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.commands.init()
+
+Creates `.mlem` directory in `path`
+
+```py
+def init(path: str = ".")  -> None
+```
+
+#### Usage:
+
+```py
+from mlem.api.commands 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.commands 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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+# load
+
+Load python object saved by MLEM
+
+    Args:
+        path (str): Path to the object. Could be local path or path inside a git repo.
+        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.
+        follow_links (bool, optional): If object we read is a MLEM link, whether to load the
+            actual object link points to. Defaults to True.
+
+    Returns:
+        Any: Python object saved by MLEM

content/docs/api-reference/load_meta.md

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+# load_meta
+
+Load MlemMeta object
+
+    Args:
+        path (str): Path to the object. Could be local path or path inside a git repo.
+        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.
+        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.
+        fs: filesystem to load from. If not provided, will be inferred from path
+        force_type: type of meta to be loaded. Defaults to MlemMeta (any mlem meta)
+    Returns:
+        MlemMeta: Saved MlemMeta object

content/docs/api-reference/ls.md

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+# 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
+
+Pack model 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.

content/docs/api-reference/save.md

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+# save()
+
+Saves given object to a given path
+
+```python
+def save(
+    obj: Any,
+    path: str,
+    repo: Optional[str] = None,
+    dvc: bool = False,
+    tmp_sample_data=None,
+    fs: Union[str, AbstractFileSystem] = None,
+    link: bool = None,
+    external: Optional[bool] = None,
+    description: str = None,
+    params: Dict[str, str] = None,
+    tags: List[str] = None,
+    update: bool = False,
+): ...
+```
+    Args:
+        obj: Object to dump
+        path: If not located on LocalFileSystem, then should be uri
+            or `fs` argument should be provided
+        repo: path to mlem repo (optional)
+        dvc: Store the object's artifacts with dvc
+        tmp_sample_data: If the object is a model or function, you can
+            provide input data sample, so MLEM will include it's schema
+            in the model's metadata
+        fs: FileSystem for the `path` argument
+        link: Whether to create a link in .mlem folder found for `path`
+        external: if obj is saved to repo, whether to put it outside of .mlem dir
+        description: description for object
+        params: arbitrary params for object
+        tags: tags for object
+        update: whether to keep old description/tags/params if new values were not provided
+
+    Returns:
+        None

content/docs/api-reference/serve.md

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+# serve
+
+Serve model via HTTP/HTTPS.
+
+    Args:
+        model (ModelMeta): The model to serve.
+        server (Union[Server, str]): Out-of-the-box supported one is "fastapi".

content/docs/cli-reference/apply-remote.md

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+# apply-remote
+
+Apply a model (deployed somewhere remotely) to a dataset. Resulting dataset will be saved as MLEM object to `output` if it is provided, otherwise will be printed.
+
+## Synopsis
+
+```usage
+usage: mlem apply-remote [options] [subtype] data
+
+arguments:
+[SUBTYPE]  Type of client. Choices: ['http', 'rmq']
+DATA       Path to dataset object  [required]
+```
+
+## Description
+
+Models which are deployed somewhere remotely or are being [served](/doc/get-started/serving) can have their methods called using the `apply-remote` command. This command is similar to [apply](/doc/cli-reference/apply), the only difference being that the model is deployed somewhere or is being served. To access the methods of this `served` model, a `client` is needed. The currently available clients are `http` and `rmq` corresponding to the `fastapi` and `rmq` server types. These clients are used to make requests to servers. The outputs and results (again as a MLEM Object) are stored or printed as usual.
+
+## Options
+
+- `-r, --repo TEXT`: Path to MLEM repo  [default: (none)]
+- `--rev TEXT`: Repo revision to use  [default: (none)]
+- `-o, --output TEXT`: Where to store the outputs.
+- `--target-repo, --tr TEXT`: Repo to save target to  [default: (none)]
+- `-m, --method TEXT`: Which model method is to be applied  [default: predict]
+- `--index / --no-index`: Whether to index output in .mlem directory
+- `--json`: Output as json
+- `-l, --load TEXT`: File to load client config from
+- `-c, --conf TEXT`: Options for client in format `field.name=value`
+- `-f, --file_conf TEXT`: File with options for client in format `field.name=path_to_config`
+- `-h, --help`: Show this message and exit.
+
+## Examples
+
+```mlem
+Apply hosted mlem model to local mlem dataset
+$ mlem apply-remote http mydataset -c host="0.0.0.0" -c port=3000 --output myprediction
+```
+
+> The hosted model above can be the example discussed [here](/doc/cli-reference/serve#examples) for instance or can be any other model.

content/docs/cli-reference/apply.md

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+# apply
+
+Apply a model to a dataset. Resulting dataset will be saved as MLEM object to
+`output` if it is provided, otherwise will be printed.
+
+## Synopsis
+
+```usage
+usage: mlem apply [options] model data
+
+arguments:
+MODEL  Path to model object  [required]
+DATA   Path to dataset object  [required]
+```
+
+## Description
+
+Models and Datasets which are [MLEM objects](/doc/user-guide/basic-concepts#mlem-objects) can be used directly through command line. Such models can be applied to a dataset i.e. their methods (such as predict) can be called with the dataset to get outputs and results (again as a MLEM Object). This workflow avoids the need of writing python scripts to load models and datasets and then subsequently use them.
+
+## Options
+
+- `-r, --repo TEXT`: Path to MLEM repo  [default: (none)]
+- `--rev TEXT`: Repo revision to use  [default: (none)]
+- `-o, --output TEXT`: Where to store the outputs.
+- `-m, --method TEXT`: Which model method is to be applied  [default: predict]
+- `--data-repo, --dr TEXT`: Repo with dataset
+- `--data-rev TEXT`: Revision of dataset
+- `-i, --import`: Try to import data on-the-fly
+- `--import-type, --it TEXT`: Specify how to read data file for import. Available types: ['pandas', 'pickle']
+- `-b, --batch_size INTEGER`: Batch size for reading data in batches.
+- `--index / --no-index`: Whether to index output in .mlem directory
+- `-e, --external`: Save result not in .mlem, but directly in repo
+- `--json`: Output as json
+- `-h, --help`: Show this message and exit.
+
+## Examples
+
+```mlem
+Apply local mlem model to local mlem dataset
+$ mlem apply mymodel mydatset --method predict --output myprediction
+
+Apply local mlem model to local data file
+$ mlem apply mymodel data.csv --method predict --import --import-type pandas[csv] --output myprediction
+
+Apply a version of remote model to a version of remote dataset
+$ mlem apply rf --repo https://github.com/iterative/example-mlem-get-started --rev main 
+                iris.csv --data-repo https://github.com/iterative/example-mlem-get-started --data-rev main
+                --method predict --output myprediction
+```

content/docs/cli-reference/clone.md

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+# clone
+
+Download MLEM object from `uri` and save it to `target`
+
+## Synopsis
+
+```usage
+usage: mlem clone [options] uri target
+
+arguments:
+URI     URI to object you want to clone  [required]
+TARGET  Path to store the downloaded object.  [required]
+```
+
+## Description
+
+Clones a [MLEM Object](/doc/user-guide/basic-concepts#mlem-objects) from source to target destination. This is useful in cases where you need the model without cloning the whole repository.
+
+## Options
+
+- `-r, --repo TEXT`: Path to MLEM repo  [default: (none)]
+- `--rev TEXT`: Repo revision to use  [default: (none)]
+- `--target-repo, --tr TEXT`: Repo to save target to  [default: (none)]
+- `-e, --external`: Save result not in .mlem, but directly in repo
+- `--link / --no-link`: Whether to create link for output in .mlem directory
+- `--help`: Show this message and exit.
+
+## Examples
+
+```mlem
+Copy remote model to local directory
+$ mlem clone rf --repo https://github.com/iterative/example-mlem-get-started --rev main mymodel
+
+Copy remote model to remote MLEM repo
+$ mlem clone rf --repo https://github.com/iterative/example-mlem-get-started --rev main mymodel --tr s3://mybucket/mymodel
+```