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/join-use-case/start-training.mdx b/join-use-case/start-training.mdx index 5f8f3b4..72ce32d 100644 --- a/join-use-case/start-training.mdx +++ b/join-use-case/start-training.mdx @@ -43,7 +43,6 @@ python -m pip install --upgrade pip # Install with the extra that matches your framework: pip install "tracebloc[pytorch]>=0.18.1" -# pip install "tracebloc[tensorflow]>=0.18.1" # pip install "tracebloc[sklearn]>=0.18.1" # pip install "tracebloc[all]>=0.18.1" # all frameworks ``` 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. diff --git a/tools-help/tracebloc.mdx b/tools-help/tracebloc.mdx index 2729f4a..7a23a98 100644 --- a/tools-help/tracebloc.mdx +++ b/tools-help/tracebloc.mdx @@ -17,7 +17,6 @@ Pick the extra that matches your ML framework — the default install ships the ```bash pip install "tracebloc[pytorch]>=0.18.1" # most users -# pip install "tracebloc[tensorflow]>=0.18.1" # TensorFlow # pip install "tracebloc[sklearn]>=0.18.1" # scikit-learn, incl. XGBoost / CatBoost / LightGBM # pip install "tracebloc[xgboost]>=0.18.1" # one boosting backend only (or: catboost, lightgbm) # pip install "tracebloc[lifelines,scikit-survival]>=0.18.1" # survival analysis @@ -28,6 +27,10 @@ pip install "tracebloc[pytorch]>=0.18.1" # most users The umbrella `[boosting]` and `[survival]` extras were removed in 0.10.0, along with `[huggingface]` (the Hugging Face stack now ships inside `[pytorch]`). Use the per-library extras above. `pip` does not fail on an unknown extra — it prints a warning, installs the core SDK only, and exits 0, so the mistake surfaces later as an `ImportError`. + + TensorFlow is deprecated. New TensorFlow model uploads are no longer accepted — install `[pytorch]` (or `[sklearn]`) for new models. The SDK raises a `FutureWarning` for TensorFlow, and TensorFlow support is removed in 1.0.0. Existing TensorFlow experiments remain readable. + + ## Key Features - Upload model files and pretrained weights