Deploy a job from a Dockerfile

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What you'll learn

  • Creating a Docker file for our training code.
  • Deploying our training code as a job using a Dockerfile via servicefoundry

This is a guide to deploy training code as a job using Dockerfile via servicefoundry

After you complete the guide, you will have a successful deployed job. Your jobs deployment dashboard will look like this:

Project structure

To complete this guide, you are going to create the following files:

  • train.py : contains our training code
  • Dockerfile: contains our docker image build instructions
  • deploy.py/deploy.yaml: contains our deployment code / deployment configuration. (Depending on whether you choose to use our python SDK or create a YAML file)
  • requirements.txt : contains our dependencies.

Your final file structure is going to look like this:

.
├── app.py
├── Dockerfile
├── deploy.py / deploy.yaml
└── requirements.txt

As you can see, all the following files are created in the same folder/directory.

Step 1: Implement the training code

The first step is to create a job that trains a scikit learn model on iris dataset

We start with a train.py containing our training code and requirements.txt with our dependencies.

.
├── train.py
└── requirements.txt

train.py

Click on the Open Recipe below to understand the train.py:

requirements.txt

pandas==1.4.4
numpy==1.22.4
scikit-learn==1.1.2

# for deploying our job deployments
servicefoundry>=0.1.97,<0.2.0

Step 2: Dockerize the training code

Now we will create the Dockerfile for the training code.

.
├── app.py
├── Dockerfile
└── requirements.txt

Dockerfile

The Dockerfile contains instructions to build the image.

Step 3: Deploying as a job

You can deploy services on TrueFoundry programmatically either using our Python SDK, or via a YAML file.

So now you can choose between either creating a deploy.py file, which will use our Python SDK.
Or you can choose to create a deploy.yaml configuration file and then use the servicefoundry deploy command

Via python SDK

File Structure

.
├── train.py
├── deploy.py
└── requirements.txt

Follow the recipe below to understand the deploy.py file :-

To deploy the job using Python API use:

python deploy.py

Via YAML file

File Structure

.
├── train.py
├── deploy.yaml
└── requirements.txt

Follow the recipe below to understand the deploy.yaml file :-

To deploy the job using Python API use:

servicefoundry deploy --workspace-fqn YOUR_WORKSPACE_FQN --file deploy.yaml

Run the above command from the same directory containing the train.py and requirements.txt files.

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.tfyignore files

If there are any files you don't want to be copied to the workspace, like a data file, or any redundant files. You can use .tfyignore files in that case.

End result

On successful deployment, the Job will be created and run immediately.

We can now visit our Applications page to check Build status, Build Logs, Runs History and monitor progress of runs.
See Monitoring and Debugging guide for more details.

See Also