sparseml.tensorflow_v1 package
Subpackages
- sparseml.tensorflow_v1.datasets package
- sparseml.tensorflow_v1.framework package
- sparseml.tensorflow_v1.models package
- Subpackages
- sparseml.tensorflow_v1.models.classification package
- Submodules
- sparseml.tensorflow_v1.models.classification.mnist module
- sparseml.tensorflow_v1.models.classification.mobilenet module
- sparseml.tensorflow_v1.models.classification.mobilenet_v2 module
- sparseml.tensorflow_v1.models.classification.resnet module
- sparseml.tensorflow_v1.models.classification.vgg module
- Module contents
- sparseml.tensorflow_v1.models.classification package
- Submodules
- sparseml.tensorflow_v1.models.estimator module
- sparseml.tensorflow_v1.models.registry module
- Module contents
- Subpackages
- sparseml.tensorflow_v1.nn package
- sparseml.tensorflow_v1.optim package
- Submodules
- sparseml.tensorflow_v1.optim.analyzer_module module
- sparseml.tensorflow_v1.optim.manager module
- sparseml.tensorflow_v1.optim.mask_creator_pruning module
- sparseml.tensorflow_v1.optim.mask_pruning module
- sparseml.tensorflow_v1.optim.modifier module
- sparseml.tensorflow_v1.optim.modifier_epoch module
- sparseml.tensorflow_v1.optim.modifier_lr module
- sparseml.tensorflow_v1.optim.modifier_params module
- sparseml.tensorflow_v1.optim.modifier_pruning module
- sparseml.tensorflow_v1.optim.schedule_lr module
- sparseml.tensorflow_v1.optim.sensitivity_pruning module
- Module contents
- sparseml.tensorflow_v1.sparsification package
- sparseml.tensorflow_v1.utils package
Submodules
sparseml.tensorflow_v1.base module
-
sparseml.tensorflow_v1.base.check_tensorflow_install(min_version: Optional[str] = '1.8.0', max_version: Optional[str] = '1.16.0', raise_on_error: bool = True, allow_env_ignore_flag: bool = True) → bool[source] Check that the tensorflow package is installed. If raise_on_error, will raise an ImportError if it is not installed or the required version range, if set, is not installed. If not raise_on_error, will return True if installed with required version and False otherwise.
- Parameters
min_version (str) – The minimum version for tensorflow that it must be greater than or equal to, if unset will require no minimum version
max_version (str) – The maximum version for tensorflow that it must be less than or equal to, if unset will require no maximum version.
raise_on_error (bool) – True to raise any issues such as not installed, minimum version, or maximum version as ImportError. False to return the result.
allow_env_ignore_flag (bool) – True to allow the env variable SPARSEML_IGNORE_TFV1 to ignore the tensorflow install and version checks. False to ignore the ignore flag.
- Returns
If raise_on_error, will return False if tensorflow is not installed or the version is outside the accepted bounds and True if everything is correct.
- Return type
bool
-
sparseml.tensorflow_v1.base.check_tf2onnx_install(min_version: Optional[str] = '1.0.0', max_version: Optional[str] = None, raise_on_error: bool = True) → bool[source] Check that the tf2onnx package is installed. If raise_on_error, will raise an ImportError if it is not installed or the required version range, if set, is not installed. If not raise_on_error, will return True if installed with required version and False otherwise.
- Parameters
min_version (str) – The minimum version for tf2onnx that it must be greater than or equal to, if unset will require no minimum version
max_version (str) – The maximum version for tf2onnx that it must be less than or equal to, if unset will require no maximum version.
raise_on_error (bool) – True to raise any issues such as not installed, minimum version, or maximum version as ImportError. False to return the result.
- Returns
If raise_on_error, will return False if tf2onnx is not installed or the version is outside the accepted bounds and True if everything is correct.
- Return type
bool
-
sparseml.tensorflow_v1.base.require_tensorflow(min_version: Optional[str] = '1.8.0', max_version: Optional[str] = '1.16.0', allow_env_ignore_flag: bool = True)[source] Decorator function to require use of tensorflow. Will check that tensorflow package is installed and within the bounding ranges of min_version and max_version if they are set before calling the wrapped function. See
check_tensorflow_install()for more info.- Parameters
min_version (str) – The minimum version for tensorflow that it must be greater than or equal to, if unset will require no minimum version
max_version (str) – The maximum version for tensorflow that it must be less than or equal to, if unset will require no maximum version.
allow_env_ignore_flag (bool) – True to allow the env variable SPARSEML_IGNORE_TFV1 to ignore the tensorflow install and version checks. False to ignore the ignore flag.
-
sparseml.tensorflow_v1.base.require_tf2onnx(min_version: Optional[str] = '1.0.0', max_version: Optional[str] = None)[source] Decorator function to require use of tf2onnx. Will check that tf2onnx package is installed and within the bounding ranges of min_version and max_version if they are set before calling the wrapped function. See
check_tf2onnx_install()for more info.- Parameters
min_version (str) – The minimum version for tf2onnx that it must be greater than or equal to, if unset will require no minimum version
max_version (str) – The maximum version for tf2onnx that it must be less than or equal to, if unset will require no maximum version.
Module contents
Functionality for working with and sparsifying Models in the TensorFlow 1.x framework