Content for /docs

content/docs/api-reference/apply.md

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+# apply
+
+Apply provided model against provided data
+
+    Args:
+        model (ModelMeta): MLEM model.
+        data (Any): Input to the model.
+        method (str, optional): Which model method to use.
+            If None, use the only method model has.
+            If more than one is available, will fail.
+        output (str, optional): If value is provided,
+            assume it's path and save output there.
+        link (bool): Whether to create a link to saved output in MLEM root folder.
+        external (bool): Whether to save result outside mlem dir
+
+    Returns:
+        If `output=None`, returns results for given data.
+            Otherwise returns None.

content/docs/api-reference/clone.md

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+# clone
+
+Clones MLEM object from `path` to `out` and returns Python representation for
+the created object
+
+    Args:
+        path (str): Path to the object. Could be local path or path inside a git repo.
+        target (str): Path to save the copy of initial object to.
+        repo (Optional[str], optional): URL to repo if object is located there.
+        rev (Optional[str], optional): revision, could be git commit SHA, branch name or tag.
+        fs (Optional[AbstractFileSystem], optional): filesystem to load object from
+        target_repo (Optional[str], optional): path to repo to save cloned object to
+        target_fs (Optional[AbstractFileSystem], optional): target filesystem
+        follow_links (bool, optional): If object we read is a MLEM link, whether to load
+            the actual object link points to. Defaults to True.
+        load_value (bool, optional): Load actual python object incorporated in MlemMeta object. Defaults to False.
+        link: whether to create link in target repo
+        external: wheter to put object inside mlem dir in target repo
+
+    Returns:
+        MlemMeta: Copy of initial object saved to `out`

content/docs/api-reference/deploy.md

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+# deploy

content/docs/api-reference/import_object.md

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+# import_object
+
+Try to load an object as MLEM model (or dataset) and return it, optionally
+saving to the specified target location

content/docs/api-reference/index.md

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+# Python API
+
+MLEM can be used as a python library, simply [install](/doc/install) with `pip`
+or `conda`. This reference provides the details about the functions in the API
+module `mlem.api`, which can be imported in any regular way, for example:
+
+```py
+import mlem.api
+```
+
+The purpose of this API is to provide programmatic access to operate on models
+and datasets from Python code.
+
+Please choose a function from the navigation sidebar to the left, or click the
+`Next` button below to jump into the first one ↘

content/docs/api-reference/init.md

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+# mlem.api.init()
+
+Creates `.mlem` directory in `path`
+
+```py
+def init(path: str = ".") -> None
+```
+
+### Usage:
+
+```py
+from mlem.api import init
+
+init(path)
+```
+
+## Description
+
+Initializes a MLEM repository by creating a `.mlem` directory inside the given
+path. A new and empty `config.yaml` is also created inside it.
+
+## Parameters
+
+- **`path`** (required) - location of the target where an MLEM repository has to
+  be initialized i.e. a `.mlem` folder has to be created.
+
+## Exceptions
+
+None
+
+## Examples
+
+```py
+from mlem.api import init
+
+init()
+```
+
+> By default, the path is `.` i.e. the current directory

content/docs/api-reference/link.md

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+# link
+
+Creates MlemLink for an `source` object and dumps it if `target` is provided
+
+    Args:
+        source (Union[str, MlemMeta]): The object to create link from.
+        source_repo (str, optional): Path to mlem repo where to load obj from
+        rev (str, optional): Revision if object is stored in git repo.
+        target (str, optional): Where to store the link object.
+        target_repo (str, optional): If provided,
+            treat `target` as link name and dump link in MLEM DIR
+        follow_links (bool): Whether to make link to the underlying object
+            if `source` is itself a link. Defaults to True.
+        external (bool): Whether to save link outside mlem dir
+        absolute (bool): Whether to make link absolute or relative to mlem repo
+
+    Returns:
+        MlemLink: Link object to the `source`.

content/docs/api-reference/load.md

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+# mlem.api.load()
+
+Load python object saved by MLEM
+
+```py
+def load(
+    path: str,
+    repo: Optional[str] = None,
+    rev: Optional[str] = None,
+    batch_size: Optional[int] = None,
+    follow_links: bool = True,
+) -> Any
+```
+
+### Usage:
+
+```py
+import os
+from mlem.api import load
+
+out_path = os.path.join(os.getcwd(), "saved-model")
+loaded = load(out_path)
+```
+
+## Description
+
+Loads a python object from a given path. The path can belong to different file
+systems (eg: `S3`). The function returns the underlying python object saved by
+MLEM.
+
+## Parameters
+
+- **`path`** (required) - Path to the object. Could be local path or path inside
+  a git repo.
+- `repo` (optional) - URL to repo if object is located there.
+- `rev` (optional) - revision, could be git commit SHA, branch name or tag.
+- `follow_links` (optional) - If object we read is a MLEM link, whether to load
+  the actual object link points to. Defaults to True.
+
+## Exceptions
+
+None
+
+## Example: Load a trained model saved with MLEM
+
+```py
+import os
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+from mlem.api import load
+
+path = os.path.join(os.getcwd(), "saved-model")
+
+model = load(path)
+assert isinstance(model, DecisionTreeClassifier)
+train, _ = load_iris(return_X_y=True)
+model.predict(train)
+```

content/docs/api-reference/load_meta.md

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+# mlem.api.load_meta()
+
+Loads MlemObject from a given path
+
+```py
+def load_meta(
+    path: str,
+    repo: Optional[str] = None,
+    rev: Optional[str] = None,
+    follow_links: bool = True,
+    load_value: bool = False,
+    fs: Optional[AbstractFileSystem] = None,
+    *,
+    force_type: Optional[Type[T]] = None,
+) -> MlemObject
+```
+
+### Usage:
+
+```py
+import os
+from mlem.api import load_meta
+
+out_path = os.path.join(os.getcwd(), "saved-model")
+loaded = load_meta(out_path)
+```
+
+## Description
+
+Loads a [MlemObject](/doc/user-guide/basic-concepts#mlem-objects) from a given
+path. This differs from [load](/doc/api-reference/load) since the latter loads
+the actual python object incorporated within MlemObject. In fact, `load` uses
+`load_meta` beneath and uses its `get_value()` method to get the underlying
+python object.
+
+## Parameters
+
+- **`path`** (required) - Path to the object. Could be local path or path inside
+  a git repo.
+- `repo` (optional) - URL to repo if object is located there.
+- `rev` (optional) - revision, could be git commit SHA, branch name or tag.
+- `follow_links` (optional) - If object we read is a MLEM link, whether to load
+  the actual object link points to. Defaults to True.
+- `load_value` (optional) - Load actual python object incorporated in
+  MlemObject. Defaults to False.
+- `fs` (optional) - filesystem to load from. If not provided, will be inferred
+  from path
+- `force_type` (optional) - type of meta to be loaded. Defaults to MlemObject
+  (any mlem meta)
+
+## Exceptions
+
+- `WrongMetaType` - Thrown if the loaded meta object has a different type than
+  what is expected (force_type or MlemObject)
+
+## Examples
+
+```py
+import os
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+
+from mlem.core.objects import MlemModel
+from mlem.api import load_meta
+
+train, _ = load_iris(return_X_y=True)
+out_path = os.path.join(os.getcwd(), "saved-model")
+meta = load_meta(out_path, load_value=True, force_type=MlemModel)
+
+model = meta.get_value()
+assert isinstance(model, DecisionTreeClassifier)
+model.predict(train)
+```

content/docs/api-reference/ls.md

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+# mlem.api.ls()
+
+Get a view of the MLEM repository by listing all of its MLEM Objects
+
+```py
+def ls(
+    repo: str = ".",
+    rev: Optional[str] = None,
+    fs: Optional[AbstractFileSystem] = None,
+    type_filter: Union[
+        Type[MlemObject], Iterable[Type[MlemObject]], None
+    ] = None,
+    include_links: bool = True,
+) -> Dict[Type[MlemObject], List[MlemObject]]
+```
+
+### Usage:
+
+```py
+from mlem.api import ls
+
+objects = ls(".", rev=None, type_filter=None, include_links=True)
+```
+
+## Description
+
+Populates a dictionary where keys are different `types` of
+[MlemObjects](/doc/user-guide/basic-concepts#mlem-objects) and values are a
+collection of MlemObjects of that type. This API is internally used by the CLI
+command [list](/doc/command-reference/list).
+
+## Parameters
+
+- **`repo`** (required) - Path or URL to repo
+- `rev` (optional) - revision, could be git commit SHA, branch name or tag.
+- `fs` (optional) - filesystem to load from. If not provided, will be inferred
+  from repo
+- `type_filter` (optional) - type of objects to be listed (eg: models / dataset
+  / etc.)
+- `include_links` (optional) - whether to include links while fetching the list
+  of MlemObjects. Defaults to True
+
+## Exceptions
+
+None
+
+## Examples
+
+```py
+from mlem.api import ls
+
+objects = ls(".")
+```

content/docs/api-reference/mlem-object.md

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+# MlemObject API
+
+[comment]: <> (list notable functions of `MlemMeta` subclasses )
+
+- MlemMeta.read
+- MLemMeta.load_value
+- MlemMeta.get_value
+- MlemMeta.dump
+- MlemMeta.make_link
+- MlemMeta.clone
+- MlemMeta.update
+
+- MlemLink.load_link
+- MlemLink.parse_link
+- MlemLink.from_location
+
+- ModelMeta.from_obj
+- DatasetMeta.from_data
+
+- TargetEnvMeta.deploy
+- TargetEnvMeta.destroy
+- TargetEnvMeta.get_status
+
+Same for DeployMeta

content/docs/api-reference/pack.md

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+# pack
+
+Package a [MLEM model](/doc/user-guide/mlem-abcs#modeltype) in
+docker-build-ready folder or directly build a docker image.
+
+    Args:
+        packager (Union[str, Packager]): Packager to use.
+            Out-of-the-box supported string values are "docker_dir" and "docker".
+        model (Union[str, ModelMeta]): The model to pack.