converter.utils.upload_model_dir()

converter.utils.upload_model_dir(model_dir, container, model_key, storage_key, storage_secret, storage_provider, platform)

Uploads model artifacts required to execute successful model converter job to cloud storage bucket of choice. Only applies to Azure ML or MLflow models.

Parameters

Parameter

Type

Description

Example

model_dir

str

Path to saved model directory

"./path/to/my/mlflow/model/directory/"

container

str

Storage provider container name

"my-bucket-name"

resources_key

str

Desired key for resource archive once uploaded to storage provider.

"path/to/resources.tar.gz"

The container and resources-key are appended to identify the full file path location for the desired uploaded file (e.g., "my-bucket-name/path/to/resources.tar.gz")

storage_key

str

Storage provider access key. For an AWS S3 bucket, this corresponds to your Access key ID. For Azure ML, this corresponds to your Storage account name. For Google Cloud Storage, this corresponds to your Client email.

storage_secret

str

Storage provider secret key. For an AWS S3 bucket, this corresponds to your Secret access key. For Azure ML, this corresponds to your Access Key. For Google Cloud storage, this corresponds to your Private key.

storage_provider

str

Storage provider name (must be one of "S3", "AZURE_BLOBS", or "GOOGLE_STORAGE").

"AZURE_BLOBS"

platform

str

Training platform used to develop model. Either "azure" or "mlflow"

"mlflow"

Returns

None

Examples

# Import some standard dependencies
import os
import json
import time
from modzy.converter.utils import upload_resources

blob_storage_provider = "S3"
blob_storage_container = "my-s3-bucket-name"

mlflow_model_dir = "local/path/to/mlflow-model/"
mlflow_model_key = "path/to/model.tar.gz"

upload_mlflow_model(
  mlflow_model_dir, 
  blob_storage_container, 
  mlflow_model_key,
  os.getenv("SP_ACCESS_KEY_ID"), 
  os.getenv("SP_SECRET_ACCESS_KEY"),
  blob_storage_provider,
  "mlflow"
)

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