ijddlmZedk(rddlZddlmZddlmZddlmZddlm Z ddlm Z dd l m Z dd l m Z dd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z ddlm!Z!ddlm"Z"ddlm#Z#edk(r ddl$ddl%mZ&nned k(r ddl'ddl(mZ&nXedk(rddl)ddl*mZ&dZ+n@ed!k(rddl,ddl-mZ&dZ+n(ed"k(rddl.ddl/mZ&dZ+ne0d#eed$Gd%d&e&Ze1Z2ed'Gd(d)e2Z1ed*d+Z3y),)backendtorchN) keras_export) result_type) KerasTensor)any_symbolic_tensors)is_keras_tensor)get_keras_mask)set_keras_mask)StatelessScope)get_stateless_scope)in_stateless_scope) SymbolicScope)in_symbolic_scope) AutocastScope)Variable)get_autocast_scope)is_float_dtype) is_int_dtype)standardize_dtype)standardize_shape)epsilon)floatx)image_data_format) set_epsilon) set_floatx)set_image_data_format)standardize_data_format tensorflow)*jaxnumpyopenvinozUnable to import backend : zkeras.Variablec eZdZy)rN__name__ __module__ __qualname__o/var/www/html/emotional.easysim.app/public_html/venv/lib/python3.12/site-packages/keras/src/backend/__init__.pyrr>r*rzkeras.name_scopec eZdZy) name_scopeNr%r)r*r+r.r.Fr,r*r.z keras.devicect|S)aiContext manager for backend-agnostic device placement. Use this context manager to control on which device operations are performed and tensors are allocated. This works across all backends (TensorFlow, JAX, PyTorch). This is useful for memory management, data preprocessing, and multi-device setups. Args: device_name: String specifying the device in format `"device_type:device_index"`. For example: `"cpu:0"`, `"gpu:0"`, `"gpu:1"`. For the PyTorch backend, `"gpu"` is automatically converted to `"cuda"`. Example: Basic usage with CPU and GPU: ```python # Allocate tensors on CPU with keras.device("cpu:0"): cpu_tensor = keras.ops.ones((2, 2)) # Allocate tensors on GPU (if available) with keras.device("gpu:0"): gpu_tensor = keras.ops.ones((2, 2)) ``` Practical example with CPU preprocessing and GPU training: ```python # Create dummy data and model x_raw = np.random.rand(128, 784) y_train = np.random.randint(0, 10, size=(128,)) model = keras.Sequential([ keras.Input(shape=(784,)), keras.layers.Dense(10) ]) model.compile( optimizer="adam", loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True) ) # Preprocess data on CPU with keras.device("cpu:0"): x_processed = keras.ops.cast(x_raw, "float32") # Train on GPU (if available) with keras.device("gpu:0"): model.fit(x_processed, y_train, epochs=2) ``` Use cases: - **Memory management**: Keep large tensors on CPU to save GPU memory - **Data preprocessing**: Process data on CPU before training on GPU - **GPU / TPU setups**: Control what runs on GPU / TPU vs CPU - **Multi-device setups**: Control which device receives which tensors Device naming conventions: - `"cpu:0"` - First CPU - `"gpu:0"` - First GPU (works across all backends) - `"gpu:1"` - Second GPU Note: For distributed training across multiple devices, see the [distributed training guides](https://keras.io/guides/distributed_training/). ) device_scope) device_names r+devicer2KsJ  $$r*)4keras.src.backend.configrrkeras.src.api_exportrkeras.src.backend.common.dtypesr%keras.src.backend.common.keras_tensorrrr keras.src.backend.common.maskingr r (keras.src.backend.common.stateless_scoper r r'keras.src.backend.common.symbolic_scoperr"keras.src.backend.common.variablesrrrrrrrrrrrrrrkeras.src.backend.tensorflow!keras.src.backend.tensorflow.coreBackendVariablekeras.src.backend.jaxkeras.src.backend.jax.corekeras.src.backend.torchkeras.src.backend.torch.coredistribution_libkeras.src.backend.numpykeras.src.backend.numpy.corekeras.src.backend.openvinokeras.src.backend.openvino.core ValueErrorr.backend_name_scoper2r)r*r+rIsD, 9-7=FA;;CHGAE<7A=;@@,+60/:< 9 .M Y%'F Y')H Y')H Y*,K 279+> ??      ! # " nD%D%r*