In Java, for instance, we have a class that represents the SageMaker client class: AmazonSageMakerClient, but I couldn't find the equivalent for Python.
I was hoping to be able to do something like:
from sagemaker import SageMakerClient
client: SageMakerClient = boto3.client("sagemaker")
I looked into the library code and docs but I couldn't find any references to such class containing the defined methods for that client. In fact, I couldn't find any classes for AWS clients like s3, sqs, etc. Are those hidden somewhere or am I missing something obvious?
In boto3, there is basically 2 levels of objects avaialble:
A client
Actual objects like you are asking about
Take a look at S3, and you will see that in addition to the Client object there are also other rich object types like Bucket.
It would seem that Sagemaker doesn't (yet) have this second level of abstraction available.
To be more productive, and work with Python classes rather than Json, try to use the SageMaker Python SDK whenever possible rather than Boto3 clients.
With Boto3 you have several SageMaker clients (As #anon said correctly):
SageMaker - Most of SageMaker features
SageMakerRuntime - Invoking endpoints
SageMaker* - Other misc SageMaker features like feature store, edge manager, ...
The boto3-stubs library can help with this.
Install using the instructions for your IDE on the package page, and then install the specific type annotations for SageMaker.
pip install 'boto3-stubs[sagemaker]'
You should be able to see type hints for the client object (type: SageMakerClient).
import boto3
client = boto3.client('sagemaker')
If you need to add hints yourself:
from mypy_boto3_sagemaker import SageMakerClient
def my_func(client: SageMakerClient):
client.create_algorithm(...)
Related
I'm creating a few GCP cloud armor policies across multiple projects using the Python client library and attaching them to several backend services using the .set_security_policy() method
I know you can do it using the console / gcloud but I need to automate this in Python
I've tried the .update() method in google-cloud-compute but that did not work out
from google.cloud import compute, compute_v1
client = compute.BackendServicesClient()
backend_service_resource = compute_v1.types.BackendService(security_policy="")
client.update(project='project_id',
backend_service='backend_service',
backend_service_resource=backend_service_resource)
The error I got when running the above code is
google.api_core.exceptions.BadRequest: 400 PUT https://compute.googleapis.com/compute/v1/projects/<project-id>/global/backendServices/<backend-name>: Invalid value for field 'resource.loadBalancingScheme': 'INVALID_LOAD_BALANCING_SCHEME'. Cannot change load balancing scheme.
When I specify loadBalancingScheme then the same error occurs with another resource value. At run-time I would not have information of all the meta data of the backend-service and some meta-data might not be initialized in the first place.
This is for anyone who had similar issues in the future. I was originally going to call the gcloud commands through python using os.system() as #giles-roberts recommended, but then I stumbled across a proper way to to do this using the Client Libraries
You simply use the same .set_security_policy() to set the security policy in the first place but this time make the policy as None. This is not quite obvious since the name of the security policy has to be a string in the documentation and it does not accept an empty string either.
from google.cloud import compute, compute_v1
client = compute.BackendServicesClient()
resource = compute_v1.types.SecurityPolicyReference(security_policy=None)
error = client.set_security_policy(project='<project_id>',
backend_service='<backend_service>',
security_policy_reference_resource=resource)
I am trying to use AWS python library boto3 to create a session. I found out we can do that either
session = boto3.Session(profile_name='profile1')
or
session2 = boto3.session.Session(profile_name='profile2')
I have checked their docs, it suppose to use boto3.session.Session().
Why both ways work ? What the different of concept behind them ?
It is just for convenience; they both refer to the same class. What is happening here is that the __init__.py for the python boto3 package includes the following:
from boto3.session import Session
This just allows you to refer to the Session class in your python code as boto3.Session rather than boto3.session.Session.
This article provides more information about this python idiom:
One common thing to do in your __init__.py is to import selected Classes, functions, etc into the package level so they can be conveniently imported from the package.
1) Instantiate an AWS Linux, micro instance using the AWS python API (include authentication to AWS)
2) Update the instance with tags: customer=ACME, environment=PROD
3) Assign a security group to the instance
To program in Python on AWS, you should use the boto3 library.
You will need to do the following:
supply credentials to the library (link)
create an EC2 client (link)
use the EC2 client to launch EC2 instances using run_instances (link)
You can specify both tags and security groups in the run_instances call. Additionally, the boto3 documentation provides some Amazon EC2 examples that will help.
Maybe you want to observe this project:
https://github.com/nchammas/flintrock
This is a hadoop and apache spark clustering project. But, it can inspire you.
Actually, there is many feature that you want like security group or filtering by tag name. Just, look around of code
I am new to python.
I need to get the Usage details using python sdk.
I am able to do the same using the usage detail API.
But unable to do so using the sdk.
I am trying to use the azure.mgmt.consumption.operations.UsageDetailsOperations class. The official docs for UsageDetailsOperations
https://learn.microsoft.com/en-us/python/api/azure-mgmt-consumption/azure.mgmt.consumption.operations.usage_details_operations.usagedetailsoperations?view=azure-python#list-by-billing-period
specifies four parameters to create the object
(i.e.client:Client for service requests,config:Configuration of service client,
serializer:An object model serializer,deserializer:An object model deserializer).
Out of these parameters I only have the client.
I need help understanding how to get the other three parameters or is there another way to create the UsageDetailsOperations object.
Or is there any other approach to get the usage details.
Thanks!
This class is not designed to be created manually, you need to create a consumption client, which will have an attribute "usages" which will be the class in question (instanciated correctly).
There is unfortunately no samples for consumption yet, but creating the client will be similar to creating any other client (see Network client creation for instance).
For consumption, what might help is the tests, since they give some idea of scenarios:
https://github.com/Azure/azure-sdk-for-python/blob/fd643a0/sdk/consumption/azure-mgmt-consumption/tests/test_mgmt_consumption.py
If you're new to Azure and Python, you might want to do this quickstart:
https://learn.microsoft.com/en-us/azure/python/python-sdk-azure-get-started
Feel free to open an issue in the main Python repo, asking for more documentation about this client (this will help prioritize it):
https://github.com/Azure/azure-sdk-for-python/issues
(I'm working at Microsoft in the Python SDK team).
I need to upload new device tokens to AWS SNS, and would rather doing it in batches instead of one token at a time.
According to the AWS documentation this is supported by their API, and an example is given for the Java SDK using a "bulkupload package".
The problem is that I wrote everything in Python and I can't find any reference to this feature in the Boto3 documentation.
Do you know of a way to do this in Python (not necessarily using Boto)? Or am I doomed to either uploading tokens one by one or rewrite everything in Java?
Thanks!