TensorFlow Serving setup - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is TensorFlow Serving?

TensorFlow Serving is a system for serving machine learning models in production. It helps you deploy models so applications can use them to make predictions.

beginner

What is the purpose of the model base path in TensorFlow Serving?

The model base path is the folder where your saved TensorFlow model files are stored. TensorFlow Serving loads models from this path to serve predictions.

intermediate

Which command starts TensorFlow Serving with a specific model directory?

You can start TensorFlow Serving using:
tensorflow_model_server --rest_api_port=8501 --model_name=my_model --model_base_path=/path/to/model
This command serves the model named 'my_model' from the given path on REST API port 8501.

beginner

What file format does TensorFlow Serving expect for models?

TensorFlow Serving expects models saved in the TensorFlow SavedModel format. This format includes the model architecture and weights in a folder structure.

intermediate

How can you check if TensorFlow Serving is running correctly?

You can check by sending a test request to the REST API endpoint, for example using curl:
curl http://localhost:8501/v1/models/my_model
If the server responds with model status, it is running correctly.

What port does TensorFlow Serving use by default for REST API?

A 8501

B 8080

C 5000

D 9000

TensorFlow Serving uses port 8501 by default for REST API requests.

Which format must your model be saved in to serve with TensorFlow Serving?

A HDF5

B Pickle

C SavedModel

D ONNX

TensorFlow Serving requires models saved in the TensorFlow SavedModel format.

What does the --model_name flag specify when starting TensorFlow Serving?

A The port number

B The name of the model to serve

C The path to the model

D The logging level

The --model_name flag tells TensorFlow Serving which model name to use for serving.

How do you specify the model location to TensorFlow Serving?

A --model_base_path

B --model_dir

C --model_path

D --model_location

The correct flag to specify the model folder is --model_base_path.

Which command can you use to test if TensorFlow Serving is running?

A ping localhost

B netstat -an

C tensorflow_model_server --status

D curl http://localhost:8501/v1/models/my_model

Sending a request to the model endpoint with curl checks if TensorFlow Serving responds.

Describe the steps to set up TensorFlow Serving for a saved model.

Think about saving, pointing to the model, and starting the server.

Explain how to verify that your TensorFlow Serving setup is working correctly.

Testing involves sending a request and checking the reply.

Practice

1. What is the main purpose of TensorFlow Serving in a machine learning workflow?

easy

A. To visualize training data and model metrics

B. To train new machine learning models from scratch

C. To preprocess data before training

D. To deploy trained models for real-time predictions

Solution

1. Step 1: Understand TensorFlow Serving's role

   TensorFlow Serving is designed to make trained models available for real-time use without retraining.

2. Step 2: Differentiate from other tasks

   Training, visualization, and preprocessing are separate steps not handled by TensorFlow Serving.

3. Final Answer:

   To deploy trained models for real-time predictions -> Option D

4. Quick Check:

   Serving = deploy models [OK]

Hint: Serving means making models ready for prediction [OK]

Common Mistakes:

• Confusing serving with training
• Thinking it handles data preprocessing
• Assuming it visualizes metrics

2. Which command correctly starts TensorFlow Serving to serve a model saved in the directory /models/my_model/1/ with model name my_model?

easy

A. tensorflow_model_server --model_name my_model --model_base_path /models/my_model/1/

B. tensorflow_model_server --model_name=my_model --model_path=/models/my_model/1/

C. tensorflow_model_server --model_name=my_model --model_base_path=/models/my_model/1/

D. tensorflow_serve --model_name=my_model --model_base_path=/models/my_model/1/

Solution

1. Step 1: Identify correct flags for TensorFlow Serving

   The correct flags are --model_name and --model_base_path with equal signs.

2. Step 2: Check command correctness

   tensorflow_model_server --model_name=my_model --model_base_path=/models/my_model/1/ uses correct command and flags with equal signs; others have syntax errors or wrong command name.

3. Final Answer:

   tensorflow_model_server --model_name=my_model --model_base_path=/models/my_model/1/ -> Option C

4. Quick Check:

   Correct flags and command = tensorflow_model_server --model_name=my_model --model_base_path=/models/my_model/1/ [OK]

Hint: Use equal signs with flags and correct command name [OK]

Common Mistakes:

• Using spaces instead of equal signs in flags
• Wrong command name like tensorflow_serve
• Using incorrect flag names like --model_path

3. Given the following Python code snippet to send data to a TensorFlow Serving REST API, what will be the printed output if the server returns a prediction of 0.85?

import requests
import json

url = 'http://localhost:8501/v1/models/my_model:predict'
data = {"instances": [[1.0, 2.0, 3.0]]}
response = requests.post(url, json=data)
prediction = response.json()['predictions'][0]
print(prediction)

medium

A. 0.85

B. [0.85]

C. {'predictions': [0.85]}

D. Error: KeyError

Solution

1. Step 1: Understand the response JSON structure

   The server returns a JSON with key 'predictions' holding a list of prediction values, e.g., {'predictions': [0.85]}.

2. Step 2: Extract the first prediction value

   Accessing response.json()['predictions'][0] extracts the float 0.85.

3. Final Answer:

   0.85 -> Option A

4. Quick Check:

   Extract first prediction value = 0.85 [OK]

Hint: Access ['predictions'][0] to get single prediction value [OK]

Common Mistakes:

• Printing the whole JSON instead of the value
• Forgetting to index the first prediction
• Assuming prediction is a list, not a float

4. You tried to start TensorFlow Serving with the command tensorflow_model_server --model_name=my_model --model_base_path=/models/my_model/1/ but got an error saying the model path does not exist. What is the most likely cause?

medium

A. The command should use --model_path instead of --model_base_path

B. The model directory path is incorrect or missing the version subfolder

C. TensorFlow Serving requires the model to be in a .pb file, not a folder

D. The model name must be the full path, not just a name

Solution

1. Step 1: Check model directory structure requirements

   TensorFlow Serving expects model_base_path to point to a directory containing version subfolders like '1', '2', etc.

2. Step 2: Identify common path mistakes

   If the path misses the version folder or is incorrect, the server cannot find the model and throws an error.

3. Final Answer:

   The model directory path is incorrect or missing the version subfolder -> Option B

4. Quick Check:

   Model path must include version folder [OK]

Hint: Model path must include version folder like /1/ [OK]

Common Mistakes:

• Using --model_path instead of --model_base_path
• Pointing to a file instead of a directory
• Confusing model name with path

5. You want to update your TensorFlow model served by TensorFlow Serving without downtime. Which approach allows you to serve the new model version while keeping the old version available for requests?

hard

A. Add the new model version in a new subfolder and update the model config file

B. Stop TensorFlow Serving, replace the old model folder, then restart the server

C. Serve the new model on a different port and manually switch clients

D. Retrain the model inside TensorFlow Serving using the new data

Solution

1. Step 1: Understand TensorFlow Serving versioning

   TensorFlow Serving supports multiple model versions by placing each version in its own numbered subfolder.

2. Step 2: Use model config file to add new version

   Adding the new version folder and updating the config allows serving both versions simultaneously without downtime.

3. Final Answer:

   Add the new model version in a new subfolder and update the model config file -> Option A

4. Quick Check:

   Versioned folders + config update = zero downtime [OK]

Hint: Use versioned folders and config to update without downtime [OK]

Common Mistakes:

• Replacing model folder while server runs
• Restarting server causes downtime
• Trying to retrain model inside serving