docs(frameworks): correct TensorFlow support claims (backend#2298)

join-use-case/how-training-works.mdx

@@ -7,6 +7,10 @@ This page documents the training and inference pipeline that the tracebloc clien
 
 If something here does not match what you observe in your run, please [contact us](mailto:support@tracebloc.io) so we can investigate together.
 
+<Note>
+  **TensorFlow is deprecated.** New TensorFlow model uploads are no longer accepted — use **PyTorch** for new models (scikit-learn is also supported where noted). Existing TensorFlow experiments remain readable, so the TensorFlow-specific behavior described below applies only to them.
+</Note>
+
 ## Shared lifecycle
 
 Every use case runs through the same outer loop on the edge:
@@ -63,7 +67,7 @@ To replicate a run locally, read the actual values your experiment was launched
 
 <Accordion title="Image classification" icon="image">
 
-**Frameworks:** PyTorch, TensorFlow
+**Frameworks:** PyTorch (TensorFlow is deprecated and read-only for existing experiments)
 
 **Input**
 - Image files (JPEG / PNG) supplied through the dataset metadata as `data_id` (the image filename) and `label` (the class name).
@@ -302,7 +306,7 @@ Each metric is computed independently; if one fails (for example, AUC-ROC on a s
 
 <Accordion title="Tabular classification" icon="table">
 
-**Frameworks:** PyTorch, TensorFlow, and any scikit-learn-compatible estimator (including XGBoost and LightGBM).
+**Frameworks:** PyTorch and any scikit-learn-compatible estimator (including XGBoost and LightGBM). (TensorFlow is deprecated and read-only for existing experiments.)
 
 **Input**
 - A tabular file with feature columns plus a label column. The label column name is configurable; categorical feature values can be strings.
@@ -353,7 +357,7 @@ Each metric is computed independently; if one fails, it falls back to zero rathe
 
 <Accordion title="Tabular regression" icon="chart-line">
 
-**Frameworks:** PyTorch, TensorFlow, and any scikit-learn-compatible regressor (including XGBoost and LightGBM).
+**Frameworks:** PyTorch and any scikit-learn-compatible regressor (including XGBoost and LightGBM). (TensorFlow is deprecated and read-only for existing experiments.)
 
 **Input**
 - A tabular file with feature columns plus a continuous target column. The target column name is configurable.

join-use-case/hyperparameters.mdx

@@ -3,6 +3,10 @@ title: "Hyperparameters"
 description: "Configure your model's training behavior by setting hyperparameters, training parameters, and augmentation options."
 ---
 
+<Note>
+  **TensorFlow is deprecated.** New TensorFlow model uploads are no longer accepted — use **PyTorch** for new models. The TensorFlow-specific optimizers, learning-rate schedules, loss functions, layer freezing, and augmentation flags documented below apply only to existing TensorFlow experiments, which remain readable.
+</Note>
+
 ## Training Parameters
 
 All parameters are set through the `training_plan` after linking your model with the dataset.

join-use-case/model-optimization.mdx

@@ -3,6 +3,10 @@ title: "Customize Models"
 description: "Learn the model format requirements, mandatory variables per framework and pre-trained weights for uploading and training models on the tracebloc workspace."
 ---
 
+<Note>
+  **TensorFlow is deprecated.** New TensorFlow model uploads are no longer accepted — author new models in **PyTorch** or **scikit-learn**. The TensorFlow mandatory variables and TensorFlow file-format examples below are retained only as reference for existing TensorFlow experiments, which remain readable.
+</Note>
+
 ## Use Pre-trained Weights
 
 Upload weights along with your model in the `user.upload_model()` step and set `weights=True`, the default value is False:
@@ -55,6 +59,10 @@ The file must contain these variables:
 
 ### Tensorflow mandatory variables
 
+<Note>
+  Deprecated — retained for existing TensorFlow experiments. New uploads must use PyTorch or scikit-learn.
+</Note>
+
 Each format must contain these variables on the main file:
 * **framework** : name of the framework for which this model file is created. For tensorflow its value will be tensorflow.
 * **model_type** : name of the model type for which this model file is created. Its value will be either *empty* or *rcnn* or *heatmap*.
@@ -172,6 +180,10 @@ The value of the above variables would look like this:
 
 ### 3. Single python file containing one/multiple methods for tensorflow
 
+<Note>
+  Formats 3 and 4 (TensorFlow) are deprecated and retained only for existing TensorFlow experiments. New uploads must use the PyTorch or scikit-learn formats above.
+</Note>
+
 All your model code needs to be contained in a **single python file** with the following structure:
 
 

join-use-case/overview.mdx

@@ -31,7 +31,11 @@ You work from a Jupyter notebook on your local machine or Google Colab. The note
 
 ## 4. Customize your model
 
-You can use any architecture: PyTorch, TensorFlow, or sklearn. The easiest way to get started is the [tracebloc model zoo](https://github.com/tracebloc/model-zoo), a collection of starter templates you can modify freely. Pick a template, adjust it to your needs, and upload. The only requirement is that a few mandatory variables at the top of your model file match the use case parameters.
+You can use any architecture: PyTorch or sklearn. The easiest way to get started is the [tracebloc model zoo](https://github.com/tracebloc/model-zoo), a collection of starter templates you can modify freely. Pick a template, adjust it to your needs, and upload. The only requirement is that a few mandatory variables at the top of your model file match the use case parameters.
+
+<Note>
+  TensorFlow is deprecated: new TensorFlow model uploads are no longer accepted. Use PyTorch (or sklearn) for new models. Existing TensorFlow experiments remain readable.
+</Note>
 
 
 → [Customize Models](/join-use-case/model-optimization)

overview/tracebloc.mdx

@@ -70,7 +70,7 @@ tracebloc is a **collaborative AI workspace** you deploy on your own infrastruct
     Whitelist contributors by email. Assign compute budgets. They never see raw data.
   </Step>
   <Step title="Build together">
-    Contributors submit models and train inside your environment. PyTorch, TensorFlow, custom containers.
+    Contributors submit models and train inside your environment. PyTorch, scikit-learn, custom containers.
   </Step>
   <Step title="Compare and decide">
     Every submission benchmarked under identical conditions. One leaderboard. Ship the winner.

tools-help/faqs.mdx

@@ -40,7 +40,7 @@ Through the [tracebloc dashboard](https://ai.tracebloc.io) — every experiment,
 The client retries transient failures automatically. Persistent failures show up in the dashboard with logs and exit codes. For ingestion-time failures, check the Job logs in the namespace you deployed into.
 
 ### Can I bring my own model?
-Yes. Use the [tracebloc Python package](/tools-help/tracebloc) to upload a model file (PyTorch, TensorFlow, or a custom container). For ready-made starting points, see the [model zoo](https://github.com/tracebloc/model-zoo).
+Yes. Use the [tracebloc Python package](/tools-help/tracebloc) to upload a model file (PyTorch, scikit-learn, or a custom container). For ready-made starting points, see the [model zoo](https://github.com/tracebloc/model-zoo). TensorFlow is deprecated — new TensorFlow uploads are no longer accepted, though existing TensorFlow experiments remain readable.
 
 ### Do you support fine-tuning?
 Yes — the same upload flow handles full training, fine-tuning with pretrained weights, and inference-only evaluation.