Upload Models to the Machine Learning Repository Using the FrogML SDK

You can upload a model to a Machine Learning repository using the frogml.log_model() function. A model can be a single file or a list of files in a directory.

This function uses checksum upload, assigning a SHA2 value to each model for retrieval from storage. If this method fails, FrogML considers regular upload.Checksum-Based Storage

After uploading the model, FrogML generates a file named model-manifest.json, which contains the model name and its related files and dependencies.

To upload a model to a Machine Learning repository, use the following function:

import frogml
frogml.files.log_model(   repository="<REPOSITORY_KEY>",    # The JFrog repository to upload the model to.
   namespace="<NAMESPACE_NAME>",     # Optional. The namespace or organization.
   model_name="<MODEL_NAME>",     # The uploaded model name
   version="<MODEL_VERSION>",     # Optional. The uploaded model version
   source_path="<FILE_PATH>",     # The file path of the model on your machine.
   properties={<PROPERTIES>}    # Optional. These are properties you can set on the model to categorize and label it.
   dependencies=[<DEPENDENCY>, <DEPENDENCY>],  # Optional
   code_path= <CODE_PATH>
)

Parameters

Example

import frogml
frogml.files.log_model(
repository="frog-ml-local",
namespace="frog-ml-beta",     # Optional
model_name="my-cool-model",
version="1.0.0",    # Optional
source_path="~/root/models/my-cool-model/",
properties={"model_type": "keras", "experiment": "my-exp"} # Optional
dependencies=["numpy>=1.19"],  # Optional
code_path= "./src" #Optional

)