table of contents
DLMODEL_SOURCE(1) | User Commands | DLMODEL_SOURCE(1) |
NAME¶
dlmodel_source - create a structured deep learning model directorySYNOPSIS¶
dlmodel_sourceDESCRIPTION¶
dlmodel_source helps to create a structured deep learning (DL) model directory via CLI. Please refer to the specification section below for more details.The structured directory will be created in /tmp.
Questions for collecting model information:
- Package name
- The name of the DL package, e.g. inception.
- Package version
- The version of the DL package, e.g. 3.
- Modle filepath
- Absolute or relative path of the model file.
- Label filepath
- Absolute or relative path of the label file.
- Config name
- Name of the config data source (see config filepath below). If there is not any config, leave it blank.
- Config filepath
- Absolute or relative path of the config file.
- Inference engine
- The inference engine supporting the DL model's format.
MODEL DIRECTORY STRUCTURE¶
Example of a structured DL model directory:<modelname-version> |-- assets | |-- labels.txt | `-- <optional-config-files> |-- LICENSE # optional currently |-- meta.json `-- model.pb
METADATA FORMAT OF MODEL PACKAGE¶
Metadata, meta.json, describes all the details in the structured model directory.Example of meta.json
# Note: model directory name is fight-detection-1.0.0 { "name": "fight-detection", "version": "1.0.0", "inference-engine": "tensorflow", "model": "model.pb", "label": "assets/labels.txt", # optional configs "config": { "<optional-key>": "<optional-value>", "<optional-key>": "<optional-value>", ... }, "checksums-sha256": { "model.pb": "<sha256sum>", "assets/labels.txt": "<sha256sum>", "<optional-file-path>": "<sha256sum>", ... } }
SEE ALSO¶
dlmodel2deb(1)October 2017 | DLMobelBox |