Private artefact storage

In most cases, you don't want your artefact storage to be exposed to the world. We therefore support the use of private artefact (blob) storage for AWS S3,  Azure Blob Storage and GCS. Therefore, Deeploy can only retrieve artefacts from these locations using a set of credentials.

Blob credentials can be added in the Keychain, under Workspace > Settings > Keychain. When you create a new key, you will be asked for the following based on your storage type:

• Name
• Description (optional)
• Blob type

We listed the credentials formats for supported blob storage below:

Blob type	AWS S3	GCS	Azure Blob
Username	The IAM user access key with access to the S3 bucket	-	-
Password	The IAM user secret access key with access to the S3 bucket	Service account JSON	The Client Secret of the Azure storage service
Tenant ID	-	-	The Tenant ID of the Azure storage service
Account	-	-	The Account name used for the Azure URL
Client ID	-	-	The Client ID of the Azure storage service

After adding the new key, copy the ID and add it to the reference.json as shown in the example below.

# this reference is used in example repository: 
# https://gitlab.com/deeploy-ml/sample-models/example-pytorch-mnist
{  
 "reference": {
     "blob": {
         "url": "s3://deeploy-examples/pytorch/mnist/model",
         "credentialsId": "8bf16fee-9ccd-41f5-ae86-06d89801c819"
      }  
  } 
}

Deeploy will now use the new credentials to fetch the artefacts from the blob storage.

S3 Region

When specifying an s3 bucket in a different region than eu-central-1 (SaaS only) you need to specify it in the "blob" section.

# this reference is used in example repository: 
# https://gitlab.com/deeploy-ml/sample-models/example-pytorch-mnist
{  
 "reference": {
     "blob": {
         "url": "s3://deeploy-examples/pytorch/mnist/model",
         "credentialsId": "8bf16fee-9ccd-41f5-ae86-06d89801c819",
         "region": "eu-west-1",
     }  
  }
}