add service deployment mnist

mnist-classifaction/deploy_model/deploy.py

@@ -0,0 +1,48 @@
+import argparse
+import logging
+from servicefoundry import (
+    Build,
+    PythonBuild,
+    Service,
+    Resources,
+    Port,
+    ArtifactsDownload,
+    TruefoundryArtifactSource,
+)
+
+logging.basicConfig(level=logging.INFO)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--workspace_fqn", type=str, required=True)
+parser.add_argument("--model_version_fqn", type=str, required=True)
+parser.add_argument("--host", type=str, required=True)
+parser.add_argument("--path", type=str, required=False)
+args = parser.parse_args()
+
+service = Service(
+    name="mnist-classification-svc",
+    image=Build(
+        build_spec=PythonBuild(
+            command="python gradio_demo.py",
+            requirements_path="requirements.txt",
+        )
+    ),
+    ports=[Port(port=8000, host=args.host, path=args.path)],
+    resources=Resources(
+        memory_limit=500,
+        memory_request=500,
+        ephemeral_storage_limit=600,
+        ephemeral_storage_request=600,
+        cpu_limit=0.3,
+        cpu_request=0.3,
+    ),
+    artifacts_download=ArtifactsDownload(
+        artifacts=[
+            TruefoundryArtifactSource(
+                artifact_version_fqn=args.model_version_fqn,
+                download_path_env_variable="MODEL_DOWNLOAD_PATH",
+            )
+        ]
+    ),
+)
+service.deploy(workspace_fqn=args.workspace_fqn)

mnist-classifaction/deploy_model/fastapi_service.py

@@ -0,0 +1,31 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from predict import predict_fn, load_model
+import tensorflow as tf
+import os
+
+model_path = os.path.join(os.environ.get("MODEL_DOWNLOAD_PATH", "."), "mnist_model.h5")
+model = load_model(model_path)
+
+app = FastAPI(docs_url="/", root_path=os.getenv("TFY_SERVICE_ROOT_PATH", "/"))
+
+
+class ImageUrl(BaseModel):
+    url: str = "https://conx.readthedocs.io/en/latest/_images/MNIST_6_0.png"
+
+
+def load_image(img_url: str):
+    img_path = tf.keras.utils.get_file("image.jpg", img_url)
+    img = tf.keras.preprocessing.image.load_img(img_path, target_size=(28, 28))
+    img_arr = tf.keras.preprocessing.image.img_to_array(img)
+    return img_arr
+
+
+app = FastAPI()
+
+
+@app.post("/predict")
+async def predict(body: ImageUrl):
+    img_arr = load_image(body.url)
+    prediction = predict_fn(model, img_arr)
+    return {"prediction": prediction}

mnist-classifaction/deploy_model/gradio_demo.py

@@ -0,0 +1,20 @@
+from predict import predict_fn, load_model
+import os
+import gradio as gr
+
+model_path = os.path.join(os.environ.get("MODEL_DOWNLOAD_PATH", "."), "mnist_model.h5")
+model = load_model(model_path)
+
+
+def get_inference(img_arr):
+    return predict_fn(model, img_arr)
+
+
+interface = gr.Interface(
+    fn=get_inference,
+    inputs="image",
+    outputs="label",
+    examples=[["sample_images/0.jpg"], ["sample_images/1.jpg"]],
+)
+
+interface.launch(server_name="0.0.0.0", server_port=8000)

mnist-classifaction/deploy_model/predict.py

@@ -0,0 +1,20 @@
+import tensorflow as tf
+import numpy as np
+
+
+def load_model(model_path: str):
+    # Load the trained model
+    model = tf.keras.models.load_model(model_path)
+    return model
+
+
+def predict_fn(model, img_arr: np.ndarray):
+    # Preprocess the image before passing it to the model
+    img_arr = tf.expand_dims(img_arr, 0)
+    img_arr = img_arr[:, :, :, 0]  # Keep only the first channel (grayscale)
+
+    # Make predictions
+    predictions = model.predict(img_arr)
+    predicted_label = tf.argmax(predictions[0]).numpy()
+
+    return str(predicted_label)

mnist-classifaction/deploy_model/requirements.txt

@@ -0,0 +1,3 @@
+tensorflow==2.15.0
+gradio==3.39.0
+fastapi==0.89.1

mnist-classifaction/train_job/deploy.py

@@ -16,18 +16,14 @@
             command="python train.py --num_epochs {{num_epochs}} --ml_repo {{ml_repo}}",
             requirements_path="requirements.txt",
         ),
-        build_source=LocalSource(local_build=False)
+        build_source=LocalSource(local_build=False),
     ),
     params=[
-            Param(name="num_epochs", default='4'),
-            Param(name="ml_repo", param_type="ml_repo"),
-        ],
+        Param(name="num_epochs", default="4"),
+        Param(name="ml_repo", param_type="ml_repo"),
+    ],
     resources=Resources(
-       cpu_request=0.5,
-       cpu_limit=0.5,
-       memory_request=1500,
-       memory_limit=2000
-    )
-
+        cpu_request=0.5, cpu_limit=0.5, memory_request=1500, memory_limit=2000
+    ),
 )
-deployment = job.deploy(workspace_fqn=args.workspace_fqn)
+deployment = job.deploy(workspace_fqn=args.workspace_fqn)

mnist-classifaction/train_job/train.py

@@ -8,12 +8,8 @@
 
 # parsing the arguments
 parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--num_epochs", type=int, default=4
-)
-parser.add_argument(
-    "--ml_repo", type=str, required=True
-)
+parser.add_argument("--num_epochs", type=int, default=4)
+parser.add_argument("--ml_repo", type=str, required=True)
 args = parser.parse_args()
 
 
@@ -36,35 +32,45 @@
 # Plot some sample images
 plt.figure(figsize=(10, 5))
 for i in range(10):
-    plt.subplot(2, 5, i+1)
-    plt.imshow(x_train[i], cmap='gray')
+    plt.subplot(2, 5, i + 1)
+    plt.imshow(x_train[i], cmap="gray")
     plt.title(f"Label: {y_train[i]}")
-    plt.axis('off')
+    plt.axis("off")
 run.log_plots({"images": plt})
 plt.tight_layout()
 
 
 # Define the model architecture
-model = tf.keras.Sequential([
-    tf.keras.layers.Flatten(input_shape=(28, 28)),
-    tf.keras.layers.Dense(128, activation='relu'),
-    tf.keras.layers.Dense(10, activation='softmax')
-])
+model = tf.keras.Sequential(
+    [
+        tf.keras.layers.Flatten(input_shape=(28, 28)),
+        tf.keras.layers.Dense(128, activation="relu"),
+        tf.keras.layers.Dense(10, activation="softmax"),
+    ]
+)
 
 # Compile the model
-model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+model.compile(
+    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
+)
 
-#logging the parameters
-run.log_params({"optimizer": "adam", "loss": "sparse_categorical_crossentropy", "metric": ["accuracy"]})
+# logging the parameters
+run.log_params(
+    {
+        "optimizer": "adam",
+        "loss": "sparse_categorical_crossentropy",
+        "metric": ["accuracy"],
+    }
+)
 
 # Train the model
 epochs = args.num_epochs
 model.fit(x_train, y_train, epochs=epochs, validation_data=(x_test, y_test))
 
 # Evaluate the model
 loss, accuracy = model.evaluate(x_test, y_test)
-print(f'Test loss: {loss}')
-print(f'Test accuracy: {accuracy}')
+print(f"Test loss: {loss}")
+print(f"Test accuracy: {accuracy}")
 
 
 # Log Metrics and Model
@@ -73,16 +79,14 @@
 run.log_metrics(metric_dict={"accuracy": accuracy, "loss": loss})
 
 # Save the trained model
-model.save('mnist_model.h5')
+model.save("mnist_model.h5")
 
 # Logging the model
 run.log_model(
     name="handwritten-digits-recognition",
-    model_file_or_folder='mnist_model.h5',
+    model_file_or_folder="mnist_model.h5",
     framework="tensorflow",
     description="sample model to recognize the handwritten digits",
     metadata={"accuracy": accuracy, "loss": loss},
     step=1,  # step number, useful when using iterative algorithms like SGD
 )
-
-

mnist-classifaction/train_model.ipynb

@@ -6,7 +6,7 @@
     "id": "QOAnoPl-dlSY"
    },
    "source": [
-    "# Train and Deploy Model on Truefoundry\n",
+    "# Train Model on Truefoundry\n",
     "This notebook demonstrates a demo on how you can train an image classification model on mnist dataset and deploy the model training job on truefoundry platform."
    ]
   },