Can any one help me how to access google cloud ml service using Rest API in python. I have tried it using postman but it gives 404 in response everytime.I have some project on google cloud I just want to use all the apis available on https://cloud.google.com/ml-engine/reference/rest/
You can find examples from Datalab:
Training service:
https://github.com/googledatalab/pydatalab/blob/master/google/datalab/ml/_job.py#L61
Prediction service:
https://github.com/googledatalab/pydatalab/blob/master/google/datalab/ml/_cloud_models.py#L225
Alternatively, instead of calling CloudML service endpoints directly, you can try Datalab's ml toolbox itself which supports structured data and image classification. You can view the notebooks first without setting up datalab:
https://github.com/googledatalab/notebooks/tree/master/samples/ML%20Toolbox
To set up Datalab and actually run these notebooks, see https://cloud.google.com/datalab/docs/quickstarts.
Related
I am trying to fetch list of available database versions using google cloud sqladmin API in python.
I am not able to figure endpoint to be used.
Here is rest api doc link.
Can you please guide me how to get solve this?
I am trying to deploy an already built image to Cloud Run using .NET Cloud Client Libraries.
I need exactly the same behavior as gcloud run deploy hello --image=us-docker.pkg.dev/cloudrun/container/hello but with .NET Cloud Client Libraries.
Unfortunately, I cannot find an API that does that in https://cloud.google.com/dotnet/docs/reference.
I also tried downloading Cloud SDK from https://cloud.google.com/sdk/docs/install and inspecting the code with PyCharm.
The API is called Cloud Run Admin API.
Cloud Run Admin API
There is an SDK for .NET.
Cloud Run Admin API Client Library for .NET
Namespace Google.Apis.CloudRun.v1
Creating a Cloud Run service is fairly complicated. I recommend that you study the REST API first so that you understand the request body. The .NET library models the REST API.
Method: namespaces.services.create
The key item is the service resource:
Resource: Service
There is a quick way to learn the API request body. Create a simple Cloud Run example and then add the command line option --log-http. Save the output to a file and then study the HTTP request parameters and request body to decipher the very large data structures that are required to create a service.
gcloud run deploy --log-http
I wrote two articles on the Cloud Run Admin API:
Google Cloud Run Deep Dive – Understanding the APIs – Part 1
Google Cloud Run Deep Dive – Understanding the APIs – Part 2
Note: I wrote those articles two years ago. Cloud Run has advanced a lot since then. However, these articles will help you understand the low-level details of the service that were not published elsewhere at the time.
I have deployed an AI model on Google Cloud Platform in the 'SavedModel' format. The model is able to make predictions when I enter sample input data through the 'Test & Use' facility on AI Platform.
Now, I would like to expose API endpoints so that other front end applications (eg. Android) can make use of this deployed model. What is the procedure to do so? I could not find much help in the documentations provided by Google.
I'm not 100% clear on your requirements, but you can make REST API calls with cURL.
ACCESS_TOKEN="$(gcloud auth application-default print-access-token)"
curl -X POST -d '{json-input}' https://ml.googleapis.com/v1/projects/{project-name}/models/{model-resource-name}:predict\?access_token\=${ACCESS_TOKEN}
Other resources:
https://cloud.google.com/ml-engine/docs/online-predict#requesting_predictions
https://cloud.google.com/ml-engine/docs/v1/predict-request#http-url-format
There are a lot of path's in order to serve your custom model but to make a concise answer to that question but you can deploy your custom model through an API endpoint following this workaround:
Upload your model into a bucket.
Create a model resource
Create a version.
Deploy
Once it's deployed, you can call the endpoint in your front-end applications.
(Bear in mind that batch predictions are not implemented on Python 3.x yet)
Endpoint URL request syntax:
POST https://ml.googleapis.com/v1/projects/{my-project}/models/{my-model}/versions/{my-version}:predict
Not specifying a version would redirect the request to your default version.
Here's some further information about prediction using the Java library an overview about ML Engine predictions.
how can i use google cloud vision with python django rest api? My task is that i have a picture,i have to find similer picture from an another picture.is there any other solution to do this task?
Here is a simple example app using the google cloud vision API with django. Of course it will take some adapting but it's something to get started with.
I've configured a project to work for Google Cloud ML beta, using instructions from https://cloud.google.com/ml/docs/how-tos/getting-set-up. Currently training a model using the instructions at https://cloud.google.com/ml/docs/quickstarts/training. Everything works like a charm, except there is no Console UI.
https://console.cloud.google.com/ml/jobs
URL not found
We couldn't find what you were looking for. Try one of the links below.
Google Developers Console Home
Google Cloud Platform
Google Developers
Where is the Google Cloud ML Console UI?
As noted, the service itself and the command line tools are up and running. The Console UI, however, is scheduled for release next week.