Can we train a model by just giving data and related column names without creating trainer in Google Cloud ML either using Rest API or command line interface
Yes. You can use Google Cloud Datalab, which comes with a structured data solution. It has an easier interface and takes care of the trainer. You can view the notebooks without setting up Datalab:
https://github.com/googledatalab/notebooks/tree/master/samples/ML%20Toolbox
Once you set up Datalab, you can run the notebook. To set up Datalab, check https://cloud.google.com/datalab/docs/quickstarts.
Instead of building a model and calling CloudML service directly, you can try Datalab's ml toolbox which supports structured data and image classification. The ml toolbox takes your data, and automatically builds and trains a model. You just have to describe your data and what you want to do.
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.
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I have a .py file containing all the instructions to generate the predictions for some data.
Those data are taken from BigQuery and the predictions should be inserted in another BigQuery table.
Right now the code is running on a AIPlatform Notebook, but I want to schedule its execution every day, is there any way to do it?
I run into the AIPlatform Jobs, but I can't understand what should my code do and what should be the structure of the code, is there any step-by-step guide to follow?
You can schedule a Notebook execution using different options:
nbconvert
Different variants of the same technology:
nbconvert: Provides a convenient way to execute the input cells of an .ipynb notebook file and save the results, both input and output cells, as a .ipynb file.
papermill: is a Python package for parameterizing and executing Jupyter Notebooks. (Uses nbconvert --execute under the hood.)
notebook executor: This tool that can be used to schedule the execution of Jupyter notebooks from anywhere (local, GCE, GCP Notebooks) to the Cloud AI Deep Learning VM. You can read more about the usage of this tool here. (Uses gcloud sdk and papermill under the hood)
KubeFlow Fairing
Is a Python package that makes it easy to train and deploy ML models on Kubeflow. Kubeflow Fairing can also be extended to train or deploy on other platforms. Currently, Kubeflow Fairing has been extended to train on Google AI Platform.
AI Platform Notebook Executor There are two core functions of the Scheduler extension:
Ability to submit a Notebook to run on AI Platform’s Machine Learning Engine as a training job with a custom container image. This allows you to experiment and write your training code in a cost-effective single VM environment, but scale out to an AI Platform job to take advantage of superior resources (ie. GPUs, TPUs, etc.).
Scheduling a Notebook for recurring runs follows the exact same sequence of steps, but requires a crontab-formatted schedule option.
Nova Plugin: This is the predecessor of the Notebook Scheduler project. Allows you to execute notebooks directly from your Jupyter UI.
Notebook training
Python package allows users to run a Jupyter notebook at Google Cloud AI Platform Training Jobs.
GCP runner: Allows running any Jupyter notebook function on Google Cloud Platform
Unlike all other solutions listed above, it allows to run training for the whole project, not single Python file or Jupyter notebook
Allows running any function with parameters, moving from local execution to cloud is just a matter of wrapping function in a: gcp_runner.run_cloud(<function_name>, …) call.
This project is production-ready without any modifications
Supports execution on local (for testing purposes), AI Platform, and Kubernetes environments Full end to end example can be found here:
https://www.github.com/vlasenkoalexey/criteo_nbdev
tensorflow_cloud (Keras for GCP) Provides APIs that will allow to easily go from debugging and training your Keras and TensorFlow code in a local environment to distributed training in the cloud.
Update July 2021:
The recommended option in GCP is Notebook Executor which is already available in EAP.
I am aware that it is possible to deploy custom containers for training jobs on google cloud and I have been able to get the same running using command.
gcloud ai-platform jobs submit training infer name --region some_region --master-image-uri=path/to/docker/image --config config.yaml
The training job was completed successfully and the model was successfully obtained, Now I want to use this model for inference, but the issue is a part of my code has system level dependencies, so I have to make some modification into the architecture in order to get it running all the time. This was the reason to have a custom container for the training job in the first place.
The documentation is only available for the training part and the inference part, (if possible) with custom containers has not been explored to the best of my knowledge.
The training part documentation is available on this link
My question is, is it possible to deploy custom containers for inference purposes on google cloud-ml?
This response refers to using Vertex AI Prediction, the newest platform for ML on GCP.
Suppose you wrote the model artifacts out to cloud storage from your training job.
The next step is to create the custom container and push to a registry, by following something like what is described here:
https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements
This section describes how you pass the model artifact directory to the custom container to be used for interence:
https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#artifacts
You will also need to create an endpoint in order to deploy the model:
https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api#aiplatform_deploy_model_custom_trained_model_sample-gcloud
Finally, you would use gcloud ai endpoints deploy-model ... to deploy the model to the endpoint:
https://cloud.google.com/sdk/gcloud/reference/ai/endpoints/deploy-model
I am trying to setup a basic pytorch pipeline with google ai platform.
I don't understand how google storage works with ai-platform jobs.
I am trying to mount several google storage blobs to my ai-platform jobs but completely can not find how I can do it. I need to do two things: 1) access dataset from my python pytorch code and 2) after train finish access logs and models
In the Google AI Platform tutorials the only relevant concept I found is manually downloading the dataset to job local storage via python google.cloud.storage API and uploading the result after the program finish. But surely this is unacceptable in the situation of quick research iterations (because of large datasets and possible crashes in the middle of training).
What is the solutions for such a basic problem?
You can use Cloud Storage Fuse to mount your bucket and use it like it was a local folder to avoid data download.
I want to use a model that I have trained for inference on Google Cloud ML. It is a NLP model, and I want my node.js server to interact with the model to get predictions at train time.
I have a process for running inference on the model manually, that I would like to duplicate in the cloud:
Use Stanford Core NLP to tokenize my text and generate data files that store my tokenized text.
Have the model use those data files, create Tensorflow Examples out of it, and run the model.
Have the model print out the predictions.
Here is how I think I can replicate it in the Cloud:
Send the text to the cloud using my node.js server.
Run my python script to generate the data file. It seems like I will have to do this inside of a custom prediction routine. I'm not sure how I can use Stanford Core NLP here.
Save the data file in a bucket in Google Cloud.
In the custom prediction routine, load the saved data file and execute the model.
Can anyone tell me if this process is correct? Also, how can I run Stanford CoreNLP on Google Cloud custom prediction routine? Also, is there a way for me to just run command line scripts (for example for creating the data files I have a simple command that I normally just run to create them)?
You can implement a custom preprocessing method in Python and invoke the Stanford toolkit from there. See this blog and associated sample code for details: https://cloud.google.com/blog/products/ai-machine-learning/ai-in-depth-creating-preprocessing-model-serving-affinity-with-custom-online-prediction-on-ai-platform-serving
I am working on GCP to predict, I'm using the census dataset, actually I'm discovering google APIs ( ML Engine ...).
When I launch the prediction job , the job runs successfully, but it doesn't display the result.
Can anyone help ? Do you have any idea why it doesn't generate an output ?
Thanks in advance :)
This is the error that occurs
https://i.stack.imgur.com/9gyTb.png
This error is common when you train with one version of TF and then try serving with a lower version. For instance, if you are using Cloud console to deploy your model, it currently has no way of letting you select the version of TensorFlow for serving, so the model is deployed using TF 1.0, but your model may have been trained with a higher version of TF (current version is 1.7).
Although the Cloud console doesn't currently let you select the version (but it will soon!), using gcloud or the REST API directly does allow you to.
In the docs, there is a section on creating a model that has code snippets under "gcloud" and "python". With gcloud you simply add the argument --runtime-version=1.6 (or whatever version) and with python you add the property "runtimeVersion": "1.6" to the body of the request.