PrimeHub
  • Introduction
  • Installation
  • Tiers and Licenses
  • End-to-End Tutorial
    • 1 - MLOps Introduction and Scoping the Project
    • 2 - Train and Manage the Model
    • 3 - Compare, Register and Deploy the Model
    • 4 - Build the Web Application
    • 5 - Summary
  • User Guide
    • User Portal
    • Notebook
      • Notebook Tips
      • Advanced Settings
      • PrimeHub Notebook Extension
      • Submit Notebook as Job
    • Jobs
      • Job Artifacts
      • Tutorial
        • (Part1) MNIST classifier training
        • (Part2) MNIST classifier training
        • (Advanced) Use Job Submission to Tune Hyperparameters
        • (Advanced) Model Serving by Seldon
        • Job Artifacts Simple Usecase
    • Models
      • Manage and Deploy Model
      • Model Management Configuration
    • Deployments
      • Pre-packaged servers
        • TensorFlow server
        • PyTorch server
        • SKLearn server
        • Customize Pre-packaged Server
        • Run Pre-packaged Server Locally
      • Package from Language Wrapper
        • Model Image for Python
        • Model Image for R
        • Reusable Base Image
      • Prediction APIs
      • Model URI
      • Tutorial
        • Model by Pre-packaged Server
        • Model by Pre-packaged Server (PHFS)
        • Model by Image built from Language Wrapper
    • Shared Files
    • Datasets
    • Apps
      • Label Studio
      • MATLAB
      • MLflow
      • Streamlit
      • Tutorial
        • Create Your Own App
        • Create an MLflow server
        • Label Dataset by Label Studio
        • Code Server
    • Group Admin
      • Images
      • Settings
    • Generate an PrimeHub API Token
    • Python SDK
    • SSH Server Feature
      • VSCode SSH Notebook Remotely
      • Generate SSH Key Pair
      • Permission Denied
      • Connection Refused
    • Advanced Tutorial
      • Labeling the data
      • Notebook as a Job
      • Custom build the Seldon server
      • PrimeHub SDK/CLI Tools
  • Administrator Guide
    • Admin Portal
      • Create User
      • Create Group
      • Assign Group Admin
      • Create/Plan Instance Type
      • Add InfuseAI Image
      • Add Image
      • Build Image
      • Gitsync Secret for GitHub
      • Pull Secret for GitLab
    • System Settings
    • User Management
    • Group Management
    • Instance Type Management
      • NodeSelector
      • Toleration
    • Image Management
      • Custom Image Guideline
    • Volume Management
      • Upload Server
    • Secret Management
    • App Settings
    • Notebooks Admin
    • Usage Reports
  • Reference
    • Jupyter Images
      • repo2docker image
      • RStudio image
    • InfuseAI Images List
    • Roadmap
  • Developer Guide
    • GitHub
    • Design
      • PrimeHub File System (PHFS)
      • PrimeHub Store
      • Log Persistence
      • PrimeHub Apps
      • Admission
      • Notebook with kernel process
      • JupyterHub
      • Image Builder
      • Volume Upload
      • Job Scheduler
      • Job Submission
      • Job Monitoring
      • Install Helper
      • User Portal
      • Meta Chart
      • PrimeHub Usage
      • Job Artifact
      • PrimeHub Apps
    • Concept
      • Architecture
      • Data Model
      • CRDs
      • GraphQL
      • Persistence Storages
      • Persistence
      • Resources Quota
      • Privilege
    • Configuration
      • How to configure PrimeHub
      • Multiple Jupyter Notebook Kernels
      • Configure SSH Server
      • Configure Job Submission
      • Configure Custom Image Build
      • Configure Model Deployment
      • Setup Self-Signed Certificate for PrimeHub
      • Chart Configuration
      • Configure PrimeHub Store
    • Environment Variables
Powered by GitBook
On this page
  • Model Information
  • Example
  1. User Guide
  2. Deployments
  3. Pre-packaged servers

TensorFlow server

PreviousPre-packaged serversNextPyTorch server

Last updated 2 years ago

Model Information

Basic

Property
Description

Model Image

infuseai/tensorflow2-prepackaged:v0.2.0

Input

ndarray or image

Output

ndarray

Repository

Compatibility of TensorFlow 2

Model Format
Support

SavedModel

Yes

HDF5

Yes

Compatibility of TensorFlow 1

Model Format
Support

*.pb

No

checkpoint

No

SavedModel

No

HDF5

Yes

Model URI Structure

SavedModel Format

We support TensorFlow2 . The model uri structure is just the output of tf.saved_model.save().

<model uri>
├── saved_model.pb
└── variables
    ├── variables.data-00000-of-00001
    └── variables.index

HDF5 Format

<model uri>
└── model.h5
  1. model.h5: The file is HDF5 format, and can be any file name with .h5 file extension.

MLflow model

<model uri>
├── MLmodel
└── <model files>

How It Works

Load the model

def load(self):
    model_uri = self.model_uri
    # check model exported from mlflow.tensorflow.autolog()
    if os.path.isfile(os.path.join(model_uri, 'MLmodel')):
        if os.path.isdir(os.path.join(model_uri, 'data/model')):
            print("Loading model from tensorflow.keras.Model.fit + mlflow.tensorflow.autolog()")
            model_uri = os.path.join(model_uri, 'data/model')
        elif os.path.isdir(os.path.join(model_uri, 'tfmodel')):
            print("Loading model from tensorflow.estimator.Estimator.train + mlflow.tensorflow.autolog()")
            model_uri = os.path.join(model_uri, 'tfmodel')

    self.use_keras_api = 1
    if tf.saved_model.contains_saved_model(model_uri):
        self.model = tf.saved_model.load(model_uri).signatures["serving_default"]
        if 'saved_model' not in str(type(self.model)):
            self.use_keras_api = 0
        else:
            del self.model
    if self.use_keras_api:
        if not glob.glob(os.path.join(model_uri, '*.h5')):
            self.model = tf.keras.models.load_model(model_uri)
        else:
            self.model = tf.keras.models.load_model(glob.glob(os.path.join(model_uri, '*.h5'))[0])
    self.loaded = True
    print(f"Use Keras API: {self.use_keras_api}")
    print(f"Model input layer: {self.model.inputs[0]}")

Predict

def predict(self, X):
    if not self.loaded:
        self.load()
    if self.use_keras_api:
        return self.model.predict(X)
    else:
        output = self.model(tf.convert_to_tensor(X, self.model.inputs[0].dtype))
        return output[next(iter(output))].numpy()

Example

Property
Description

Model Image

infuseai/tensorflow2-prepackaged:v0.2.0

Model URI

gs://primehub-models/tensorflow2/mnist (SavedModel) or gs://primehub-models/tensorflow2/mnist-h5 (HDF5)

ndarray

Test Request

curl -X POST http://localhost:9000/api/v1.0/predictions \
    -H 'Content-Type: application/json' \
    -d '{ "data": {"ndarray": [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.32941176470588235, 0.7254901960784313, 0.6235294117647059, 0.592156862745098, 0.23529411764705882, 0.1411764705882353, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8705882352941177, 0.996078431372549, 0.996078431372549, 0.996078431372549, 0.996078431372549, 0.9450980392156862, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.7764705882352941, 0.6666666666666666, 0.20392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2627450980392157, 0.4470588235294118, 0.2823529411764706, 0.4470588235294118, 0.6392156862745098, 0.8901960784313725, 0.996078431372549, 0.8823529411764706, 0.996078431372549, 0.996078431372549, 0.996078431372549, 0.9803921568627451, 0.8980392156862745, 0.996078431372549, 0.996078431372549, 0.5490196078431373, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06666666666666667, 0.25882352941176473, 0.054901960784313725, 0.2627450980392157, 0.2627450980392157, 0.2627450980392157, 0.23137254901960785, 0.08235294117647059, 0.9254901960784314, 0.996078431372549, 0.41568627450980394, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3254901960784314, 0.9921568627450981, 0.8196078431372549, 0.07058823529411765, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08627450980392157, 0.9137254901960784, 1.0, 0.3254901960784314, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5058823529411764, 0.996078431372549, 0.9333333333333333, 0.17254901960784313, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23137254901960785, 0.9764705882352941, 0.996078431372549, 0.24313725490196078, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5215686274509804, 0.996078431372549, 0.7333333333333333, 0.0196078431372549, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03529411764705882, 0.803921568627451, 0.9725490196078431, 0.22745098039215686, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.49411764705882355, 0.996078431372549, 0.7137254901960784, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.29411764705882354, 0.984313725490196, 0.9411764705882353, 0.2235294117647059, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07450980392156863, 0.8666666666666667, 0.996078431372549, 0.6509803921568628, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.011764705882352941, 0.796078431372549, 0.996078431372549, 0.8588235294117647, 0.13725490196078433, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14901960784313725, 0.996078431372549, 0.996078431372549, 0.30196078431372547, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12156862745098039, 0.8784313725490196, 0.996078431372549, 0.45098039215686275, 0.00392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5215686274509804, 0.996078431372549, 0.996078431372549, 0.20392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23921568627450981, 0.9490196078431372, 0.996078431372549, 0.996078431372549, 0.20392156862745098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4745098039215686, 0.996078431372549, 0.996078431372549, 0.8588235294117647, 0.1568627450980392, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4745098039215686, 0.996078431372549, 0.8117647058823529, 0.07058823529411765, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]] } }'

Test Result

{"data":{"names":[],"ndarray":[[2.2179587233495113e-07,1.2331390131237185e-08,2.5685869331937283e-05,0.0001267452462343499,3.6731301333858823e-10,8.802298339105619e-07,1.7313735514723483e-11,0.9998445510864258,5.112421490593988e-07,1.4923105027264683e-06]]},"meta":{"requestPath":{"model":"infuseai/tensorflow2-prepackaged:v0.2.0"}}}

Image

Test Request

curl -F 'binData=@test_image.jpg' http://localhost:9000/api/v1.0/predictions

Test Result

{"data":{"names":[],"tensor":{"shape":[1,10],"values":[2.240761034499883e-07,1.2446706776358951e-08,2.6079718736582436e-05,0.00012795037764590234,3.6888223031716905e-10,8.873528258845909e-07,1.7562255469338872e-11,0.9998427629470825,5.136774916536524e-07,1.4995322317190585e-06]}},"meta":{"requestPath":{"model":"infuseai/tensorflow2-prepackaged:v0.2.0"}}}

We also support which is saved from Keras API in both TensorFlow 2 and TensorFlow 1.

We also support MLflow model in Tensorflow Flavor and Keras Flavor which are exported from .

You can check the detailed code in the . Here, we brief the code as follows.

The example uses the , which is used in .

SavedModel format
HDF5 format
MLflow autologging API
Github
Keras MNIST dataset
tensorflow tutorial
Link