Update

.env

@@ -4,9 +4,9 @@
 # Copy this file to .env and update with your production values
 #
 # Usage:
-#   cp .env.production.example .env
-#   nano .env  # Edit with your values
-#   docker compose -f docker-compose.production.yml up -d
+#   cp .env.production.example .env
+#   nano .env  # Edit with your values
+#   docker compose -f docker-compose.production.yml up -d
 
 # =============================================================================
 # FLASK CONFIGURATION (Required)
@@ -15,7 +15,7 @@ FLASK_ENV=production
 DEBUG=False
 
 # Generate a new secret key with:
-#   python -c 'import secrets; print(secrets.token_hex(32))'
+#   python -c 'import secrets; print(secrets.token_hex(32))'
 SECRET_KEY=87d27fd71f96abf9e24e998f0717912bd15185a7f82370202272cc2ad59123f2
 
 # =============================================================================
@@ -25,7 +25,7 @@ SECRET_KEY=87d27fd71f96abf9e24e998f0717912bd15185a7f82370202272cc2ad59123f2
 POSTGRES_HOST=192.168.5.71
 POSTGRES_PORT=5432
 POSTGRES_DB=writebot
-POSTGRES_USER=writebot_user
+POSTGRES_USER=postgres
 POSTGRES_PASSWORD=Writebot01
 
 # Alternative: Set DATABASE_URL directly (overrides above settings)
@@ -48,7 +48,8 @@ CUDA_VISIBLE_DEVICES=0
 # DOCKER BUILD ARGUMENTS
 # =============================================================================
 # These can be customized at build time
-CUDA_VERSION=12.3.1
+CUDA_VERSION=12.3.2
+CUDNN_VERSION=9
 UBUNTU_VERSION=22.04
 PYTHON_VERSION=3.11
 
@@ -83,9 +84,9 @@ SESSION_COOKIE_SAMESITE=Lax
 # The tunnel will connect to localhost:${APP_PORT}
 #
 # Setup commands:
-#   cloudflared tunnel login
-#   cloudflared tunnel create writebot
-#   cloudflared tunnel route dns writebot your-domain.com
+#   cloudflared tunnel login
+#   cloudflared tunnel create writebot
+#   cloudflared tunnel route dns writebot your-domain.com
 #
 # Tunnel token (if using token-based auth instead of cert)
 # CLOUDFLARE_TUNNEL_TOKEN=your-tunnel-token
@@ -101,11 +102,11 @@ APP_VERSION=1.0.0
 # =============================================================================
 # Default network layout (customize in proxmox-setup.sh):
 #
-# Component        | VMID | IP Address    | Purpose
+# Component        | VMID | IP Address    | Purpose
 # -----------------|------|---------------|------------------
-# Docker VM        | 100  | 10.10.10.10   | WriteBot app + GPU
-# PostgreSQL LXC   | 101  | 10.10.10.11   | Database server
-# Redis LXC        | 102  | 10.10.10.12   | Cache/rate limiting
-# Gateway          | -    | 10.10.10.1    | Network gateway
+# Docker VM        | 100  | 10.10.10.10   | WriteBot app + GPU
+# PostgreSQL LXC   | 101  | 10.10.10.11   | Database server
+# Redis LXC        | 102  | 10.10.10.12   | Cache/rate limiting
+# Gateway          | -    | 10.10.10.1    | Network gateway
 #
 # Network: vmbr0 (10.10.10.0/24)

Dockerfile.gpu

@@ -1,13 +1,13 @@
 # GPU-enabled Dockerfile for WriteBot
 # Optimized for NVIDIA RTX 50 series (Blackwell) and RTX 40/30 series GPUs
-# Uses CUDA 12.x for best performance with modern GPUs
+# Uses CUDA 13.x for RTX 50 series (Blackwell) support
 
 #=============================================================================
 # BUILD ARGUMENTS - Customize these at build time
 #=============================================================================
-ARG CUDA_VERSION=12.3.2
+ARG CUDA_VERSION=13.0.1
 ARG CUDNN_VERSION=9
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04
 ARG PYTHON_VERSION=3.11
 ARG GUNICORN_WORKERS=2
 ARG GUNICORN_THREADS=4
@@ -52,16 +52,17 @@ RUN python -m pip install --upgrade pip
 # Copy requirements and install Python dependencies
 COPY requirements.txt .
 
-# Install TensorFlow with CUDA support
-RUN pip install --no-cache-dir --user tensorflow[and-cuda]>=2.15.0 tensorflow-probability>=0.23.0
+# Install TensorFlow 2.18+ with CUDA 13.0 support for RTX 50 series (Blackwell)
+# Also install tf-keras for Keras 2 compatibility (TF 2.16+ defaults to Keras 3)
+RUN pip install --no-cache-dir --user "tensorflow[and-cuda]>=2.18.0" "tensorflow-probability>=0.25.0" "tf-keras>=2.18.0"
 
 # Install remaining dependencies
 RUN pip install --no-cache-dir --user -r requirements.txt
 
 # Production stage
-ARG CUDA_VERSION=12.3.2
+ARG CUDA_VERSION=13.0.1
 ARG CUDNN_VERSION=9
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04
 FROM nvidia/cuda:${CUDA_VERSION}-cudnn${CUDNN_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
 # Re-declare ARGs for production stage
@@ -116,6 +117,8 @@ ENV CUDA_VISIBLE_DEVICES=0
 ENV TF_FORCE_GPU_ALLOW_GROWTH=true
 # Enable TensorFloat-32 for RTX 30/40/50 series
 ENV TF_ENABLE_TF32=1
+# Use Keras 2 (tf-keras) instead of Keras 3 for TF1 compat code compatibility
+ENV TF_USE_LEGACY_KERAS=1
 ENV NVIDIA_VISIBLE_DEVICES=all
 ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
 

handwriting_synthesis/rnn/LSTMAttentionCell.py

@@ -226,7 +226,7 @@ def termination_condition(self, state):
         past_final_char = char_idx >= self.attention_values_lengths
         output = self.output_function(state)
         es = tf.cast(output[:, 2], tf.int32)
-        is_eos = tf.equal(es, tf.experimental.numpy.ones_like(es))
+        is_eos = tf.equal(es, tf.ones_like(es))
         return tf.logical_or(tf.logical_and(final_char, is_eos), past_final_char)
 
     def _parse_parameters(self, gmm_params, eps=1e-8, sigma_eps=1e-4):

handwriting_synthesis/rnn/operations.py

@@ -8,12 +8,45 @@
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope as vs
-from tensorflow.python.ops.rnn import _maybe_tensor_shape_from_tensor
-from tensorflow.python.ops.rnn_cell_impl import _concat, assert_like_rnncell
-from tensorflow.python.util import is_in_graph_mode
 from tensorflow.python.util import nest
 
 
+def _maybe_tensor_shape_from_tensor(shape):
+    """Convert tensor or TensorShape to TensorShape for compatibility."""
+    if isinstance(shape, ops.Tensor):
+        return tensor_shape.TensorShape(None)
+    return tensor_shape.TensorShape(shape)
+
+
+def _concat(prefix, suffix, static=False):
+    """Concat prefix and suffix, handling both static and dynamic shapes."""
+    if isinstance(prefix, ops.Tensor):
+        p = prefix
+    else:
+        p = tf.constant(prefix, dtype=tf.int32) if not isinstance(prefix, list) else tf.constant([prefix], dtype=tf.int32)
+    if isinstance(suffix, ops.Tensor):
+        s = suffix
+    else:
+        s = tf.constant(suffix, dtype=tf.int32) if not isinstance(suffix, list) else tf.constant(suffix, dtype=tf.int32)
+    # Ensure both are at least 1D
+    if len(p.shape) == 0:
+        p = tf.expand_dims(p, 0)
+    if len(s.shape) == 0:
+        s = tf.expand_dims(s, 0)
+    return tf.concat([p, s], axis=0)
+
+
+def assert_like_rnncell(name, cell):
+    """Check that cell behaves like an RNNCell (compatibility shim for TF 2.x)."""
+    conditions = [
+        hasattr(cell, "output_size"),
+        hasattr(cell, "state_size"),
+        hasattr(cell, "__call__") or callable(cell),
+    ]
+    if not all(conditions):
+        raise TypeError(f"{name} is not an RNNCell: {type(cell)}")
+
+
 def raw_rnn(cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=None):
     """
     Computes a recurrent neural network where inputs can be fed adaptively.
@@ -56,7 +89,8 @@ def raw_rnn(cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=No
     # determined by the parent scope, or is set to place the cached
     # Variable using the same placement as for the rest of the RNN.
     with vs.variable_scope(scope or "rnn") as varscope:
-        if is_in_graph_mode.IS_IN_GRAPH_MODE():
+        # TF 2.x compatibility: check if we're in graph mode (not eager)
+        if not tf.executing_eagerly():
             if varscope.caching_device is None:
                 varscope.set_caching_device(lambda op: op.device)