content/en/guides/models/_index.md @@ -3,7 +3,7 @@ menu: default: identifier: models title: W&B Models -weight: 3 +weight: 30 no_list: true --- diff --git a/content/en/guides/models_quickstart.md b/content/en/guides/models_quickstart.md index a43e3b1717..d95b8693de 100644 --- a/content/en/guides/models_quickstart.md +++ b/content/en/guides/models_quickstart.md @@ -1,6 +1,6 @@ --- title: Get Started with W&B Models -weight: 2 +weight: 20 --- Learn when and how to use W&B to track, share, and manage model artifacts in your machine learning workflows. This page covers logging experiments, generating reports, and accessing logged data using the appropriate W&B API for each task. diff --git a/content/en/guides/quickstart.md b/content/en/guides/quickstart.md index 5110e5f9b4..fab57a57d8 100644 --- a/content/en/guides/quickstart.md +++ b/content/en/guides/quickstart.md @@ -6,7 +6,7 @@ menu: parent: guides title: W&B Quickstart url: quickstart -weight: 1 +weight: 10 --- Install W&B to track, visualize, and manage machine learning experiments of any size. diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index f48a9ba636..0a407d4280 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -4,7 +4,7 @@ menu: identifier: training title: W&B Training description: Post-train your models using reinforcement learning. -weight: 6 +weight: 60 --- Now in public preview, W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. diff --git a/content/en/guides/training/serverless-rl/using-trained-models.md b/content/en/guides/training/serverless-rl/using-trained-models.md index 3deedf986d..7d0aa495b0 100644 --- a/content/en/guides/training/serverless-rl/using-trained-models.md +++ b/content/en/guides/training/serverless-rl/using-trained-models.md @@ -1,6 +1,6 @@ --- -title: "Use Trained Models" -linkTitle: "Use Trained Models" +title: "Use your trained models" +linkTitle: "Use trained models" weight: 20 description: > Make inference requests to the models you've trained. diff --git a/content/en/guides/weave/_index.md b/content/en/guides/weave/_index.md index a9b7c782c0..d6d9b73cf9 100644 --- a/content/en/guides/weave/_index.md +++ b/content/en/guides/weave/_index.md @@ -3,7 +3,7 @@ menu: default: identifier: weave title: W&B Weave -weight: 4 +weight: 40 --- {{% alert %}} From 8c8896ea68d170c2fbb4729023e275fb97533db4 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 19:59:01 -0400 Subject: [PATCH 47/59] Makes product layout 2x2 on front page --- content/en/_index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/en/_index.md b/content/en/_index.md index 769f959ec4..e788680e56 100644 --- a/content/en/_index.md +++ b/content/en/_index.md @@ -46,7 +46,12 @@ Use [W&B Weave](https://weave-docs.wandb.ai/) to manage AI models in your code. - [Use Weave in your W&B runs]({{< relref "/guides/weave/set-up-weave" >}})

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@@ -99,7 +99,7 @@ Now in public preview, use [W&B Training]({{< relref "/guides/training/" >}}) to p { overflow: hidden; display: block; } ul { margin-left: 50px; } -/* Make all cards uniform size in 3x2 grid */ +/* Make all cards uniform size in 2x2 grid */ .top-row-cards .td-card-group, .bottom-row-cards .td-card-group { max-width: 100%; From b2c5a52fa2119672dcf74b41251cea7178e3ba8a Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 21:15:37 -0400 Subject: [PATCH 48/59] Feedback via Noah, Matt, and David --- content/en/guides/_index.md | 2 ++ content/en/guides/training/_index.md | 10 +++++++--- .../guides/training/serverless-rl/_index.md | 13 +++++-------- .../serverless-rl/available-models.md | 8 ++++---- .../training/serverless-rl/serverless-rl.md | 6 +++--- .../training/serverless-rl/usage-limits.md | 14 +++++++------- .../serverless-rl/using-trained-models.md | 19 ++++++++++--------- 7 files changed, 38 insertions(+), 34 deletions(-) diff --git a/content/en/guides/_index.md b/content/en/guides/_index.md index 4ceb532872..62ae807b51 100644 --- a/content/en/guides/_index.md +++ b/content/en/guides/_index.md @@ -33,6 +33,8 @@ W&B consists of three major components: [Models]({{< relref "/guides/models.md" **[W&B Inference]({{< relref "/guides/inference/" >}})** is a set of tools for accessing open-source foundation models through W&B Weave and an OpenAI-compatible API. +**[W&B Training]({{< relref "/guides/training/" >}})** provides serverless reinforcement learning for post-training LLMs to improve reliability on multi-turn agentic tasks. + {{% alert %}} Learn about recent releases in the [W&B release notes]({{< relref "/ref/release-notes/" >}}). {{% /alert %}} diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index 0a407d4280..4cdfa6fe85 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -7,8 +7,12 @@ description: Post-train your models using reinforcement learning. weight: 60 --- -Now in public preview, W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. +Now in public preview, W&B Training offers serverless reinforcement learning (RL) for post-training large language models (LLMs) to improve their reliability performing multi-turn, agentic tasks while also increasing speed and reducing costs. RL is a training technique where models learn to improve their behavior through feedback on their outputs. -W&B Training includes integration with [ART](https://art.openpipe.ai/getting-started/about), a flexible RL fine-tuning framework; [RULER](https://openpipe.ai/blog/ruler), a universal verifier; and a fully managed serverless RL backend on CoreWeave cloud, so you can run RL training loops without provisioning, configuring, or managing GPUs and infrastructure. +W&B Training includes integration with: -Learn how to use W&B Training's [Serverless RL]({{< relref "/guides/training/serverless-rl" >}}) to post-train your models. \ No newline at end of file +* [ART](https://art.openpipe.ai/getting-started/about), a flexible RL fine-tuning framework. +* [RULER](https://openpipe.ai/blog/ruler), a universal verifier. +* A fully-managed backend on CoreWeave cloud. + +To get started, satisfy the [prerequisites]({{< relref "/guides/training/prerequisites" >}}) to start using the service and then see [OpenPipe's Serverless RL quickstart](https://art.openpipe.ai/getting-started/quick-start) to learn how to post-train your models. \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl/_index.md b/content/en/guides/training/serverless-rl/_index.md index 5ba835f893..ba81669a77 100644 --- a/content/en/guides/training/serverless-rl/_index.md +++ b/content/en/guides/training/serverless-rl/_index.md @@ -21,20 +21,17 @@ Serverless RL trains low-rank adapters (LoRAs) to specialize a model for your ag Reinforcement learning (RL) is a set of powerful training techniques that you can use in many kinds of training setups, including on GPUs that you own or rent directly. Serverless RL can provide the following advantages in your RL post-training: -* Lower training costs - * By multiplexing shared infrastructure across many users, skipping the setup process for each job, and scaling your GPU costs down to 0 when you're not actively training, Serverless RL reduces training costs significantly. -* Faster training time - * By splitting inference requests across many GPUs and immediately provisioning training infrstructure when you need it, Serverless RL speeds up your training jobs and lets you iterate faster. -* Automatic deployment - * Serverless RL automatically deploys every checkpoint you train, eliminating the need to manually set up hosting infrastructure. Trained models can be accessed and tested immediately in local, staging, or production environments. +* **Lower training costs**: By multiplexing shared infrastructure across many users, skipping the setup process for each job, and scaling your GPU costs down to 0 when you're not actively training, Serverless RL reduces training costs significantly. +* **Faster training time**: By splitting inference requests across many GPUs and immediately provisioning training infrstructure when you need it, Serverless RL speeds up your training jobs and lets you iterate faster. +* **Automatic deployment**: Serverless RL automatically deploys every checkpoint you train, eliminating the need to manually set up hosting infrastructure. Trained models can be accessed and tested immediately in local, staging, or production environments. ## How Serverless RL uses W&B services -Serverless RL uses a combination of the following W&B services to operate: +Serverless RL uses a combination of the following W&B components to operate: * [Inference]({{< relref "guides/inference" >}}): To run your models * [Models]({{< relref "guides/models" >}}): To track performance metrics during the LoRA adapter's training * [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters * [Weave (optional)]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop -Serverless RL is currently in public preview. We only charge for the use of inference and the storage of artifacts. We do not charge for adapter training during the preview period. We will make training pricing details available in the next few weeks. +Serverless RL is in public preview. During the preview, you are charged only for the use of inference and the storage of artifacts, during the preview period, not for adapter training. We do not charge for adapter training during the preview period. \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl/available-models.md b/content/en/guides/training/serverless-rl/available-models.md index a5604f652d..19c66cb460 100644 --- a/content/en/guides/training/serverless-rl/available-models.md +++ b/content/en/guides/training/serverless-rl/available-models.md @@ -1,14 +1,14 @@ --- -title: "Available Models" -linkTitle: "Available Models" +title: "Available models" +linkTitle: "Available models" weight: 40 description: > See the models you can train with Serverless RL. --- -Serverless RL currently only supports a single open-source foundation model for training. We will add more models soon. +Serverless RL currently only supports a single open-source foundation model for training. -If there's a particular model you're interested in getting support for, email use at support@wandb.com. +To express interest in a particular model, contact [support](mailto:support@wandb.ai). ## Model catalog diff --git a/content/en/guides/training/serverless-rl/serverless-rl.md b/content/en/guides/training/serverless-rl/serverless-rl.md index c141c03a23..64a5117639 100644 --- a/content/en/guides/training/serverless-rl/serverless-rl.md +++ b/content/en/guides/training/serverless-rl/serverless-rl.md @@ -1,9 +1,9 @@ --- -description: Directions on how to get started using Serverless RL. +description: Get started using Serverless RL. title: Use Serverless RL weight: 10 --- -Serverless RL is supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and the W&B Training API. +Serverless RL is supported through [OpenPipe's ART framework](https://art.openpipe.ai/getting-started/about) and the [W&B Training API]({{< relref "ref/training" >}}). -To start using Serverless RL, see the ART [quickstart](https://art.openpipe.ai/getting-started/quick-start) for code examples and workflows. To learn about Serverless RL's API endpoints, see the [W&B Training API]({{< relref "ref/training" >}}). \ No newline at end of file +To start using Serverless RL, see the ART [quickstart](https://art.openpipe.ai/getting-started/quick-start) for code examples and workflows. To learn about Serverless RL's API endpoints, see the W&B Training API. \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index dbb4ce836a..9c6cb849f6 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -1,6 +1,6 @@ --- -title: "Usage Information and Limits" -linkTitle: "Usage & Limits" +title: "Usage information and limits" +linkTitle: "Usage & limits" weight: 30 description: > Understand pricing, usage limits, and account restrictions for W&B Serverless RL. @@ -29,17 +29,17 @@ Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serv By default, Serverless RL currently supports up to 2000 concurrent requests per user and 6000 per project. If you exceed your rate limit, the Inference API returns a `429 Concurrency limit reached for requests` response. To avoid this error, reduce the number of concurrent requests your training job or production workload makes at once. If you need a higher rate limit, you can request one at support@wandb.com. -{{< alert title="Note" >}} -## Personal entities unsupported +{{< alert title="Personal entities unsupported" >}} Serverless RL and W&B Inference don't support personal entities (personal accounts). To access Serverless RL, switch to a non-personal account by [creating a Team]({{< relref "/guides/hosting/iam/access-management/manage-organization/#add-and-manage-teams" >}}). Personal entities (personal accounts) were deprecated in May 2024, so this advisory only applies to legacy accounts. -## Geographic restrictions - -Serverless RL is only available in supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). {{< /alert >}} +{{< alert title="Geographic restrictions" >}} +Serverless RL is only available in supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). + +{{< /alert >}} diff --git a/content/en/guides/training/serverless-rl/using-trained-models.md b/content/en/guides/training/serverless-rl/using-trained-models.md index 7d0aa495b0..4329ae499e 100644 --- a/content/en/guides/training/serverless-rl/using-trained-models.md +++ b/content/en/guides/training/serverless-rl/using-trained-models.md @@ -6,21 +6,23 @@ description: > Make inference requests to the models you've trained. --- -After training a model with Serverless RL, it is automatically deployed and ready to use in production. To make a request to the OpenAI-compatible endpoint, follow the instructions below. - -### Overview +After training a model with Serverless RL, it is automatically available for inference. To make a request to the OpenAI-compatible endpoint, follow the instructions below. When making inference requests, set your base URL to `https://api.training.wandb.ai/v1/` and use your W&B API key for authentication. Finally, set your model name to match the following schema: -`wandb-artifact://///:` +``` +wandb-artifact://///:` +``` For example, if your W&B team is named `email-specialists`, your project is called `mail-search`, your trained model is named `agent-001`, and you wanted to deploy it on step 25 (presumably because that step did the best on your evals), you would set the model name in your chat completion requests to the following: -`wandb-artifact:///email-specialists/mail-search/agent-001:step25` +``` +wandb-artifact:///email-specialists/mail-search/agent-001:step25` +``` This tells W&B Inference exactly where to find your model when initially loading it for inference. -The following examples show you how to make inference requests to your trained model using a CuRL request or the [Python OpenAI SDK](https://openai.github.io/openai-agents-python/). +The following examples show you how to make inference requests to your trained model using a CuRL request or the [Python OpenAI SDK](https://github.com/openai/openai-python). ### CuRL @@ -49,9 +51,8 @@ ENTITY = "my-entity" PROJECT = "my-project" client = OpenAI( - base_url="https://api.inference.coreweave.com/v1", - api_key=WANDB_API_KEY, - default_headers={"OpenAI-Project": f"{ENTITY}/{PROJECT}"}, + base_url="https://api.training.wandb.ai/v1", + api_key=WANDB_API_KEY ) response = client.chat.completions.create( From 97e56b10a33e0f0a0130c383622db09d828b8312 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 21:33:27 -0400 Subject: [PATCH 49/59] Fixes API reference page --- content/en/_index.md | 2 +- content/en/guides/training/api-reference.md | 12 ++---------- content/en/ref/training.md | 5 +++++ 3 files changed, 8 insertions(+), 11 deletions(-) diff --git a/content/en/_index.md b/content/en/_index.md index e788680e56..1bcb3545ba 100644 --- a/content/en/_index.md +++ b/content/en/_index.md @@ -84,7 +84,7 @@ Now in public preview, use [W&B Training]({{< relref "/guides/training/" >}}) to - [Introduction]({{< relref "/guides/training/" >}}) - [Serverless RL]({{< relref "/guides/training/serverless-rl/" >}}) - [Prerequisites]({{< relref "/guides/training/prerequisites/" >}}) -- [API Reference]({{< relref "/guides/training/api-reference/" >}}) +- [API Reference]({{< relref "/ref/training" >}})
{{% /card %}} {{< /cardpane >}} diff --git a/content/en/guides/training/api-reference.md b/content/en/guides/training/api-reference.md index c06888e05d..ac0480a9a8 100644 --- a/content/en/guides/training/api-reference.md +++ b/content/en/guides/training/api-reference.md @@ -2,15 +2,7 @@ title: "API Reference" linkTitle: "API Reference" weight: 100 +manualLink: "/ref/training" description: > Complete API documentation for W&B Training. ---- - -View the complete [W&B Training API Reference]({{< relref "/ref/training" >}}). - -The API reference includes: -* All available endpoints -* Request and response schemas -* Authentication details -* Example requests and responses - +--- \ No newline at end of file diff --git a/content/en/ref/training.md b/content/en/ref/training.md index c9cf6b31cb..c84877b7d9 100644 --- a/content/en/ref/training.md +++ b/content/en/ref/training.md @@ -2,4 +2,9 @@ title: W&B Training API description: Generated documentation for W&B APIs layout: redoc +menu: + default: + parent: training + name: API Reference + weight: 100 --- From b96d37747f6c9bdb7d2fbb8b9d9604305f3348f5 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 21:34:20 -0400 Subject: [PATCH 50/59] remove unnecessary menu frontmatter from reference --- content/en/ref/training.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/content/en/ref/training.md b/content/en/ref/training.md index c84877b7d9..c9cf6b31cb 100644 --- a/content/en/ref/training.md +++ b/content/en/ref/training.md @@ -2,9 +2,4 @@ title: W&B Training API description: Generated documentation for W&B APIs layout: redoc -menu: - default: - parent: training - name: API Reference - weight: 100 --- From a3a2fa5a650bf1d87bd5e2cfa9736648e7125e8d Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 21:42:54 -0400 Subject: [PATCH 51/59] Adds coreweave link --- content/en/guides/training/serverless-rl/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/en/guides/training/serverless-rl/_index.md b/content/en/guides/training/serverless-rl/_index.md index ba81669a77..c2549700b2 100644 --- a/content/en/guides/training/serverless-rl/_index.md +++ b/content/en/guides/training/serverless-rl/_index.md @@ -7,7 +7,7 @@ description: Learn about how to more efficiently post-train your models using re weight: 5 --- -Now in public preview, Serverless RL helps developers post-train LLMs to learn new behaviors and improve reliability, speed, and costs when performing multi-turn agentic tasks. We provision the training infrastructure for you while allowing full flexibility in your environment's setup. Serverless RL gives you instant access to a managed training cluster that elastically auto-scales to dozens of GPUs. By splitting RL workflows into inference and training phases and multiplexing them across jobs, Serverless RL increases GPU utilization and reduces your training time and costs. +Now in public preview, Serverless RL helps developers post-train LLMs to learn new behaviors and improve reliability, speed, and costs when performing multi-turn agentic tasks. W&B provision the training infrastructure ([on CoreWeave](https://docs.coreweave.com/docs/platform)) for you while allowing full flexibility in your environment's setup. Serverless RL gives you instant access to a managed training cluster that elastically auto-scales to dozens of GPUs. By splitting RL workflows into inference and training phases and multiplexing them across jobs, Serverless RL increases GPU utilization and reduces your training time and costs. Serverless RL is ideal for tasks like: * Voice agents @@ -34,4 +34,4 @@ Serverless RL uses a combination of the following W&B components to operate: * [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters * [Weave (optional)]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop -Serverless RL is in public preview. During the preview, you are charged only for the use of inference and the storage of artifacts, during the preview period, not for adapter training. We do not charge for adapter training during the preview period. \ No newline at end of file +Serverless RL is in public preview. During the preview, you are charged only for the use of inference and the storage of artifacts, during the preview period, not for adapter training. W&B do not charge for adapter training during the preview period. \ No newline at end of file From efe3bcdf4efde38a5cffdab6333492b642e6ca0e Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 22:09:30 -0400 Subject: [PATCH 52/59] rewrite inference doc --- .../serverless-rl/using-trained-models.md | 26 +++++++++++++------ 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/content/en/guides/training/serverless-rl/using-trained-models.md b/content/en/guides/training/serverless-rl/using-trained-models.md index 4329ae499e..a82e538fae 100644 --- a/content/en/guides/training/serverless-rl/using-trained-models.md +++ b/content/en/guides/training/serverless-rl/using-trained-models.md @@ -6,23 +6,33 @@ description: > Make inference requests to the models you've trained. --- -After training a model with Serverless RL, it is automatically available for inference. To make a request to the OpenAI-compatible endpoint, follow the instructions below. +After training a model with Serverless RL. it is automatically available for inference. -When making inference requests, set your base URL to `https://api.training.wandb.ai/v1/` and use your W&B API key for authentication. Finally, set your model name to match the following schema: +To send requests to your trained model, you need: +* Your [W&B API key](https://wandb.ai/authorize) +* The [Training API's]({{< relref "/ref/training" >}}) base URL, `https://api.training.wandb.ai/v1/` +* Your model's endpoint + +The model's endpoint uses the following schema: ``` -wandb-artifact://///:` +wandb-artifact://///: ``` -For example, if your W&B team is named `email-specialists`, your project is called `mail-search`, your trained model is named `agent-001`, and you wanted to deploy it on step 25 (presumably because that step did the best on your evals), you would set the model name in your chat completion requests to the following: +The schema consists of: + +* Your W&B entity's (team) name +* The name of the project associated with your model +* The trained model's name +* The training step of the model you want to deploy (this is usually the step where the model performed best in your evaluations) + +For example, if your W&B team is named `email-specialists`, your project is called `mail-search`, your trained model is named `agent-001`, and you wanted to deploy it on step 25, the endpoint looks like this: ``` -wandb-artifact:///email-specialists/mail-search/agent-001:step25` +wandb-artifact:///email-specialists/mail-search/agent-001:step25 ``` -This tells W&B Inference exactly where to find your model when initially loading it for inference. - -The following examples show you how to make inference requests to your trained model using a CuRL request or the [Python OpenAI SDK](https://github.com/openai/openai-python). +Once you have your endpoint, you can integrate into your normal inference workflows. The following examples show how to make inference requests to your trained model using a CuRL request or the [Python OpenAI SDK](https://github.com/openai/openai-python). ### CuRL From db815ab586a96e430aba6eed0f0366cdfe1ae70a Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 22:27:33 -0400 Subject: [PATCH 53/59] minor copy changes --- content/en/guides/training/serverless-rl/_index.md | 2 +- content/en/guides/training/serverless-rl/usage-limits.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/en/guides/training/serverless-rl/_index.md b/content/en/guides/training/serverless-rl/_index.md index c2549700b2..f370874dad 100644 --- a/content/en/guides/training/serverless-rl/_index.md +++ b/content/en/guides/training/serverless-rl/_index.md @@ -34,4 +34,4 @@ Serverless RL uses a combination of the following W&B components to operate: * [Artifacts]({{< relref "guides/core/artifacts" >}}): To store and version the LoRA adapters * [Weave (optional)]({{< relref "guides/models" >}}): To gain observability into how the model responds at each step of the training loop -Serverless RL is in public preview. During the preview, you are charged only for the use of inference and the storage of artifacts, during the preview period, not for adapter training. W&B do not charge for adapter training during the preview period. \ No newline at end of file +Serverless RL is in public preview. During the preview, you are charged only for the use of inference and the storage of artifacts. W&B does not charge for adapter training during the preview period. \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index 9c6cb849f6..9b50801a15 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -18,7 +18,7 @@ Pricing for Serverless RL inference requests matches W&B Inference pricing. See At each training step, Serverless RL collects batches of trajectories that include your agent's outputs and associated rewards (calculated by your reward function). The batched trajectories are then used to update the weights of a LoRA adapter that specializes a base model for your task. The training jobs to update these LoRAs run on dedicated GPU clusters managed by Serverless RL. -During the public preview, training is free. We will announce pricing for training soon. +Training is free during the public preview period. ### Model storage From 87ab4d057df2cd45c9f34ff32b9dd24b819a2ffb Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 22:54:13 -0400 Subject: [PATCH 54/59] renames file and corrects some grammar --- .../serverless-rl/using-trained-models.md | 79 ------------------- 1 file changed, 79 deletions(-) delete mode 100644 content/en/guides/training/serverless-rl/using-trained-models.md diff --git a/content/en/guides/training/serverless-rl/using-trained-models.md b/content/en/guides/training/serverless-rl/using-trained-models.md deleted file mode 100644 index a82e538fae..0000000000 --- a/content/en/guides/training/serverless-rl/using-trained-models.md +++ /dev/null @@ -1,79 +0,0 @@ ---- -title: "Use your trained models" -linkTitle: "Use trained models" -weight: 20 -description: > - Make inference requests to the models you've trained. ---- - -After training a model with Serverless RL. it is automatically available for inference. - -To send requests to your trained model, you need: -* Your [W&B API key](https://wandb.ai/authorize) -* The [Training API's]({{< relref "/ref/training" >}}) base URL, `https://api.training.wandb.ai/v1/` -* Your model's endpoint - -The model's endpoint uses the following schema: - -``` -wandb-artifact://///: -``` - -The schema consists of: - -* Your W&B entity's (team) name -* The name of the project associated with your model -* The trained model's name -* The training step of the model you want to deploy (this is usually the step where the model performed best in your evaluations) - -For example, if your W&B team is named `email-specialists`, your project is called `mail-search`, your trained model is named `agent-001`, and you wanted to deploy it on step 25, the endpoint looks like this: - -``` -wandb-artifact:///email-specialists/mail-search/agent-001:step25 -``` - -Once you have your endpoint, you can integrate into your normal inference workflows. The following examples show how to make inference requests to your trained model using a CuRL request or the [Python OpenAI SDK](https://github.com/openai/openai-python). - -### CuRL - -```shell -curl https://api.training.wandb.ai/v1/chat/completions \ - -H "Authorization: Bearer $WANDB_API_KEY" \ - -H "Content-Type: application/json" \ - -d '{ - "model": "wandb-artifact://///:", - "messages": [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Summarize our training run."} - ], - "temperature": 0.7, - "top_p": 0.95 - }' -``` - -### OpenAI SDK - -```python -from openai import OpenAI - -WANDB_API_KEY = "your-wandb-api-key" -ENTITY = "my-entity" -PROJECT = "my-project" - -client = OpenAI( - base_url="https://api.training.wandb.ai/v1", - api_key=WANDB_API_KEY -) - -response = client.chat.completions.create( - model=f"wandb-artifact:///{ENTITY}/{PROJECT}/my-model:step100", - messages=[ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Summarize our training run."}, - ], - temperature=0.7, - top_p=0.95, -) - -print(response.choices[0].message.content) -``` \ No newline at end of file From 29e1aaedc71b7a9105d2cea0466068a8eb8d5ff8 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Mon, 6 Oct 2025 22:59:04 -0400 Subject: [PATCH 55/59] renames file for real --- .../serverless-rl/use-trained-models.md | 79 +++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 content/en/guides/training/serverless-rl/use-trained-models.md diff --git a/content/en/guides/training/serverless-rl/use-trained-models.md b/content/en/guides/training/serverless-rl/use-trained-models.md new file mode 100644 index 0000000000..b82c2dfbc4 --- /dev/null +++ b/content/en/guides/training/serverless-rl/use-trained-models.md @@ -0,0 +1,79 @@ +--- +title: "Use your trained models" +linkTitle: "Use trained models" +weight: 20 +description: > + Make inference requests to the models you've trained. +--- + +After training a model with Serverless RL, it is automatically available for inference. + +To send requests to your trained model, you need: +* Your [W&B API key](https://wandb.ai/authorize) +* The [Training API's]({{< relref "/ref/training" >}}) base URL, `https://api.training.wandb.ai/v1/` +* Your model's endpoint + +The model's endpoint uses the following schema: + +``` +wandb-artifact://///: +``` + +The schema consists of: + +* Your W&B entity's (team) name +* The name of the project associated with your model +* The trained model's name +* The training step of the model you want to deploy (this is usually the step where the model performed best in your evaluations) + +For example, if your W&B team is named `email-specialists`, your project is called `mail-search`, your trained model is named `agent-001`, and you wanted to deploy it on step 25, the endpoint looks like this: + +``` +wandb-artifact:///email-specialists/mail-search/agent-001:step25 +``` + +Once you have your endpoint, you can integrate it into your normal inference workflows. The following examples show how to make inference requests to your trained model using a cURL request or the [Python OpenAI SDK](https://github.com/openai/openai-python). + +### cURL + +```shell +curl https://api.training.wandb.ai/v1/chat/completions \ + -H "Authorization: Bearer $WANDB_API_KEY" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "wandb-artifact://///:", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Summarize our training run."} + ], + "temperature": 0.7, + "top_p": 0.95 + }' +``` + +### OpenAI SDK + +```python +from openai import OpenAI + +WANDB_API_KEY = "your-wandb-api-key" +ENTITY = "my-entity" +PROJECT = "my-project" + +client = OpenAI( + base_url="https://api.training.wandb.ai/v1", + api_key=WANDB_API_KEY +) + +response = client.chat.completions.create( + model=f"wandb-artifact:///{ENTITY}/{PROJECT}/my-model:step100", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Summarize our training run."}, + ], + temperature=0.7, + top_p=0.95, +) + +print(response.choices[0].message.content) +``` \ No newline at end of file From 519adc43fd43b0b86c39124523fc7287b0ceed0c Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Tue, 7 Oct 2025 10:53:58 -0400 Subject: [PATCH 56/59] Feedback from M. Linville and N. Luna --- content/en/guides/training/_index.md | 2 +- content/en/guides/training/prerequisites.md | 14 ++++--------- .../training/serverless-rl/usage-limits.md | 21 ++++--------------- .../serverless-rl/use-trained-models.md | 3 +-- 4 files changed, 10 insertions(+), 30 deletions(-) diff --git a/content/en/guides/training/_index.md b/content/en/guides/training/_index.md index 4cdfa6fe85..44c8325d3b 100644 --- a/content/en/guides/training/_index.md +++ b/content/en/guides/training/_index.md @@ -13,6 +13,6 @@ W&B Training includes integration with: * [ART](https://art.openpipe.ai/getting-started/about), a flexible RL fine-tuning framework. * [RULER](https://openpipe.ai/blog/ruler), a universal verifier. -* A fully-managed backend on CoreWeave cloud. +* A fully-managed backend on [CoreWeave Cloud](https://docs.coreweave.com/docs/platform). To get started, satisfy the [prerequisites]({{< relref "/guides/training/prerequisites" >}}) to start using the service and then see [OpenPipe's Serverless RL quickstart](https://art.openpipe.ai/getting-started/quick-start) to learn how to post-train your models. \ No newline at end of file diff --git a/content/en/guides/training/prerequisites.md b/content/en/guides/training/prerequisites.md index 6113b2eb5e..01d9811cbb 100644 --- a/content/en/guides/training/prerequisites.md +++ b/content/en/guides/training/prerequisites.md @@ -12,18 +12,12 @@ Complete these steps before using W&B Training features through the OpenPipe ART Before starting, review the [usage information and limits]({{< relref "guides/training/serverless-rl/usage-limits" >}}) to understand costs and restrictions. {{< /alert >}} -## Set up your W&B account and project +## Sign up and create an API key -You need these items to access W&B Training: +To authenticate your machine with W&B, you must first generate an API key at [wandb.ai/authorize](https://wandb.ai/authorize). Copy the API key and store it securely. -1. **A W&B account** - Sign up at [W&B](https://app.wandb.ai/login?signup=true) - -2. **A W&B API key** - Get your API key at [https://wandb.ai/authorize](https://wandb.ai/authorize) - -3. **A W&B project** - Create a project in your W&B account to track usage, record training metrics, and save trained models +## Create a project in W&B +Create a project in your W&B account to track usage, record training metrics, and save trained models. See [Insert link to a quickstart or tutorial] ## Next steps diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index 9b50801a15..c16a28ae7b 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -24,23 +24,10 @@ Training is free during the public preview period. Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB of free storage, which is enough for roughly 30 LoRAs. We recommend deleting low-performing LoRAs to save space. See the [ART SDK](https://art.openpipe.ai) for instructions on how to do this. +## Limits -## Inference Concurrency limits - -By default, Serverless RL currently supports up to 2000 concurrent requests per user and 6000 per project. If you exceed your rate limit, the Inference API returns a `429 Concurrency limit reached for requests` response. To avoid this error, reduce the number of concurrent requests your training job or production workload makes at once. If you need a higher rate limit, you can request one at support@wandb.com. - -{{< alert title="Personal entities unsupported" >}} - -Serverless RL and W&B Inference don't support personal entities (personal accounts). To access Serverless RL, switch to a non-personal account by [creating a Team]({{< relref "/guides/hosting/iam/access-management/manage-organization/#add-and-manage-teams" >}}). Personal entities (personal accounts) were deprecated in May 2024, so this advisory only applies to legacy accounts. - -{{< /alert >}} - -{{< alert title="Geographic restrictions" >}} - -Serverless RL is only available in supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). - -{{< /alert >}} - - +* **Inference concurrency limits**: By default, Serverless RL currently supports up to 2000 concurrent requests per user and 6000 per project. If you exceed your rate limit, the Inference API returns a `429 Concurrency limit reached for requests` response. To avoid this error, reduce the number of concurrent requests your training job or production workload makes at once. If you need a higher rate limit, you can request one at support@wandb.com. +* **Personal entities unsupported**: Serverless RL and W&B Inference don't support personal entities (personal accounts). To access Serverless RL, switch to a non-personal account by [creating a Team]({{< relref "/guides/hosting/iam/access-management/manage-organization/#add-and-manage-teams" >}}). Personal entities (personal accounts) were deprecated in May 2024, so this advisory only applies to legacy accounts. +* **Geographic restrictions**: Serverless RL is only available in supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). \ No newline at end of file diff --git a/content/en/guides/training/serverless-rl/use-trained-models.md b/content/en/guides/training/serverless-rl/use-trained-models.md index b82c2dfbc4..c87d037697 100644 --- a/content/en/guides/training/serverless-rl/use-trained-models.md +++ b/content/en/guides/training/serverless-rl/use-trained-models.md @@ -2,8 +2,7 @@ title: "Use your trained models" linkTitle: "Use trained models" weight: 20 -description: > - Make inference requests to the models you've trained. +description: Make inference requests to the models you've trained. --- After training a model with Serverless RL, it is automatically available for inference. From f2376ada893f919e49b869368c5c58c2ade993af Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Wed, 8 Oct 2025 11:56:35 -0400 Subject: [PATCH 57/59] Updates ToS link and LoRA deletion link, fixes spelling error --- content/en/guides/training/serverless-rl/_index.md | 2 +- content/en/guides/training/serverless-rl/usage-limits.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/content/en/guides/training/serverless-rl/_index.md b/content/en/guides/training/serverless-rl/_index.md index f370874dad..72e3c4f7a3 100644 --- a/content/en/guides/training/serverless-rl/_index.md +++ b/content/en/guides/training/serverless-rl/_index.md @@ -22,7 +22,7 @@ Serverless RL trains low-rank adapters (LoRAs) to specialize a model for your ag Reinforcement learning (RL) is a set of powerful training techniques that you can use in many kinds of training setups, including on GPUs that you own or rent directly. Serverless RL can provide the following advantages in your RL post-training: * **Lower training costs**: By multiplexing shared infrastructure across many users, skipping the setup process for each job, and scaling your GPU costs down to 0 when you're not actively training, Serverless RL reduces training costs significantly. -* **Faster training time**: By splitting inference requests across many GPUs and immediately provisioning training infrstructure when you need it, Serverless RL speeds up your training jobs and lets you iterate faster. +* **Faster training time**: By splitting inference requests across many GPUs and immediately provisioning training infrastructure when you need it, Serverless RL speeds up your training jobs and lets you iterate faster. * **Automatic deployment**: Serverless RL automatically deploys every checkpoint you train, eliminating the need to manually set up hosting infrastructure. Trained models can be accessed and tested immediately in local, staging, or production environments. ## How Serverless RL uses W&B services diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index c16a28ae7b..61ad5c73c0 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -22,7 +22,7 @@ Training is free during the public preview period. ### Model storage -Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB of free storage, which is enough for roughly 30 LoRAs. We recommend deleting low-performing LoRAs to save space. See the [ART SDK](https://art.openpipe.ai) for instructions on how to do this. +Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serve, or continue training them at any time. Storage is billed monthly based on total checkpoint size and your [pricing plan](https://wandb.ai/site/pricing). Every plan includes at least 5GB of free storage, which is enough for roughly 30 LoRAs. We recommend deleting low-performing LoRAs to save space. See the [ART SDK](https://art.openpipe.ai/features/checkpoint-deletion) for instructions on how to do this. ## Limits @@ -30,4 +30,4 @@ Serverless RL stores checkpoints of your trained LoRAs so you can evaluate, serv * **Personal entities unsupported**: Serverless RL and W&B Inference don't support personal entities (personal accounts). To access Serverless RL, switch to a non-personal account by [creating a Team]({{< relref "/guides/hosting/iam/access-management/manage-organization/#add-and-manage-teams" >}}). Personal entities (personal accounts) were deprecated in May 2024, so this advisory only applies to legacy accounts. -* **Geographic restrictions**: Serverless RL is only available in supported geographic locations. For more information, see the [Terms of Service](https://docs.coreweave.com/docs/policies/terms-of-service/terms-of-use#geographic-restrictions). \ No newline at end of file +* **Geographic restrictions**: Serverless RL is only available in supported geographic locations. For more information, see the [Terms of Service](https://site.wandb.ai/terms/). \ No newline at end of file From 8682337827294c186d1ca9fe5ad4b07b5d11e026 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Wed, 8 Oct 2025 12:54:06 -0400 Subject: [PATCH 58/59] Updates from launch meeting --- content/en/_index.md | 2 +- content/en/guides/training/prerequisites.md | 3 ++- content/en/guides/training/serverless-rl/usage-limits.md | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/content/en/_index.md b/content/en/_index.md index 1bcb3545ba..a303b77bb4 100644 --- a/content/en/_index.md +++ b/content/en/_index.md @@ -82,8 +82,8 @@ Use [W&B Inference]({{< relref "/guides/inference/" >}}) to access leading open- Now in public preview, use [W&B Training]({{< relref "/guides/training/" >}}) to post-train large language models using serverless reinforcement learning (RL). Features include fully managed GPU infrastructure, integration with ART and RULER, and automatic scaling for multi-turn agentic tasks. - [Introduction]({{< relref "/guides/training/" >}}) -- [Serverless RL]({{< relref "/guides/training/serverless-rl/" >}}) - [Prerequisites]({{< relref "/guides/training/prerequisites/" >}}) +- [Serverless RL]({{< relref "/guides/training/serverless-rl/" >}}) - [API Reference]({{< relref "/ref/training" >}})
{{% /card %}} diff --git a/content/en/guides/training/prerequisites.md b/content/en/guides/training/prerequisites.md index 01d9811cbb..3bf4faa10d 100644 --- a/content/en/guides/training/prerequisites.md +++ b/content/en/guides/training/prerequisites.md @@ -17,7 +17,8 @@ Before starting, review the [usage information and limits]({{< relref "guides/tr To authenticate your machine with W&B, you must first generate an API key at [wandb.ai/authorize](https://wandb.ai/authorize). Copy the API key and store it securely. ## Create a project in W&B -Create a project in your W&B account to track usage, record training metrics, and save trained models. See [Insert link to a quickstart or tutorial] + +Create a project in your W&B account to track usage, record training metrics, and save trained models. See the [Projects guide]({{< relref "/guides/track/project-page" >}}) for more information. ## Next steps diff --git a/content/en/guides/training/serverless-rl/usage-limits.md b/content/en/guides/training/serverless-rl/usage-limits.md index 61ad5c73c0..1a518868a6 100644 --- a/content/en/guides/training/serverless-rl/usage-limits.md +++ b/content/en/guides/training/serverless-rl/usage-limits.md @@ -12,7 +12,7 @@ Pricing has three components: inference, training, and storage. For specific bil ### Inference -Pricing for Serverless RL inference requests matches W&B Inference pricing. See [model-specific costs](https://wandb.ai/site/pricing/inference) for more details. Learn more about purchasing credits, account tiers, and usage caps in the [W&B Inference docs]({{< relref "/guides/inference/usage-limits/#purchase-more-credits" >}}). +Pricing for Serverless RL inference requests matches W&B Inference pricing. See [model-specific costs](https://site.wandb.ai/pricing/reinforcement-learning) for more details. Learn more about purchasing credits, account tiers, and usage caps in the [W&B Inference docs]({{< relref "/guides/inference/usage-limits/#purchase-more-credits" >}}). ### Training From fc91eccb7b2fe9004efa5afa795583aba7d90f78 Mon Sep 17 00:00:00 2001 From: dbrian57 Date: Wed, 8 Oct 2025 12:56:09 -0400 Subject: [PATCH 59/59] link fix --- content/en/guides/training/prerequisites.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/en/guides/training/prerequisites.md b/content/en/guides/training/prerequisites.md index 3bf4faa10d..1023f1cf98 100644 --- a/content/en/guides/training/prerequisites.md +++ b/content/en/guides/training/prerequisites.md @@ -18,7 +18,7 @@ To authenticate your machine with W&B, you must first generate an API key at [wa ## Create a project in W&B -Create a project in your W&B account to track usage, record training metrics, and save trained models. See the [Projects guide]({{< relref "/guides/track/project-page" >}}) for more information. +Create a project in your W&B account to track usage, record training metrics, and save trained models. See the [Projects guide](https://docs.wandb.ai/guides/track/project-page) for more information. ## Next steps