Deploy Google TimesFM using Inferless

Introduction

Google introduces TimesFM, a state-of-the-art time series forecasting model designed to push the boundaries of temporal data analysis. The TimesFM models leverage advanced deep learning techniques to provide accurate and robust forecasts. The models come pretrained and fine-tuned on diverse datasets, ensuring robust performance out of the box while also allowing for further customization and optimization. In this tutorial, we will explore how to deploy and utilize TimesFM using Inferless.

Our Observations

We have used Timesfm to deploy the model on a A100(80GB) system. Here are our observations:

Inference Time	Cold Start Time
0.12 sec	35.84 sec

Defining Dependencies

We have deployed the model using the Timesfm offical package.

Constructing the GitHub/GitLab Template

Now quickly construct the GitHub/GitLab template, this process is mandatory and make sure you don’t add any file named model.py.

Timesfm/
├── app.py
├── inferless-runtime-config.yaml
├── inferless.yaml
└── input_schema.py

You can also add other files to this directory.

Create the class for inference

In the app.py we will define the class and import all the required functions

def initialize: In this function, you will initialize your model and define any variable that you want to use during inference.
def infer: This function gets called for every request that you send. Here you can define all the steps that are required for the inference. You can also pass custom values for inference and pass it through inputs(dict) parameter.
def finalize: This function cleans up all the allocated memory.

import timesfm

class InferlessPythonModel:
    def initialize(self):
        self.tfm = timesfm.TimesFm(
            context_len=128,
            horizon_len=96,
            input_patch_len=32,
            output_patch_len=128,
            num_layers=20,
            model_dims=1280,
            backend="cpu",
        )
        self.tfm.load_from_checkpoint(repo_id="google/timesfm-1.0-200m")

    def infer(self,inputs):
        forecast_input = inputs["forecast_input"]
        frequency_input = inputs["frequency_input"]
        point_forecast, experimental_quantile_forecast = self.tfm.forecast(
            [forecast_input],
            freq=[frequency_input],
        )
        return {
            "point_forecast":point_forecast,
            "experimental_quantile_forecast":experimental_quantile_forecast
        }

    def finalize(self):
        pass

Create the Input Schema

We have to create a input_schema.py in your GitHub/Gitlab repository this will help us create the Input parameters. You can checkout our documentation on Input / Output Schema. For this tutorial, we have defined a parameter prompt which is required during the API call. Now lets create the input_schema.py.

INPUT_SCHEMA = {
    "forecast_input": {
        'datatype': 'FP32',
        'required': True,
        'shape': [100],
        'example': [0.0, 0.20064886, 0.39313661, 0.56963411, 0.72296256, 0.84688556, 0.93636273, 0.98775469, 0.99897117, 0.96955595, 0.90070545, 0.79522006, 0.65739025, 0.49282204, 0.30820902, 0.11106004, -0.09060615, -0.28858706, -0.47483011, -0.64176014, -0.7825875, -0.89158426, -0.96431712, -0.99782778, -0.99075324, -0.94338126, -0.85763861, -0.73701276, -0.58640998, -0.41195583, -0.22074597, -0.0205576, 0.18046693, 0.37415123, 0.55261747, 0.7086068, 0.83577457, 0.92894843, 0.98433866, 0.99969234, 0.97438499, 0.90944594, 0.8075165, 0.6727425, 0.51060568, 0.32770071, 0.13146699, -0.07011396, -0.26884313, -0.45663749, -0.62585878, -0.76962418, -0.88208623, -0.95867071, -0.99626264, -0.99333304, -0.95000106, -0.86802917, -0.75075145, -0.60293801, -0.43060093, -0.24074979, -0.0411065, 0.16020873, 0.35500771, 0.53536727, 0.69395153, 0.82431033, 0.9211415, 0.98050658, 0.99999098, 0.9788022, 0.91780205, 0.81947165, 0.68781042, 0.5281735, 0.34705389, 0.15181837, -0.04959214, -0.24898556, -0.43825186, -0.6096929, -0.75633557, -0.87221538, -0.95261911, -0.99427643, -0.995493, -0.95621934, -0.87805285, -0.76417283, -0.61921119, -0.44906404, -0.26065185, -0.06163804, 0.13988282, 0.33571414, 0.51789078, 0.67900297, 0.81249769, 0.91294525
        ]
    },
    "frequency_input": {
        'datatype': 'INT8',
        'required': True,
        'shape': [1],
        'example': [0]
    }
}

Creating the Custom Runtime

This is a mandatory step where we allow the users to upload their custom runtime through inferless-runtime-config.yaml.

build:
  run:
    - "git clone https://github.com/google-research/timesfm.git"
    - "cd timesfm"
    - "pip install -e ."
    - "pip install huggingface_hub[cli]==0.23.0 utilsforecast==0.1.10 praxis==1.4.0 paxml==1.4.0 einshape==1.0"
    - "pip install jax[cuda] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html jax==0.4.28"
    - "huggingface-cli login --token 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 --add-to-git-credential"

Method A: Deploying the model on Inferless Platform

Inferless supports multiple ways of importing your model. For this tutorial, we will use GitHub. Navigate to your desired workspace in Inferless and Click on Add a custom model button that you see on the top right. An import wizard will open up.

Step 2: Follow the UI to complete the model Import

Select the GitHub/GitLab Integration option to connect your source code repository with the deployment environment.
Navigate to the specific GitHub repository that contains your model’s code. Here, you will need to identify and enter the name of the model you wish to import.
Choose the appropriate type of machine that suits your model’s requirements. Additionally, specify the minimum and maximum number of replicas to define the scalability range for deploying your model.
Optionally, you have the option to enable automatic build and deployment. This feature triggers a new deployment automatically whenever there is a new code push to your repository.
If your model requires additional software packages, configure the Custom Runtime settings by including necessary pip or apt packages. Also, set up environment variables such as Inference Timeout, Container Concurrency, and Scale Down Timeout to tailor the runtime environment according to your needs.
Wait for the validation process to complete, ensuring that all settings are correct and functional. Once validation is successful, click on the “Import” button to finalize the import of your model.

Step 3: Wait for the model build to complete usually takes ~5-10 minutes

Step 4: Use the APIs to call the model

Once the model is in ‘Active’ status you can click on the ‘API’ page to call the model

Here is the Demo:

Method B: Deploying the model on Inferless CLI

Inferless allows you to deploy your model using Inferless-CLI. Follow the steps to deploy using Inferless CLI.

Clone the repository of the model

Let’s begin by cloning the model repository:

git clone https://github.com/inferless/Timesfm.git

Deploy the Model

To deploy the model using Inferless CLI, execute the following command:

inferless deploy --gpu A100 --runtime inferless-runtime-config.yaml

Explanation of the Command:

--gpu A100: Specifies the GPU type for deployment. Available options include A10, A100, and T4.
--runtime inferless-runtime-config.yaml: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.

How-to Guides

​Introduction

​Our Observations

​Defining Dependencies

​Constructing the GitHub/GitLab Template

​Create the class for inference

​Create the Input Schema

​Creating the Custom Runtime

​Method A: Deploying the model on Inferless Platform

​Step 1: Login to the inferless dashboard can click on Import model button

​Step 2: Follow the UI to complete the model Import

​Step 3: Wait for the model build to complete usually takes ~5-10 minutes

​Step 4: Use the APIs to call the model

​Here is the Demo:

​Method B: Deploying the model on Inferless CLI

​Clone the repository of the model

​Deploy the Model