added mnist example

mnist-classifaction/train_job/deploy.py

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+import logging, os, argparse
+from servicefoundry import Build, Job, PythonBuild, Param, Port, LocalSource, Resources
+
+# parsing the arguments
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--workspace_fqn", type=str, required=True, help="fqn of the workspace to deploy to"
+)
+args = parser.parse_args()
+
+# defining the job specifications
+job = Job(
+    name="mnist-train-job",
+    image=Build(
+        build_spec=PythonBuild(
+            command="python train.py --num_epochs {{num_epochs}} --ml_repo {{ml_repo}}",
+            requirements_path="requirements.txt",
+        ),
+        build_source=LocalSource(local_build=False)
+    ),
+    params=[
+            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=1000,
+       memory_limit=1500
+    )
+
+)
+deployment = job.deploy(workspace_fqn=args.workspace_fqn)

mnist-classifaction/train_job/requirements.txt

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+matplotlib==3.8.2
+tensorflow==2.15.0
+mlfoundry==0.10.4

mnist-classifaction/train_job/train.py

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+import mlfoundry
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist
+import matplotlib.pyplot as plt
+from tensorflow.keras.datasets import mnist
+import os
+import argparse
+
+# parsing the arguments
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--num_epochs", type=int, default=4
+)
+parser.add_argument(
+    "--ml_repo", type=str, required=True
+)
+args = parser.parse_args()
+
+
+# Load the MNIST dataset
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+print(f"The number of train images: {len(x_train)}")
+print(f"The number of test images: {len(x_test)}")
+
+# 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.title(f"Label: {y_train[i]}")
+    plt.axis('off')
+plt.tight_layout()
+plt.show()
+
+
+# Load the MNIST dataset
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+# Normalize the pixel values between 0 and 1
+x_train = x_train / 255.0
+x_test = x_test / 255.0
+
+
+# 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')
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+
+# Creating client for logging the metadata
+client = mlfoundry.get_client()
+
+client.create_ml_repo(args.ml_repo)
+run = client.create_run(ml_repo=args.ml_repo)
+
+
+#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}')
+
+
+# Log Metrics and Model
+
+# Logging the metrics of the model
+run.log_metrics(metric_dict={"accuracy": accuracy, "loss": loss})
+
+# Save the trained model
+model.save('mnist_model.h5')
+
+# Logging the model
+run.log_model(
+    name="handwritten-digits-recognition",
+    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
+)
+
+