diff --git a/join-use-case/how-training-works.mdx b/join-use-case/how-training-works.mdx
index c233b40..8a67f2c 100644
--- a/join-use-case/how-training-works.mdx
+++ b/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.
+
+ **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.
+
+
## 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
-**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
-**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
-**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.
diff --git a/join-use-case/hyperparameters.mdx b/join-use-case/hyperparameters.mdx
index 43c472a..56518ca 100644
--- a/join-use-case/hyperparameters.mdx
+++ b/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."
---
+
+ **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.
+
+
## Training Parameters
All parameters are set through the `training_plan` after linking your model with the dataset.
diff --git a/join-use-case/model-optimization.mdx b/join-use-case/model-optimization.mdx
index 56ecde7..c05cccd 100644
--- a/join-use-case/model-optimization.mdx
+++ b/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."
---
+
+ **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.
+
+
## 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
+
+ Deprecated — retained for existing TensorFlow experiments. New uploads must use PyTorch or scikit-learn.
+
+
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
+
+ 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.
+
+
All your model code needs to be contained in a **single python file** with the following structure:
diff --git a/join-use-case/overview.mdx b/join-use-case/overview.mdx
index cae5a74..b56b8a8 100644
--- a/join-use-case/overview.mdx
+++ b/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.
+
+
+ TensorFlow is deprecated: new TensorFlow model uploads are no longer accepted. Use PyTorch (or sklearn) for new models. Existing TensorFlow experiments remain readable.
+
→ [Customize Models](/join-use-case/model-optimization)
diff --git a/overview/tracebloc.mdx b/overview/tracebloc.mdx
index 374aef2..b176c3b 100644
--- a/overview/tracebloc.mdx
+++ b/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.
- 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.
Every submission benchmarked under identical conditions. One leaderboard. Ship the winner.
diff --git a/tools-help/faqs.mdx b/tools-help/faqs.mdx
index 2a0f847..24a35db 100644
--- a/tools-help/faqs.mdx
+++ b/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.