Added: Tensorlfow and PyTorch with separate dockerfiles and builds

Author: animikhaich

.dockerignore

@@ -0,0 +1,2 @@
+.venv
+data

.github/workflows/docker-build-push.yml

@@ -88,29 +88,3 @@ jobs:
                   labels: ${{ steps.meta-pytorch.outputs.labels }}
                   cache-from: type=gha
                   cache-to: type=gha,mode=max
-
-            # Both image
-            - name: Extract metadata (both)
-              id: meta-both
-              uses: docker/metadata-action@v5
-              with:
-                  images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}-both
-                  tags: |
-                      type=ref,event=branch
-                      type=ref,event=pr
-                      type=semver,pattern={{version}}
-                      type=semver,pattern={{major}}.{{minor}}
-                      type=semver,pattern={{major}}
-                      type=sha,prefix={{branch}}-
-                      type=raw,value=latest,enable={{is_default_branch}}
-
-            - name: Build and push Both image
-              uses: docker/build-push-action@v5
-              with:
-                  context: .
-                  file: Dockerfile.both
-                  push: ${{ github.event_name != 'pull_request' }}
-                  tags: ${{ steps.meta-both.outputs.tags }}
-                  labels: ${{ steps.meta-both.outputs.labels }}
-                  cache-from: type=gha
-                  cache-to: type=gha,mode=max

.gitignore

@@ -143,4 +143,7 @@ dmypy.json
 model/
 logs*
 old_data/
-test*
+test*
+
+
+data

Dockerfile

@@ -1,11 +1,9 @@
-FROM nvidia/cuda:12.4.0-cudnn-runtime-ubuntu22.04
+FROM astral/uv:python3.12-bookworm-slim
 
 ARG DEBIAN_FRONTEND=noninteractive
 
 # Install Python and necessary tools
 RUN apt-get update && apt-get install -y \
-    python3.10 \
-    python3-pip \
     parallel \
     && rm -rf /var/lib/apt/lists/*
 
@@ -14,9 +12,10 @@ RUN mkdir /app
 WORKDIR /app
 
 # Copy requirements and install dependencies
-RUN pip install uv
-COPY ./requirements-both.txt /app
-RUN uv pip install --no-cache-dir -r requirements-both.txt --system
+COPY ./requirements-tensorflow.txt /app
+COPY ./requirements-pytorch.txt /app
+RUN uv pip install --no-cache-dir -r requirements-tensorflow.txt --system
+RUN uv pip install --no-cache-dir -r requirements-pytorch.txt --system
 
 # Copy application files
 COPY . /app

Dockerfile.both

@@ -1,4 +1,4 @@
-FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
+FROM astral/uv:python3.12-bookworm-slim
 
 ARG DEBIAN_FRONTEND=noninteractive
 
@@ -9,7 +9,6 @@ RUN mkdir /app
 WORKDIR /app
 COPY ./requirements-pytorch.txt /app
 
-RUN pip install uv
 RUN uv pip install -r requirements-pytorch.txt --system
 COPY . /app
 

Dockerfile.pytorch

@@ -1,4 +1,4 @@
-FROM tensorflow/tensorflow:2.18.0-gpu
+FROM astral/uv:python3.12-bookworm-slim
 
 ARG DEBIAN_FRONTEND=noninteractive
 
@@ -9,7 +9,6 @@ RUN mkdir /app
 WORKDIR /app
 COPY ./requirements-tensorflow.txt /app
 
-RUN pip install uv
 RUN uv pip install -r requirements-tensorflow.txt --system
 COPY . /app
 

Dockerfile.tensorflow

@@ -82,7 +82,7 @@ Select from:
 ### PyTorch
 Enable/disable using the checkbox:
 - Unchecked: Full Precision (FP32)
-- Checked: Automatic Mixed Precision (AMP) using torch.cuda.amp
+- Checked: Automatic Mixed Precision (AMP) using torch.amp
 
 ## Docker Images
 

FRAMEWORK_GUIDE.md

@@ -150,6 +150,46 @@ The above is just used for development and by no means is necessary to run this
         └── *.jpg
 ```
 
+### Using Preset Datasets (quick start)
+
+If you don't have your own dataset ready, the toolkit supports downloading common image classification datasets (CIFAR10, CIFAR100, MNIST, FashionMNIST, STL10) and preparing them in the required folder-per-class layout.
+
+Example (Streamlit UI progress integration):
+
+```python
+import streamlit as st
+from core.data_loader_pytorch import ImageClassificationDataLoaderPyTorch
+from utils.add_ons_pytorch import make_streamlit_progress_callback
+
+st.title('Preset Dataset Download')
+cb = make_streamlit_progress_callback(prefix='Downloading dataset')
+# This will download CIFAR10 into ./data/CIFAR10 (if not present) and show progress in Streamlit
+dl = ImageClassificationDataLoaderPyTorch(
+  data_dir='./data/CIFAR10',
+  image_dims=(224,224),
+  preset_name='CIFAR10',
+  preset_target_dir='./data/CIFAR10',
+  progress_callback=cb,
+)
+
+st.write('Dataset ready at:', dl.data_dir)
+```
+
+Or use from Python (no Streamlit callback):
+
+```python
+from core.data_loader_pytorch import ImageClassificationDataLoaderPyTorch
+
+# download into ./data/MNIST and prepare folder layout automatically
+dl = ImageClassificationDataLoaderPyTorch(
+  data_dir='./data/MNIST',
+  preset_name='MNIST',
+  preset_target_dir='./data/MNIST',
+)
+
+dataloader, dataset = dl.create_dataloader(batch_size=32, augment=False)
+```
+
 4. **Choose your Docker image** based on your needs:
 
    **Option A: Pull from Docker Hub (when available)**

README.md

@@ -24,6 +24,4 @@ echo "All images built successfully!"
 echo ""
 echo "Available images:"
 echo "  - ghcr.io/animikhaich/zero-code-classifier:tensorflow (TensorFlow only)"
-echo "  - ghcr.io/animikhaich/zero-code-classifier:pytorch (PyTorch only)"
-echo "  - ghcr.io/animikhaich/zero-code-classifier:both (TensorFlow + PyTorch)"
-echo "  - ghcr.io/animikhaich/zero-code-classifier:latest (same as tensorflow)"
+echo "  - ghcr.io/animikhaich/zero-code-classifier:pytorch (PyTorch only)"