I am attempting to train an AI model using Google Vertex AI.
I am using a custom container to and starting with specified machineType and acceleratorType.
My application requires nvidia-driver >455, but the machines come with 450.51.06.
Is there a way to update the nvidia driver version on the machine running my custom container?
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I am trying to deploy a simple support vector regression (SVR) model to Vertex AI using scikit-learn version 1.0, but I am encountering the following error:
Failed to create endpoint "adpool_eval" due to the error: model server container out of memory,
please use a larger machine type for model deployment:
https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types.
The model is saved as "model.joblib" in a cloud storage bucket as required by Vertex AI.
The model is very small and simple so I can't understand how it is running out of memory. I tried machines with much more RAM and CPUs and the issue still persists. The cloud storage bucket, model registry, and endpoint are all in the same region (europe-west1).
The issue seems to be with saving the model using the joblib library. I used the pickle library instead and I managed to deploy the same model without any issues on the n1-standard machine.
Because of certain VPC restrictions I am forced to use custom containers for predictions for a model trained on Tensorflow. According to the documentation requirements I have created a HTTP server using Tensorflow Serving. The Dockerfile used to build the image is as follows:
FROM tensorflow/serving:2.4.1-gpu
# copy the model file
ENV MODEL_NAME=my_model
COPY my_model /models/my_model
Where my_model contains the saved_model inside a folder named 1/.
I have then pushed this image to a Google Container Repository and then created a Model by using Import an existing custom container and changing the Port to 8501. However when trying to deploy the model to an endpoint using a single compute node of type n1-standard-16 and 1 P100 GPU the deployment runs into the below error:
Failed to create session: Internal: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
I am unable to figure how this is happening. I am able to run the same docker image on my local machine and I am able to successfully get predictions by hitting the endpoint that is created: http://localhost:8501/v1/models/my_model:predict
Any help is this regard will be appreciated.
The issue has been solved by downgrading Tensorflow serving image to 2.3.0-gpu version. According to the error context, the CUDA driver in the custom model image doesn't match the appropriate driver version in GCP AI Platform training cluster.
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 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.