Remove deprecated presets and update versions for release 2025.2.0

docs/matrix.md

@@ -23,16 +23,10 @@
             {{ read_csv('assets/max-pvc.csv') }}
 
 === "[Preset Containers](https://github.com/intel/ai-containers/blob/main/preset/README.md)"
-    === "[Classical ML](https://hub.docker.com/r/intel/classical-ml)"
-        {{ read_csv('assets/classical_ml.csv') }}
     === "[Deep Learning Pytorch* CPU](https://hub.docker.com/r/intel/deep-learning)"
         {{ read_csv('assets/deep_learning_pytorch_cpu.csv') }}
     === "[Deep Learning PyTorch* GPU](https://hub.docker.com/r/intel/deep-learning)"
         {{ read_csv('assets/deep_learning_pytorch_gpu.csv') }}
-    === "[Deep Learning TensorFlow* CPU](https://hub.docker.com/r/intel/deep-learning)"
-        {{ read_csv('assets/deep_learning_tensorflow_cpu.csv') }}
-    === "[Deep Learning TensorFlow* GPU](https://hub.docker.com/r/intel/deep-learning)"
-        {{ read_csv('assets/deep_learning_tensorflow_gpu.csv') }}
     === "[Deep Learning JAX CPU](https://hub.docker.com/r/intel/deep-learning)"
         {{ read_csv('assets/deep_learning_jax_cpu.csv') }}
 

docs/scripts/hook.py

@@ -37,16 +37,9 @@ def create_support_matrix():
     compose_to_csv("jax", None)
 
     # get_repo(models)
-    compose_to_csv("preset/classical-ml", "classical_ml")
+    compose_to_csv("preset/deep-learning-jax-cpu", "deep_learning_jax_cpu")
     compose_to_csv("preset/deep-learning-pytorch-cpu", "deep_learning_pytorch_cpu")
     compose_to_csv("preset/deep-learning-pytorch-gpu", "deep_learning_pytorch_gpu")
-    compose_to_csv(
-        "preset/deep-learning-tensorflow-cpu", "deep_learning_tensorflow_cpu"
-    )
-    compose_to_csv(
-        "preset/deep-learning-tensorflow-gpu", "deep_learning_tensorflow_gpu"
-    )
-    compose_to_csv("preset/deep-learning-jax-cpu", "deep_learning_jax_cpu")
 
 
 def on_pre_build(*args, **kwargs):

preset/README.md

@@ -6,11 +6,8 @@ AI Tools Preset Containers provide data scientists and developers with ready-to-
 
 | Preset Container Name | Purpose | Tools | Image Name |
 | -----------------------------| ------------- | ------------- | ----------------- |
-| Classical Machine Learning | Accelerate your machine learning and data science pipelines with the power of open libraries optimized for Intel® architectures | <li>[Intel® Extension for Scikit-learn\*](https://github.com/intel/scikit-learn-intelex),<br /> <li>[Intel® Optimization for XGBoost*](https://github.com/dmlc/xgboost),<br /> <li>[Modin\*](https://github.com/modin-project/modin) | [`intel/classical-ml:latest-py3.11`](https://hub.docker.com/r/intel/classical-ml/tags) |
 | Deep Learning PyTorch\* CPU | Boost the performance of your workloads, reduce model size, and improve the speed of your Deep Learning deployments on Intel® Xeon® processors with Intel® Extension for PyTorch* | <li>[Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), <br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor),<br /> <li>[ONNX Runtime\*](https://github.com/microsoft/onnxruntime) | [`intel/deep-learning:pytorch-latest-py3.11`](https://hub.docker.com/r/intel/deep-learning/tags) |
 | Deep Learning PyTorch\* GPU | Boost the performance of your workloads, reduce model size, and improve the speed of your Deep Learning deployments on Intel® Data Center GPU Max Series with Intel® Extension for PyTorch\* | <li>[Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch), <br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor), <br /> <li>[DeepSpeed\*](https://github.com/microsoft/DeepSpeed)  | [`intel/deep-learning:pytorch-gpu-latest-py3.11`](https://hub.docker.com/r/intel/deep-learning/tags) |
-| Deep Learning TensorFlow\* CPU| Boost the performance of your workloads, reduce model size, and improve the speed of your Deep Learning deployments on Intel® Xeon® processors with Intel® Extension for TensorFlow\* | <li>[Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor),<br /> <li>[ONNX Runtime*](https://github.com/microsoft/onnxruntime) | [`intel/deep-learning:tensorflow-latest-py3.11`](https://hub.docker.com/r/intel/deep-learning/tags) |
-| Deep Learning TensorFlow\* GPU| Boost the performance of your workloads, reduce model size, and improve the speed of your Deep Learning deployments on Intel® Data Center GPU Max Series with Intel® Extension for TensorFlow\* | <li>[Intel® Extension for Tensorflow](https://github.com/intel/intel-extension-for-tensorflow),<br /> <li>[Intel® Optimization for Horovod\*](https://github.com/intel/intel-optimization-for-horovod),<br /> <li>[Intel® Neural Compressor](https://github.com/intel/neural-compressor) | [`intel/deep-learning:tensorflow-gpu-latest-py3.11`](https://hub.docker.com/r/intel/deep-learning/tags) |
 | Deep Learning JAX\* CPU| Reduce model size and improve the speed of your Deep Learning deployments on Intel® Xeon® processors with JAX\* | <li>[JAX\*](https://github.com/jax-ml/jax) | [`intel/deep-learning:jax-latest-py3.11`](https://hub.docker.com/r/intel/deep-learning/tags) |
 
 ## Prerequisites
@@ -29,7 +26,7 @@ There are three modes to run these containers:
 
 * [Jupyter Notebook](#run-using-jupyter-notebook)
 * [Interactive](#run-in-interactive-mode)
-* [Multi-Node Distributed Training](#run-in-multi-node-distributed-mode-advanced) (Deep Learning PyTorch GPU Deep Learning TensorFlow GPU)
+* [Multi-Node Distributed Training](#run-in-multi-node-distributed-mode-advanced) (Deep Learning PyTorch GPU)
 
 > [!NOTE]
 > Modify the commands below to fit your use case, including the image, environment variables, and GPU device path.
@@ -51,7 +48,7 @@ docker run -it --rm \
 ```
 
 > [!NOTE]
-> Some applications use shared memory to share data between processes. The default shared memory segment size for Docker containers is 64MB, which may be insufficient for multithreaded applications (for example, Modin*). Increase the shared memory size using `--shm-size`.
+> Some applications use shared memory to share data between processes. The default shared memory segment size for Docker containers is 64MB, which may be insufficient for multithreaded applications. Increase the shared memory size using `--shm-size`.
 
 #### Run on Jupyter GPU
 
@@ -83,7 +80,7 @@ docker run -it --rm \
 
 2. Select a notebook sample from the Overview notebook found in directory where you launched the server. In this example, the `intel/deep-learning:pytorch-latest-py3.11` container has a notebook titled [`Deep_Learning_Samples_Overview.ipynb`](https://github.com/intel/ai-containers/blob/main/preset/deep-learning-pytorch/notebooks/Deep_Learning_Samples_Overview.ipynb) when launched in Jupyter mode.
 
-3. After selecting a notebook sample, choose the appropriate kernel from the dropdown menu available when loading the notebook. For each container, there is a kernel with the environment name: `classical-ml`, `jax-cpu`, `pytorch-cpu`, `pytorch-gpu`, `tensorflow-cpu`, and  `tensorflow-gpu`.
+3. After selecting a notebook sample, choose the appropriate kernel from the dropdown menu available when loading the notebook. For each container, there is a kernel with the environment name: `jax-cpu`, `pytorch-cpu` and `pytorch-gpu`.
 
 ##### Advanced Jupyter Server Configuration
 
@@ -130,15 +127,15 @@ docker run -it --rm \
     -p 8888:8888 --shm-size=12G \
     -v ${PWD}:/home/dev/workdir \
     -v /dev/dri/by-path:/dev/dri/by-path \
-    intel/deep-learning:pytorch-latest-py3.11 bash
+    intel/deep-learning:pytorch-gpu-latest-py3.11 bash
 ```
 
 > [!NOTE]
-> Some applications use shared memory to share data between processes. The default shared memory segment size for Docker containers is 64MB, which may be insufficient for multithreaded applications (for example, Modin\*). Increase the shared memory size using `--shm-size`.
+> Some applications use shared memory to share data between processes. The default shared memory segment size for Docker containers is 64MB, which may be insufficient for multithreaded applications. Increase the shared memory size using `--shm-size`.
 
 #### Next Steps
 
-1. For each container, there is a kernel with the environment name: `classical-ml`, `jax`, `pytorch-cpu`, `pytorch-gpu`, `tensorflow-cpu`, and  `tensorflow-gpu`. Use the command below to activate the `pytorch-cpu` environment in the Deep Learning PyTorch\* CPU preset:
+1. For each container, there is a kernel with the environment name: `jax`, `pytorch-cpu` and `pytorch-gpu`. Use the command below to activate the `pytorch-cpu` environment in the Deep Learning PyTorch\* CPU preset:
 
     ```bash
     conda activate pytorch-cpu
@@ -160,7 +157,7 @@ docker run -it --rm \
 
 ## Run in Multi-Node Distributed Mode [Advanced]
 
-Follow the instructions provided for [Intel® Extension for TensorFlow\*](https://github.com/intel/ai-containers/tree/main/preset/deep-learning/demo/tensorflow-distributed/README.md) or [Intel® Extension for PyTorch\*](https://github.com/intel/ai-containers/tree/main/preset/deep-learning/demo/pytorch-distributed/README.md) using the Deep Learning TensorFlow\* GPU or Deep Learning PyTorch\* GPU presets, respectively.
+Follow the instructions provided for [Intel® Extension for PyTorch\*](https://github.com/intel/ai-containers/tree/main/preset/deep-learning/demo/pytorch-distributed/README.md) using the Deep Learning PyTorch\* GPU presets, respectively.
 
 ## Troubleshooting and Support