tensorplay.package API

A file structure representation. Organized as Directory nodes that have lists of their Directory children. Directories for a package are created by calling PackageImporter.file_structure().

has_file(filename: str) → bool[source]

Checks if a file is present in a Directory.

Parameters:

filename (str) – Path of file to search for.

Returns:

If a Directory contains the specified file.

Return type:

bool

A set of patterns that candidate strings will be matched against.

A candidate is composed of a list of segments separated by separator, e.g. "foo.bar.baz".

A pattern contains one or more segments. Segments can be:

• A literal string (e.g. "foo"), which matches exactly.

• A string containing a wildcard (e.g. "tensorplay*", or "foo*baz*"). The wildcard matches any string, including the empty string.

• A double wildcard ("**"). This matches against zero or more complete segments.

Examples

tensorplay.**: matches tensorplay and all its submodules, e.g. tensorplay.nn and tensorplay.nn.functional. tensorplay.*: matches tensorplay.nn or tensorplay.utils, but not tensorplay.nn.functional. tensorplay*.**: matches tensorplay, tensorplay_vision, and all their submodules.

A candidate will match the GlobGroup if it matches any of the include patterns and none of the exclude patterns.

Parameters:

• include (str | Iterable[str]) – A string or list of strings, each representing a pattern to be matched against. A candidate will match if it matches any include pattern

• exclude (str | Iterable[str]) – A string or list of strings, each representing a pattern to be matched against. A candidate will be excluded from matching if it matches any exclude pattern.

• separator (str) – A string that delimits segments in candidates and patterns. By default this is “.” which corresponds to how modules are named in Python. Another common value for this is “/”, which is the Unix path separator.

Represents an environment to import modules from.

By default, you can figure out what module an object belongs by checking __module__ and importing the result using __import__ or importlib.import_module.

tensorplay.package introduces module importers other than the default one. Each PackageImporter introduces a new namespace. Potentially a single name (e.g. ‘foo.bar’) is present in multiple namespaces.

It supports two main operations:

import_module: module_name -> module object get_name: object -> (parent module name, name of obj within module)

The guarantee is that following round-trip will succeed or throw an ObjNotFoundError/ObjMisMatchError.

module_name, obj_name = env.get_name(obj) module = env.import_module(module_name) obj2 = getattr(module, obj_name) assert obj1 is obj2

get_name(obj: Any, name: str | None = None) → tuple[str, str][source]

Given an object, return a name that can be used to retrieve the object from this environment.

Parameters:

• obj – An object to get the module-environment-relative name for.

• name – If set, use this name instead of looking up __name__ or __qualname__ on obj. This is only here to match how Pickler handles __reduce__ functions that return a string, don’t use otherwise.

Returns:

A tuple (parent_module_name, attr_name) that can be used to retrieve obj from this environment. To use it:

mod = importer.import_module(parent_module_name)
obj = getattr(mod, attr_name)

Raises:

• ObjNotFoundError – we couldn’t retrieve obj by name.

• ObjMisMatchError – we found a different object with the same name as obj.

abstractmethod import_module(module_name: str) → ModuleType[source]

Import module_name from this environment.

The contract is the same as for importlib.import_module.

whichmodule(obj: Any, name: str) → str[source]

Find the module name an object belongs to.

This should be considered internal for end-users, but developers of an importer can override it to customize the behavior.

Based on the classic pickle.py approach, but modified to exclude the search into sys.modules.

A compound importer that takes a list of importers and tries them one at a time.

The first importer in the list that returns a result “wins”.

Exporters allow you to write packages of code, pickled Python data, and arbitrary binary and text resources into a self-contained package.

Imports can load this code in a hermetic way, such that code is loaded from the package rather than the normal Python import system. This allows for the packaging of model code and data so that it can be run on a server or used in the future for transfer learning.

The code contained in packages is copied file-by-file from the original source when it is created, and the file format is a specially organized zip file. Future users of the package can unzip the package, and edit the code in order to perform custom modifications to it.

The importer for packages ensures that code in the module can only be loaded from within the package, except for modules explicitly listed as external using extern(). The file extern_modules in the zip archive lists all the modules that a package externally depends on. This prevents “implicit” dependencies where the package runs locally because it is importing a locally-installed package, but then fails when the package is copied to another machine.

When source code is added to the package, the exporter can optionally scan it for further code dependencies (dependencies=True). It looks for import statements, resolves relative references to qualified module names, and performs an action specified by the user (See: extern(), mock(), and intern()).

Only plain Python modules are supported: C extension modules and other compiled or interpreter-specific artifacts cannot be interned. Objects embedded with save_pickle() serialize tensors through tensorplay’s own pickling support.

add_dependency(module_name: str, dependencies=True)[source]

Given a module, add it to the dependency graph according to patterns specified by the user.

all_paths(src: str, dst: str) → str[source]

Return a dot representation of the subgraph

that has all paths from src to dst.

Returns:

A dot representation containing all paths from src to dst. (https://graphviz.org/doc/info/lang.html)

close()[source]

Write the package to the filesystem. Any calls after close() are now invalid. It is preferable to use resource guard syntax instead:

with PackageExporter("file.zip") as e:
    ...

denied_modules() → list[str][source]

Return all modules that are currently denied.

Returns:

A list containing the names of modules which will be denied in this package.

deny(include: str | Iterable[str], *, exclude: str | Iterable[str] = ())[source]

Blocklist modules whose names match the given glob patterns from the list of modules the package can import. If a dependency on any matching packages is found, a PackagingError is raised.

Parameters:

• include (list[str] | str) – A string e.g. "my_package.my_subpackage", or list of strings for the names of the modules to be externed. This can also be a glob-style pattern, as described in mock().

• exclude (list[str] | str) – An optional pattern that excludes some patterns that match the include string.

dependency_graph_string() → str[source]

Returns digraph string representation of dependencies in package.

Returns:

A string representation of dependencies in package.

extern(include: str | Iterable[str], *, exclude: str | Iterable[str] = (), allow_empty: bool = True)[source]

Include module in the list of external modules the package can import. This will prevent dependency discovery from saving it in the package. The importer will load an external module directly from the standard import system. Code for extern modules must also exist in the process loading the package.

Parameters:

• include (list[str] | str) – A string e.g. "my_package.my_subpackage", or list of strings for the names of the modules to be externed. This can also be a glob-style pattern, as described in mock().

• exclude (list[str] | str) – An optional pattern that excludes some patterns that match the include string.

• allow_empty (bool) – An optional flag that specifies whether the extern modules specified by this call to the extern method must be matched to some module during packaging. If an extern module glob pattern is added with allow_empty=False, and close() is called (either explicitly or via __exit__) before any modules match that pattern, an exception is thrown. If allow_empty=True, no such exception is thrown.

externed_modules() → list[str][source]

Return all modules that are currently externed.

Returns:

A list containing the names of modules which will be externed in this package.

get_rdeps(module_name: str) → list[str][source]

Return a list of all modules which depend on the module module_name.

Returns:

A list containing the names of modules which depend on module_name.

get_unique_id() → str[source]

Get an id. This id is guaranteed to only be handed out once for this package.

intern(include: str | Iterable[str], *, exclude: str | Iterable[str] = (), allow_empty: bool = True)[source]

Specify modules that should be packaged. A module must match some intern pattern in order to be included in the package and have its dependencies processed recursively.

Parameters:

• include (list[str] | str) – A string e.g. “my_package.my_subpackage”, or list of strings for the names of the modules to be externed. This can also be a glob-style pattern, as described in mock().

• exclude (list[str] | str) – An optional pattern that excludes some patterns that match the include string.

• allow_empty (bool) – An optional flag that specifies whether the intern modules specified by this call to the intern method must be matched to some module during packaging. If an intern module glob pattern is added with allow_empty=False, and close() is called (either explicitly or via __exit__) before any modules match that pattern, an exception is thrown. If allow_empty=True, no such exception is thrown.

interned_modules() → list[str][source]

Return all modules that are currently interned.

Returns:

A list containing the names of modules which will be interned in this package.

mock(include: str | Iterable[str], *, exclude: str | Iterable[str] = (), allow_empty: bool = True)[source]

Replace some required modules with a mock implementation. Mocked modules will return a fake object for any attribute accessed from it. Because we copy file-by-file, the dependency resolution will sometimes find files that are imported by model files but whose functionality is never used (e.g. custom serialization code or training helpers). Use this function to mock this functionality out without having to modify the original code.

Parameters:

• include (list[str] | str) –

  A string e.g. "my_package.my_subpackage", or list of strings for the names of the modules to be mocked out. Strings can also be a glob-style pattern string that may match multiple modules. Any required dependencies that match this pattern string will be mocked out automatically.

  Examples :

  'tensorplay.**' – matches tensorplay and all submodules of tensorplay, e.g. 'tensorplay.nn' and 'tensorplay.nn.functional'

  'tensorplay.*' – matches 'tensorplay.nn' or 'tensorplay.functional', but not 'tensorplay.nn.functional'

• exclude (list[str] | str) – An optional pattern that excludes some patterns that match the include string. e.g. include='tensorplay.**', exclude='tensorplay.foo' will mock all tensorplay packages except 'tensorplay.foo', Default: is [].

• allow_empty (bool) – An optional flag that specifies whether the mock implementation(s) specified by this call to the mock() method must be matched to some module during packaging. If a mock is added with allow_empty=False, and close() is called (either explicitly or via __exit__) and the mock has not been matched to a module used by the package being exported, an exception is thrown. If allow_empty=True, no such exception is thrown.

mocked_modules() → list[str][source]

Return all modules that are currently mocked.

Returns:

A list containing the names of modules which will be mocked in this package.

register_extern_hook(hook: Callable[[PackageExporter, str], None]) → RemovableHandle[source]

Registers an extern hook on the exporter.

The hook will be called each time a module matches against an extern() pattern. It should have the following signature:

hook(exporter: PackageExporter, module_name: str) -> None

Hooks will be called in order of registration.

Returns:

A handle that can be used to remove the added hook by calling handle.remove().

Return type:

tensorplay.utils.hooks.RemovableHandle

register_intern_hook(hook: Callable[[PackageExporter, str], None]) → RemovableHandle[source]

Registers an intern hook on the exporter.

The hook will be called each time a module matches against an intern() pattern. It should have the following signature:

hook(exporter: PackageExporter, module_name: str) -> None

Hooks will be called in order of registration.

Returns:

A handle that can be used to remove the added hook by calling handle.remove().

Return type:

tensorplay.utils.hooks.RemovableHandle

register_mock_hook(hook: Callable[[PackageExporter, str], None]) → RemovableHandle[source]

Registers a mock hook on the exporter.

The hook will be called each time a module matches against a mock() pattern. It should have the following signature:

hook(exporter: PackageExporter, module_name: str) -> None

Hooks will be called in order of registration.

Returns:

A handle that can be used to remove the added hook by calling handle.remove().

Return type:

tensorplay.utils.hooks.RemovableHandle

save_binary(package, resource, binary: bytes)[source]

Save raw bytes to the package.

Parameters:

• package (str) – The name of module package this resource should go in (e.g. "my_package.my_subpackage").

• resource (str) – A unique name for the resource, used to identify it to load.

• binary (str) – The data to save.

save_module(module_name: str, dependencies=True)[source]

Save the code for module into the package. Code for the module is resolved using the importers path to find the module object, and then using its __file__ attribute to find the source code.

Parameters:

• module_name (str) – e.g. my_package.my_subpackage, code will be saved to provide code for this package.

• dependencies (bool, optional) – If True, we scan the source for dependencies.

save_pickle(package: str, resource: str, obj: Any, dependencies: bool = True, pickle_protocol: int = 3)[source]

Save a python object to the archive using pickle. Equivalent to tensorplay.save() but saving into the archive rather than a stand-alone file. Standard pickle does not save the code, only the objects. If dependencies is true, this method will also scan the pickled objects for which modules are required to reconstruct them and save the relevant code.

To be able to save an object where type(obj).__name__ is my_module.MyObject, my_module.MyObject must resolve to the class of the object according to the importer order. When saving objects that have previously been packaged, the importer’s import_module method will need to be present in the importer list for this to work.

Tensors inside obj are serialized by tensorplay’s own pickling support.

Parameters:

• package (str) – The name of module package this resource should go in (e.g. "my_package.my_subpackage").

• resource (str) – A unique name for the resource, used to identify it to load.

• obj (Any) – The object to save, must be picklable.

• dependencies (bool, optional) – If True, we scan the source for dependencies.

save_source_file(module_name: str, file_or_directory: str, dependencies=True)[source]

Adds the local file system file_or_directory to the source package to provide the code for module_name.

Parameters:

• module_name (str) – e.g. "my_package.my_subpackage", code will be saved to provide code for this package.

• file_or_directory (str) – the path to a file or directory of code. When a directory, all python files in the directory are recursively copied using save_source_file(). If a file is named "/__init__.py" the code is treated as a package.

• dependencies (bool, optional) – If True, we scan the source for dependencies.

save_source_string(module_name: str, src: str, is_package: bool = False, dependencies: bool = True)[source]

Adds src as the source code for module_name in the exported package.

Parameters:

• module_name (str) – e.g. my_package.my_subpackage, code will be saved to provide code for this package.

• src (str) – The Python source code to save for this package.

• is_package (bool, optional) – If True, this module is treated as a package. Packages are allowed to have submodules (e.g. my_package.my_subpackage.my_subsubpackage), and resources can be saved inside them. Defaults to False.

• dependencies (bool, optional) – If True, we scan the source for dependencies.

save_text(package: str, resource: str, text: str)[source]

Save text data to the package.

Parameters:

• package (str) – The name of module package this resource should go in (e.g. "my_package.my_subpackage").

• resource (str) – A unique name for the resource, used to identify it to load.

• text (str) – The contents to save.

Importers allow you to load code written to packages by PackageExporter. Code is loaded in a hermetic way, using files from the package rather than the normal python import system. This allows for the packaging of model code and data so that it can be run on a server or used in the future for transfer learning.

The importer for packages ensures that code in the module can only be loaded from within the package, except for modules explicitly listed as external during export. The file extern_modules in the zip archive lists all the modules that a package externally depends on. This prevents “implicit” dependencies where the package runs locally because it is importing a locally-installed package, but then fails when the package is copied to another machine.

file_structure(*, include: GlobPattern = '**', exclude: GlobPattern = ()) → Directory[source]

Returns a file structure representation of package’s zipfile.

Parameters:

• include (list[str] | str) – An optional string e.g. "my_package.my_subpackage", or optional list of strings for the names of the files to be included in the zipfile representation. This can also be a glob-style pattern, as described in PackageExporter.mock()

• exclude (list[str] | str) – An optional pattern that excludes files whose name match the pattern.

Returns:

Directory

get_name(obj: Any, name: str | None = None) → tuple[str, str]

Given an object, return a name that can be used to retrieve the object from this environment.

Parameters:

• obj – An object to get the module-environment-relative name for.

• name – If set, use this name instead of looking up __name__ or __qualname__ on obj. This is only here to match how Pickler handles __reduce__ functions that return a string, don’t use otherwise.

Returns:

A tuple (parent_module_name, attr_name) that can be used to retrieve obj from this environment. To use it:

mod = importer.import_module(parent_module_name)
obj = getattr(mod, attr_name)

Raises:

• ObjNotFoundError – we couldn’t retrieve obj by name.

• ObjMisMatchError – we found a different object with the same name as obj.

id()[source]

Returns internal identifier that tensorplay.package uses to distinguish PackageImporter instances. Looks like:

<tensorplay_package_0>

import_module(name: str, package=None)[source]

Load a module from the package if it hasn’t already been loaded, and then return the module. Modules are loaded locally to the importer and will appear in self.modules rather than sys.modules.

Parameters:

• name (str) – Fully qualified name of the module to load.

• package ([type], optional) – Unused, but present to match the signature of importlib.import_module. Defaults to None.

Returns:

The (possibly already) loaded module.

Return type:

types.ModuleType

load_binary(package: str, resource: str) → bytes[source]

Load raw bytes.

Parameters:

• package (str) – The name of module package (e.g. "my_package.my_subpackage").

• resource (str) – The unique name for the resource.

Returns:

The loaded data.

Return type:

bytes

load_pickle(package: str, resource: str, map_location=None) → Any[source]

Unpickles the resource from the package, loading any modules that are needed to construct the objects using import_module().

Parameters:

• package (str) – The name of module package (e.g. "my_package.my_subpackage").

• resource (str) – The unique name for the resource.

• map_location – Retained for interface compatibility; tensorplay embeds tensor payloads directly in the pickle stream, so no remapping of storage records takes place. Defaults to None.

Returns:

The unpickled object.

Return type:

Any

load_text(package: str, resource: str, encoding: str = 'utf-8', errors: str = 'strict') → str[source]

Load a string.

Parameters:

• package (str) – The name of module package (e.g. "my_package.my_subpackage").

• resource (str) – The unique name for the resource.

• encoding (str, optional) – Passed to decode. Defaults to 'utf-8'.

• errors (str, optional) – Passed to decode. Defaults to 'strict'.

Returns:

The loaded text.

Return type:

str

python_version()[source]

Returns the version of python that was used to create this package.

Note: this function is experimental and not Forward Compatible. The plan is to move this into a lock file later on.

Returns:

str | None a python version e.g. 3.8.9 or None if no version was stored with this package

whichmodule(obj: Any, name: str) → str

Find the module name an object belongs to.

This should be considered internal for end-users, but developers of an importer can override it to customize the behavior.

Based on the classic pickle.py approach, but modified to exclude the search into sys.modules.

Listing of different reasons a dependency may fail to package.

This enum is used to provide good error messages when PackagingError is raised.