tracebloc · saadqbal · May 11, 2026 · May 6, 2026 · May 6, 2026 · May 6, 2026
diff --git a/.mintignore b/.mintignore
@@ -1,6 +1,10 @@
-# Mintlify automatically ignores these files and directories:
-# .git, .github, .claude, .agents, .idea, node_modules,
-# README.md, LICENSE.md, CHANGELOG.md, CONTRIBUTING.md
+# Mintlify is supposed to auto-ignore .git, .github, .claude, .agents,
+# .idea, node_modules, README.md, LICENSE.md, CHANGELOG.md, CONTRIBUTING.md
+# — but in practice the dev server still tries to MDX-parse files inside
+# .github (e.g. pull_request_template.md, which uses HTML <!-- comments),
+# so list it explicitly.
+.github/
+.github/**
 
 # Draft content
 drafts/

diff --git a/create-use-case/prerequisites.mdx b/create-use-case/prerequisites.mdx
@@ -1,13 +1,13 @@
 ---
 title: "Prerequisites"
 description: "Supported data types, tasks, and requirements for creating a use case on tracebloc."
 ---

 tracebloc is continually expanding supported data types and tasks to enable your use cases. In case your use case is not yet supported, please reach out to us at [support@tracebloc.io](mailto:support@tracebloc.io).

 Before you can create a use case on the [tracebloc website](https://ai.tracebloc.io/my-use-cases), make sure the following requirements are met:

 - You are registered as a user on the tracebloc platform
 - You have a client deployed using kubernetes either [locally or in the cloud](/environment-setup/setup-guide)
 - Your dataset is cleaned and preprocessed
 - You are familiar with the supported data types and tasks
@@ -23,22 +23,22 @@

 ### Image Data

 **Requirements for all image data tasks:** Uniform image sizes and uniform file types. For example all images as 256x256 rgb .jpg files. Convert files if necessary and in case your images do not fit the supported size, crop or resize accordingly.

 <Note>
 **Filenames in the label CSV:** For all image and text tasks, the `filename` column in the label CSV **must not include the file extension** (e.g. use `cat01`, not `cat01.jpg`). The extension is configured once on the ingestor side via `file_options.extension` in the template and applied to every row at ingestion time.
 </Note>

 All images are validated before ingestion by the data ingestor. The ingestion process only starts when every file meets the requirements. Fix or remove any invalid images, then retry.

 For object detection, images and annotations are automatically up- or downsized to 448x448 pixels.

 | Task | Input file type | Color mode | Supported image size | Label file type | Requirements | Links |
 |-----------------------|-----------------|--------------------------------------------------------------|----------------|-----------------------------------|----------------------------------------------|----------|
 | Classification | PNG, JPG, JPEG | rgb (3 channels) or grayscale (1 channel), 8-bit per channel | Square (height = width) | CSV | Uniform image size and file type per dataset | [Detailed structure](#image-classification) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/image_classification) |
-| Keypoint Detection | PNG, JPG, JPEG | rgb (3 channels) or grayscale (1 channel), 8-bit per channel | Square (height = width) | CSV | Uniform image sizes <br></br> Same number of keypoints per image and class | [Detailed structure](#image-keypoint-detection) <br></br>Template coming soon — contact [support@tracebloc.io](mailto:support@tracebloc.io) |
+| Keypoint Detection | PNG, JPG, JPEG | rgb (3 channels) or grayscale (1 channel), 8-bit per channel | Square (height = width) | CSV | Uniform image sizes <br></br> Same number of keypoints per image and class | [Detailed structure](#image-keypoint-detection) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/keypoint_detection) |
 | Object Detection | PNG, JPG, JPEG | rgb (3 channels) or grayscale (1 channel), 8-bit per channel | Square (height = width). Images and annotations will be resized to 448x448 px automatically | Pascal VOC | Uniform image sizes, one xml per image | [Detailed structure](#image-object-detection) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/object_detection) |
-| Semantic Segmentation | PNG, JPG, JPEG | rgb (3 channels) or grayscale (1 channel), 8-bit per channel | Square (height = width) | PNG, JPG, JPEG | Uniform image and mask sizes | [Detailed structure](#image-semantic-segmentation) <br></br>Template coming soon — contact [support@tracebloc.io](mailto:support@tracebloc.io) |
+| Semantic Segmentation | PNG, JPG, JPEG | rgb (3 channels) or grayscale (1 channel), 8-bit per channel | Square (height = width) | PNG, JPG, JPEG | Uniform image and mask sizes | [Detailed structure](#image-semantic-segmentation) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/semantic_segmentation) |
 
 ### Image Classification
 
@@ -60,11 +60,11 @@
 ...
 ```

 The `filename` column must not include the file extension. Set the expected extension once via `file_options.extension` in the ingestor template.

 ### Image Keypoint Detection

 The number of keypoints per class and per image must be fixed. For example, in a person/car keypoint detection project, both classes must define the same keypoints, and every image must contain the full set for its class. You cannot mix classes with different keypoint counts (e.g., 16 for person and 32 for car) or annotate some images with fewer keypoints for the same class.

 ``` structure
 train/
@@ -85,8 +85,8 @@
 image02,car,115,145,2
 ```

 - **X and Y** determine the X-/Y-coordinates of a keypoint
 - **Visibility** indicates whether a keypoint is visible in the image or not: 0 = not visible (point outside the image or point is in the image but occluded), 1 = visible
 - **Filename** should not include the file extension.

 ### Image Object Detection
@@ -141,8 +141,8 @@

 ### Image Semantic Segmentation

 Each mask is an rgb image whose pixel values map to classes defined in labels.csv. The labels.csv contains a global list per image and class. All masks must exactly match their corresponding image sizes and file names.
 For binary segmentation (two classes), provide a single-channel grayscale mask where background pixels are black (0) and foreground pixels are white (255).
 For three or more classes, supply an RGB mask where each class is represented by a unique color (or pixel value). The filename should not include the file extension.


@@ -174,13 +174,13 @@

 ### Tabular Data

 **Requirements for all tabular data tasks:** Each dataset must be provided as a single CSV file with a header row. Every column must contain uniform data types, for example numeric values for features and a categorical or alphanumeric column for labels. Use UTF-8 encoding with comma separators and validate that your schema matches the expected types. Invalid rows are skipped by the ingestor.

 | Task | Data file type | Requirements | Links |
 |--------------------------|---------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|----------|
 | Classification | CSV (features and label in one single file) | Uniform data formats per column. <br></br>Feature columns: Numeric <br></br>Label columns: Alphanumeric | [Detailed structure](#tabular-classification) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/tabular_classification) |
 | Regression | CSV (features and label in one single file) | Uniform data formats per column. <br></br>Feature columns: Numeric <br></br>Label column: Numeric (continuous target) | [Detailed structure](#tabular-regression) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/tabular_regression) |
 | Time Series Forecasting | CSV (timestamp, features and target in one single file) | A timestamp column in a parsable format (e.g. `YYYY-MM-DD` or ISO 8601). <br></br>Feature columns: Numeric <br></br>Target column: Numeric | [Detailed structure](#time-series-forecasting) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/time_series_forecasting) |
 | Time to Event Prediction | CSV (features, time and event in one single file) | A `time` column (duration until event or censoring, integer or numeric). <br></br>An event column (binary 0/1 indicating whether the event occurred). <br></br>Feature columns: Numeric | [Detailed structure](#time-to-event-prediction) <br></br>[Example](https://github.com/tracebloc/data-ingestors/tree/develop/templates/time_to_event_prediction) |

 ### Tabular Classification
@@ -239,7 +239,7 @@

 ### Text Classification

 The `filename` column must not include the file extension. The extension is set once via `file_options.extension` in the ingestor template (e.g. `FileExtension.TXT`).

 ``` structure
 train/

diff --git a/create-use-case/templates.mdx b/create-use-case/templates.mdx
@@ -3,7 +3,7 @@
 description: "Ready-made data ingestion templates for every supported task — clone, configure, deploy."
 ---

 Each task tracebloc supports comes with a runnable data-ingestion template — a working `Dockerfile`, `ingestor.py`, and `ingestor-job.yaml` you can copy, point at your data, and ship.

 ## Available templates

@@ -11,13 +11,15 @@
 |---|---|
 | Image classification | [`templates/image_classification`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/image_classification) |
 | Object detection | [`templates/object_detection`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/object_detection) |
+| Keypoint detection | [`templates/keypoint_detection`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/keypoint_detection) |
+| Semantic segmentation | [`templates/semantic_segmentation`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/semantic_segmentation) |
 | Text classification | [`templates/text_classification`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/text_classification) |
 | Tabular classification | [`templates/tabular_classification`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/tabular_classification) |
 | Tabular regression | [`templates/tabular_regression`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/tabular_regression) |
 | Time series forecasting | [`templates/time_series_forecasting`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/time_series_forecasting) |
 | Time-to-event prediction | [`templates/time_to_event_prediction`](https://github.com/tracebloc/data-ingestors/tree/develop/templates/time_to_event_prediction) |
 
-Keypoint detection and semantic segmentation are supported, but ready-made templates are still in flight — contact [support@tracebloc.io](mailto:support@tracebloc.io) for guidance in the meantime. See [Prerequisites](/create-use-case/prerequisites) for the full list of supported tasks and required data formats.
+See [Prerequisites](/create-use-case/prerequisites) for the full list of supported tasks and required data formats.
 
 ## How to use a template
 

diff --git a/docs.json b/docs.json
@@ -67,6 +67,7 @@
  "join-use-case/join-use-case",
  "join-use-case/start-training",
  "join-use-case/hyperparameters",
+ "join-use-case/how-training-works",
  "join-use-case/model-optimization",
  "join-use-case/model-evaluation"
  ]

diff --git a/environment-setup/setup-guide.mdx b/environment-setup/setup-guide.mdx
@@ -1,6 +1,6 @@
 ---
 title: "Setup Guide"
 description: "Detailed walkthrough for deploying a tracebloc workspace — requirements, installer internals, GPU support, and verification."
 ---

 Everything the installer does, explained. This page walks through the full setup process — what each step does, what to expect, and how to verify it worked.
@@ -19,7 +19,7 @@

 **Supported platforms:** macOS (Intel & Apple Silicon) · Linux (x86_64 & arm64) · Windows (x86_64 & arm64)

 **Outbound access needed:** The installer downloads container images and connects to the tracebloc platform. Make sure your network allows traffic to `*.docker.io`, `*.tracebloc.io`, `github.com`, and `pypi.org`.

 ---

@@ -29,7 +29,7 @@

 ## 2. Register a Client

 A **client** is your workspace's identity on the platform. It ties a specific machine to your account and controls what data and use cases are accessible from it.

 Open the [client page](https://ai.tracebloc.io/clients) and click **"+"**.

@@ -93,11 +93,11 @@
 **Step 2/4 — Set up secure compute environment**
 Provisions a lightweight local Kubernetes cluster inside Docker. First run takes 1–2 minutes to download components.

 **Step 3/4 — Install tracebloc client**
 Prompts for a **workspace name** (e.g. `berlin-team`, `vision-lab`, `ml-mardan`). This identifies the client on your machine and becomes the Kubernetes namespace.

 **Step 4/4 — Connect to tracebloc network**
 Prompts for your **Client ID** and **password** from step 2 above. This links your secure local environment to the tracebloc platform so vendors can submit models for evaluation.

 When it finishes you'll see a summary like:

@@ -138,7 +138,7 @@
 | `mysql-...` | Local metadata store — tracks jobs, metrics, and configuration |
 | `tracebloc-jobs-manager-...` | The client — executes training jobs and communicates with the platform |

 Then open [ai.tracebloc.io](https://ai.tracebloc.io) and check that your client status shows **Online**. This confirms the client has established a secure connection to the tracebloc backend.

 <Tip>
 Stuck on Offline? Check the [Troubleshooting](/environment-setup/troubleshooting) page — most issues resolve with a pod restart or credential check.
@@ -150,7 +150,7 @@
 
 Your workspace is running. Here's where to go from here:
 
-**Data owners:** [Create a use case](/create-use-case/prerequisites) — select datasets, define evaluation metrics, and invite contributors to submit models.
+**Data owners:** [Ingest your dataset](/create-use-case/prepare-dataset), then [create a use case](/create-use-case/prerequisites) — pick from prepared datasets, define evaluation metrics, and invite contributors to submit models.
 
 **Data scientists:** [Join a use case](/join-use-case/join-use-case) — connect to a workspace, train models on real data, and submit results.
 

diff --git a/evals.json b/evals.json
@@ -1,4 +1,4 @@
-[
+[
  {
  "id": 67,
  "formula": "{c_index}",