From cabb62f86daee34afb06686ddb025df4f7e8144e Mon Sep 17 00:00:00 2001 From: alnoam Date: Thu, 6 Mar 2025 16:58:07 +0200 Subject: [PATCH 1/4] Small edits --- .../deploying_clearml/enterprise_deploy/multi_tenant_k8s.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/deploying_clearml/enterprise_deploy/multi_tenant_k8s.md b/docs/deploying_clearml/enterprise_deploy/multi_tenant_k8s.md index 45b4d4d2..c3f4003b 100644 --- a/docs/deploying_clearml/enterprise_deploy/multi_tenant_k8s.md +++ b/docs/deploying_clearml/enterprise_deploy/multi_tenant_k8s.md @@ -332,8 +332,8 @@ must be substituted with valid domain names or values from responses. ``` APISERVER_URL="https://api." - APISERVER_KEY="GGS9F4M6XB2DXJ5AFT9F" - APISERVER_SECRET="2oGujVFhPfaozhpuz2GzQfA5OyxmMsR3WVJpsCR5hrgHFs20PO" + APISERVER_KEY="" + APISERVER_SECRET="" ``` 2. Create a *Tenant* (company): @@ -525,7 +525,7 @@ Install the [Task Traffic Router](appgw.md) in your Kubernetes cluster, allowing apiServerUrlReference: "" apiserverKey: "" apiserverSecret: "" - jwksKey: "ymLh1ok5k5xNUQfS944Xdx9xjf0wueokqKM2dMZfHuH9ayItG2" + jwksKey: "" ingress: enabled: true hostName: "" From f5e4b4d4713ff296f935faeadd738f4e34de8921 Mon Sep 17 00:00:00 2001 From: alnoam Date: Thu, 6 Mar 2025 17:00:09 +0200 Subject: [PATCH 2/4] Small edits --- docs/guides/ide/google_colab.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guides/ide/google_colab.md b/docs/guides/ide/google_colab.md index dbee1a2d..e6b9d2a2 100644 --- a/docs/guides/ide/google_colab.md +++ b/docs/guides/ide/google_colab.md @@ -40,8 +40,8 @@ and running, users can send Tasks to be executed on Google Colab's hardware. api_host="https://api.clear.ml", web_host="https://app.clear.ml", files_host="https://files.clear.ml", - key='6ZHX9UQMYL874A1NE8', - secret='=2h6#%@Y&m*tC!VLEXq&JI7QhZPKuJfbaYD4!uUk(t7=9ENv' + key='', + secret='' ) ``` From 64f13593de7bde52d5cc5e4255e2461a4389515d Mon Sep 17 00:00:00 2001 From: pollfly <75068813+pollfly@users.noreply.github.com> Date: Thu, 6 Mar 2025 19:11:59 +0200 Subject: [PATCH 3/4] Add Visualization embedding admonitions in app pages (#1075) --- docs/webapp/applications/apps_embed_model_deployment.md | 7 +++++++ docs/webapp/applications/apps_llama_deployment.md | 7 +++++++ docs/webapp/applications/apps_model_deployment.md | 6 ++++++ 3 files changed, 20 insertions(+) diff --git a/docs/webapp/applications/apps_embed_model_deployment.md b/docs/webapp/applications/apps_embed_model_deployment.md index d78afd25..ee8be58c 100644 --- a/docs/webapp/applications/apps_embed_model_deployment.md +++ b/docs/webapp/applications/apps_embed_model_deployment.md @@ -48,6 +48,13 @@ After starting an Embedding Model Deployment instance, you can view the followin ![Embedding Model Deployment app](../../img/apps_embedding_model_deployment.png#light-mode-only) ![Embedding Model Deployment app](../../img/apps_embedding_model_deployment_dark.png#dark-mode-only) +:::tip EMBEDDING CLEARML VISUALIZATION +You can embed plots from the app instance dashboard into [ClearML Reports](../webapp_reports.md). These visualizations +are updated live as the app instance(s) updates. The Enterprise Plan supports embedding resources in +external tools (e.g. Notion). Hover over the plot and click Embed code +to copy the embed code, and navigate to a report to paste the embed code. +::: + ## Embedding Model Deployment Instance Configuration When configuring a new Embedding Model Deployment instance, you can fill in the required parameters or reuse the diff --git a/docs/webapp/applications/apps_llama_deployment.md b/docs/webapp/applications/apps_llama_deployment.md index e1233569..442640b9 100644 --- a/docs/webapp/applications/apps_llama_deployment.md +++ b/docs/webapp/applications/apps_llama_deployment.md @@ -46,6 +46,13 @@ After starting a llama.cpp Model Deployment instance, you can view the following ![llama deployment dashboard](../../img/apps_llama_dashboard.png) +:::tip EMBEDDING CLEARML VISUALIZATION +You can embed plots from the app instance dashboard into [ClearML Reports](../webapp_reports.md). These visualizations +are updated live as the app instance(s) updates. The Enterprise Plan supports embedding resources in +external tools (e.g. Notion). Hover over the plot and click Embed code +to copy the embed code, and navigate to a report to paste the embed code. +::: + ### Llama.cpp Model Deployment Instance Configuration When configuring a new llama.cpp Model Deployment instance, you can fill in the required parameters or reuse the diff --git a/docs/webapp/applications/apps_model_deployment.md b/docs/webapp/applications/apps_model_deployment.md index 6bddd620..eba05532 100644 --- a/docs/webapp/applications/apps_model_deployment.md +++ b/docs/webapp/applications/apps_model_deployment.md @@ -49,6 +49,12 @@ etc. ![vLLM Model Deployment App](../../img/apps_model_deployment.png#light-mode-only) ![vLLM Model Deployment App](../../img/apps_model_deployment_dark.png#dark-mode-only) +:::tip EMBEDDING CLEARML VISUALIZATION +You can embed plots from the app instance dashboard into [ClearML Reports](../webapp_reports.md). These visualizations +are updated live as the app instance(s) updates. The Enterprise Plan supports embedding resources in +external tools (e.g. Notion). Hover over the plot and click Embed code +to copy the embed code, and navigate to a report to paste the embed code. +::: ## vLLM Model Deployment Instance Configuration From 42b9807b724d4ab2d7821ad3fd6dc9baeccff6c8 Mon Sep 17 00:00:00 2001 From: pollfly <75068813+pollfly@users.noreply.github.com> Date: Thu, 6 Mar 2025 19:18:09 +0200 Subject: [PATCH 4/4] Small edits (#1076) --- docs/integrations/autokeras.md | 2 +- docs/integrations/catboost.md | 2 +- docs/integrations/fastai.md | 2 +- docs/integrations/keras.md | 2 +- docs/integrations/lightgbm.md | 2 +- docs/integrations/megengine.md | 2 +- docs/integrations/pytorch.md | 2 +- docs/integrations/pytorch_lightning.md | 2 +- docs/integrations/scikit_learn.md | 2 +- docs/integrations/tao.md | 2 +- docs/integrations/tensorflow.md | 2 +- docs/integrations/xgboost.md | 2 +- docs/integrations/yolov5.md | 2 +- docs/integrations/yolov8.md | 2 +- docs/overview.md | 2 +- 15 files changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/integrations/autokeras.md b/docs/integrations/autokeras.md index dcf38cff..ece401ad 100644 --- a/docs/integrations/autokeras.md +++ b/docs/integrations/autokeras.md @@ -93,7 +93,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/catboost.md b/docs/integrations/catboost.md index f3e60261..3d5caa09 100644 --- a/docs/integrations/catboost.md +++ b/docs/integrations/catboost.md @@ -91,7 +91,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/fastai.md b/docs/integrations/fastai.md index f532be3a..aabc00c9 100644 --- a/docs/integrations/fastai.md +++ b/docs/integrations/fastai.md @@ -90,7 +90,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/keras.md b/docs/integrations/keras.md index d9ac7a0d..e1270272 100644 --- a/docs/integrations/keras.md +++ b/docs/integrations/keras.md @@ -103,7 +103,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/lightgbm.md b/docs/integrations/lightgbm.md index 7f6d2628..b611ac22 100644 --- a/docs/integrations/lightgbm.md +++ b/docs/integrations/lightgbm.md @@ -92,7 +92,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/megengine.md b/docs/integrations/megengine.md index 3ad13771..6cbeb627 100644 --- a/docs/integrations/megengine.md +++ b/docs/integrations/megengine.md @@ -88,7 +88,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/pytorch.md b/docs/integrations/pytorch.md index 9373b4e7..5a9184d3 100644 --- a/docs/integrations/pytorch.md +++ b/docs/integrations/pytorch.md @@ -112,7 +112,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/pytorch_lightning.md b/docs/integrations/pytorch_lightning.md index 41e95bba..977c545e 100644 --- a/docs/integrations/pytorch_lightning.md +++ b/docs/integrations/pytorch_lightning.md @@ -118,7 +118,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md), to cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/scikit_learn.md b/docs/integrations/scikit_learn.md index c0fb490a..61049811 100644 --- a/docs/integrations/scikit_learn.md +++ b/docs/integrations/scikit_learn.md @@ -94,7 +94,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/tao.md b/docs/integrations/tao.md index 6a2376b2..07a11249 100644 --- a/docs/integrations/tao.md +++ b/docs/integrations/tao.md @@ -111,7 +111,7 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/tensorflow.md b/docs/integrations/tensorflow.md index 49040835..2f175e7b 100644 --- a/docs/integrations/tensorflow.md +++ b/docs/integrations/tensorflow.md @@ -105,7 +105,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/xgboost.md b/docs/integrations/xgboost.md index 876f5fb2..831c379c 100644 --- a/docs/integrations/xgboost.md +++ b/docs/integrations/xgboost.md @@ -118,7 +118,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/yolov5.md b/docs/integrations/yolov5.md index 9818d8c9..1f353427 100644 --- a/docs/integrations/yolov5.md +++ b/docs/integrations/yolov5.md @@ -167,7 +167,7 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: and shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only) ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only) diff --git a/docs/integrations/yolov8.md b/docs/integrations/yolov8.md index f3080412..b6091864 100644 --- a/docs/integrations/yolov8.md +++ b/docs/integrations/yolov8.md @@ -112,7 +112,7 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: shuts down instances as needed, according to a resource budget that you set. -### Cloning, Editing, and Enqueuing +### Reproducing Tasks ClearML logs all the information required to reproduce a task, but you may also want to change a few parameters and task details when you re-run it, which you can do through ClearML's UI. diff --git a/docs/overview.md b/docs/overview.md index 12cb5402..d6ddb186 100644 --- a/docs/overview.md +++ b/docs/overview.md @@ -7,7 +7,7 @@ slug: / # ClearML Documentation ## Overview -Welcome to the documentation for ClearML, the end to end platform for streamlining AI development and deployment. ClearML consists of three essential layers: +Welcome to the documentation for ClearML, the end-to-end platform for streamlining AI development and deployment. ClearML consists of three essential layers: 1. [**Infrastructure Control Plane**](#infrastructure-control-plane) (Cloud/On-Prem Agnostic) 2. [**AI Development Center**](#ai-development-center) 3. [**GenAI App Engine**](#genai-app-engine)