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Truss configuration

Truss is configurable to its core. Every Truss must include a file config.yaml in its root directory, which is automatically generated when the Truss is created. However, configuration is optional. Every configurable value has a sensible default, and a completely empty config file is valid.
The iris sklearn Truss has a good example of a typical Truss config:
model_framework: sklearn
model_metadata:
model_binary_dir: model
supports_predict_proba: true
python_version: py39
requirements:
- scikit-learn==1.0.2
- threadpoolctl==3.0.0
- joblib==1.1.0
- numpy==1.20.3
- scipy==1.7.3
More examples can be found here.
Let's investigate the various values that can be set in the config file.

model_framework

There are many popular frameworks for creating ML models, and this field represents the framework used. Many models are made with multiple frameworks, such as a keras model wrapping tensorflow inside. This field refers to the outermost layer of any such composite model. Supported values:
custom
huggingface
keras
lightgbm
pytorch
sklearn
tensorflow
xgboost
This list is expected to grow. In fact, adding support for an ML framework you're familiar with can be a good contribution to Truss.

model_type

A model type is typically a subcategory under a model framework.
For example for HuggingFace the model_type field maps to the pipeline task such as text-classification.

model_name

This is the display name of a model. This name may be used for identifying a model when deployed to model serving platforms such as Baseten.

model_module_dir

A model in a Truss is ultimately a Python module. This is the directory in a truss that represents that module. This directory is called model by default.

model_class_filename

Truss models are represented by a model class inside a python module. This field represents the file that holds that class. This file is called model.py by default.

model_class_name

This is the class that represents a model in Truss. This class is called Model by default.

data_dir

Most Truss models need access to a serialized model binary. For example, Tensorflow models use the SavedModel representation, while Joblib and pickle are other common serialization formats. Truss provides a way of bundling any blobs that are then made available to the model class constructor. Any blobs simply need to be placed in a data directory. This field is used to specify the name of that directory under a truss and is called data by default.
When the model class is instantiated at serving time, the model class constructor is passed a parameter called data_dir, which points to a pathlib path to a directory on disk where the blobs are provided. This mechanism provides flexibility to the model server on how to make the blobs available, and ease to the model code.
For example, the iris model bundles the model binary at data/model/model.joblib.
The model constructor in the model class then uses the data_dir parameter to load the model binary from.

input_type

This field is reserved for representing the type of input that the model expects. It isn't used currently, but is a good place to put this type information for documentation.

model_metadata

This is a catchall for any information that a model needs, that can't be represented in one of the other fields. The whole config is available to the model at runtime, so this is a good place to store any custom information that model needs then. For example, sklearn models include a flag here that indicates whether the model supports returning probabilities alongside predictions.
model_metadata:
supports_predict_proba: true

requirements

This field represents the Python dependencies that the model module depends on. The requirements should be provided in the pip requirements file format, but as a list, with each list entry being the line in the requirements file.
requirements:
- scikit-learn==1.0.2
- threadpoolctl==3.0.0
- joblib==1.1.0
- numpy==1.20.3
- scipy==1.7.3

system_packages

Truss assumes the Debian operating system. This field can be used to specify any system packages that you would typically install using apt.
system_packages:
- ffmpeg
- libsm6
- libxext6

environment_variables

Do not store secret values directly in environment variables (or anywhere in the config file). See the section on secrets below for information on properly managing secrets.
Any environment variables can be provided here as key value pairs and are exposed to the environment that the model executes in. Many Python libraries can be customized using environment variables, so this field can be quite handy in those scenarios.
environment_variables:
AWS_ACCESS_KEY_ID: AKIAIOSFODNN7EXAMPLE
AWS_SECRET_ACCESS_KEY: DummyDefaultToBeReplacedAtRuntime

resources

Indicate runtime resources such as CPU, RAM and GPU that the model ideally needs. These requirements are a suggestion, the model serving environment may not always be able to honor them.
resources:
cpu: 800m
memory: 1Gi
use_gpu: true

python_version

Version of python to use for serving the model. Currently supported python version are:
  1. 1.
    py37 -- python 3.7
  2. 2.
    py38 -- python 3.8
  3. 3.
    py39 -- python 3.9

examples_filename

Having inputs to try a model with is extremely handy, it makes the model much more approachable. Examples can be bundled with a Truss, in the form of a list. Each element of the list should be a dictionary with:
  1. 1.
    name Name of the example
  2. 2.
    input Input for that example
Currently, this file should be yaml and is called examples.yaml by default. This field can be used to customize the name of this file although there's hardly ever a reason to.
The truss cli has handy methods for listing and running examples.

secrets

This field can be used to specify the keys for such secrets and dummy default values -- Never store actual secret values in the config. Dummy default values are instructive of what the actual values look like and thus act as documentation of the format.
A model may depend on certain secret values that can't be bundled with the model and need to be bound securely at runtime. For example, a model may need to download information from s3 and may need access to AWS credentials for that.
It's important that the model lists all the secret keys that it expects at runtime. This not only serves as good practice but certain model serving frameworks may make available only the secrets for which keys are listed here.
secrets:
openai_api_key:
Secret names need to confirm to the k8s secret name guidelines.
How secrets are mounted at runtime depends on the serving environment. For example, when running on Docker locally, Truss allows specifying secrets in ~/.truss/config.yaml, while on Baseten, secrets can be specified as regular organization secrets.