diff --git a/frameworks/tensorflow/2.18.1/Dockerfile b/frameworks/tensorflow/2.18.1/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..2e9abf9f57f54981f56e3a2e4ba1cf12a59ad9a2
--- /dev/null
+++ b/frameworks/tensorflow/2.18.1/Dockerfile
@@ -0,0 +1,19 @@
+
+FROM opencloudos/opencloudos9-cuda-devel:12.8
+
+LABEL maintainer="stronking 363133710@qq.com"
+LABEL org.opencontainers.image.source="https://gitee.com/OpenCloudOS/ai-agent-container"
+LABEL org.opencontainers.image.description="TensorFlow 2.18.1 (GPU) on OpenCloudOS 9"
+
+ENV NVIDIA_VISIBLE_DEVICES=all \
+    PYTHONUNBUFFERED=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PIP_DEFAULT_TIMEOUT=120
+
+
+WORKDIR /home
+
+RUN --mount=type=cache,id=pip-cache-opencloudos9-cu128,target=/root/.cache/pip \
+    pip install "tensorflow[and-cuda]==2.18.1"
+
+CMD ["python3"]
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.18.1/README.md b/frameworks/tensorflow/2.18.1/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2c8ceffd85879e39a90fd324bda5ab5cfd5ec89b
--- /dev/null
+++ b/frameworks/tensorflow/2.18.1/README.md
@@ -0,0 +1,31 @@
+# tensorflow 2.18.1 on OpenCloudOS 9
+
+## 基本信息
+- **框架版本**：v2.18.1
+- **基础镜像**：opencloudos9-cuda-devel:12.8
+- **Python 版本**：3.11
+- **CUDA 版本**： 12.x 或 更高
+
+## 构建
+
+docker build -t oc9-tensorflow:2.18.1 .
+
+## 镜像启动命令
+
+docker run -d --gpus all --name oc9-tensorflow oc9-tensorflow:2.18.1
+
+## 镜像测试命令
+
+docker run --rm --gpus all oc9-tensorflow:2.18.1 bash test.sh
+docker run --rm --gpus all oc9-tensorflow:2.18.1 python tensor_smoke_test.py
+
+## 已知问题
+```
+对于如下日志输出，属于正常现象。这些日志通常不是致命错误。
+核心原因是：TensorFlow 在 import tensorflow as tf 时，会初始化 CUDA/XLA 相关组件，并注册 cuFFT、cuDNN、cuBLAS 等 GPU 插件工厂。
+日志里的意思是：这些插件工厂已经注册过一次，现在又尝试注册，所以打印了“already been registered”。
+TensorFlow 官方 GitHub 上有多个相同现象的 issue，日志内容和图片中的告警日志基本一致，
+包括 Unable to register cuFFT factory、cuDNN factory、cuBLAS factory、computation placer already registered 等
+```
+如下图所示告警日志：
+![告警日志](./warring_log.png)
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.18.1/build.conf b/frameworks/tensorflow/2.18.1/build.conf
new file mode 100644
index 0000000000000000000000000000000000000000..b1f5736581ab05c9c6b15b8d9442e063afe25d8a
--- /dev/null
+++ b/frameworks/tensorflow/2.18.1/build.conf
@@ -0,0 +1,4 @@
+# tensorflow 2.18.1 on OpenCloudOS 9 (GPU)
+IMAGE_NAME=oc9-tensorflow
+IMAGE_TAG=2.18.1
+GPU_TEST=false
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.18.1/tensor_smoke_test.py b/frameworks/tensorflow/2.18.1/tensor_smoke_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..02cec134116518270c21bcf37daf410a619ca0a1
--- /dev/null
+++ b/frameworks/tensorflow/2.18.1/tensor_smoke_test.py
@@ -0,0 +1,52 @@
+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import numpy as np
+import tensorflow as tf
+
+
+def main():
+    print("TensorFlow version:", tf.__version__)
+
+    gpus = tf.config.list_physical_devices("GPU")
+    print("GPUs:", gpus)
+
+    # 基础张量计算
+    a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+    b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
+    c = tf.matmul(a, b)
+
+    print("matmul result:")
+    print(c.numpy())
+
+    expected = tf.constant([[19.0, 22.0], [43.0, 50.0]])
+    tf.debugging.assert_near(c, expected)
+
+    # 简单 Keras 模型
+    x = np.array([[0.0], [1.0], [2.0], [3.0]], dtype=np.float32)
+    y = np.array([[1.0], [3.0], [5.0], [7.0]], dtype=np.float32)
+
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=(1,)),
+        tf.keras.layers.Dense(1),
+    ])
+
+    model.compile(
+        optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
+        loss="mse",
+    )
+
+    history = model.fit(x, y, epochs=3, verbose=0)
+
+    pred = model.predict(np.array([[4.0]], dtype=np.float32), verbose=0)
+
+    assert pred.shape == (1, 1)
+    assert np.isfinite(pred).all()
+
+    print("final loss:", float(history.history["loss"][-1]))
+    print("prediction:", float(pred[0][0]))
+    print("TensorFlow smoke test passed")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.18.1/test.sh b/frameworks/tensorflow/2.18.1/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c2fed93f16a25af247974d29f8d80998f0fb4dcc
--- /dev/null
+++ b/frameworks/tensorflow/2.18.1/test.sh
@@ -0,0 +1,336 @@
+#!/usr/bin/env bash
+# 在容器外执行：验证指定 Docker 镜像中的 TensorFlow / Keras / CUDA 基础功能。
+#
+# 用法：
+#   bash test.sh <image:tag> [额外 docker run 参数...]
+#
+# 示例：
+#   bash test.sh my-tensorflow:latest
+#   bash test.sh my-tensorflow:latest --ipc=host
+#   REQUIRE_CUDA=0 bash test.sh tensorflow-cpu:test
+#   EXPECTED_TF_VERSION_PREFIX=2.19 bash test.sh my-tensorflow:latest
+
+set -Eeuo pipefail
+
+IMAGE="${1:-}"
+
+if [[ -z "${IMAGE}" || "${IMAGE}" == "-h" || "${IMAGE}" == "--help" ]]; then
+  cat <<'USAGE'
+用法:
+  bash test.sh <image:tag> [额外 docker run 参数...]
+
+环境变量:
+  REQUIRE_CUDA=1|0               是否强制要求 CUDA/GPU 可用，默认 1
+  GPUS=all                       传给 docker run --gpus 的值，默认 all
+  PYTHON_BIN=python3             容器内 Python 命令，默认 python3
+  DOCKER_NETWORK=none            docker 网络模式，默认 none
+  TIMEOUT_SECONDS=180            整体测试超时时间，默认 180 秒
+  EXPECTED_TF_VERSION_PREFIX=2.19 可选，检查 TensorFlow 版本前缀
+
+示例:
+  bash test.sh registry.example.com/ai/tensorflow:2.19-cu12
+  REQUIRE_CUDA=0 bash test.sh tensorflow-cpu:test
+  EXPECTED_TF_VERSION_PREFIX=2.19 bash test.sh my-image:tag
+  bash test.sh my-image:tag --ipc=host --shm-size=2g
+USAGE
+  exit 1
+fi
+
+shift || true
+
+REQUIRE_CUDA="${REQUIRE_CUDA:-1}"
+GPUS="${GPUS:-all}"
+PYTHON_BIN="${PYTHON_BIN:-python3}"
+DOCKER_NETWORK="${DOCKER_NETWORK:-none}"
+TIMEOUT_SECONDS="${TIMEOUT_SECONDS:-180}"
+EXPECTED_TF_VERSION_PREFIX="${EXPECTED_TF_VERSION_PREFIX:-}"
+EXTRA_DOCKER_ARGS=("$@")
+
+log()  { printf '\033[1;34m%s\033[0m\n' "$*"; }
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+
+[[ "${REQUIRE_CUDA}" =~ ^[01]$ ]] || fail "REQUIRE_CUDA 只能是 1 或 0，当前值: ${REQUIRE_CUDA}"
+
+command -v docker >/dev/null 2>&1 || fail "未找到 docker 命令"
+docker info >/dev/null 2>&1 || fail "docker daemon 不可用，请确认 Docker 服务已启动且当前用户有权限访问"
+
+if ! docker image inspect "${IMAGE}" >/dev/null 2>&1; then
+  warn "本地未找到镜像 ${IMAGE}；docker run 可能会尝试拉取镜像"
+fi
+
+DOCKER_ARGS=(run --rm -i)
+
+if [[ "${REQUIRE_CUDA}" == "1" ]]; then
+  DOCKER_ARGS+=(--gpus "${GPUS}")
+fi
+
+DOCKER_ARGS+=(
+  -e "REQUIRE_CUDA=${REQUIRE_CUDA}"
+  -e "PYTHON_BIN=${PYTHON_BIN}"
+  -e "EXPECTED_TF_VERSION_PREFIX=${EXPECTED_TF_VERSION_PREFIX}"
+  -e "TF_CPP_MIN_LOG_LEVEL=2"
+)
+
+DOCKER_ARGS+=("${EXTRA_DOCKER_ARGS[@]}")
+DOCKER_ARGS+=("${IMAGE}" /bin/bash -s)
+
+log "=== TensorFlow Docker 镜像功能测试 ==="
+printf '镜像: %s\n' "${IMAGE}"
+printf 'CUDA 强制检查: %s\n' "${REQUIRE_CUDA}"
+printf 'Docker 网络: %s\n' "${DOCKER_NETWORK}"
+printf 'Python 命令: %s\n' "${PYTHON_BIN}"
+
+if [[ -n "${EXPECTED_TF_VERSION_PREFIX}" ]]; then
+  printf '期望 TensorFlow 版本前缀: %s\n' "${EXPECTED_TF_VERSION_PREFIX}"
+fi
+
+if ((${#EXTRA_DOCKER_ARGS[@]} > 0)); then
+  printf '额外 docker 参数: %s\n' "${EXTRA_DOCKER_ARGS[*]}"
+fi
+
+RUN_CMD=(docker "${DOCKER_ARGS[@]}")
+echo "##############################################"
+echo "${RUN_CMD} ${DOCKER_ARGS[@]}"
+echo "##############################################"
+
+if command -v timeout >/dev/null 2>&1; then
+  RUN_CMD=(timeout --preserve-status "${TIMEOUT_SECONDS}s" "${RUN_CMD[@]}")
+fi
+
+"${RUN_CMD[@]}" <<'IN_CONTAINER'
+set -Eeuo pipefail
+
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+section() { printf '\n\033[1;34m=== %s ===\033[0m\n' "$*"; }
+
+PY="${PYTHON_BIN:-python3}"
+
+if [[ -d /usr/local/cuda ]]; then
+  export CUDA_HOME="${CUDA_HOME:-/usr/local/cuda}"
+  export PATH="${CUDA_HOME}/bin:${PATH}"
+  export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH:-}"
+fi
+
+export TF_CPP_MIN_LOG_LEVEL="${TF_CPP_MIN_LOG_LEVEL:-2}"
+
+section "1. Python 基础检查"
+
+command -v "${PY}" >/dev/null 2>&1 || fail "容器内未找到 Python 命令: ${PY}"
+"${PY}" --version
+ok "Python 可用"
+
+section "2. TensorFlow 导入与版本检查"
+
+"${PY}" - <<'PY'
+import os
+import platform
+import sys
+
+import tensorflow as tf
+
+print("python_executable:", sys.executable)
+print("platform:", platform.platform())
+print("tensorflow_version:", tf.__version__)
+
+expected = os.environ.get("EXPECTED_TF_VERSION_PREFIX", "").strip()
+if expected:
+    assert tf.__version__.startswith(expected), (
+        f"期望 TensorFlow 版本前缀 {expected}，实际版本 {tf.__version__}"
+    )
+    print("expected_version_prefix:", expected)
+PY
+
+ok "TensorFlow import 正常"
+
+section "3. TensorFlow 构建信息检查"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+print("tf_version:", tf.__version__)
+
+try:
+    build_info = tf.sysconfig.get_build_info()
+    print("build_info:")
+    for k in sorted(build_info.keys()):
+        print(f"  {k}: {build_info[k]}")
+except Exception as e:
+    print("无法读取 tf.sysconfig.get_build_info():", repr(e))
+
+try:
+    print("is_built_with_cuda:", tf.test.is_built_with_cuda())
+except Exception as e:
+    print("无法读取 tf.test.is_built_with_cuda():", repr(e))
+PY
+
+ok "TensorFlow 构建信息读取完成"
+
+section "4. CUDA / GPU 环境检查"
+
+if [[ "${REQUIRE_CUDA:-1}" == "1" ]]; then
+  command -v nvidia-smi >/dev/null 2>&1 || fail "nvidia-smi 不可用；请检查宿主机 NVIDIA 驱动、nvidia-container-toolkit、docker run --gpus 参数"
+
+  echo "nvidia-smi 摘要:"
+  nvidia-smi --query-gpu=name,driver_version,memory.total --format=csv,noheader || fail "nvidia-smi 执行失败"
+
+  "${PY}" - <<'PY'
+import tensorflow as tf
+
+gpus = tf.config.list_physical_devices("GPU")
+logical_gpus = tf.config.list_logical_devices("GPU")
+
+print("physical_gpus:", gpus)
+print("logical_gpus:", logical_gpus)
+
+assert len(gpus) > 0, "TensorFlow 未检测到物理 GPU"
+assert len(logical_gpus) > 0, "TensorFlow 未检测到逻辑 GPU"
+
+for gpu in gpus:
+    try:
+        tf.config.experimental.set_memory_growth(gpu, True)
+        print(f"memory_growth_enabled: {gpu}")
+    except Exception as e:
+        print(f"memory_growth_skip: {gpu}, reason={e!r}")
+PY
+
+  ok "TensorFlow GPU 识别正常"
+
+  section "5. TensorFlow GPU 实际计算检查"
+
+  "${PY}" - <<'PY'
+import tensorflow as tf
+
+with tf.device("/GPU:0"):
+    a = tf.random.normal((1024, 1024))
+    b = tf.random.normal((1024, 1024))
+    c = tf.matmul(a, b)
+
+result = c.numpy()
+
+assert result.shape == (1024, 1024)
+assert not tf.math.reduce_any(tf.math.is_nan(c)).numpy(), "GPU 计算结果包含 NaN"
+
+print("gpu_matmul_shape:", result.shape)
+print("gpu_matmul_mean:", float(tf.reduce_mean(c).numpy()))
+PY
+
+  ok "TensorFlow GPU 计算正常"
+
+  if command -v nvcc >/dev/null 2>&1; then
+    echo "nvcc 版本:"
+    nvcc --version | sed -n '1,5p'
+  else
+    warn "未检测到 nvcc：运行时镜像通常不需要 nvcc，只有编译 CUDA 扩展时才需要"
+  fi
+else
+  warn "REQUIRE_CUDA=0，跳过强制 CUDA / GPU 检查"
+fi
+
+section "6. TensorFlow CPU 基础张量计算"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
+
+c = tf.matmul(a, b)
+
+expected = tf.constant([[19.0, 22.0], [43.0, 50.0]])
+tf.debugging.assert_near(c, expected)
+
+print("matmul_result:")
+print(c.numpy())
+PY
+
+ok "TensorFlow 基础张量计算正常"
+
+section "7. Keras 模型构建、训练、推理检查"
+
+"${PY}" - <<'PY'
+import os
+import tempfile
+
+import numpy as np
+import tensorflow as tf
+
+np.random.seed(123)
+tf.random.set_seed(123)
+
+# 构造简单训练数据：y = 2x + 1
+x = np.array([[0.0], [1.0], [2.0], [3.0], [4.0], [5.0]], dtype=np.float32)
+y = np.array([[1.0], [3.0], [5.0], [7.0], [9.0], [11.0]], dtype=np.float32)
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape=(1,)),
+    tf.keras.layers.Dense(8, activation="relu"),
+    tf.keras.layers.Dense(1),
+])
+
+model.compile(
+    optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
+    loss="mse",
+)
+
+history = model.fit(x, y, epochs=5, batch_size=2, verbose=0)
+
+pred = model.predict(np.array([[6.0]], dtype=np.float32), verbose=0)
+
+assert pred.shape == (1, 1), f"推理结果 shape 异常：{pred.shape}"
+assert np.isfinite(pred).all(), "推理结果包含 NaN 或 Inf"
+
+print("final_loss:", float(history.history["loss"][-1]))
+print("prediction_shape:", pred.shape)
+print("prediction_value:", float(pred[0][0]))
+
+with tempfile.TemporaryDirectory() as tmp_dir:
+    keras_path = os.path.join(tmp_dir, "tiny_model.keras")
+    model.save(keras_path)
+
+    loaded_model = tf.keras.models.load_model(keras_path)
+    loaded_pred = loaded_model.predict(np.array([[6.0]], dtype=np.float32), verbose=0)
+
+    assert loaded_pred.shape == (1, 1), f"加载模型推理 shape 异常：{loaded_pred.shape}"
+    assert np.isfinite(loaded_pred).all(), "加载模型推理结果包含 NaN 或 Inf"
+
+    print("keras_save_load: ok")
+PY
+
+ok "Keras 模型训练、推理、保存、加载正常"
+
+section "8. tf.data 数据管道检查"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+dataset = tf.data.Dataset.from_tensor_slices(
+    (
+        tf.constant([[1.0], [2.0], [3.0], [4.0]]),
+        tf.constant([[2.0], [4.0], [6.0], [8.0]]),
+    )
+)
+
+dataset = dataset.batch(2).prefetch(tf.data.AUTOTUNE)
+
+batch_count = 0
+for xb, yb in dataset:
+    assert xb.shape[0] <= 2
+    assert yb.shape[0] <= 2
+    batch_count += 1
+
+assert batch_count == 2, f"batch 数量异常: {batch_count}"
+
+print("tf_data_batch_count:", batch_count)
+PY
+
+ok "tf.data 数据管道正常"
+
+section "测试结果"
+ok "所有 TensorFlow 检查通过"
+IN_CONTAINER
+
+ok "宿主机侧 docker run 验证完成"
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.18.1/warring_log.png b/frameworks/tensorflow/2.18.1/warring_log.png
new file mode 100644
index 0000000000000000000000000000000000000000..1461c0cedad8d45c8d32554de19c341ba88d37bc
Binary files /dev/null and b/frameworks/tensorflow/2.18.1/warring_log.png differ
diff --git a/frameworks/tensorflow/2.19.0/Dockerfile b/frameworks/tensorflow/2.19.0/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..4e44e6d6fbac6897481074fd86c5caa7291f83fd
--- /dev/null
+++ b/frameworks/tensorflow/2.19.0/Dockerfile
@@ -0,0 +1,19 @@
+
+FROM opencloudos/opencloudos9-cuda-devel:12.8
+
+LABEL maintainer="stronking 363133710@qq.com"
+LABEL org.opencontainers.image.source="https://gitee.com/OpenCloudOS/ai-agent-container"
+LABEL org.opencontainers.image.description="TensorFlow 2.19.0 (GPU) on OpenCloudOS 9"
+
+ENV NVIDIA_VISIBLE_DEVICES=all \
+    PYTHONUNBUFFERED=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PIP_DEFAULT_TIMEOUT=120
+
+
+WORKDIR /home
+
+RUN --mount=type=cache,id=pip-cache-opencloudos9-cu128,target=/root/.cache/pip \
+    pip install "tensorflow[and-cuda]==2.19.0"
+
+CMD ["python3"]
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.19.0/README.md b/frameworks/tensorflow/2.19.0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b2cb9370da93241d386b2c14cc4d3c4ac2798946
--- /dev/null
+++ b/frameworks/tensorflow/2.19.0/README.md
@@ -0,0 +1,31 @@
+# tensorflow 2.19.0 on OpenCloudOS 9
+
+## 基本信息
+- **框架版本**：v2.19.0
+- **基础镜像**：opencloudos9-cuda-devel:12.8
+- **Python 版本**：3.11
+- **CUDA 版本**： 12.x 或 更高
+
+## 构建
+
+docker build -t oc9-tensorflow:2.19.0 .
+
+## 镜像启动命令
+
+docker run -d --gpus all --name oc9-tensorflow oc9-tensorflow:2.19.0
+
+## 镜像测试命令
+
+docker run --rm --gpus all oc9-tensorflow:2.19.0 bash test.sh
+docker run --rm --gpus all oc9-tensorflow:2.19.0 python tensor_smoke_test.py
+
+## 已知问题
+```
+对于如下日志输出，属于正常现象。这些日志通常不是致命错误。
+核心原因是：TensorFlow 在 import tensorflow as tf 时，会初始化 CUDA/XLA 相关组件，并注册 cuFFT、cuDNN、cuBLAS 等 GPU 插件工厂。
+日志里的意思是：这些插件工厂已经注册过一次，现在又尝试注册，所以打印了“already been registered”。
+TensorFlow 官方 GitHub 上有多个相同现象的 issue，日志内容和图片中的告警日志基本一致，
+包括 Unable to register cuFFT factory、cuDNN factory、cuBLAS factory、computation placer already registered 等
+```
+如下图所示告警日志：
+![告警日志](./warring_log.png)
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.19.0/build.conf b/frameworks/tensorflow/2.19.0/build.conf
new file mode 100644
index 0000000000000000000000000000000000000000..e0a7020614dd0eaf103411ab1c481950f6592219
--- /dev/null
+++ b/frameworks/tensorflow/2.19.0/build.conf
@@ -0,0 +1,4 @@
+# tensorflow 2.19.0 on OpenCloudOS 9 (GPU)
+IMAGE_NAME=oc9-tensorflow
+IMAGE_TAG=2.19.0
+GPU_TEST=false
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.19.0/tensor_smoke_test.py b/frameworks/tensorflow/2.19.0/tensor_smoke_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..02cec134116518270c21bcf37daf410a619ca0a1
--- /dev/null
+++ b/frameworks/tensorflow/2.19.0/tensor_smoke_test.py
@@ -0,0 +1,52 @@
+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import numpy as np
+import tensorflow as tf
+
+
+def main():
+    print("TensorFlow version:", tf.__version__)
+
+    gpus = tf.config.list_physical_devices("GPU")
+    print("GPUs:", gpus)
+
+    # 基础张量计算
+    a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+    b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
+    c = tf.matmul(a, b)
+
+    print("matmul result:")
+    print(c.numpy())
+
+    expected = tf.constant([[19.0, 22.0], [43.0, 50.0]])
+    tf.debugging.assert_near(c, expected)
+
+    # 简单 Keras 模型
+    x = np.array([[0.0], [1.0], [2.0], [3.0]], dtype=np.float32)
+    y = np.array([[1.0], [3.0], [5.0], [7.0]], dtype=np.float32)
+
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=(1,)),
+        tf.keras.layers.Dense(1),
+    ])
+
+    model.compile(
+        optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
+        loss="mse",
+    )
+
+    history = model.fit(x, y, epochs=3, verbose=0)
+
+    pred = model.predict(np.array([[4.0]], dtype=np.float32), verbose=0)
+
+    assert pred.shape == (1, 1)
+    assert np.isfinite(pred).all()
+
+    print("final loss:", float(history.history["loss"][-1]))
+    print("prediction:", float(pred[0][0]))
+    print("TensorFlow smoke test passed")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.19.0/test.sh b/frameworks/tensorflow/2.19.0/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cbbce1b29c54efd794c0a4fe5b99b4fc028e41fa
--- /dev/null
+++ b/frameworks/tensorflow/2.19.0/test.sh
@@ -0,0 +1,336 @@
+#!/usr/bin/env bash
+# 在容器外执行：验证指定 Docker 镜像中的 TensorFlow / Keras / CUDA 基础功能。
+#
+# 用法：
+#   bash test_tensorflow_docker.sh <image:tag> [额外 docker run 参数...]
+#
+# 示例：
+#   bash test_tensorflow_docker.sh my-tensorflow:latest
+#   bash test_tensorflow_docker.sh my-tensorflow:latest --ipc=host
+#   REQUIRE_CUDA=0 bash test_tensorflow_docker.sh tensorflow-cpu:test
+#   EXPECTED_TF_VERSION_PREFIX=2.19 bash test_tensorflow_docker.sh my-tensorflow:latest
+
+set -Eeuo pipefail
+
+IMAGE="${1:-}"
+
+if [[ -z "${IMAGE}" || "${IMAGE}" == "-h" || "${IMAGE}" == "--help" ]]; then
+  cat <<'USAGE'
+用法:
+  bash test.sh <image:tag> [额外 docker run 参数...]
+
+环境变量:
+  REQUIRE_CUDA=1|0               是否强制要求 CUDA/GPU 可用，默认 1
+  GPUS=all                       传给 docker run --gpus 的值，默认 all
+  PYTHON_BIN=python3             容器内 Python 命令，默认 python3
+  DOCKER_NETWORK=none            docker 网络模式，默认 none
+  TIMEOUT_SECONDS=180            整体测试超时时间，默认 180 秒
+  EXPECTED_TF_VERSION_PREFIX=2.19 可选，检查 TensorFlow 版本前缀
+
+示例:
+  bash test.sh registry.example.com/ai/tensorflow:2.19-cu12
+  REQUIRE_CUDA=0 bash test.sh tensorflow-cpu:test
+  EXPECTED_TF_VERSION_PREFIX=2.19 bash test.sh my-image:tag
+  bash test.sh my-image:tag --ipc=host --shm-size=2g
+USAGE
+  exit 1
+fi
+
+shift || true
+
+REQUIRE_CUDA="${REQUIRE_CUDA:-1}"
+GPUS="${GPUS:-all}"
+PYTHON_BIN="${PYTHON_BIN:-python3}"
+DOCKER_NETWORK="${DOCKER_NETWORK:-none}"
+TIMEOUT_SECONDS="${TIMEOUT_SECONDS:-180}"
+EXPECTED_TF_VERSION_PREFIX="${EXPECTED_TF_VERSION_PREFIX:-}"
+EXTRA_DOCKER_ARGS=("$@")
+
+log()  { printf '\033[1;34m%s\033[0m\n' "$*"; }
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+
+[[ "${REQUIRE_CUDA}" =~ ^[01]$ ]] || fail "REQUIRE_CUDA 只能是 1 或 0，当前值: ${REQUIRE_CUDA}"
+
+command -v docker >/dev/null 2>&1 || fail "未找到 docker 命令"
+docker info >/dev/null 2>&1 || fail "docker daemon 不可用，请确认 Docker 服务已启动且当前用户有权限访问"
+
+if ! docker image inspect "${IMAGE}" >/dev/null 2>&1; then
+  warn "本地未找到镜像 ${IMAGE}；docker run 可能会尝试拉取镜像"
+fi
+
+DOCKER_ARGS=(run --rm -i)
+
+if [[ "${REQUIRE_CUDA}" == "1" ]]; then
+  DOCKER_ARGS+=(--gpus "${GPUS}")
+fi
+
+DOCKER_ARGS+=(
+  -e "REQUIRE_CUDA=${REQUIRE_CUDA}"
+  -e "PYTHON_BIN=${PYTHON_BIN}"
+  -e "EXPECTED_TF_VERSION_PREFIX=${EXPECTED_TF_VERSION_PREFIX}"
+  -e "TF_CPP_MIN_LOG_LEVEL=2"
+)
+
+DOCKER_ARGS+=("${EXTRA_DOCKER_ARGS[@]}")
+DOCKER_ARGS+=("${IMAGE}" /bin/bash -s)
+
+log "=== TensorFlow Docker 镜像功能测试 ==="
+printf '镜像: %s\n' "${IMAGE}"
+printf 'CUDA 强制检查: %s\n' "${REQUIRE_CUDA}"
+printf 'Docker 网络: %s\n' "${DOCKER_NETWORK}"
+printf 'Python 命令: %s\n' "${PYTHON_BIN}"
+
+if [[ -n "${EXPECTED_TF_VERSION_PREFIX}" ]]; then
+  printf '期望 TensorFlow 版本前缀: %s\n' "${EXPECTED_TF_VERSION_PREFIX}"
+fi
+
+if ((${#EXTRA_DOCKER_ARGS[@]} > 0)); then
+  printf '额外 docker 参数: %s\n' "${EXTRA_DOCKER_ARGS[*]}"
+fi
+
+RUN_CMD=(docker "${DOCKER_ARGS[@]}")
+echo "##############################################"
+echo "${RUN_CMD} ${DOCKER_ARGS[@]}"
+echo "##############################################"
+
+if command -v timeout >/dev/null 2>&1; then
+  RUN_CMD=(timeout --preserve-status "${TIMEOUT_SECONDS}s" "${RUN_CMD[@]}")
+fi
+
+"${RUN_CMD[@]}" <<'IN_CONTAINER'
+set -Eeuo pipefail
+
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+section() { printf '\n\033[1;34m=== %s ===\033[0m\n' "$*"; }
+
+PY="${PYTHON_BIN:-python3}"
+
+if [[ -d /usr/local/cuda ]]; then
+  export CUDA_HOME="${CUDA_HOME:-/usr/local/cuda}"
+  export PATH="${CUDA_HOME}/bin:${PATH}"
+  export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH:-}"
+fi
+
+export TF_CPP_MIN_LOG_LEVEL="${TF_CPP_MIN_LOG_LEVEL:-2}"
+
+section "1. Python 基础检查"
+
+command -v "${PY}" >/dev/null 2>&1 || fail "容器内未找到 Python 命令: ${PY}"
+"${PY}" --version
+ok "Python 可用"
+
+section "2. TensorFlow 导入与版本检查"
+
+"${PY}" - <<'PY'
+import os
+import platform
+import sys
+
+import tensorflow as tf
+
+print("python_executable:", sys.executable)
+print("platform:", platform.platform())
+print("tensorflow_version:", tf.__version__)
+
+expected = os.environ.get("EXPECTED_TF_VERSION_PREFIX", "").strip()
+if expected:
+    assert tf.__version__.startswith(expected), (
+        f"期望 TensorFlow 版本前缀 {expected}，实际版本 {tf.__version__}"
+    )
+    print("expected_version_prefix:", expected)
+PY
+
+ok "TensorFlow import 正常"
+
+section "3. TensorFlow 构建信息检查"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+print("tf_version:", tf.__version__)
+
+try:
+    build_info = tf.sysconfig.get_build_info()
+    print("build_info:")
+    for k in sorted(build_info.keys()):
+        print(f"  {k}: {build_info[k]}")
+except Exception as e:
+    print("无法读取 tf.sysconfig.get_build_info():", repr(e))
+
+try:
+    print("is_built_with_cuda:", tf.test.is_built_with_cuda())
+except Exception as e:
+    print("无法读取 tf.test.is_built_with_cuda():", repr(e))
+PY
+
+ok "TensorFlow 构建信息读取完成"
+
+section "4. CUDA / GPU 环境检查"
+
+if [[ "${REQUIRE_CUDA:-1}" == "1" ]]; then
+  command -v nvidia-smi >/dev/null 2>&1 || fail "nvidia-smi 不可用；请检查宿主机 NVIDIA 驱动、nvidia-container-toolkit、docker run --gpus 参数"
+
+  echo "nvidia-smi 摘要:"
+  nvidia-smi --query-gpu=name,driver_version,memory.total --format=csv,noheader || fail "nvidia-smi 执行失败"
+
+  "${PY}" - <<'PY'
+import tensorflow as tf
+
+gpus = tf.config.list_physical_devices("GPU")
+logical_gpus = tf.config.list_logical_devices("GPU")
+
+print("physical_gpus:", gpus)
+print("logical_gpus:", logical_gpus)
+
+assert len(gpus) > 0, "TensorFlow 未检测到物理 GPU"
+assert len(logical_gpus) > 0, "TensorFlow 未检测到逻辑 GPU"
+
+for gpu in gpus:
+    try:
+        tf.config.experimental.set_memory_growth(gpu, True)
+        print(f"memory_growth_enabled: {gpu}")
+    except Exception as e:
+        print(f"memory_growth_skip: {gpu}, reason={e!r}")
+PY
+
+  ok "TensorFlow GPU 识别正常"
+
+  section "5. TensorFlow GPU 实际计算检查"
+
+  "${PY}" - <<'PY'
+import tensorflow as tf
+
+with tf.device("/GPU:0"):
+    a = tf.random.normal((1024, 1024))
+    b = tf.random.normal((1024, 1024))
+    c = tf.matmul(a, b)
+
+result = c.numpy()
+
+assert result.shape == (1024, 1024)
+assert not tf.math.reduce_any(tf.math.is_nan(c)).numpy(), "GPU 计算结果包含 NaN"
+
+print("gpu_matmul_shape:", result.shape)
+print("gpu_matmul_mean:", float(tf.reduce_mean(c).numpy()))
+PY
+
+  ok "TensorFlow GPU 计算正常"
+
+  if command -v nvcc >/dev/null 2>&1; then
+    echo "nvcc 版本:"
+    nvcc --version | sed -n '1,5p'
+  else
+    warn "未检测到 nvcc：运行时镜像通常不需要 nvcc，只有编译 CUDA 扩展时才需要"
+  fi
+else
+  warn "REQUIRE_CUDA=0，跳过强制 CUDA / GPU 检查"
+fi
+
+section "6. TensorFlow CPU 基础张量计算"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
+
+c = tf.matmul(a, b)
+
+expected = tf.constant([[19.0, 22.0], [43.0, 50.0]])
+tf.debugging.assert_near(c, expected)
+
+print("matmul_result:")
+print(c.numpy())
+PY
+
+ok "TensorFlow 基础张量计算正常"
+
+section "7. Keras 模型构建、训练、推理检查"
+
+"${PY}" - <<'PY'
+import os
+import tempfile
+
+import numpy as np
+import tensorflow as tf
+
+np.random.seed(123)
+tf.random.set_seed(123)
+
+# 构造简单训练数据：y = 2x + 1
+x = np.array([[0.0], [1.0], [2.0], [3.0], [4.0], [5.0]], dtype=np.float32)
+y = np.array([[1.0], [3.0], [5.0], [7.0], [9.0], [11.0]], dtype=np.float32)
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape=(1,)),
+    tf.keras.layers.Dense(8, activation="relu"),
+    tf.keras.layers.Dense(1),
+])
+
+model.compile(
+    optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
+    loss="mse",
+)
+
+history = model.fit(x, y, epochs=5, batch_size=2, verbose=0)
+
+pred = model.predict(np.array([[6.0]], dtype=np.float32), verbose=0)
+
+assert pred.shape == (1, 1), f"推理结果 shape 异常：{pred.shape}"
+assert np.isfinite(pred).all(), "推理结果包含 NaN 或 Inf"
+
+print("final_loss:", float(history.history["loss"][-1]))
+print("prediction_shape:", pred.shape)
+print("prediction_value:", float(pred[0][0]))
+
+with tempfile.TemporaryDirectory() as tmp_dir:
+    keras_path = os.path.join(tmp_dir, "tiny_model.keras")
+    model.save(keras_path)
+
+    loaded_model = tf.keras.models.load_model(keras_path)
+    loaded_pred = loaded_model.predict(np.array([[6.0]], dtype=np.float32), verbose=0)
+
+    assert loaded_pred.shape == (1, 1), f"加载模型推理 shape 异常：{loaded_pred.shape}"
+    assert np.isfinite(loaded_pred).all(), "加载模型推理结果包含 NaN 或 Inf"
+
+    print("keras_save_load: ok")
+PY
+
+ok "Keras 模型训练、推理、保存、加载正常"
+
+section "8. tf.data 数据管道检查"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+dataset = tf.data.Dataset.from_tensor_slices(
+    (
+        tf.constant([[1.0], [2.0], [3.0], [4.0]]),
+        tf.constant([[2.0], [4.0], [6.0], [8.0]]),
+    )
+)
+
+dataset = dataset.batch(2).prefetch(tf.data.AUTOTUNE)
+
+batch_count = 0
+for xb, yb in dataset:
+    assert xb.shape[0] <= 2
+    assert yb.shape[0] <= 2
+    batch_count += 1
+
+assert batch_count == 2, f"batch 数量异常: {batch_count}"
+
+print("tf_data_batch_count:", batch_count)
+PY
+
+ok "tf.data 数据管道正常"
+
+section "测试结果"
+ok "所有 TensorFlow 检查通过"
+IN_CONTAINER
+
+ok "宿主机侧 docker run 验证完成"
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.19.0/test_result.png b/frameworks/tensorflow/2.19.0/test_result.png
new file mode 100644
index 0000000000000000000000000000000000000000..48f15883c5c9d8cc6e2e22cd6ede0169048d2c0f
Binary files /dev/null and b/frameworks/tensorflow/2.19.0/test_result.png differ
diff --git a/frameworks/tensorflow/2.19.0/warring_log.png b/frameworks/tensorflow/2.19.0/warring_log.png
new file mode 100644
index 0000000000000000000000000000000000000000..1461c0cedad8d45c8d32554de19c341ba88d37bc
Binary files /dev/null and b/frameworks/tensorflow/2.19.0/warring_log.png differ
diff --git a/frameworks/tensorflow/2.21.0/Dockerfile b/frameworks/tensorflow/2.21.0/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..8def434782612f47c4349ee88d0a5adc24c81872
--- /dev/null
+++ b/frameworks/tensorflow/2.21.0/Dockerfile
@@ -0,0 +1,19 @@
+
+FROM opencloudos/opencloudos9-cuda-devel:12.8
+
+LABEL maintainer="stronking 363133710@qq.com"
+LABEL org.opencontainers.image.source="https://gitee.com/OpenCloudOS/ai-agent-container"
+LABEL org.opencontainers.image.description="TensorFlow 2.21.0 (GPU) on OpenCloudOS 9"
+
+ENV NVIDIA_VISIBLE_DEVICES=all \
+    PYTHONUNBUFFERED=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PIP_DEFAULT_TIMEOUT=120
+
+
+WORKDIR /home
+
+RUN --mount=type=cache,id=pip-cache-opencloudos9-cu128,target=/root/.cache/pip \
+    pip install "tensorflow[and-cuda]==2.21.0"
+
+CMD ["python3"]
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.21.0/README.md b/frameworks/tensorflow/2.21.0/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..36bc11abcc366fe05d8bb2f853fa83f391cf676a
--- /dev/null
+++ b/frameworks/tensorflow/2.21.0/README.md
@@ -0,0 +1,31 @@
+# tensorflow 2.21.0 on OpenCloudOS 9
+
+## 基本信息
+- **框架版本**：v2.21.0
+- **基础镜像**：opencloudos9-cuda-devel:12.8
+- **Python 版本**：3.11
+- **CUDA 版本**： 12.x 或 更高
+
+## 构建
+
+docker build -t oc9-tensorflow:2.21.0 .
+
+## 镜像启动命令
+
+docker run -d --gpus all --name oc9-tensorflow oc9-tensorflow:2.21.0
+
+## 镜像测试命令
+
+docker run --rm --gpus all oc9-tensorflow:2.21.0 bash test.sh
+docker run --rm --gpus all oc9-tensorflow:2.21.0 python tensor_smoke_test.py
+
+## 已知问题
+```
+对于如下日志输出，属于正常现象。这些日志通常不是致命错误。
+核心原因是：TensorFlow 在 import tensorflow as tf 时，会初始化 CUDA/XLA 相关组件，并注册 cuFFT、cuDNN、cuBLAS 等 GPU 插件工厂。
+日志里的意思是：这些插件工厂已经注册过一次，现在又尝试注册，所以打印了“already been registered”。
+TensorFlow 官方 GitHub 上有多个相同现象的 issue，日志内容和图片中的告警日志基本一致，
+包括 Unable to register cuFFT factory、cuDNN factory、cuBLAS factory、computation placer already registered 等
+```
+如下图所示告警日志：
+![告警日志](./warring_log.png)
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.21.0/build.conf b/frameworks/tensorflow/2.21.0/build.conf
new file mode 100644
index 0000000000000000000000000000000000000000..b0255949f5a755fb67ece75bc5eeb3900eddb169
--- /dev/null
+++ b/frameworks/tensorflow/2.21.0/build.conf
@@ -0,0 +1,4 @@
+# tensorflow 2.21.0 on OpenCloudOS 9 (GPU)
+IMAGE_NAME=oc9-tensorflow
+IMAGE_TAG=2.21.0
+GPU_TEST=false
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.21.0/tensor_smoke_test.py b/frameworks/tensorflow/2.21.0/tensor_smoke_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..02cec134116518270c21bcf37daf410a619ca0a1
--- /dev/null
+++ b/frameworks/tensorflow/2.21.0/tensor_smoke_test.py
@@ -0,0 +1,52 @@
+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import numpy as np
+import tensorflow as tf
+
+
+def main():
+    print("TensorFlow version:", tf.__version__)
+
+    gpus = tf.config.list_physical_devices("GPU")
+    print("GPUs:", gpus)
+
+    # 基础张量计算
+    a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+    b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
+    c = tf.matmul(a, b)
+
+    print("matmul result:")
+    print(c.numpy())
+
+    expected = tf.constant([[19.0, 22.0], [43.0, 50.0]])
+    tf.debugging.assert_near(c, expected)
+
+    # 简单 Keras 模型
+    x = np.array([[0.0], [1.0], [2.0], [3.0]], dtype=np.float32)
+    y = np.array([[1.0], [3.0], [5.0], [7.0]], dtype=np.float32)
+
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=(1,)),
+        tf.keras.layers.Dense(1),
+    ])
+
+    model.compile(
+        optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
+        loss="mse",
+    )
+
+    history = model.fit(x, y, epochs=3, verbose=0)
+
+    pred = model.predict(np.array([[4.0]], dtype=np.float32), verbose=0)
+
+    assert pred.shape == (1, 1)
+    assert np.isfinite(pred).all()
+
+    print("final loss:", float(history.history["loss"][-1]))
+    print("prediction:", float(pred[0][0]))
+    print("TensorFlow smoke test passed")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/frameworks/tensorflow/2.21.0/test.sh b/frameworks/tensorflow/2.21.0/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c2fed93f16a25af247974d29f8d80998f0fb4dcc
--- /dev/null
+++ b/frameworks/tensorflow/2.21.0/test.sh
@@ -0,0 +1,336 @@
+#!/usr/bin/env bash
+# 在容器外执行：验证指定 Docker 镜像中的 TensorFlow / Keras / CUDA 基础功能。
+#
+# 用法：
+#   bash test.sh <image:tag> [额外 docker run 参数...]
+#
+# 示例：
+#   bash test.sh my-tensorflow:latest
+#   bash test.sh my-tensorflow:latest --ipc=host
+#   REQUIRE_CUDA=0 bash test.sh tensorflow-cpu:test
+#   EXPECTED_TF_VERSION_PREFIX=2.19 bash test.sh my-tensorflow:latest
+
+set -Eeuo pipefail
+
+IMAGE="${1:-}"
+
+if [[ -z "${IMAGE}" || "${IMAGE}" == "-h" || "${IMAGE}" == "--help" ]]; then
+  cat <<'USAGE'
+用法:
+  bash test.sh <image:tag> [额外 docker run 参数...]
+
+环境变量:
+  REQUIRE_CUDA=1|0               是否强制要求 CUDA/GPU 可用，默认 1
+  GPUS=all                       传给 docker run --gpus 的值，默认 all
+  PYTHON_BIN=python3             容器内 Python 命令，默认 python3
+  DOCKER_NETWORK=none            docker 网络模式，默认 none
+  TIMEOUT_SECONDS=180            整体测试超时时间，默认 180 秒
+  EXPECTED_TF_VERSION_PREFIX=2.19 可选，检查 TensorFlow 版本前缀
+
+示例:
+  bash test.sh registry.example.com/ai/tensorflow:2.19-cu12
+  REQUIRE_CUDA=0 bash test.sh tensorflow-cpu:test
+  EXPECTED_TF_VERSION_PREFIX=2.19 bash test.sh my-image:tag
+  bash test.sh my-image:tag --ipc=host --shm-size=2g
+USAGE
+  exit 1
+fi
+
+shift || true
+
+REQUIRE_CUDA="${REQUIRE_CUDA:-1}"
+GPUS="${GPUS:-all}"
+PYTHON_BIN="${PYTHON_BIN:-python3}"
+DOCKER_NETWORK="${DOCKER_NETWORK:-none}"
+TIMEOUT_SECONDS="${TIMEOUT_SECONDS:-180}"
+EXPECTED_TF_VERSION_PREFIX="${EXPECTED_TF_VERSION_PREFIX:-}"
+EXTRA_DOCKER_ARGS=("$@")
+
+log()  { printf '\033[1;34m%s\033[0m\n' "$*"; }
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+
+[[ "${REQUIRE_CUDA}" =~ ^[01]$ ]] || fail "REQUIRE_CUDA 只能是 1 或 0，当前值: ${REQUIRE_CUDA}"
+
+command -v docker >/dev/null 2>&1 || fail "未找到 docker 命令"
+docker info >/dev/null 2>&1 || fail "docker daemon 不可用，请确认 Docker 服务已启动且当前用户有权限访问"
+
+if ! docker image inspect "${IMAGE}" >/dev/null 2>&1; then
+  warn "本地未找到镜像 ${IMAGE}；docker run 可能会尝试拉取镜像"
+fi
+
+DOCKER_ARGS=(run --rm -i)
+
+if [[ "${REQUIRE_CUDA}" == "1" ]]; then
+  DOCKER_ARGS+=(--gpus "${GPUS}")
+fi
+
+DOCKER_ARGS+=(
+  -e "REQUIRE_CUDA=${REQUIRE_CUDA}"
+  -e "PYTHON_BIN=${PYTHON_BIN}"
+  -e "EXPECTED_TF_VERSION_PREFIX=${EXPECTED_TF_VERSION_PREFIX}"
+  -e "TF_CPP_MIN_LOG_LEVEL=2"
+)
+
+DOCKER_ARGS+=("${EXTRA_DOCKER_ARGS[@]}")
+DOCKER_ARGS+=("${IMAGE}" /bin/bash -s)
+
+log "=== TensorFlow Docker 镜像功能测试 ==="
+printf '镜像: %s\n' "${IMAGE}"
+printf 'CUDA 强制检查: %s\n' "${REQUIRE_CUDA}"
+printf 'Docker 网络: %s\n' "${DOCKER_NETWORK}"
+printf 'Python 命令: %s\n' "${PYTHON_BIN}"
+
+if [[ -n "${EXPECTED_TF_VERSION_PREFIX}" ]]; then
+  printf '期望 TensorFlow 版本前缀: %s\n' "${EXPECTED_TF_VERSION_PREFIX}"
+fi
+
+if ((${#EXTRA_DOCKER_ARGS[@]} > 0)); then
+  printf '额外 docker 参数: %s\n' "${EXTRA_DOCKER_ARGS[*]}"
+fi
+
+RUN_CMD=(docker "${DOCKER_ARGS[@]}")
+echo "##############################################"
+echo "${RUN_CMD} ${DOCKER_ARGS[@]}"
+echo "##############################################"
+
+if command -v timeout >/dev/null 2>&1; then
+  RUN_CMD=(timeout --preserve-status "${TIMEOUT_SECONDS}s" "${RUN_CMD[@]}")
+fi
+
+"${RUN_CMD[@]}" <<'IN_CONTAINER'
+set -Eeuo pipefail
+
+ok()   { printf '\033[1;32m✓ %s\033[0m\n' "$*"; }
+warn() { printf '\033[1;33m! %s\033[0m\n' "$*"; }
+fail() { printf '\033[1;31m✗ %s\033[0m\n' "$*" >&2; exit 1; }
+section() { printf '\n\033[1;34m=== %s ===\033[0m\n' "$*"; }
+
+PY="${PYTHON_BIN:-python3}"
+
+if [[ -d /usr/local/cuda ]]; then
+  export CUDA_HOME="${CUDA_HOME:-/usr/local/cuda}"
+  export PATH="${CUDA_HOME}/bin:${PATH}"
+  export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${LD_LIBRARY_PATH:-}"
+fi
+
+export TF_CPP_MIN_LOG_LEVEL="${TF_CPP_MIN_LOG_LEVEL:-2}"
+
+section "1. Python 基础检查"
+
+command -v "${PY}" >/dev/null 2>&1 || fail "容器内未找到 Python 命令: ${PY}"
+"${PY}" --version
+ok "Python 可用"
+
+section "2. TensorFlow 导入与版本检查"
+
+"${PY}" - <<'PY'
+import os
+import platform
+import sys
+
+import tensorflow as tf
+
+print("python_executable:", sys.executable)
+print("platform:", platform.platform())
+print("tensorflow_version:", tf.__version__)
+
+expected = os.environ.get("EXPECTED_TF_VERSION_PREFIX", "").strip()
+if expected:
+    assert tf.__version__.startswith(expected), (
+        f"期望 TensorFlow 版本前缀 {expected}，实际版本 {tf.__version__}"
+    )
+    print("expected_version_prefix:", expected)
+PY
+
+ok "TensorFlow import 正常"
+
+section "3. TensorFlow 构建信息检查"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+print("tf_version:", tf.__version__)
+
+try:
+    build_info = tf.sysconfig.get_build_info()
+    print("build_info:")
+    for k in sorted(build_info.keys()):
+        print(f"  {k}: {build_info[k]}")
+except Exception as e:
+    print("无法读取 tf.sysconfig.get_build_info():", repr(e))
+
+try:
+    print("is_built_with_cuda:", tf.test.is_built_with_cuda())
+except Exception as e:
+    print("无法读取 tf.test.is_built_with_cuda():", repr(e))
+PY
+
+ok "TensorFlow 构建信息读取完成"
+
+section "4. CUDA / GPU 环境检查"
+
+if [[ "${REQUIRE_CUDA:-1}" == "1" ]]; then
+  command -v nvidia-smi >/dev/null 2>&1 || fail "nvidia-smi 不可用；请检查宿主机 NVIDIA 驱动、nvidia-container-toolkit、docker run --gpus 参数"
+
+  echo "nvidia-smi 摘要:"
+  nvidia-smi --query-gpu=name,driver_version,memory.total --format=csv,noheader || fail "nvidia-smi 执行失败"
+
+  "${PY}" - <<'PY'
+import tensorflow as tf
+
+gpus = tf.config.list_physical_devices("GPU")
+logical_gpus = tf.config.list_logical_devices("GPU")
+
+print("physical_gpus:", gpus)
+print("logical_gpus:", logical_gpus)
+
+assert len(gpus) > 0, "TensorFlow 未检测到物理 GPU"
+assert len(logical_gpus) > 0, "TensorFlow 未检测到逻辑 GPU"
+
+for gpu in gpus:
+    try:
+        tf.config.experimental.set_memory_growth(gpu, True)
+        print(f"memory_growth_enabled: {gpu}")
+    except Exception as e:
+        print(f"memory_growth_skip: {gpu}, reason={e!r}")
+PY
+
+  ok "TensorFlow GPU 识别正常"
+
+  section "5. TensorFlow GPU 实际计算检查"
+
+  "${PY}" - <<'PY'
+import tensorflow as tf
+
+with tf.device("/GPU:0"):
+    a = tf.random.normal((1024, 1024))
+    b = tf.random.normal((1024, 1024))
+    c = tf.matmul(a, b)
+
+result = c.numpy()
+
+assert result.shape == (1024, 1024)
+assert not tf.math.reduce_any(tf.math.is_nan(c)).numpy(), "GPU 计算结果包含 NaN"
+
+print("gpu_matmul_shape:", result.shape)
+print("gpu_matmul_mean:", float(tf.reduce_mean(c).numpy()))
+PY
+
+  ok "TensorFlow GPU 计算正常"
+
+  if command -v nvcc >/dev/null 2>&1; then
+    echo "nvcc 版本:"
+    nvcc --version | sed -n '1,5p'
+  else
+    warn "未检测到 nvcc：运行时镜像通常不需要 nvcc，只有编译 CUDA 扩展时才需要"
+  fi
+else
+  warn "REQUIRE_CUDA=0，跳过强制 CUDA / GPU 检查"
+fi
+
+section "6. TensorFlow CPU 基础张量计算"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
+
+c = tf.matmul(a, b)
+
+expected = tf.constant([[19.0, 22.0], [43.0, 50.0]])
+tf.debugging.assert_near(c, expected)
+
+print("matmul_result:")
+print(c.numpy())
+PY
+
+ok "TensorFlow 基础张量计算正常"
+
+section "7. Keras 模型构建、训练、推理检查"
+
+"${PY}" - <<'PY'
+import os
+import tempfile
+
+import numpy as np
+import tensorflow as tf
+
+np.random.seed(123)
+tf.random.set_seed(123)
+
+# 构造简单训练数据：y = 2x + 1
+x = np.array([[0.0], [1.0], [2.0], [3.0], [4.0], [5.0]], dtype=np.float32)
+y = np.array([[1.0], [3.0], [5.0], [7.0], [9.0], [11.0]], dtype=np.float32)
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape=(1,)),
+    tf.keras.layers.Dense(8, activation="relu"),
+    tf.keras.layers.Dense(1),
+])
+
+model.compile(
+    optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
+    loss="mse",
+)
+
+history = model.fit(x, y, epochs=5, batch_size=2, verbose=0)
+
+pred = model.predict(np.array([[6.0]], dtype=np.float32), verbose=0)
+
+assert pred.shape == (1, 1), f"推理结果 shape 异常：{pred.shape}"
+assert np.isfinite(pred).all(), "推理结果包含 NaN 或 Inf"
+
+print("final_loss:", float(history.history["loss"][-1]))
+print("prediction_shape:", pred.shape)
+print("prediction_value:", float(pred[0][0]))
+
+with tempfile.TemporaryDirectory() as tmp_dir:
+    keras_path = os.path.join(tmp_dir, "tiny_model.keras")
+    model.save(keras_path)
+
+    loaded_model = tf.keras.models.load_model(keras_path)
+    loaded_pred = loaded_model.predict(np.array([[6.0]], dtype=np.float32), verbose=0)
+
+    assert loaded_pred.shape == (1, 1), f"加载模型推理 shape 异常：{loaded_pred.shape}"
+    assert np.isfinite(loaded_pred).all(), "加载模型推理结果包含 NaN 或 Inf"
+
+    print("keras_save_load: ok")
+PY
+
+ok "Keras 模型训练、推理、保存、加载正常"
+
+section "8. tf.data 数据管道检查"
+
+"${PY}" - <<'PY'
+import tensorflow as tf
+
+dataset = tf.data.Dataset.from_tensor_slices(
+    (
+        tf.constant([[1.0], [2.0], [3.0], [4.0]]),
+        tf.constant([[2.0], [4.0], [6.0], [8.0]]),
+    )
+)
+
+dataset = dataset.batch(2).prefetch(tf.data.AUTOTUNE)
+
+batch_count = 0
+for xb, yb in dataset:
+    assert xb.shape[0] <= 2
+    assert yb.shape[0] <= 2
+    batch_count += 1
+
+assert batch_count == 2, f"batch 数量异常: {batch_count}"
+
+print("tf_data_batch_count:", batch_count)
+PY
+
+ok "tf.data 数据管道正常"
+
+section "测试结果"
+ok "所有 TensorFlow 检查通过"
+IN_CONTAINER
+
+ok "宿主机侧 docker run 验证完成"
\ No newline at end of file
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