Truefoundry Docs

In this guide, we’ll deploy a Job to train a machine learning model. The model will learn to predict the species of an iris flower based on its sepal length, sepal width, petal length, and petal width. There are three species: Iris setosa, Iris versicolor, and Iris virginica.

Project Setup

We’ve already prepared the training script that trains a model on the Iris dataset, and you can find the code in our GitHub Repository. Clone the GitHub repository with the following command:

git clone https://github.com/truefoundry/getting-started-examples.git

Navigate to the project directory:

cd train-model

Please review the job code to become familiar with the code you’ll deploy.

Project Structure

The project files are organised as follows:

.
├── train.py - Contains the training script code.
└── requirements.txt - Contains the list of all dependencies.

All these files are located in the same directory.

Prerequisites

Before you proceed with the guide, make sure you have the following:

TrueFoundry CLI: Set up and configure the TrueFoundry CLI tool on your local machine by following the Setup for CLI guide.
Workspace: To deploy your job, you’ll need a workspace. If you don’t have one, you can create it using this guide: Creating a Workspace or seek assistance from your cluster administrator.

Deploying the Job

Create a deploy.py file in the same directory as your Job code (app.py). This file will contain the necessary configuration for your Job. Your directory structure will then appear as follows: File Structure

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

`deploy.py`

import argparse
import logging
from truefoundry.deploy import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

# Create a TrueFoundry **Job** object to configure your service
job = Job(
  # Specify the name of the job
  name="your-job",
  # Define how to build your code into a Docker image
  image=Build(
    # `PythonBuild` helps specify the details of your Python Code.
    # These details will be used to templatize a DockerFile to build your Docker Image
    build_spec=PythonBuild(
      # Define the command to run the training script
      command="python train.py",
      # Specify the path to requirements file
      requirements_path="requirements.txt",
    )
  ),
  # Define the resource constraints.
  #
  # Requests are the minimum amount of resources that a container needs to run.
  # Limits are the maximum amount of resources that a container can use.
  #
  # If a container tries to use more resources than its limits, it will be throttled or killed.
  resources=Resources(
    # CPU is specified as a number. 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core.
    cpu_request=0.2,
    cpu_limit=0.5,

    # Memory is defined as an integer and the unit is Megabytes.
    memory_request=200,
    memory_limit=500,

    # Ephemeral storage is defined as an integer and the unit is Megabytes.
    ephemeral_storage_request=1000,
    ephemeral_storage_limit=2000,
  ),
  # Define environment variables that your Job will have access to
  env={
    "ENVIRONMENT": "dev"
  }
)


# Deploy the job to the specified workspace, copy workspace FQN using the following guide
# https://docs.truefoundry.com/docs/key-concepts#creating-a-workspace
job.deploy(workspace_fqn="your-workspace-fqn")

To understand the code, you can click the following recipe:

Deploy Training Code as a Job

Setup the project

First, you need to import the following modules:

argparse from Python’s standard library.
logging from Python’s standard library.
Import the necessary classes (Build, PythonBuild, Service, Resources, and Port) from servicefoundry.

Set up logging to display servicefoundry logs by configuring the log level to INFO.

import argparse
import logging
from servicefoundry import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

Setup Job

Now, it’s time to define the properties of the Job:

Specify the name of the job, which will be its identifier in TrueFoundry’s deployments dashboard.
Define the image with instructions on how to build the container image.
Configure the resources for the application.

import argparse
import logging
from servicefoundry import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

# Create a TrueFoundry **Job** object to configure your service
job = Job(
  # Specify the name of the job
  name="iris-train-job",
  # Define how to build your code into a Docker image
  image=Build(
    # `PythonBuild` helps specify the details of your Python Code.
    # These details will be used to templatize a DockerFile to build your Docker Image
    build_spec=PythonBuild(
      # Define the command to run the training script
      command="python train.py",
      # Specify the path to requirements file
      requirements_path="requirements.txt",
    )
  ),
  # Define the resource constraints.
  #
  # Requests are the minimum amount of resources that a container needs to run.
  # Limits are the maximum amount of resources that a container can use.
  #
  # If a container tries to use more resources than its limits, it will be throttled or killed.
  resources=Resources(
    # CPU is specified as a number. 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core.
    cpu_request=0.2,
    cpu_limit=0.5,

    # Memory is defined as an integer and the unit is Megabytes.
    memory_request=200,
    memory_limit=500,

    # Ephemeral storage is defined as an integer and the unit is Megabytes.
    ephemeral_storage_request=1000,
    ephemeral_storage_limit=2000,
  ),
  # Define environment variables that your Job will have access to
  env={
    "ENVIRONMENT": "dev"
  }
)


# Deploy the job to the specified workspace, copy workspace FQN using the following guide
# https://docs.truefoundry.com/docs/creating-a-workspace#workspace-fully-qualified-name-fqn
job.deploy(workspace_fqn="your-workspace-fqn")

Define Code to Docker Image Build Instructions

For defining how to build your code into a Docker image, use the Build class:

Specify the build_source to determine the source code location. If not provided, the current working directory is used.
Define the build_spec using the PythonBuild class to set up a Python environment.

import argparse
import logging
from servicefoundry import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

# Create a TrueFoundry **Job** object to configure your service
job = Job(
  # Specify the name of the job
  name="iris-train-job",
  # Define how to build your code into a Docker image
  image=Build(
    # `PythonBuild` helps specify the details of your Python Code.
    # These details will be used to templatize a DockerFile to build your Docker Image
    build_spec=PythonBuild(
      # Define the command to run the training script
      command="python train.py",
      # Specify the path to requirements file
      requirements_path="requirements.txt",
    )
  ),
  # Define the resource constraints.
  #
  # Requests are the minimum amount of resources that a container needs to run.
  # Limits are the maximum amount of resources that a container can use.
  #
  # If a container tries to use more resources than its limits, it will be throttled or killed.
  resources=Resources(
    # CPU is specified as a number. 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core.
    cpu_request=0.2,
    cpu_limit=0.5,

    # Memory is defined as an integer and the unit is Megabytes.
    memory_request=200,
    memory_limit=500,

    # Ephemeral storage is defined as an integer and the unit is Megabytes.
    ephemeral_storage_request=1000,
    ephemeral_storage_limit=2000,
  ),
  # Define environment variables that your Job will have access to
  env={
    "ENVIRONMENT": "dev"
  }
)


# Deploy the job to the specified workspace, copy workspace FQN using the following guide
# https://docs.truefoundry.com/docs/creating-a-workspace#workspace-fully-qualified-name-fqn
job.deploy(workspace_fqn="your-workspace-fqn")

Configure the Python Build

In the PythonBuild class, provide the following arguments:

command: The command to start your service.
requirements_path: The path to your dependencies file.

import argparse
import logging
from servicefoundry import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

# Create a TrueFoundry **Job** object to configure your service
job = Job(
  # Specify the name of the job
  name="iris-train-job",
  # Define how to build your code into a Docker image
  image=Build(
    # `PythonBuild` helps specify the details of your Python Code.
    # These details will be used to templatize a DockerFile to build your Docker Image
    build_spec=PythonBuild(
      # Define the command to run the training script
      command="python train.py",
      # Specify the path to requirements file
      requirements_path="requirements.txt",
    )
  ),
  # Define the resource constraints.
  #
  # Requests are the minimum amount of resources that a container needs to run.
  # Limits are the maximum amount of resources that a container can use.
  #
  # If a container tries to use more resources than its limits, it will be throttled or killed.
  resources=Resources(
    # CPU is specified as a number. 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core.
    cpu_request=0.2,
    cpu_limit=0.5,

    # Memory is defined as an integer and the unit is Megabytes.
    memory_request=200,
    memory_limit=500,

    # Ephemeral storage is defined as an integer and the unit is Megabytes.
    ephemeral_storage_request=1000,
    ephemeral_storage_limit=2000,
  ),
  # Define environment variables that your Job will have access to
  env={
    "ENVIRONMENT": "dev"
  }
)


# Deploy the job to the specified workspace, copy workspace FQN using the following guide
# https://docs.truefoundry.com/docs/creating-a-workspace#workspace-fully-qualified-name-fqn
job.deploy(workspace_fqn="your-workspace-fqn")

Specify resource constraints.

For all deployments, specify resource constraints such as CPU and memory using the Resources class. This ensures proper deployment on the cluster.

cpu_request: Specifies the minimum CPU reserved for the application (0.5 represents 50% of CPU resources).
cpu_limit: Defines the upper limit on CPU usage, beyond which the application is throttled.
memory_request: Specifies the minimum required memory (e.g., 1 means 1 MB).
memory_limit: Sets the maximum memory allowed; exceeding this limit triggers an Out of Memory (OOM) error.

import argparse
import logging
from servicefoundry import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

# Create a TrueFoundry **Job** object to configure your service
job = Job(
  # Specify the name of the job
  name="iris-train-job",
  # Define how to build your code into a Docker image
  image=Build(
    # `PythonBuild` helps specify the details of your Python Code.
    # These details will be used to templatize a DockerFile to build your Docker Image
    build_spec=PythonBuild(
      # Define the command to run the training script
      command="python train.py",
      # Specify the path to requirements file
      requirements_path="requirements.txt",
    )
  ),
  # Define the resource constraints.
  #
  # Requests are the minimum amount of resources that a container needs to run.
  # Limits are the maximum amount of resources that a container can use.
  #
  # If a container tries to use more resources than its limits, it will be throttled or killed.
  resources=Resources(
    # CPU is specified as a number. 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core.
    cpu_request=0.2,
    cpu_limit=0.5,

    # Memory is defined as an integer and the unit is Megabytes.
    memory_request=200,
    memory_limit=500,

    # Ephemeral storage is defined as an integer and the unit is Megabytes.
    ephemeral_storage_request=1000,
    ephemeral_storage_limit=2000,
  ),
  # Define environment variables that your Job will have access to
  env={
    "ENVIRONMENT": "dev"
  }
)


# Deploy the job to the specified workspace, copy workspace FQN using the following guide
# https://docs.truefoundry.com/docs/creating-a-workspace#workspace-fully-qualified-name-fqn
job.deploy(workspace_fqn="your-workspace-fqn")

Specifying environment variables

You can also provide environment variables using a dictionary of the format {"env_var_name": "env_var_value". This is helpful for configurations like environment type (dev/prod) or model registry links.

import argparse
import logging
from servicefoundry import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

# Create a TrueFoundry **Job** object to configure your service
job = Job(
  # Specify the name of the job
  name="iris-train-job",
  # Define how to build your code into a Docker image
  image=Build(
    # `PythonBuild` helps specify the details of your Python Code.
    # These details will be used to templatize a DockerFile to build your Docker Image
    build_spec=PythonBuild(
      # Define the command to run the training script
      command="python train.py",
      # Specify the path to requirements file
      requirements_path="requirements.txt",
    )
  ),
  # Define the resource constraints.
  #
  # Requests are the minimum amount of resources that a container needs to run.
  # Limits are the maximum amount of resources that a container can use.
  #
  # If a container tries to use more resources than its limits, it will be throttled or killed.
  resources=Resources(
    # CPU is specified as a number. 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core.
    cpu_request=0.2,
    cpu_limit=0.5,

    # Memory is defined as an integer and the unit is Megabytes.
    memory_request=200,
    memory_limit=500,

    # Ephemeral storage is defined as an integer and the unit is Megabytes.
    ephemeral_storage_request=1000,
    ephemeral_storage_limit=2000,
  ),
  # Define environment variables that your Job will have access to
  env={
    "ENVIRONMENT": "dev"
  }
)


# Deploy the job to the specified workspace, copy workspace FQN using the following guide
# https://docs.truefoundry.com/docs/creating-a-workspace#workspace-fully-qualified-name-fqn
job.deploy(workspace_fqn="your-workspace-fqn")

Deploy the job

Use job.deploy() to initiate the deployment. Provide the workspace_fqn (fully qualified workspace name) to specify where the service should be deployed.

import argparse
import logging
from servicefoundry import Build, Job, PythonBuild, Resources

# Set up logging to display informational messages
logging.basicConfig(level=logging.INFO)

# Create a TrueFoundry **Job** object to configure your service
job = Job(
  # Specify the name of the job
  name="iris-train-job",
  # Define how to build your code into a Docker image
  image=Build(
    # `PythonBuild` helps specify the details of your Python Code.
    # These details will be used to templatize a DockerFile to build your Docker Image
    build_spec=PythonBuild(
      # Define the command to run the training script
      command="python train.py",
      # Specify the path to requirements file
      requirements_path="requirements.txt",
    )
  ),
  # Define the resource constraints.
  #
  # Requests are the minimum amount of resources that a container needs to run.
  # Limits are the maximum amount of resources that a container can use.
  #
  # If a container tries to use more resources than its limits, it will be throttled or killed.
  resources=Resources(
    # CPU is specified as a number. 1 CPU unit is equivalent to 1 physical CPU core, or 1 virtual core.
    cpu_request=0.2,
    cpu_limit=0.5,

    # Memory is defined as an integer and the unit is Megabytes.
    memory_request=200,
    memory_limit=500,

    # Ephemeral storage is defined as an integer and the unit is Megabytes.
    ephemeral_storage_request=1000,
    ephemeral_storage_limit=2000,
  ),
  # Define environment variables that your Job will have access to
  env={
    "ENVIRONMENT": "dev"
  }
)


# Deploy the job to the specified workspace, copy workspace FQN using the following guide
# https://docs.truefoundry.com/docs/creating-a-workspace#workspace-fully-qualified-name-fqn
job.deploy(workspace_fqn="your-workspace-fqn")

To deploy using Python SDK use the following command from the same directory containing the train.py and requirements.txt files.

python deploy.py

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

Exclude files when building and deploying your source code:

To exclude specific files from being built and deployed, create a .tfyignore file in the directory containing your deployment script (deploy.py). The .tfyignore file follows the same rules as the .gitignore file.If your repository already has a .gitignore file, you don’t need to create a .tfyignore file. Service Foundry will automatically detect the files to ignore.Place the .tfyignore file in the project’s root directory, alongside deploy.py.

After running the command mentioned above, wait for the deployment process to complete. Monitor the status until it shows DEPLOY_SUCCESS:, indicating a successful deployment.

Once deployed, you’ll receive a dashboard access link in the output, typically mentioned as You can find the application on the dashboard:. Click that link to access the deployment dashboard.

View your deployed job

On successful deployment your Job will be displayed as Suspended (yellow) indicating that your Job has been deployed but it will not run automatically.

Run your job

To run your Job you will have to trigger it manually.

Congratulations! You have successfully deployed and run your training Job. To learn how to interact with your job check out this guide: Interacting with your Job

Getting Started

Train and Deploy Models

Service Deployment

Job Deployment

LLM Deployment

LLM Finetuning

Workflow Deployment

Async Service Deployment

Volumes

ML Repository

LLM Tracing

Platform

Deploying On Your Own Cloud

Deploy And Run Job Using Python SDK

Project Setup

Project Structure

Prerequisites

Deploying the Job

`deploy.py`

Exclude files when building and deploying your source code:

View your deployed job

Run your job

Getting Started

Train and Deploy Models

Service Deployment

Job Deployment

LLM Deployment

LLM Finetuning

Workflow Deployment

Async Service Deployment

Volumes

ML Repository

LLM Tracing

Platform

Deploying On Your Own Cloud

​Project Setup

​Project Structure

​Prerequisites

​Deploying the Job

​deploy.py

​Exclude files when building and deploying your source code:

​View your deployed job

​Run your job

Project Setup

Project Structure

Prerequisites

Deploying the Job

`deploy.py`

Exclude files when building and deploying your source code:

View your deployed job

Run your job