MLEM now offers deployment to Kubernetes and Sagemaker with a single command.
Apply a deployed-model (possibly remotely) to data. The results will be saved as
a MLEM Object to output if provided.
Otherwise, it will be printed to stdout.
usage: mlem apply-remote [--raw] [-d <path>] [-p <path>]
[--rev <commitish>] [-o <path>]
[--tp <path>] [-m <text>] [--json]
[-f <text>] [-h]
[<client> [client options] | --load <declaration>]
Builtin clients:
- http
- rmq
- sagemakerModels which are deployed somewhere remotely or are being served locally, can
have their methods called using the apply-remote command. This command is
similar to apply, with the only difference being
the model is deployed remotely using a deployment, or served locally. To access
the methods of the served model, a client is needed. Currently, the
available clients are http and rmq - which are used to launch requests
against the fastapi and rmq server types, correspondingly.
--raw <boolean> - Pass values as-is without serializers [default: False]-d <path>, --data <path> - Path to MLEM dataset [required]-p <path>, --project <path> - Path to MLEM project [default: (none)]--rev <commitish> - Repo revision to use [default: (none)]-o <path>, --output <path> - Where to save model outputs--tp <path>, --target-project <path> - Project to save target to [default:
(none)]-m <text>, --method <text> - Which model method is to be applied [default:
predict]--json - Output as json-f <text>, --file_conf <text> - File with options for client in format
field.name=path_to_config-h, --help - Show this message and exit.Given a hosted model server (see
serve example as a way to easily do
this) and a local MLEM dataset mydataset, run the following command to infer
the entire dataset with the model and save the output result to myprediction
$ mlem apply-remote http mydataset --host="127.0.0.1" --port=3000 --output myprediction