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deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/batch_pred_evaluator.py | """
This component evaluates the performance of a currently deployed model, and
the evaluation is based on the result of batch prediction on Vertex AI from the previous component.
At the end, this component will output true or false to indicate if retraining is needed.
Reference: https://bit.ly/vertex-batch
"""
from tfx.dsl.component.experimental.annotations import Parameter, OutputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
import os
import json
@component
def PerformanceEvaluator(
gcs_destination: Parameter[str],
local_directory: Parameter[str],
threshold: Parameter[float],
trigger_pipeline: OutputArtifact[Dataset],
):
"""
gcs_destination: GCS location where the files containing
the result of batch prediction is
local_directory: Temporary directory to hold files copied
from the gcs_destination
threshold: threshold to decide if retraining is needed or not
it is based on the measured accuracy
trigger_pipeline: an output artifact which hold true or false
to indicate if retraining is needed or not
"""
full_gcs_results_dir = f"{gcs_destination}/{local_directory}"
# Create missing directories.
os.makedirs(local_directory, exist_ok=True)
# Get the Cloud Storage paths for each result.
os.system(f"gsutil -m cp -r {full_gcs_results_dir} {local_directory}")
# Get most recently modified directory.
latest_directory = max(
[os.path.join(local_directory, d) for d in os.listdir(local_directory)],
key=os.path.getmtime,
)
# Get downloaded results in directory.
results_files = []
for dirpath, subdirs, files in os.walk(latest_directory):
for file in files:
if file.startswith("prediction.results"):
results_files.append(os.path.join(dirpath, file))
# Consolidate all the results into a list.
results = []
for results_file in results_files:
# Download each result.
with open(results_file, "r") as file:
results.extend([json.loads(line) for line in file.readlines()])
# Calculate performance.
num_correct = 0
for result in results:
label = os.path.basename(result["instance"]).split("_")[0]
prediction = result["prediction"]["label"]
if label == prediction:
num_correct = num_correct + 1
accuracy = num_correct / len(results)
logging.info(f"Accuracy: {accuracy*100}%")
# Store the boolean result.
trigger_pipeline.set_string_custom_property("result", str(accuracy >= threshold))
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/batch_prediction_vertex.py | """
This component launches a Batch Prediction job on Vertex AI.
Know more about Vertex AI Batch Predictions jobs, go here:
https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions.
"""
from google.cloud import storage
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.standard_artifacts import String
import google.cloud.aiplatform as vertex_ai
from absl import logging
@component
def BatchPredictionGen(
gcs_source: InputArtifact[String],
project: Parameter[str],
location: Parameter[str],
model_resource_name: Parameter[str],
job_display_name: Parameter[str],
gcs_destination: Parameter[str],
instances_format: Parameter[str] = "file-list",
machine_type: Parameter[str] = "n1-standard-2",
accelerator_count: Parameter[int] = 0,
accelerator_type: Parameter[str] = None,
starting_replica_count: Parameter[int] = 1,
max_replica_count: Parameter[int] = 1,
):
"""
gcs_source: A location inside GCS to be used by the Batch Prediction job to get its inputs.
Rest of the parameters are explained here: https://git.io/JiUyU.
"""
storage_client = storage.Client()
# Read GCS Source (gcs_source contains the full path of GCS object).
# 1-1. get bucketname from gcs_source
gcs_source_uri = gcs_source.uri.split("//")[1:][0].split("/")
bucketname = gcs_source_uri[0]
bucket = storage_client.get_bucket(bucketname)
logging.info(f"bucketname: {bucketname}")
# 1-2. get object path without the bucket name.
objectpath = "/".join(gcs_source_uri[1:])
# 1-3. read the object to get value set by OutputArtifact from FileListGen.
blob = bucket.blob(objectpath)
logging.info(f"objectpath: {objectpath}")
gcs_source = f"gs://{blob.download_as_text()}"
# Get Model.
vertex_ai.init(project=project, location=location)
model = vertex_ai.Model.list(
filter=f"display_name={model_resource_name}", order_by="update_time"
)[-1]
# Launch a Batch Prediction job.
logging.info("Starting batch prediction job.")
logging.info(f"GCS path where file list is: {gcs_source}")
batch_prediction_job = model.batch_predict(
job_display_name=job_display_name,
instances_format=instances_format,
gcs_source=gcs_source,
gcs_destination_prefix=gcs_destination,
machine_type=machine_type,
accelerator_count=accelerator_count,
accelerator_type=accelerator_type,
starting_replica_count=starting_replica_count,
max_replica_count=max_replica_count,
sync=True,
)
logging.info(batch_prediction_job.display_name)
logging.info(batch_prediction_job.resource_name)
logging.info(batch_prediction_job.state)
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/file_list_gen.py | """
Generate a txt file formatted required by Vertex AI's Batch Prediction
There are few options, and this component generate "file list" formatted txt.
(https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)
"""
import tensorflow as tf
from absl import logging
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter, OutputArtifact
from tfx.types.standard_artifacts import String
@component
def FileListGen(
outpath: OutputArtifact[String],
gcs_src_bucket: Parameter[str],
gcs_src_prefix: Parameter[str] = "",
output_filename: Parameter[str] = "test-images.txt",
):
"""
: param outpath: OutputArtifact to hold where output_filename will be located
This will be used in the downstream component, BatchPredictionGen
: param gcs_src_bucket: GCS bucket name where the list of raw data is
: param gcs_src_prefix: prefix to be added to gcs_src_bucket
: param output_filename: output filename whose content is a list of file paths of raw data
"""
logging.info("FileListGen started")
# 1. get the list of data
gcs_src_prefix = (
f"{gcs_src_prefix}/" if len(gcs_src_prefix) != 0 else gcs_src_prefix
)
img_paths = tf.io.gfile.glob(f"gs://{gcs_src_bucket}/{gcs_src_prefix}*.jpg")
logging.info("Successfully retrieve the file(jpg) list from GCS path")
# 2. write the list of data in the expected format in Vertex AI Batch Prediction to a local file
with open(output_filename, "w", encoding="utf-8") as f:
f.writelines("%s\n" % img_path for img_path in img_paths)
logging.info(
f"Successfully created the file list file({output_filename}) in local storage"
)
# 3. upload the local file to GCS location
gcs_dst = f"{gcs_src_bucket}/{gcs_src_prefix}{output_filename}"
tf.io.gfile.copy(output_filename, f"gs://{gcs_dst}", overwrite=True)
logging.info(f"Successfully uploaded the file list ({gcs_dst})")
# 4. store the GCS location where the local file is
outpath.value = gcs_dst
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/span_preparator.py | """
This component is responsible for separating provided samples into training and
validation splits. It then converts them to TFRecords and stores those inside
a GCS location. Finally, it returns the latest span id calculated from the current
samples in `gcs_source_bucket`.
"""
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact, InputArtifact
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
from datetime import datetime
import tensorflow as tf
import random
import os
# Label-mapping.
LABEL_DICT = {
"airplane": 0,
"automobile": 1,
"bird": 2,
"cat": 3,
"deer": 4,
"dog": 5,
"frog": 6,
"horse": 7,
"ship": 8,
"truck": 9,
}
# Images are byte-strings.
def _bytestring_feature(list_of_bytestrings):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=list_of_bytestrings))
# Classes would be integers.
def _int_feature(list_of_ints):
return tf.train.Feature(int64_list=tf.train.Int64List(value=list_of_ints))
# Function that prepares a record for the tfrecord file
# a record contains the image and its label.
def to_tfrecord(img_bytes, label):
feature = {
"image": _bytestring_feature([img_bytes]),
"label": _int_feature([label]),
}
return tf.train.Example(features=tf.train.Features(feature=feature))
def write_tfrecords(filepaths, dest_gcs, tfrecord_filename, new_span, is_train):
# For this project, we are serializing the images in one TFRecord only.
# For more realistic purposes, this should be sharded.
folder = "train" if is_train else "test"
with tf.io.TFRecordWriter(tfrecord_filename) as writer:
for path in filepaths:
image_string = tf.io.read_file(path).numpy()
class_name = path.split("/")[-1].split("_")[0]
label = LABEL_DICT[class_name]
example = to_tfrecord(image_string, label)
writer.write(example.SerializeToString())
# Copy over the zipped TFRecord file to the GCS Bucket and
# remove the temporary files.
logging.info(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.system(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.remove(tfrecord_filename)
@component
def SpanPreparator(
is_retrain: InputArtifact[Dataset],
gcs_source_bucket: Parameter[str],
gcs_destination_bucket: Parameter[str],
latest_span_id: OutputArtifact[Dataset],
gcs_source_prefix: Parameter[str] = "",
):
"""
:param is_retrain: Boolean to indicate if we are retraining.
:param gcs_source_bucket: GCS location where the entry samples are residing.
:param gcs_destination_bucket: GCS location where the converted TFRecords will be serialized.
:param latest_span_id: Data span.
:param gcs_source_prefix: Location prefix.
"""
if is_retrain.get_string_custom_property("result") == "False":
# Get the latest span and determine the new span.
last_span_str = tf.io.gfile.glob(f"{gcs_destination_bucket}/span-*")[-1]
last_span = int(last_span_str.split("-")[-1])
new_span = last_span + 1
timestamp = datetime.utcnow().strftime("%y%m%d-%H%M%S")
# Get images from the provided GCS source.
image_paths = tf.io.gfile.glob(f"gs://{gcs_source_bucket}/*.jpg")
logging.info(image_paths)
random.shuffle(image_paths)
# Create train and validation splits.
val_split = 0.2
split_index = int(len(image_paths) * (1 - val_split))
training_paths = image_paths[:split_index]
validation_paths = image_paths[split_index:]
# Write as TFRecords.
write_tfrecords(
training_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_training_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=True,
)
write_tfrecords(
validation_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_validation_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=False,
)
logging.info("Removing images from batch prediction bucket.")
os.system(
f"gsutil mv gs://{gcs_source_bucket}/{gcs_source_prefix} gs://{gcs_source_bucket}/{gcs_source_prefix}_old"
)
latest_span_id.set_string_custom_property("latest_span", str(new_span))
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/training_pipeline_trigger.py | """
Component responsible for triggering a training job given a pipeline specification.
"""
import json
from google.cloud import storage
from kfp.v2.google.client import AIPlatformClient
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
@component
def PipelineTrigger(
is_retrain: InputArtifact[Dataset],
latest_span_id: InputArtifact[Dataset],
pipeline_spec_path: Parameter[str],
project_id: Parameter[str],
region: Parameter[str],
):
"""
:param is_retrain: Boolean to indicate if we are retraining.
:param latest_span_id: Latest span id to craft training data for the model.
:param pipeline_spec_path: Training pipeline specification path.
:param project_id: GCP project id.
:param region: GCP region.
"""
if is_retrain.get_string_custom_property("result") == "False":
# Check if the pipeline spec exists.
storage_client = storage.Client()
path_parts = pipeline_spec_path.replace("gs://", "").split("/")
bucket_name = path_parts[0]
blob_name = "/".join(path_parts[1:])
bucket = storage_client.bucket(bucket_name)
blob = storage.Blob(bucket=bucket, name=blob_name)
if not blob.exists(storage_client):
raise ValueError(f"{pipeline_spec_path} does not exist.")
# Initialize Vertex AI API client and submit for pipeline execution.
api_client = AIPlatformClient(project_id=project_id, region=region)
# Fetch the latest span.
latest_span = latest_span_id.get_string_custom_property("latest_span")
# Create a training job from pipeline spec.
response = api_client.create_run_from_job_spec(
pipeline_spec_path,
enable_caching=False,
parameter_values={
"input-config": json.dumps(
{
"splits": [
{
"name": "train",
"pattern": f"span-[{int(latest_span)-1}{latest_span}]/train/*.tfrecord",
},
{
"name": "val",
"pattern": f"span-[{int(latest_span)-1}{latest_span}]/test/*.tfrecord",
},
]
}
),
"output-config": json.dumps({}),
},
)
logging.info(response)
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/01_Dataset_Prep.ipynb | from google.colab import auth
auth.authenticate_user()TARGET_ROOT_DIR = "cifar10"
TARGET_TRAIN_DIR = TARGET_ROOT_DIR + "/span-1/train"
TARGET_TEST_DIR = TARGET_ROOT_DIR + "/span-1/test"
!mkdir -p {TARGET_TRAIN_DIR}
!mkdir -p {TARGET_TEST_DIR}import tensorflow_datasets as tfds
# Generate TFRecords with TFDS
builder = tfds.builder("cifar10")
builder.download_and_prepare()#@title GCS
#@markdown You should change these values as per your preferences. The copy operation can take ~5 minutes.
BUCKET_PATH = "gs://cifar10-csp-public2" #@param {type:"string"}
REGION = "us-central1" #@param {type:"string"}
!gsutil mb -l {REGION} {BUCKET_PATH}
!gsutil -m cp -r {TARGET_ROOT_DIR}/* {BUCKET_PATH}from tfx import v1 as tfx
from tfx.components.example_gen import utilsfrom tfx.proto import example_gen_pb2
_DATA_PATH = "gs://cifar10-csp-public"
splits = [
example_gen_pb2.Input.Split(name="train", pattern="span-{SPAN}/train/*"),
example_gen_pb2.Input.Split(name="val", pattern="span-{SPAN}/test/*"),
]
_, span, version = utils.calculate_splits_fingerprint_span_and_version(
_DATA_PATH, splits
)span, version |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/02_TFX_Training_Pipeline.ipynb | from google.colab import auth
auth.authenticate_user()import tensorflow as tf
print("TensorFlow version: {}".format(tf.__version__))
from tfx import v1 as tfx
print("TFX version: {}".format(tfx.__version__))
import kfp
print("KFP version: {}".format(kfp.__version__))
from google.cloud import aiplatform as vertex_ai
import osGOOGLE_CLOUD_PROJECT = "gcp-ml-172005" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1" # @param {type:"string"}
GCS_BUCKET_NAME = "cifar10-experimental-csp2" # @param {type:"string"}
DATA_ROOT = "gs://cifar10-csp-public2" # @param {type:"string"}
if not (GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION and GCS_BUCKET_NAME):
from absl import logging
logging.error("Please set all required parameters.")PIPELINE_NAME = "continuous-adaptation-for-data-changes"
# Path to various pipeline artifact.
PIPELINE_ROOT = "gs://{}/pipeline_root/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
# Paths for users' Python module.
MODULE_ROOT = "gs://{}/pipeline_module/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
# This is the path where your model will be pushed for serving.
SERVING_MODEL_DIR = "gs://{}/serving_model/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
print("PIPELINE_ROOT: {}".format(PIPELINE_ROOT))_trainer_module_file = 'trainer.py'%%writefile {_trainer_module_file}
from typing import List
from absl import logging
from tensorflow import keras
from tfx import v1 as tfx
import tensorflow as tf
_IMAGE_FEATURES = {
"image": tf.io.FixedLenFeature([], tf.string),
"label": tf.io.FixedLenFeature([], tf.int64),
}
_CONCRETE_INPUT = "numpy_inputs"
_TRAIN_BATCH_SIZE = 64
_EVAL_BATCH_SIZE = 64
_INPUT_SHAPE = (32, 32, 3)
_EPOCHS = 2
def _parse_fn(example):
example = tf.io.parse_single_example(example, _IMAGE_FEATURES)
image = tf.image.decode_jpeg(example["image"], channels=3)
class_label = tf.cast(example["label"], tf.int32)
return image, class_label
def _input_fn(file_pattern: List[str], batch_size: int) -> tf.data.Dataset:
print(f"Reading data from: {file_pattern}")
tfrecord_filenames = tf.io.gfile.glob(file_pattern[0] + ".gz")
print(tfrecord_filenames)
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
dataset = dataset.map(_parse_fn).batch(batch_size)
return dataset.repeat()
def _make_keras_model() -> tf.keras.Model:
"""Creates a ResNet50-based model for classifying flowers data.
Returns:
A Keras Model.
"""
inputs = keras.Input(shape=_INPUT_SHAPE)
base_model = keras.applications.ResNet50(
include_top=False, input_shape=_INPUT_SHAPE, pooling="avg"
)
base_model.trainable = False
x = tf.keras.applications.resnet.preprocess_input(inputs)
x = base_model(
x, training=False
) # Ensures BatchNorm runs in inference model in this model
outputs = keras.layers.Dense(10, activation="softmax")(x)
model = keras.Model(inputs, outputs)
model.compile(
optimizer=keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
model.summary(print_fn=logging.info)
return model
def _preprocess(bytes_input):
decoded = tf.io.decode_jpeg(bytes_input, channels=3)
resized = tf.image.resize(decoded, size=(32, 32))
return resized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def preprocess_fn(bytes_inputs):
decoded_images = tf.map_fn(
_preprocess, bytes_inputs, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
[tf.TensorSpec(shape=[None, 32, 32, 3], dtype=tf.float32, name=_CONCRETE_INPUT)]
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(bytes_inputs):
# This function comes from the Computer Vision book from O'Reilly.
labels = tf.constant(
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
],
dtype=tf.string,
)
images = preprocess_fn(bytes_inputs)
probs = m_call(**images)
indices = tf.argmax(probs, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def run_fn(fn_args: tfx.components.FnArgs):
print(fn_args)
train_dataset = _input_fn(fn_args.train_files, batch_size=_TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(fn_args.eval_files, batch_size=_EVAL_BATCH_SIZE)
model = _make_keras_model()
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
epochs=_EPOCHS,
)
_, acc = model.evaluate(eval_dataset, steps=fn_args.eval_steps)
logging.info(f"Validation accuracy: {round(acc * 100, 2)}%")
# The result of the training should be saved in `fn_args.serving_model_dir`
# directory.
tf.saved_model.save(
model,
fn_args.serving_model_dir,
signatures={"serving_default": _model_exporter(model)},
)os.path.join(MODULE_ROOT, _trainer_module_file)_vertex_uploader_module_file = "vertex_uploader.py"
_vertex_deployer_module_file = "vertex_deployer.py"%%writefile {_vertex_uploader_module_file}
import os
import tensorflow as tf
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.types.standard_artifacts import String
from google.cloud import aiplatform as vertex_ai
from tfx import v1 as tfx
from absl import logging
@component
def VertexUploader(
project: Parameter[str],
region: Parameter[str],
model_display_name: Parameter[str],
pushed_model_location: Parameter[str],
serving_image_uri: Parameter[str],
uploaded_model: tfx.dsl.components.OutputArtifact[String],
):
vertex_ai.init(project=project, location=region)
pushed_model_dir = os.path.join(
pushed_model_location, tf.io.gfile.listdir(pushed_model_location)[-1]
)
logging.info(f"Model registry location: {pushed_model_dir}")
vertex_model = vertex_ai.Model.upload(
display_name=model_display_name,
artifact_uri=pushed_model_dir,
serving_container_image_uri=serving_image_uri,
parameters_schema_uri=None,
instance_schema_uri=None,
explanation_metadata=None,
explanation_parameters=None,
)
uploaded_model.set_string_custom_property(
"model_resource_name", str(vertex_model.resource_name)
)
logging.info(f"Model resource: {str(vertex_model.resource_name)}")%%writefile {_vertex_deployer_module_file}
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.types.standard_artifacts import String
from google.cloud import aiplatform as vertex_ai
from tfx import v1 as tfx
from absl import logging
@component
def VertexDeployer(
project: Parameter[str],
region: Parameter[str],
model_display_name: Parameter[str],
deployed_model_display_name: Parameter[str],
):
logging.info(f"Endpoint display: {deployed_model_display_name}")
vertex_ai.init(project=project, location=region)
endpoints = vertex_ai.Endpoint.list(
filter=f"display_name={deployed_model_display_name}", order_by="update_time"
)
if len(endpoints) > 0:
logging.info(f"Endpoint {deployed_model_display_name} already exists.")
endpoint = endpoints[-1]
else:
endpoint = vertex_ai.Endpoint.create(deployed_model_display_name)
model = vertex_ai.Model.list(
filter=f"display_name={model_display_name}", order_by="update_time"
)[-1]
endpoint = vertex_ai.Endpoint.list(
filter=f"display_name={deployed_model_display_name}", order_by="update_time"
)[-1]
deployed_model = endpoint.deploy(
model=model,
# Syntax from here: https://git.io/JBQDP
traffic_split={"0": 100},
machine_type="n1-standard-4",
min_replica_count=1,
max_replica_count=1,
)
logging.info(f"Model deployed to: {deployed_model}")DATASET_DISPLAY_NAME = "cifar10"
VERSION = "tfx-1-2-0"
TFX_IMAGE_URI = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{DATASET_DISPLAY_NAME}:{VERSION}"
print(f"URI of the custom image: {TFX_IMAGE_URI}")%%writefile Dockerfile
FROM gcr.io/tfx-oss-public/tfx:1.2.0
RUN mkdir -p custom_components
COPY custom_components/* ./custom_components/
RUN pip install --upgrade google-cloud-aiplatform# Specify training worker configurations. To minimize costs we can even specify two
# different configurations: a beefier machine for the Endpoint model and slightly less
# powerful machine for the mobile model.
TRAINING_JOB_SPEC = {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": "gcr.io/tfx-oss-public/tfx:{}".format(tfx.__version__),
},
}
],
}SERVING_JOB_SPEC = {
"endpoint_name": PIPELINE_NAME.replace("-", "_"), # '-' is not allowed.
"project_id": GOOGLE_CLOUD_PROJECT,
"min_replica_count": 1,
"max_replica_count": 1,
"machine_type": "n1-standard-2",
}from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")import tfxfrom tfx.orchestration import data_types
from tfx import v1 as tfx
from tfx.proto import example_gen_pb2, range_config_pb2
from tfx.components.example_gen import utils
from custom_components.vertex_uploader import VertexUploader
from custom_components.vertex_deployer import VertexDeployer
def _create_pipeline(
input_config: data_types.RuntimeParameter,
output_config: data_types.RuntimeParameter,
pipeline_name: str,
pipeline_root: str,
data_root: str,
serving_model_dir: str,
trainer_module: str,
project_id: str,
region: str,
) -> tfx.dsl.Pipeline:
"""Creates a three component flowers pipeline with TFX."""
example_gen = tfx.components.ImportExampleGen(
input_base=data_root, input_config=input_config, output_config=output_config
)
# Trainer
trainer = tfx.extensions.google_cloud_ai_platform.Trainer(
module_file=trainer_module,
examples=example_gen.outputs["examples"],
train_args=tfx.proto.TrainArgs(splits=["train"], num_steps=50000 // 64),
eval_args=tfx.proto.EvalArgs(splits=["val"], num_steps=10000 // 64),
custom_config={
tfx.extensions.google_cloud_ai_platform.ENABLE_VERTEX_KEY: True,
tfx.extensions.google_cloud_ai_platform.VERTEX_REGION_KEY: region,
tfx.extensions.google_cloud_ai_platform.TRAINING_ARGS_KEY: TRAINING_JOB_SPEC,
"use_gpu": True,
},
).with_id("trainer")
# Pushes the model to a filesystem destination.
pushed_model_location = os.path.join(serving_model_dir, "resnet50")
resnet_pusher = tfx.components.Pusher(
model=trainer.outputs["model"],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=pushed_model_location
)
),
).with_id("resnet_pusher")
# Vertex AI upload.
model_display_name = "resnet_cifar_latest"
uploader = VertexUploader(
project=project_id,
region=region,
model_display_name=model_display_name,
pushed_model_location=pushed_model_location,
serving_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-5:latest",
).with_id("vertex_uploader")
uploader.add_upstream_node(resnet_pusher)
# Create an endpoint.
deployer = VertexDeployer(
project=project_id,
region=region,
model_display_name=model_display_name,
deployed_model_display_name=model_display_name + "_" + TIMESTAMP,
).with_id("vertex_deployer")
deployer.add_upstream_node(uploader)
components = [
example_gen,
trainer,
resnet_pusher,
uploader,
deployer,
]
return tfx.dsl.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
)import os
PIPELINE_DEFINITION_FILE = PIPELINE_NAME + "_pipeline.json"
# Important: We need to pass the custom Docker image URI to the
# `KubeflowV2DagRunnerConfig` to take effect.
runner = tfx.orchestration.experimental.KubeflowV2DagRunner(
config=tfx.orchestration.experimental.KubeflowV2DagRunnerConfig(
default_image=TFX_IMAGE_URI
),
output_filename=PIPELINE_DEFINITION_FILE,
)
_ = runner.run(
_create_pipeline(
input_config=tfx.dsl.experimental.RuntimeParameter(
name="input-config",
default='{"input_config": {"splits": [{"name":"train", "pattern":"span-1/train/tfrecord"}, {"name":"val", "pattern":"span-1/test/tfrecord"}]}}',
ptype=str,
),
output_config=tfx.dsl.experimental.RuntimeParameter(
name="output-config", default="{}", ptype=str,
),
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
serving_model_dir=SERVING_MODEL_DIR,
trainer_module=os.path.join(MODULE_ROOT, _trainer_module_file),
project_id=GOOGLE_CLOUD_PROJECT,
region=GOOGLE_CLOUD_REGION,
)
)from kfp.v2.google import client
pipelines_client = client.AIPlatformClient(
project_id=GOOGLE_CLOUD_PROJECT, region=GOOGLE_CLOUD_REGION,
)import json
from tfx.orchestration import data_types
_ = pipelines_client.create_run_from_job_spec(
PIPELINE_DEFINITION_FILE,
enable_caching=False,
parameter_values={
"input-config": json.dumps(
{
"splits": [
{"name": "train", "pattern": "span-[12]/train/*.tfrecord"},
{"name": "val", "pattern": "span-[12]/test/*.tfrecord"},
]
}
),
"output-config": json.dumps({}),
},
) |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/03_Batch_Prediction_Pipeline.ipynb | from google.colab import auth
auth.authenticate_user()# @title
from fastdot.core import *
tfx_components = [
"FileListGen",
"BatchPredictionGen",
"PerformanceEvaluator",
"SpanPreparator",
"PipelineTrigger",
]
block = "TFX Component Workflow"
g = graph_items(seq_cluster(tfx_components, block))
g_file_list_gen_module_file = "file_list_gen.py"%%writefile {_file_list_gen_module_file}
import tfx
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact
from tfx.types.standard_artifacts import String
from google.cloud import storage
from absl import logging
@component
def FileListGen(
outpath: OutputArtifact[String],
project: Parameter[str],
gcs_source_bucket: Parameter[str],
gcs_source_prefix: Parameter[str] = "",
output_filename: Parameter[str] = "test-images.txt",
):
logging.info("FileListGen started")
client = storage.Client(project=project)
bucket = client.get_bucket(gcs_source_bucket)
blobs = bucket.list_blobs(prefix=gcs_source_prefix)
logging.info("Successfully retrieve the file(jpg) list from GCS path")
f = open(output_filename, "w")
for blob in blobs:
if blob.name.split(".")[-1] == "jpg":
prefix = ""
if gcs_source_prefix != "":
prefix = f"/{gcs_source_prefix}"
line = f"gs://{gcs_source_bucket}{prefix}/{blob.name}\n"
f.write(line)
f.close()
logging.info(
f"Successfully created the file list file({output_filename}) in local storage"
)
prefix = ""
if gcs_source_prefix != "":
prefix = f"{gcs_source_prefix}/"
blob = bucket.blob(f"{prefix}{output_filename}")
blob.upload_from_filename(output_filename)
logging.info(f"Successfully uploaded the file list ({prefix}{output_filename})")
outpath.value = gcs_source_bucket + "/" + prefix + output_filename_batch_pred_module_file = 'batch_prediction_vertex.py'%%writefile {_batch_pred_module_file}
from google.cloud import storage
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types import artifact_utils
from tfx.types.standard_artifacts import String
import google.cloud.aiplatform as vertex_ai
from typing import Union, Sequence
from absl import logging
@component
def BatchPredictionGen(
gcs_source: InputArtifact[String],
project: Parameter[str],
location: Parameter[str],
model_resource_name: Parameter[str],
job_display_name: Parameter[str],
gcs_destination: Parameter[str],
instances_format: Parameter[str] = "file-list",
machine_type: Parameter[str] = "n1-standard-2",
accelerator_count: Parameter[int] = 0,
accelerator_type: Parameter[str] = None,
starting_replica_count: Parameter[int] = 1,
max_replica_count: Parameter[int] = 1,
):
storage_client = storage.Client()
# Read GCS Source (gcs_source contains the full path of GCS object)
# 1-1. get bucketname from gcs_source
gcs_source_uri = gcs_source.uri.split("//")[1:][0].split("/")
bucketname = gcs_source_uri[0]
bucket = storage_client.get_bucket(bucketname)
logging.info(f"bucketname: {bucketname}")
# 1-2. get object path without the bucketname
objectpath = "/".join(gcs_source_uri[1:])
# 1-3. read the object to get value set by OutputArtifact from FileListGen
blob = bucket.blob(objectpath)
logging.info(f"objectpath: {objectpath}")
gcs_source = f"gs://{blob.download_as_text()}"
# Get Model
vertex_ai.init(project=project, location=location)
model = vertex_ai.Model.list(
filter=f"display_name={model_resource_name}", order_by="update_time"
)[-1]
# Batch Predictions
logging.info("Starting batch prediction job.")
logging.info(f"GCS path where file list is: {gcs_source}")
batch_prediction_job = model.batch_predict(
job_display_name=job_display_name,
instances_format=instances_format,
gcs_source=gcs_source,
gcs_destination_prefix=gcs_destination,
machine_type=machine_type,
accelerator_count=accelerator_count,
accelerator_type=accelerator_type,
starting_replica_count=starting_replica_count,
max_replica_count=max_replica_count,
sync=True,
)
logging.info(batch_prediction_job.display_name)
logging.info(batch_prediction_job.resource_name)
logging.info(batch_prediction_job.state)_evaluator_module_file = 'batch_pred_evaluator.py'%%writefile {_evaluator_module_file}
# Reference: https://bit.ly/vertex-batch
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
import os
import json
@component
def PerformanceEvaluator(
gcs_destination: Parameter[str],
local_directory: Parameter[str],
threshold: Parameter[float],
trigger_pipeline: OutputArtifact[Dataset],
):
full_gcs_results_dir = f"{gcs_destination}/{local_directory}"
# Create missing directories.
os.makedirs(local_directory, exist_ok=True)
# Get the Cloud Storage paths for each result.
os.system(f"gsutil -m cp -r {full_gcs_results_dir} {local_directory}")
# Get most recently modified directory.
latest_directory = max(
[os.path.join(local_directory, d) for d in os.listdir(local_directory)],
key=os.path.getmtime,
)
# Get downloaded results in directory.
results_files = []
for dirpath, subdirs, files in os.walk(latest_directory):
for file in files:
if file.startswith("prediction.results"):
results_files.append(os.path.join(dirpath, file))
# Consolidate all the results into a list.
results = []
for results_file in results_files:
# Download each result.
with open(results_file, "r") as file:
results.extend([json.loads(line) for line in file.readlines()])
# Calculate performance.
num_correct = 0
for result in results:
label = os.path.basename(result["instance"]).split("_")[0]
prediction = result["prediction"]["label"]
if label == prediction:
num_correct = num_correct + 1
accuracy = num_correct / len(results)
logging.info(f"Accuracy: {accuracy*100}%")
trigger_pipeline.set_string_custom_property("result", str(accuracy >= threshold))_span_preparator_module_file = 'span_preparator.py'%%writefile {_span_preparator_module_file}
import tfx
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact, InputArtifact
from tfx.types.experimental.simple_artifacts import Dataset
from google.cloud import storage
from absl import logging
from datetime import datetime
import tensorflow as tf
import random
import gzip
import os
# Label-mapping.
LABEL_DICT = {
"airplane": 0,
"automobile": 1,
"bird": 2,
"cat": 3,
"deer": 4,
"dog": 5,
"frog": 6,
"horse": 7,
"ship": 8,
"truck": 9,
}
# Images are byte-strings.
def _bytestring_feature(list_of_bytestrings):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=list_of_bytestrings))
# Classes would be integers.
def _int_feature(list_of_ints):
return tf.train.Feature(int64_list=tf.train.Int64List(value=list_of_ints))
# Function that prepares a record for the tfrecord file
# a record contains the image and its label.
def to_tfrecord(img_bytes, label):
feature = {
"image": _bytestring_feature([img_bytes]),
"label": _int_feature([label]),
}
return tf.train.Example(features=tf.train.Features(feature=feature))
def write_tfrecords(filepaths, dest_gcs, tfrecord_filename, new_span, is_train):
# For this project, we are serializing the images in one TFRecord only.
# For more realistic purposes, this should be sharded.
folder = "train" if is_train else "test"
with tf.io.TFRecordWriter(tfrecord_filename) as writer:
for path in filepaths:
image_string = tf.io.read_file(path).numpy()
class_name = path.split("/")[-1].split("_")[0]
label = LABEL_DICT[class_name]
example = to_tfrecord(image_string, label)
writer.write(example.SerializeToString())
# Copy over the zipped TFRecord file to the GCS Bucket and
# remove the temporary files.
logging.info(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.system(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.remove(tfrecord_filename)
@component
def SpanPreparator(
is_retrain: InputArtifact[Dataset],
gcs_source_bucket: Parameter[str],
gcs_destination_bucket: Parameter[str],
latest_span_id: OutputArtifact[Dataset],
gcs_source_prefix: Parameter[str] = "",
):
if is_retrain.get_string_custom_property("result") == "False":
last_span_str = tf.io.gfile.glob(f"{gcs_destination_bucket}/span-*")[-1]
last_span = int(last_span_str.split("-")[-1])
new_span = last_span + 1
timestamp = datetime.utcnow().strftime("%y%m%d-%H%M%S")
image_paths = tf.io.gfile.glob(f"gs://{gcs_source_bucket}/*.jpg")
logging.info(image_paths)
random.shuffle(image_paths)
val_split = 0.2
split_index = int(len(image_paths) * (1 - val_split))
training_paths = image_paths[:split_index]
validation_paths = image_paths[split_index:]
write_tfrecords(
training_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_training_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=True,
)
write_tfrecords(
validation_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_validation_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=False,
)
logging.info("Removing images from batch prediction bucket.")
os.system(
f"gsutil mv gs://{gcs_source_bucket}/{gcs_source_prefix} gs://{gcs_source_bucket}/{gcs_source_prefix}_old"
)
# os.system(f"gsutil rm -rf gs://{gcs_source_bucket}/*")
latest_span_id.set_string_custom_property("latest_span", str(new_span))_pipeline_trigger_module_file = 'training_pipeline_trigger.py'%%writefile {_pipeline_trigger_module_file}
import json
from google.cloud import storage
from kfp.v2.google.client import AIPlatformClient
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
@component
def PipelineTrigger(
is_retrain: InputArtifact[Dataset],
latest_span_id: InputArtifact[Dataset],
pipeline_spec_path: Parameter[str],
project_id: Parameter[str],
region: Parameter[str],
):
if is_retrain.get_string_custom_property('result') == 'False':
# Check if the pipeline spec exists.
storage_client = storage.Client()
path_parts = pipeline_spec_path.replace("gs://", "").split("/")
bucket_name = path_parts[0]
blob_name = "/".join(path_parts[1:])
bucket = storage_client.bucket(bucket_name)
blob = storage.Blob(bucket=bucket, name=blob_name)
if not blob.exists(storage_client):
raise ValueError(f"{pipeline_spec_path} does not exist.")
# Initialize Vertex AI API client and submit for pipeline execution.
api_client = AIPlatformClient(project_id=project_id, region=region)
# Fetch the latest span.
latest_span = latest_span_id.get_string_custom_property('latest_span')
# Create a training job from pipeline spec.
response = api_client.create_run_from_job_spec(pipeline_spec_path,
enable_caching=False,
parameter_values={
'input-config': json.dumps({
'splits': [
{'name': 'train', 'pattern': f'span-[{int(latest_span)-1}{latest_span}]/train/*.tfrecord'},
{'name': 'val', 'pattern': f'span-[{int(latest_span)-1}{latest_span}]/test/*.tfrecord'}
]
}),
'output-config': json.dumps({})
})
logging.info(response)# This bucket will be responsible for storing the pipeline related artifacts.
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = "cifar10-experimental-csp2" # @param {type:"string"}
MODEL_RESOURCE_NAME = "resnet_cifar_latest" # @param {type: "string"}
TEST_FILENAME = "test-images.txt" # @param {type:"string"}
TEST_GCS_BUCKET = "batch-prediction-collection-3" # @param {type:"string"}
TEST_GCS_PREFIX = "" # @param {type: "string"}
TRAINING_PIPELINE_SPEC = "gs://cifar10-experimental-csp2/pipeline_root/continuous-adaptation-for-data-changes/continuous-adaptation-for-data-changes_pipeline.json" # @param {type: "string"}
TRAINING_DATA_PATH = "gs://cifar10-csp-public2" # @param {type: "string"}
if not (GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION and GCS_BUCKET_NAME):
from absl import logging
logging.error("Please set all required parameters.")PIPELINE_NAME = 'continuous-adaptation-for-data-changes-batch'
# Path to various pipeline artifact.
PIPELINE_ROOT = 'gs://{}/pipeline_root/{}'.format(
GCS_BUCKET_NAME, PIPELINE_NAME)
print('PIPELINE_ROOT: {}'.format(PIPELINE_ROOT))DISPLAY_NAME = "batch-predictions-pipeline"
VERSION = "tfx-1-2-0-34"
TFX_IMAGE_URI = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{DISPLAY_NAME}:{VERSION}"
print(f"URI of the custom image: {TFX_IMAGE_URI}")%%writefile Dockerfile
FROM gcr.io/tfx-oss-public/tfx:1.2.0
RUN mkdir -p custom_components
COPY custom_components/* ./custom_components/
RUN pip install --upgrade google-cloud-aiplatform google-cloud-storage kfp==1.6.1from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")from tfx.orchestration import data_types
from tfx import v1 as tfx
from tfx.orchestration.pipeline import Pipeline
from custom_components.file_list_gen import FileListGen
from custom_components.batch_prediction_vertex import BatchPredictionGen
from custom_components.batch_pred_evaluator import PerformanceEvaluator
from custom_components.span_preparator import SpanPreparator
from custom_components.training_pipeline_trigger import PipelineTrigger
def _create_pipeline(
pipeline_name: str,
pipeline_root: str,
data_gcs_bucket: str,
data_gcs_prefix: data_types.RuntimeParameter,
batch_job_gcs: str,
job_display_name: str,
model_resource_name: str,
project_id: str,
region: str,
threshold: float,
data_gcs_destination: str,
training_pipeline_spec: str,
) -> Pipeline:
# Generate a file list for batch preditions.
# More details on the structure of this file here:
# https://bit.ly/3BzfHVu.
filelist_gen = FileListGen(
project=project_id,
gcs_source_bucket=data_gcs_bucket,
gcs_source_prefix=data_gcs_prefix,
).with_id("filelist_gen")
# Submit a batch prediction job.
batch_pred_component = BatchPredictionGen(
project=project_id,
location=region,
job_display_name=job_display_name,
model_resource_name=model_resource_name,
gcs_source=filelist_gen.outputs["outpath"],
gcs_destination=f"gs://{batch_job_gcs}/results/",
accelerator_count=0,
accelerator_type=None,
).with_id("bulk_inferer_vertex")
batch_pred_component.add_upstream_node(filelist_gen)
# Evaluate the performance of the predictions.
# In a real-world project, this evaluation takes place
# separately, typically with the help of domain experts.
final_gcs_destination = f"gs://{batch_job_gcs}/results/"
evaluator = PerformanceEvaluator(
gcs_destination=f'gs://{final_gcs_destination.split("/")[2]}',
local_directory=final_gcs_destination.split("/")[-2],
threshold=threshold,
).with_id("batch_prediction_evaluator")
evaluator.add_upstream_node(batch_pred_component)
span_preparator = SpanPreparator(
is_retrain=evaluator.outputs["trigger_pipeline"],
gcs_source_bucket=data_gcs_bucket,
gcs_source_prefix=data_gcs_prefix,
gcs_destination_bucket=data_gcs_destination,
).with_id("span_preparator")
span_preparator.add_upstream_node(evaluator)
trigger = PipelineTrigger(
is_retrain=evaluator.outputs["trigger_pipeline"],
latest_span_id=span_preparator.outputs["latest_span_id"],
pipeline_spec_path=training_pipeline_spec,
project_id=project_id,
region=region,
).with_id("training_pipeline_trigger")
trigger.add_upstream_node(span_preparator)
components = [
filelist_gen,
batch_pred_component,
evaluator,
span_preparator,
trigger,
]
return Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
)import os
import tfx
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2.kubeflow_v2_dag_runner import KubeflowV2DagRunner
from tfx.orchestration.kubeflow.v2.kubeflow_v2_dag_runner import (
KubeflowV2DagRunnerConfig,
)
PIPELINE_DEFINITION_FILE = PIPELINE_NAME + "_pipeline.json"
THRESHOLD = 0.9
# Important: We need to pass the custom Docker image URI to the
# `KubeflowV2DagRunnerConfig` to take effect.
runner = KubeflowV2DagRunner(
config=KubeflowV2DagRunnerConfig(default_image=TFX_IMAGE_URI),
output_filename=PIPELINE_DEFINITION_FILE,
)
_ = runner.run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_gcs_bucket=TEST_GCS_BUCKET,
data_gcs_prefix=data_types.RuntimeParameter(
name="data_gcs_prefix", default="", ptype=str
),
batch_job_gcs=GCS_BUCKET_NAME,
job_display_name=f"{MODEL_RESOURCE_NAME}_{TIMESTAMP}",
project_id=GOOGLE_CLOUD_PROJECT,
region=GOOGLE_CLOUD_REGION,
model_resource_name=MODEL_RESOURCE_NAME,
threshold=THRESHOLD,
data_gcs_destination=TRAINING_DATA_PATH,
training_pipeline_spec=TRAINING_PIPELINE_SPEC,
)
)from kfp.v2.google import client
pipelines_client = client.AIPlatformClient(
project_id=GOOGLE_CLOUD_PROJECT, region=GOOGLE_CLOUD_REGION,
)
_ = pipelines_client.create_run_from_job_spec(
PIPELINE_DEFINITION_FILE,
enable_caching=False,
parameter_values={"data_gcs_prefix": "2021-10"},
) |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/04_Cloud_Scheduler_Trigger.ipynb | from google.colab import auth
auth.authenticate_user()GOOGLE_CLOUD_PROJECT = "gcp-ml-172005" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = "cifar10-experimental-csp2" # @param {type:"string"}
PIPELINE_NAME = "continuous-adaptation-for-data-changes-batch" # @param {type:"string"}
PIPELINE_ROOT = "gs://{}/pipeline_root/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
PIPELINE_LOCATION = f"{PIPELINE_ROOT}/{PIPELINE_NAME}_pipeline.json"
PUBSUB_TOPIC = f"trigger-{PIPELINE_NAME}"
SCHEDULER_JOB_NAME = f"scheduler-job-{PUBSUB_TOPIC}"
IMAGE_LOCATION_BUCKET = "batch-prediction-collection-3" # @param {type:"string"}IMAGE_LOCATION_BUCKET_cloud_function_dep = "cloud_function/requirements.txt"%%writefile {_cloud_function_dep}
kfp==1.6.2
google-cloud-aiplatform
google-cloud-storage_cloud_function_file = "cloud_function/main.py"%%writefile {_cloud_function_file}
import os
import re
import json
import logging
import base64
from datetime import datetime
from kfp.v2.google.client import AIPlatformClient
from google.cloud import storage
def get_number_of_images(storage_client, bucket, latest_directory):
blobs = storage_client.list_blobs(bucket, prefix=latest_directory)
count = 0
for blob in blobs:
if blob.name.split(".")[-1] == "jpg":
count = count + 1
return count
def is_there_enough_images(storage_client, bucket, latest_directory, threshold):
number_of_images = get_number_of_images(storage_client, bucket, latest_directory)
print(f"number of images = {number_of_images}")
return number_of_images >= threshold
def get_latest_directory(storage_client, bucket):
blobs = storage_client.list_blobs(bucket)
folders = list(
set(
[
os.path.dirname(blob.name)
for blob in blobs
if bool(
re.match(
"[1-9][0-9][0-9][0-9]-[0-1][0-9]", os.path.dirname(blob.name)
)
)
is True
]
)
)
folders.sort(key=lambda date: datetime.strptime(date, "%Y-%m"))
print(folders[0])
return folders[0]
def trigger_pipeline(event, context):
# Parse the environment variables.
project = os.getenv("PROJECT")
region = os.getenv("REGION")
gcs_pipeline_file_location = os.getenv("GCS_PIPELINE_FILE_LOCATION")
gcs_image_bucket = os.getenv("GCS_IMAGE_BUCKET")
print(project)
print(region)
print(gcs_pipeline_file_location)
print(gcs_image_bucket)
threshold = 100
# Check if the pipeline file exists in the provided GCS Bucket.
storage_client = storage.Client()
latest_directory = get_latest_directory(storage_client, gcs_image_bucket)
if is_there_enough_images(
storage_client, gcs_image_bucket, latest_directory, threshold
):
path_parts = gcs_pipeline_file_location.replace("gs://", "").split("/")
pipeline_bucket = path_parts[0]
pipeline_blob = "/".join(path_parts[1:])
pipeline_bucket = storage_client.bucket(pipeline_bucket)
blob = storage.Blob(bucket=pipeline_bucket, name=pipeline_blob)
if not blob.exists(storage_client):
raise ValueError(f"{gcs_pipeline_file_location} does not exist.")
# Initialize Vertex AI API client and submit for pipeline execution.
api_client = AIPlatformClient(project_id=project, region=region)
response = api_client.create_run_from_job_spec(
job_spec_path=gcs_pipeline_file_location,
parameter_values={"data_gcs_prefix": latest_directory},
enable_caching=True,
)
logging.info(response)ENV_VARS=f"""\
PROJECT={GOOGLE_CLOUD_PROJECT},\
REGION={GOOGLE_CLOUD_REGION},\
GCS_PIPELINE_FILE_LOCATION={PIPELINE_LOCATION},\
GCS_IMAGE_BUCKET={IMAGE_LOCATION_BUCKET}
"""
!echo {ENV_VARS}BUCKET = f'gs://{GCS_BUCKET_NAME}'
CLOUD_FUNCTION_NAME = f'trigger-{PIPELINE_NAME}-fn'
!gcloud functions deploy {CLOUD_FUNCTION_NAME} \
--region={GOOGLE_CLOUD_REGION} \
--trigger-topic={PUBSUB_TOPIC} \
--runtime=python37 \
--source=cloud_function\
--entry-point=trigger_pipeline\
--stage-bucket={BUCKET}\
--update-env-vars={ENV_VARS}import IPython
cloud_fn_url = f"https://console.cloud.google.com/functions/details/{GOOGLE_CLOUD_REGION}/{CLOUD_FUNCTION_NAME}"
html = (
f'See the Cloud Function details <a href="{cloud_fn_url}" target="_blank">here</a>.'
)
IPython.display.display(IPython.display.HTML(html)) |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/98_Batch_Prediction_Test.ipynb | from google.colab import auth
auth.authenticate_user()GOOGLE_CLOUD_PROJECT = "central-hangar-321813" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1" # @param {type:"string"}
MODEL_NAME = "resnet_cifar_latest" # @param {type:"string"}
TEST_FILENAME = "test-images.txt" # @param {type:"string"}
TEST_GCS_BUCKET = "gs://batch-prediction-collection" # @param {type:"string"}
TEST_LOCAL_PATH = "Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes/notebooks/test-images" # @param {type:"string"}from os import listdir
test_files = listdir(TEST_LOCAL_PATH)
test_filesf = open(TEST_FILENAME, "w")
for filename in test_files:
f.write(f"{TEST_GCS_BUCKET}/{filename}\n")
f.close()import google.cloud.aiplatform as aiplatform
from typing import Union, Sequence
def create_batch_prediction_job_dedicated_resources_sample(
project: str,
location: str,
model_resource_name: str,
job_display_name: str,
gcs_source: Union[str, Sequence[str]],
gcs_destination: str,
instances_format: str = "file-list",
machine_type: str = "n1-standard-2",
accelerator_count: int = 1,
accelerator_type: str = "NVIDIA_TESLA_K80",
starting_replica_count: int = 1,
max_replica_count: int = 1,
sync: bool = True,
):
aiplatform.init(project=project, location=location)
my_model = aiplatform.Model(model_resource_name)
batch_prediction_job = my_model.batch_predict(
job_display_name=job_display_name,
instances_format=instances_format,
gcs_source=gcs_source,
gcs_destination_prefix=gcs_destination,
machine_type=machine_type,
accelerator_count=accelerator_count,
accelerator_type=accelerator_type,
starting_replica_count=starting_replica_count,
max_replica_count=max_replica_count,
sync=sync,
)
batch_prediction_job.wait()
print(batch_prediction_job.display_name)
print(batch_prediction_job.resource_name)
print(batch_prediction_job.state)
return batch_prediction_jobfrom datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")create_batch_prediction_job_dedicated_resources_sample(
project=GOOGLE_CLOUD_PROJECT,
location=GOOGLE_CLOUD_REGION,
model_resource_name="2008244793993330688",
job_display_name=f"{MODEL_NAME}-{TIMESTAMP}",
gcs_source=[f"{TEST_GCS_BUCKET}/{TEST_FILENAME}"],
gcs_destination=f"{TEST_GCS_BUCKET}/results/",
accelerator_type=None,
accelerator_count=None,
)import os
import json
RESULTS_DIRECTORY = "results"
RESULTS_DIRECTORY_FULL = f'{TEST_GCS_BUCKET}/{RESULTS_DIRECTORY}'
# Create missing directories
os.makedirs(RESULTS_DIRECTORY, exist_ok=True)
# Get the Cloud Storage paths for each result
!gsutil -m cp -r $RESULTS_DIRECTORY_FULL $RESULTS_DIRECTORY
# Get most recently modified directory
latest_directory = max(
[
os.path.join(RESULTS_DIRECTORY, d)
for d in os.listdir(RESULTS_DIRECTORY)
],
key=os.path.getmtime,
)
# Get downloaded results in directory
results_files = []
for dirpath, subdirs, files in os.walk(latest_directory):
for file in files:
if file.startswith("prediction.results"):
results_files.append(os.path.join(dirpath, file))
# Consolidate all the results into a list
results = []
for results_file in results_files:
# Download each result
with open(results_file, "r") as file:
results.extend([json.loads(line) for line in file.readlines()])resultsnum_correct = 0
for result in results:
label = os.path.basename(result["instance"]).split("_")[0]
prediction = result["prediction"]["label"]
print(f"label({label})/prediction({prediction})")
if label == prediction:
num_correct = num_correct + 1
print()
print(f"number of results: {len(results)}")
print(f"number of correct: {num_correct}")
print(f"Accuracy: {num_correct/len(results)}") |
deep-diver/mlops-hf-tf-vision-models | advanced_part1/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
eval_configs=configs.EVAL_CONFIGS,
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
eval_configs=configs.EVAL_CONFIGS,
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from tfx.proto import tuner_pb2
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
eval_configs=configs.EVAL_CONFIGS,
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_tuner_args=configs.GCP_AI_PLATFORM_TUNER_ARGS,
tuner_args=tuner_pb2.TuneArgs(
num_parallel_trials=configs.NUM_PARALLEL_TRIALS
),
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
hyperparameters=configs.HYPER_PARAMETERS,
eval_configs=configs.EVAL_CONFIGS,
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | basic/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
},
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | basic/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
},
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | dataset/create_tfrecords.py | """
Script to generate TFRecord shards from the Sidewalks dataset as shown in this
blog post: https://huggingface.co/blog/fine-tune-segformer.
The recommended way to obtain TFRecord shards is via an Apache Beam
Pipeline with an execution runner like Dataflow. Example:
https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/jpeg_to_tfrecord.py.
Usage:
python create_tfrecords --batch_size 16
python create_tfrecords --resize 256 # without --resize flag, no resizing is applied
References:
* https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/05_split_tfrecord.ipynb
* https://www.tensorflow.org/tutorials/images/segmentation
"""
import argparse
import math
import os
from typing import Tuple
import datasets
import numpy as np
import tensorflow as tf
import tqdm
from PIL import Image
RESOLUTION = 256
def load_beans_dataset(args):
hf_dataset_identifier = "beans"
ds = datasets.load_dataset(hf_dataset_identifier)
ds = ds.shuffle(seed=1)
ds = ds["train"].train_test_split(test_size=args.split, seed=args.seed)
train_ds = ds["train"]
val_ds = ds["test"]
return train_ds, val_ds
def resize_img(
image: tf.Tensor, label: tf.Tensor, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = tf.image.resize(image, (resize, resize))
return image, label
def process_image(
image: Image, label: Image, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = np.array(image)
label = np.array(label)
image = tf.convert_to_tensor(image)
label = tf.convert_to_tensor(label)
if resize:
image, label = resize_img(image, label, resize)
return image, label
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def create_tfrecord(image: Image, label: Image, resize: int):
image, label = process_image(image, label, resize)
image_dims = image.shape
image = tf.reshape(image, [-1]) # flatten to 1D array
label = tf.reshape(label, [-1]) # flatten to 1D array
return tf.train.Example(
features=tf.train.Features(
feature={
"image": _float_feature(image.numpy()),
"image_shape": _int64_feature(
[image_dims[0], image_dims[1], image_dims[2]]
),
"label": _int64_feature(label.numpy()),
}
)
).SerializeToString()
def write_tfrecords(root_dir, dataset, split, batch_size, resize):
print(f"Preparing TFRecords for split: {split}.")
for step in tqdm.tnrange(int(math.ceil(len(dataset) / batch_size))):
temp_ds = dataset[step * batch_size : (step + 1) * batch_size]
shard_size = len(temp_ds["image"])
filename = os.path.join(
root_dir, "{}-{:02d}-{}.tfrec".format(split, step, shard_size)
)
with tf.io.TFRecordWriter(filename) as out_file:
for i in range(shard_size):
image = temp_ds["image"][i]
label = temp_ds["labels"][i]
example = create_tfrecord(image, label, resize)
out_file.write(example)
print("Wrote file {} containing {} records".format(filename, shard_size))
def main(args):
train_ds, val_ds = load_beans_dataset(args)
print("Dataset loaded from HF.")
if not os.path.exists(args.root_tfrecord_dir):
os.makedirs(args.root_tfrecord_dir, exist_ok=True)
print(args.resize)
write_tfrecords(
args.root_tfrecord_dir, train_ds, "train", args.batch_size, args.resize
)
write_tfrecords(args.root_tfrecord_dir, val_ds, "val", args.batch_size, args.resize)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--split", help="Train and test split.", default=0.2, type=float
)
parser.add_argument(
"--seed",
help="Seed to be used while performing train-test splits.",
default=2022,
type=int,
)
parser.add_argument(
"--root_tfrecord_dir",
help="Root directory where the TFRecord shards will be serialized.",
default="beans-tfrecords",
type=str,
)
parser.add_argument(
"--batch_size",
help="Number of samples to process in a batch before serializing a single TFRecord shard.",
default=32,
type=int,
)
parser.add_argument(
"--resize",
help="Width and height size the image will be resized to. No resizing will be applied when this isn't set.",
type=int,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from tfx.proto import tuner_pb2
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
eval_configs=configs.EVAL_CONFIGS,
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_tuner_args=configs.GCP_AI_PLATFORM_TUNER_ARGS,
tuner_args=tuner_pb2.TuneArgs(
num_parallel_trials=configs.NUM_PARALLEL_TRIALS
),
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
hf_pusher_args=configs.HF_PUSHER_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
hyperparameters=configs.HYPER_PARAMETERS,
eval_configs=configs.EVAL_CONFIGS,
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | intermediate/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | intermediate/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | notebooks/advanced_part1.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
proprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"
schema_file = "schema.pbtxt"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import SchemaGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import LatestBlessedModelResolver
import tensorflow_model_analysis as tfmacontext = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)
context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])%%writefile {schema_file}
feature {
name: "image"
type: FLOAT
presence {
min_fraction: 1.0
}
float_domain {
min: 0
max: 255
}
shape {
dim { size: 256 }
dim { size: 256 }
dim { size: 3 }
}
}
feature {
name: "image_shape"
type: INT
presence {
min_fraction: 1.0
}
shape {
dim { size: 3 }
}
}
feature {
name: "label"
type: INT
presence {
min_fraction: 1.0
}
int_domain {
min: 0
max: 2
}
shape {
dim { size: 1 }
}
}schema_gen = tfx.components.ImportSchemaGen(
schema_file=schema_file)
context.run(schema_gen)example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema']
)
context.run(example_validator)context.show(example_validator.outputs['anomalies'])%%writefile {proprocessing_file}
import tensorflow as tf
IMAGE_KEY = "image"
LABEL_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
INPUT_IMG_SIZE = 224
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_KEY] = tf.image.resize(
inputs[IMAGE_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_KEY] = inputs[IMAGE_KEY] / 255.0
inputs[IMAGE_KEY] = tf.transpose(inputs[IMAGE_KEY], [0, 3, 1, 2])
outputs[MODEL_INPUT_IMAGE_KEY] = inputs[IMAGE_KEY]
outputs[MODEL_INPUT_LABEL_KEY] = inputs[LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
_MODEL_INPUT_LABEL_KEY = "labels"
def INFO(text: str):
absl.logging.info(text)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
###
def _transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def _tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {_MODEL_INPUT_LABEL_KEY: logits}
return serve_tf_examples_fn
###
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 32,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_MODEL_INPUT_LABEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _build_model():
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable=False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam', loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
model = _build_model()
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _EVAL_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": _model_exporter(model),
"transform_features": _transform_features_signature(
model, tf_transform_output
),
"from_examples": _tf_examples_serving_signature(model, tf_transform_output),
},
)trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
)context.run(trainer)model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")context.run(model_resolver)eval_configs = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)context.run(evaluator) |
deep-diver/mlops-hf-tf-vision-models | notebooks/advanced_part2.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
tuner_fn = "modules.model.tuner_fn"
proprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"
schema_file = "schema.pbtxt"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import SchemaGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Tuner
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import LatestBlessedModelResolver
import tensorflow_model_analysis as tfmacontext = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)
context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])%%writefile {schema_file}
feature {
name: "image"
type: FLOAT
presence {
min_fraction: 1.0
}
float_domain {
min: 0
max: 255
}
shape {
dim { size: 256 }
dim { size: 256 }
dim { size: 3 }
}
}
feature {
name: "image_shape"
type: INT
presence {
min_fraction: 1.0
}
shape {
dim { size: 3 }
}
}
feature {
name: "label"
type: INT
presence {
min_fraction: 1.0
}
int_domain {
min: 0
max: 2
}
shape {
dim { size: 1 }
}
}schema_gen = tfx.components.ImportSchemaGen(
schema_file=schema_file)
context.run(schema_gen)example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema']
)
context.run(example_validator)context.show(example_validator.outputs['anomalies'])%%writefile {proprocessing_file}
import tensorflow as tf
IMAGE_KEY = "image"
LABEL_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
INPUT_IMG_SIZE = 224
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_KEY] = tf.image.resize(
inputs[IMAGE_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_KEY] = inputs[IMAGE_KEY] / 255.0
inputs[IMAGE_KEY] = tf.transpose(inputs[IMAGE_KEY], [0, 3, 1, 2])
outputs[MODEL_INPUT_IMAGE_KEY] = inputs[IMAGE_KEY]
outputs[MODEL_INPUT_LABEL_KEY] = inputs[LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import keras_tuner
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.v1.components import TunerFnResult
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
_MODEL_INPUT_LABEL_KEY = "labels"
def INFO(text: str):
absl.logging.info(text)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
###
def _transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def _tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {_MODEL_INPUT_LABEL_KEY: logits}
return serve_tf_examples_fn
###
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 32,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_MODEL_INPUT_LABEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _get_hyperparameters() -> keras_tuner.HyperParameters:
hp = keras_tuner.HyperParameters()
hp.Choice("learning_rate", [1e-3, 1e-2], default=1e-3)
return hp
def _build_model(hparams: keras_tuner.HyperParameters):
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable=False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.get("learning_rate"))
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
hparams = keras_tuner.HyperParameters.from_config(fn_args.hyperparameters)
INFO(f"HyperParameters for training: {hparams.get_config()}")
model = _build_model(hparams)
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _EVAL_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": _model_exporter(model),
"transform_features": _transform_features_signature(
model, tf_transform_output
),
"from_examples": _tf_examples_serving_signature(model, tf_transform_output),
},
)
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
tuner = keras_tuner.RandomSearch(
_build_model,
max_trials=6,
hyperparameters=_get_hyperparameters(),
allow_new_entries=False,
objective=keras_tuner.Objective("val_accuracy", "max"),
directory=fn_args.working_dir,
project_name="img_classification_tuning",
)
tf_transform_output = tft.TFTransformOutput(fn_args.transform_graph_path)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
"x": train_dataset,
"validation_data": eval_dataset,
"steps_per_epoch": _TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
"validation_steps": _EVAL_LENGTH // _EVAL_BATCH_SIZE,
},
)
tuner = Tuner(
tuner_fn=tuner_fn,
examples=transform.outputs["transformed_examples"],
schema=schema_gen.outputs["schema"],
transform_graph=transform.outputs["transform_graph"],
)context.run(tuner)trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
hyperparameters=tuner.outputs["best_hyperparameters"],
)context.run(trainer)model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")context.run(model_resolver)eval_configs = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)context.run(evaluator) |
deep-diver/mlops-hf-tf-vision-models | notebooks/basic.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import Trainer
from tfx.components import Pusher
from tfx.components import StatisticsGen
from tfx.proto import example_gen_pb2context = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)context.run(example_gen)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
def INFO(text: str):
absl.logging.info(text)
###
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
###
def _parse_tfr(proto):
feature_description = {
"image": tf.io.VarLenFeature(tf.float32),
"image_shape": tf.io.VarLenFeature(tf.int64),
"label": tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec["image_shape"])
image = tf.reshape(tf.sparse.to_dense(rec["image"]), image_shape)
label = tf.sparse.to_dense(rec["label"])
return {"pixel_values": image, "labels": label}
def _preprocess(example_batch):
images = example_batch["pixel_values"]
images = tf.transpose(images, perm=[0, 1, 2, 3]) # (batch_size, height, width, num_channels)
images = tf.image.resize(images, (224, 224))
images = tf.transpose(images, perm=[0, 3, 1, 2])
labels = example_batch["labels"]
labels = tf.transpose(labels, perm=[0, 1]) # So, that TF can evaluation the shapes.
return {"pixel_values": images, "labels": labels}
def _input_fn(
file_pattern: List[str],
batch_size: int = 32,
is_train: bool = False,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = tf.data.TFRecordDataset(
tf.io.gfile.glob(file_pattern[0] + ".gz"),
num_parallel_reads=tf.data.AUTOTUNE,
compression_type="GZIP",
).map(_parse_tfr, num_parallel_calls=tf.data.AUTOTUNE)
if is_train:
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
dataset = dataset.map(_preprocess)
return dataset
def _build_model():
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam', loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
train_dataset = _input_fn(
fn_args.train_files,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
model = _build_model()
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _TRAIN_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir, save_format="tf", signatures=_model_exporter(model)
)trainer = Trainer(
run_fn=model_fn,
examples=example_gen.outputs["examples"],
)context.run(trainer) |
deep-diver/mlops-hf-tf-vision-models | notebooks/intermediate.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
proprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"
schema_file = "schema.pbtxt"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import SchemaGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Pusher
from tfx.proto import example_gen_pb2context = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)
context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])%%writefile {schema_file}
feature {
name: "image"
type: FLOAT
presence {
min_fraction: 1.0
}
float_domain {
min: 0
max: 255
}
shape {
dim { size: 256 }
dim { size: 256 }
dim { size: 3 }
}
}
feature {
name: "image_shape"
type: INT
presence {
min_fraction: 1.0
}
shape {
dim { size: 3 }
}
}
feature {
name: "label"
type: INT
presence {
min_fraction: 1.0
}
int_domain {
min: 0
max: 2
}
shape {
dim { size: 1 }
}
}schema_gen = tfx.components.ImportSchemaGen(
schema_file=schema_file)
context.run(schema_gen)example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema']
)
context.run(example_validator)context.show(example_validator.outputs['anomalies'])%%writefile {proprocessing_file}
import tensorflow as tf
IMAGE_KEY = "image"
LABEL_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
INPUT_IMG_SIZE = 224
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_KEY] = tf.image.resize(
inputs[IMAGE_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_KEY] = inputs[IMAGE_KEY] / 255.0
inputs[IMAGE_KEY] = tf.transpose(inputs[IMAGE_KEY], [0, 3, 1, 2])
outputs[MODEL_INPUT_IMAGE_KEY] = inputs[IMAGE_KEY]
outputs[MODEL_INPUT_LABEL_KEY] = inputs[LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
_MODEL_INPUT_LABEL_KEY = "labels"
def INFO(text: str):
absl.logging.info(text)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 32,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_MODEL_INPUT_LABEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _build_model():
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable=False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam', loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
model = _build_model()
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _EVAL_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures=_model_exporter(model)
)trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
)context.run(trainer) |
deep-diver/mlops-hf-tf-vision-models | notebooks/parse_tfrecord.ipynb | import tensorflow as tfGCS_PATH_FULL_RESOUTION = "gs://beans-fullres/tfrecords"
GCS_PATH_LOW_RESOLUTION = "gs://beans-lowres/tfrecords"BATCH_SIZE = 4
AUTO = tf.data.AUTOTUNEdef parse_tfr(proto):
feature_description = {
"image": tf.io.VarLenFeature(tf.float32),
"image_shape": tf.io.VarLenFeature(tf.int64),
"label": tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec["image_shape"])
image = tf.reshape(tf.sparse.to_dense(rec["image"]), image_shape)
label = tf.sparse.to_dense(rec["label"])
return {"pixel_values": image, "label": label}
def prepare_dataset(GCS_PATH=GCS_PATH_FULL_RESOUTION,
split="train", batch_size=BATCH_SIZE):
if split not in ["train", "val"]:
raise ValueError(
"Invalid split provided. Supports splits are: `train` and `val`."
)
dataset = tf.data.TFRecordDataset(
[filename for filename in tf.io.gfile.glob(f"{GCS_PATH}/{split}-*")],
num_parallel_reads=AUTO,
).map(parse_tfr, num_parallel_calls=AUTO)
if split == "train":
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(AUTO)
return datasettrain_dataset = prepare_dataset()
val_dataset = prepare_dataset(split="val")for batch in train_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)for batch in val_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)train_dataset = prepare_dataset(GCS_PATH_LOW_RESOLUTION)
val_dataset = prepare_dataset(GCS_PATH_LOW_RESOLUTION, split="val")for batch in train_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)for batch in val_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape) |
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/ViT.py | import tensorflow as tf
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model():
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/hyperparams.py | EPOCHS = 1
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
INPUT_IMG_SIZE = 224
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/preprocessing.py | import tensorflow as tf
from .common import IMAGE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import INPUT_IMG_SIZE
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_TFREC_KEY] = tf.image.resize(
inputs[IMAGE_TFREC_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_TFREC_KEY] = inputs[IMAGE_TFREC_KEY] / 255.0
inputs[IMAGE_TFREC_KEY] = tf.transpose(inputs[IMAGE_TFREC_KEY], [0, 3, 1, 2])
outputs[IMAGE_MODEL_KEY] = inputs[IMAGE_TFREC_KEY]
outputs[LABEL_MODEL_KEY] = inputs[LABEL_TFREC_KEY]
return outputs
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/signatures.py | from typing import Dict
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
from .common import LABEL_MODEL_KEY
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {LABEL_MODEL_KEY: logits}
return serve_tf_examples_fn
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/train.py | import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import (
model_exporter,
transform_features_signature,
tf_examples_serving_signature,
)
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
model = build_model()
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": model_exporter(model),
"transform_features": transform_features_signature(
model, tf_transform_output
),
"from_examples": tf_examples_serving_signature(model, tf_transform_output),
},
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/train_data.py | from typing import List
import tensorflow as tf
import tensorflow_transform as tft
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
from .utils import INFO
from .common import LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = BATCH_SIZE,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=LABEL_MODEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/pipeline/configs.py | import os
import tensorflow_model_analysis as tfma
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
PIPELINE_NAME = "vit-e2e-pipeline-advanced-part1"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
SCHEMA_PATH = "pipeline/schema.pbtxt"
TRAINING_FN = "modules.train.run_fn"
PREPROCESSING_FN = "modules.preprocessing.preprocessing_fn"
EXAMPLE_GEN_BEAM_ARGS = None
TRANSFORM_BEAM_ARGS = None
EVAL_CONFIGS = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
TRANSFORM_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
"--worker_harness_container_image=" + PIPELINE_IMAGE,
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
transform_beam_args: Optional[List] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
if transform_beam_args is not None:
transform.with_beam_pipeline_args(transform_beam_args)
components.append(transform)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
components.append(transform)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
}
trainer = Trainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/ViT.py | import tensorflow as tf
import keras_tuner
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model(hparams: keras_tuner.HyperParameters):
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.get("learning_rate"))
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/hyperparams.py | import keras_tuner
EPOCHS = 10
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
INPUT_IMG_SIZE = 224
def get_hyperparameters(hyperparameters) -> keras_tuner.HyperParameters:
hp_set = keras_tuner.HyperParameters()
for hp in hyperparameters:
hp_set.Choice(
hp, hyperparameters[hp]["values"], default=hyperparameters[hp]["default"]
)
return hp_set
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/preprocessing.py | import tensorflow as tf
from .common import IMAGE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import INPUT_IMG_SIZE
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_TFREC_KEY] = tf.image.resize(
inputs[IMAGE_TFREC_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_TFREC_KEY] = inputs[IMAGE_TFREC_KEY] / 255.0
inputs[IMAGE_TFREC_KEY] = tf.transpose(inputs[IMAGE_TFREC_KEY], [0, 3, 1, 2])
outputs[IMAGE_MODEL_KEY] = inputs[IMAGE_TFREC_KEY]
outputs[LABEL_MODEL_KEY] = inputs[LABEL_TFREC_KEY]
return outputs
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/signatures.py | from typing import Dict
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
from .common import LABEL_MODEL_KEY
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {LABEL_MODEL_KEY: logits}
return serve_tf_examples_fn
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/train.py | import keras_tuner
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import (
model_exporter,
transform_features_signature,
tf_examples_serving_signature,
)
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
from .utils import INFO
def run_fn(fn_args: FnArgs):
custom_config = fn_args.custom_config
epochs = EPOCHS
if custom_config is not None:
if "is_local" in custom_config:
epochs = 1
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
hparams = keras_tuner.HyperParameters.from_config(fn_args.hyperparameters)
INFO(f"HyperParameters for training: {hparams.get_config()}")
model = build_model(hparams)
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=epochs,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": model_exporter(model),
"transform_features": transform_features_signature(
model, tf_transform_output
),
"from_examples": tf_examples_serving_signature(model, tf_transform_output),
},
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/train_data.py | from typing import List
import tensorflow as tf
import tensorflow_transform as tft
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
from .utils import INFO
from .common import LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = BATCH_SIZE,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=LABEL_MODEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/tuning.py | import keras_tuner
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.v1.components import TunerFnResult
from .train_data import input_fn
from .ViT import build_model
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import get_hyperparameters
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
hyperparameters = fn_args.custom_config["hyperparameters"]
tuner = keras_tuner.RandomSearch(
build_model,
max_trials=6,
hyperparameters=get_hyperparameters(hyperparameters),
allow_new_entries=False,
objective=keras_tuner.Objective("val_accuracy", "max"),
directory=fn_args.working_dir,
project_name="ViT MLOps Advanced Part2",
)
tf_transform_output = tft.TFTransformOutput(fn_args.transform_graph_path)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
"x": train_dataset,
"validation_data": eval_dataset,
"steps_per_epoch": TRAIN_LENGTH // TRAIN_BATCH_SIZE,
"validation_steps": EVAL_LENGTH // EVAL_BATCH_SIZE,
},
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/pipeline/configs.py | import os
import tensorflow_model_analysis as tfma
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
import tfx.extensions.google_cloud_ai_platform.tuner.executor as vertex_tuner_const
PIPELINE_NAME = "vit-e2e-pipeline-advanced-part2"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
SCHEMA_PATH = "pipeline/schema.pbtxt"
TRAINING_FN = "modules.train.run_fn"
TUNER_FN = "modules.tuning.tuner_fn"
PREPROCESSING_FN = "modules.preprocessing.preprocessing_fn"
EXAMPLE_GEN_BEAM_ARGS = None
TRANSFORM_BEAM_ARGS = None
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
HYPER_PARAMETERS = {
"learning_rate": {"values": [1e-3, 1e-2, 1e-1], "default": 1e-3},
}
EVAL_CONFIGS = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
TRANSFORM_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
"--worker_harness_container_image=" + PIPELINE_IMAGE,
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
NUM_PARALLEL_TRIALS = 3
GCP_AI_PLATFORM_TUNER_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_tuner_const.TUNING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"job_spec": {
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
},
vertex_tuner_const.REMOTE_TRIALS_WORKING_DIR_KEY: os.path.join(
PIPELINE_ROOT, "trials"
),
"use_gpu": True,
"hyperparameters": HYPER_PARAMETERS,
}
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.extensions.google_cloud_ai_platform.tuner.component import Tuner as VertexTuner
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.proto import tuner_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_tuner_args: Optional[Dict[Text, Text]] = None,
tuner_args: tuner_pb2.TuneArgs = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
transform_beam_args: Optional[List] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
if transform_beam_args is not None:
transform.with_beam_pipeline_args(transform_beam_args)
components.append(transform)
tuner = VertexTuner(
tuner_fn=modules["tuner_fn"],
examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
tune_args=tuner_args,
custom_config=ai_platform_tuner_args,
)
components.append(tuner)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"hyperparameters": tuner.outputs["best_hyperparameters"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Tuner
from tfx.components import Trainer
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
hyperparameters: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
components.append(transform)
tuner = Tuner(
tuner_fn=modules["tuner_fn"],
examples=transform.outputs["transformed_examples"],
schema=schema_gen.outputs["schema"],
transform_graph=transform.outputs["transform_graph"],
custom_config={"hyperparameters": hyperparameters},
)
components.append(tuner)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"hyperparameters": tuner.outputs["best_hyperparameters"],
"custom_config": {"is_local": True},
}
trainer = Trainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/ViT.py | import tensorflow as tf
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model():
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/hyperparams.py | EPOCHS = 1
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/signatures.py | import tensorflow as tf
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/train.py | from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import model_exporter
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
def run_fn(fn_args: FnArgs):
train_dataset = input_fn(
fn_args.train_files,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
model = build_model()
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=EPOCHS,
)
model.save(
fn_args.serving_model_dir, save_format="tf", signatures=model_exporter(model)
)
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/train_data.py | from typing import List
import tensorflow as tf
from .utils import INFO
from .common import IMAGE_TFREC_KEY, IMAGE_SHAPE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def _parse_tfr(proto):
feature_description = {
IMAGE_TFREC_KEY: tf.io.VarLenFeature(tf.float32),
IMAGE_SHAPE_TFREC_KEY: tf.io.VarLenFeature(tf.int64),
LABEL_TFREC_KEY: tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec[IMAGE_SHAPE_TFREC_KEY])
image = tf.reshape(tf.sparse.to_dense(rec[IMAGE_TFREC_KEY]), image_shape)
label = tf.sparse.to_dense(rec[LABEL_TFREC_KEY])
return {IMAGE_MODEL_KEY: image, LABEL_MODEL_KEY: label}
def _preprocess(example_batch):
images = example_batch[IMAGE_MODEL_KEY]
images = tf.transpose(
images, perm=[0, 1, 2, 3]
) # (batch_size, height, width, num_channels)
images = tf.image.resize(images, (224, 224))
images = tf.transpose(images, perm=[0, 3, 1, 2])
labels = example_batch[LABEL_MODEL_KEY]
labels = tf.transpose(labels, perm=[0, 1]) # So, that TF can evaluation the shapes.
return {IMAGE_MODEL_KEY: images, LABEL_MODEL_KEY: labels}
def input_fn(
file_pattern: List[str],
batch_size: int = BATCH_SIZE,
is_train: bool = False,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = tf.data.TFRecordDataset(
tf.io.gfile.glob(file_pattern[0] + ".gz"),
num_parallel_reads=tf.data.AUTOTUNE,
compression_type="GZIP",
).map(_parse_tfr, num_parallel_calls=tf.data.AUTOTUNE)
if is_train:
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
dataset = dataset.map(_preprocess)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | basic/pipeline/configs.py | import os # pylint: disable=unused-import
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
PIPELINE_NAME = "vit-e2e-pipeline-basic"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
TRAINING_FN = "modules.train.run_fn"
EXAMPLE_GEN_BEAM_ARGS = None
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
|
deep-diver/mlops-hf-tf-vision-models | basic/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
modules: Dict[Text, Text],
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
trainer_args = {
"run_fn": modules["training_fn"],
"examples": example_gen.outputs["examples"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
pusher_args = {
"model": trainer.outputs["model"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | basic/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import Trainer
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
modules: Dict[Text, Text],
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
trainer = Trainer(
run_fn=modules["training_fn"],
examples=example_gen.outputs["examples"],
)
components.append(trainer)
pusher_args = {
"model": trainer.outputs["model"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/ViT.py | import tensorflow as tf
import keras_tuner
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model(hparams: keras_tuner.HyperParameters):
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.get("learning_rate"))
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
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