class MyModel(ModelBase):
cfg = config.setting(config.req("MODEL.learning_rate"),
config.req("MODEL.classes"),
config.req("MODEL.layers"),
config.opt("MODEL.batch_size", 8))
def __init__(self, fmap):
super(MyModel, self).__init__(fmap)
self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
self.compute_loss = tf.keras.losses.SparseCategoricalCrossentropy()
self.mean_loss = tf.keras.metrics.Mean()
self.acc = tf.keras.metrics.SparseCategoricalAccuracy()
self.metrics = {"mean_loss": self.mean_loss, "acc": self.acc}
self.msg_frac = 10
def build(self):
concat_list = self.get_inputs(tp="nums")
images = tf.concat(concat_list, axis=1)
images = tf.reshape(images, (-1, 28, 28, 1))
output = CNN(n_class=10)(images)
arg_max = tf.argmax(output, axis=1)
self.set_output(output, "softmax")
self.set_output(arg_max, "argmax")
@tf.function
def train(self, feature, label):
_label = label["label"]
with tf.GradientTape() as tape:
output, _ = self.model(feature)
loss = self.compute_loss(_label, output)
grads = tape.gradient(loss, self.model.trainable_variables)
self.optimizer.apply_gradients(
zip(grads, self.model.trainable_variables))
self.mean_loss(loss)
self.acc(_label, output)
@tf.function
def evaluate(self, feature, label):
_label = label["label"]
output, _ = self.model(feature)
loss = self.compute_loss(_label, output)
self.mean_loss(loss)
self.acc(_label, output)
@tf.function
def predict(self, feature):
pred = self.model(feature)
return pred
class _Merge(TaskNode):
parent_tag = TaskNode.set_tag("_START")
output_tag = TaskNode.set_tag("RAW_FEATURE")
cfg = config.setting(config.req("SPARK"), config.req("MERGE.config_file"),
config.req("MERGE.fit"), config.req("MERGE.seed_sql"))
def __init__(self):
super(_Merge, self).__init__()
@TaskNode.timeit
def run(self):
fit = config.MERGE.fit
seed_sql = config.MERGE.seed_sql
feature_config_file = Path(config.MERGE.config_file).resolve()
if fit:
assert feature_config_file.is_file(), \
i18n("Input feature configuration is not exists "
"when fit=true: {}") \
.format(feature_config_file)
_spark_conf = config.SPARK.dense_dict
params = []
for k, v in _spark_conf.items():
if k.startswith("spark."):
_param = '--conf "{}={}"'.format(k, v)
else:
_param = '--{} "{}"'.format(k, v)
params.append(_param)
spark_conf = "\n".join(params)
_model_dir = Path(config.HDFS_FEMODEL_DIR)
hdfs_merge_dir = _model_dir.joinpath("merge").as_posix()
out_dir = Path(config.RAW_FEATURES).as_posix()
spark_submit = SparkJARExec(spark_conf=spark_conf,
seed_sql=seed_sql,
feature_config=feature_config_file,
feature_date=config.FEATURE_DATE,
feature_model_hdfs=hdfs_merge_dir,
feature_out_hdfs=out_dir,
fit=fit)
spark_submit.run()
logging.info(i18n("Feature merge done."))
class DataPrepare(TaskNode):
parent_tag = TaskNode.set_tag("_START")
output_tag = TaskNode.set_tag("RAW_FEATURE")
cfg = config.setting(
config.req("SPARK"),
config.req("IRIS_DATASET_FOR"),
config.opt("DATA_DIR", "$LOCAL_WORKSPACE/data_dir"),
config.opt("HDFS_FEATURE_DIR", _HDFS_FEATURE_DIR),
)
def __init__(self):
super(DataPrepare, self).__init__()
@TaskNode.timeit
def run(self):
spark_app = SparkBaseApp()
spark = spark_app.spark
hdfs = spark_app.hdfs
hdfs_save_path = Path(config.HDFS_FEATURE_DIR)
if hdfs.exists(hdfs_save_path / "_SUCCESS"):
logging.info("Exists data found. {}".format(hdfs_save_path))
logging.info("Data is ready! Skip prepare...")
return
train_data_path, test_data_path = get_data()
if config.IRIS_DATASET_FOR == "train":
local_data_path = train_data_path
else:
local_data_path = test_data_path
pdf = pd.read_csv(local_data_path, header=None)
pdf.columns = data_header
pdf["idx"] = [i for i in range(1, len(pdf) + 1)]
df = spark.createDataFrame(pdf)
logging.info("Saving Dataset to {}".format(hdfs_save_path))
df.repartition(1).write.save(hdfs_save_path.as_posix())
class _Root(TaskNode):
parent_tag = TaskNode.set_tag()
output_tag = TaskNode.set_tag(UNIVERSAL_TAG, "_START", "_ROOT")
cfg = config.setting(config.req("MODEL_TAG"), config.req("MODEL_DATE"),
config.req("HDFS_WORKSPACE"),
config.req("PRIMARY_KEYS"), config.req("FEATURE_TAG"),
config.req("FEATURE_DATE"),
config.opt("HDFS_FEATURE_DIR", _HDFS_FEATURE_DIR),
config.opt("HDFS_MODEL_DIR", _HDFS_MODEL_DIR),
config.opt("HDFS_FEMODEL_DIR", _HDFS_FEMODEL_DIR),
config.opt("LOCAL_WORKSPACE", "./dlflow_default"),
config.opt("LOCAL_MODEL_DIR", _LOCAL_MODEL_DIR),
config.opt("LOCAL_FEMODEL_DIR", _LOCAL_FEMODEL_DIR),
config.opt("LOCAL_TMP_DIR", _LOCAL_TMP_DIR),
config.opt("DROP_COLUMNS", []))
def __init__(self):
super(_Root, self).__init__()
@TaskNode.timeit
def run(self):
def solve_local_path(raw_path):
path = Path(raw_path).resolve()
if not path.is_dir():
path.mkdir(parents=True)
return path.as_posix()
config.LOCAL_WORKSPACE = solve_local_path(config.LOCAL_WORKSPACE)
config.LOCAL_MODEL_DIR = solve_local_path(config.LOCAL_MODEL_DIR)
config.LOCAL_FEMODEL_DIR = solve_local_path(config.LOCAL_FEMODEL_DIR)
config.LOCAL_TMP_DIR = solve_local_path(config.LOCAL_TMP_DIR)
def seq_conf_parser(v, sign=","):
if isinstance(v, str):
iter_v = v.split(sign)
elif isinstance(v, list):
iter_v = v
else:
iter_v = []
return [i for i in map(lambda x: x.strip(), iter_v) if i]
config.PRIMARY_KEYS = seq_conf_parser(config.PRIMARY_KEYS)
config.LABELS = seq_conf_parser(config.LABELS)
config.DROP_COLUMNS = seq_conf_parser(config.DROP_COLUMNS)
if "SPARK" in config:
app_name = ".".join(["DLFlow", config.uuid])
spark_conf = config.SPARK.dense_dict if config.SPARK else {}
spark_app = SparkBaseApp()
spark_app.initialize_spark(app_name, spark_conf)
else:
hdfs = HDFS()
hdfs.initialize_hdfs()
class DemoTask(TaskNode):
parent_tag = TaskNode.set_tag("PARENT_TAG")
output_tag = TaskNode.set_tag("OUTPUT_TAG")
bind_tasks = "task name or list of tasks"
cfg = config.setting(
config.req("DemoParam")
)
def __init__(self):
super(DemoTask, self).__init__()
@TaskNode.timeit
def run(self):
logging.info("Running {}".format(self.__class__.__name__))
class _Predict(TaskNode):
parent_tag = TaskNode.set_tag("_BUILD", "TRAINED_MODEL", "ENCODE_FEATURE")
output_tag = TaskNode.set_tag("PREDICT_RESULT")
bind_tasks = "_Build"
cfg = config.setting(
config.req("SPARK"),
config.opt("HDFS_PREDICT_DIR", _HDFS_PREDICT_DIR),
)
def __init__(self):
super(_Predict, self).__init__()
@TaskNode.timeit
def run(self):
spark_app = SparkBaseApp()
spark = spark_app.spark
sc = spark_app.sc
hdfs = spark_app.hdfs
dirs = config._build_dirs
tmp_fmap_dir = dirs["tmp_fmap_dir"]
hdfs_ckpt_dir = dirs["hdfs_ckpt_dir"]
hdfs_static_dir = dirs["hdfs_static_dir"]
sc.addFile(hdfs.hdfs_whole_path(hdfs_static_dir.as_posix()),
recursive=True)
sc.addFile(hdfs.hdfs_whole_path(hdfs_ckpt_dir.as_posix()),
recursive=True)
fmap = Fmap.load(tmp_fmap_dir)
bc_static_model_dir = sc.broadcast("static")
bc_fmap = sc.broadcast(fmap)
bc_config = sc.broadcast(config)
def predict_map(rdd):
from pyspark.files import SparkFiles
config = bc_config.value
fmap = bc_fmap.value
static_dir = SparkFiles.get(bc_static_model_dir.value)
ckpt_dir = SparkFiles.get("ckpt")
from dlflow.mgr import Collector, model
collect = Collector()
collect(static_dir, "Static models")
input_cls = model[config.MODEL.input_name]
dataset = input_cls(fmap).rdd_inputs(rdd, config.MODEL.batch_size)
model_cls = model[config.MODEL.model_name]
model_ins = model_cls(fmap)
model_ins.load_model(ckpt_dir)
return model_ins.predict_act(dataset)
local_model = model[config.MODEL.model_name](fmap)
df_title = local_model.pkey_names
df_title.extend(local_model.output_names)
df = spark.read.parquet(config.HDFS_ENCODE_DIR)
parallelism = spark.conf.get("spark.default.parallelism", None)
if parallelism:
num_partitions = int(parallelism)
else:
num_partitions = df.rdd.getNumPartitions()
pred_df = df.repartition(num_partitions) \
.rdd \
.mapPartitions(predict_map) \
.toDF(df_title)
hdfs_predict_dir = config.HDFS_PREDICT_DIR
spark_app.save_compress(pred_df, hdfs_predict_dir)
logging.info(
i18n("Predicting result save to {}").format(hdfs_predict_dir))
logging.info(i18n("Predicting Done."))
class DNNRegression(ModelBase):
cfg = config.setting(config.req("MODEL.learning_rate"),
config.req("MODEL.layers"),
config.opt("MODEL.batch_size", 128))
def __init__(self, fmap):
super(DNNRegression, self).__init__(fmap)
self.optimizer = tf.keras.optimizers.Adam(
learning_rate=config.MODEL.learning_rate)
self.compute_loss = tf.keras.losses.MeanSquaredError()
self.mean_loss = tf.keras.metrics.Mean()
self.metrics = {
"mean_loss": self.mean_loss,
}
self.msg_frac = 100
def build(self):
concat_list = self.get_inputs(tp="nums")
for ctg_inp, depth in self.get_inputs(tp="ctgs", with_depth=True):
_emb = _Embedding(depth, 4)(ctg_inp)
concat_list.append(_emb)
net = tf.concat(concat_list, axis=1)
for size in config.MODEL.layers:
net = tf.keras.layers.Dense(size, activation=tf.nn.relu)(net)
output = tf.keras.layers.Dense(1)(net)
sigmoid = tf.nn.sigmoid(output)
self.set_output(output, "output")
self.set_output(sigmoid, "sigmoid")
@tf.function
def train(self, feature, label):
_label = label["label"]
with tf.GradientTape() as tape:
logits, _ = self.model(feature)
loss = self.compute_loss(_label, logits)
grads = tape.gradient(loss, self.model.trainable_variables)
self.optimizer.apply_gradients(
zip(grads, self.model.trainable_variables))
self.mean_loss(loss)
@tf.function
def evaluate(self, feature, label):
_label = label["label"]
logits, _ = self.model(feature)
loss = self.compute_loss(_label, logits)
self.mean_loss(loss)
@tf.function
def predict(self, feature):
pred = self.model(feature)
return pred
class _Encode(TaskNode):
parent_tag = TaskNode.set_tag("RAW_FEATURE")
output_tag = TaskNode.set_tag("ENCODE_FEATURE", "TFRECORD_FEATURE")
cfg = config.setting(
config.req("SPARK"),
config.req("BUCKET", None),
config.opt("HDFS_ENCODE_DIR", _HDFS_ENCODE_DIR),
)
def __init__(self):
super(_Encode, self).__init__()
@TaskNode.timeit
def run(self):
spark_app = SparkBaseApp()
spark = spark_app.spark
hdfs = spark_app.hdfs
if "HDFS_TFRECORD_DIR" in config:
hdsf_tfrecord_dir = Path(config.HDFS_TFRECORD_DIR)
if hdfs.exists(hdsf_tfrecord_dir / "_SUCCESS"):
logging.info(i18n("TFRecords already exists, skip encoding."))
return
elif "HDFS_ENCODE_DIR" in config:
hdfs_encode_dir = Path(config.HDFS_ENCODE_DIR)
if hdfs.exists(hdfs_encode_dir / "_SUCCESS"):
logging.info(
i18n("Encode result already exists, encoding done."))
return
fmap_dir = "fmap_{}".format(config.uuid)
tmp_fmap_dir = Path(config.LOCAL_TMP_DIR).joinpath(fmap_dir)
local_fmap_dir = Path(config.LOCAL_FEMODEL_DIR).joinpath("fmap")
local_norm_dir = Path(config.LOCAL_FEMODEL_DIR).joinpath("norm")
hdfs_fmap_dir = Path(config.HDFS_FEMODEL_DIR).joinpath("fmap")
hdfs_norm_dir = Path(config.HDFS_FEMODEL_DIR).joinpath("norm")
if not hdfs.exists(config.HDFS_FEMODEL_DIR):
hdfs.mkdirs(hdfs_fmap_dir.parent)
spark_parser = Parser("spark")
parser_cls = spark_parser.get_parser()
normalizer_cls = spark_parser.get_normalizer()
if "SPARK.spark.default.parallelism" in config:
parallelism = int(config.SPARK.spark.default.parallelism)
else:
parallelism = _DEFAULT_PARALLELISM
df = spark.read \
.parquet(config.HDFS_FEATURE_DIR) \
.repartition(parallelism)
parser = parser_cls()
if hdfs.exists(hdfs_fmap_dir.joinpath("fmap.meta")):
logging.info(i18n("Using HDFS fmap: {}").format(hdfs_fmap_dir))
hdfs.get(hdfs_fmap_dir, tmp_fmap_dir)
else:
logging.info(
i18n("There is no fmap available, start to "
"generate fmap by parsing features."))
primary_keys = config.PRIMARY_KEYS
labels = config.LABELS
drop_columns = config.DROP_COLUMNS
buckets = None if config.BUCKET is None else config.BUCKET.dict
parser.fit(df,
buckets=buckets,
drop_columns=drop_columns,
primary_keys=primary_keys,
labels=labels)
parser.save(tmp_fmap_dir)
logging.info(i18n("Put fmap to HDFS: {}").format(hdfs_fmap_dir))
hdfs.delete(hdfs_fmap_dir)
hdfs.put(tmp_fmap_dir, hdfs_fmap_dir)
if local_fmap_dir.exists():
logging.warning(
i18n("Local directory {} already exists, "
"it will be overwritten: {}").format(
"fmap", local_fmap_dir))
shutil.rmtree(local_fmap_dir)
shutil.copytree(tmp_fmap_dir, local_fmap_dir)
parser.load(tmp_fmap_dir)
encode_df = parser.transform(df)
normalizer = normalizer_cls()
if hdfs.exists(
hdfs_norm_dir.joinpath("normalizers_metadata", "_SUCCESS")):
normalizer.load(hdfs_norm_dir)
else:
hdfs.mkdirs(hdfs_norm_dir)
try:
bucket_conf = config.BUCKET.dict
except AttributeError:
bucket_conf = None
if config.BUCKET is not None:
logging.error(i18n("Get wrong type bucket configuration."))
normalizer.fit(encode_df, parser.fmap, bucket_conf=bucket_conf)
normalizer.save(hdfs_norm_dir)
if local_norm_dir.exists():
logging.warning(
i18n("Local directory {} already exists, "
"it will be overwritten: {}").format(
"norm", local_norm_dir))
shutil.rmtree(local_norm_dir)
hdfs.get(hdfs_norm_dir, local_norm_dir)
norm_df = normalizer.transform(encode_df)
spark_app.save_compress(norm_df, config.HDFS_ENCODE_DIR, use_tfr=False)
if "HDFS_TFRECORD_DIR" in config:
spark_app.save_compress(norm_df,
config.HDFS_TFRECORD_DIR,
use_tfr=True)
logging.info(i18n("Encoding done."))
class _Build(TaskNode):
parent_tag = TaskNode.set_tag("ENCODE_FEATURE", "TFRECORD_FEATURE")
output_tag = TaskNode.set_tag("_BUILD")
cfg = config.setting(
config.req("MODELS_DIR"),
config.req("MODEL.model_name"),
config.req("MODEL.input_name"),
config.opt("HDFS_ENCODE_DIR", _HDFS_ENCODE_DIR),
config.opt("HDFS_TFRECORD_DIR", _HDFS_TFRECORD_DIR),
)
def __init__(self):
super(_Build, self).__init__()
@TaskNode.timeit
def run(self):
hdfs = HDFS()
local_static_dir = Path(config.MODELS_DIR).resolve()
hdfs_static_dir = Path(config.HDFS_MODEL_DIR).joinpath("static")
fmap_dir = "fmap_{}".format(config.uuid)
tmp_fmap_dir = Path(config.LOCAL_TMP_DIR).joinpath(fmap_dir)
local_fmap_dir = Path(config.LOCAL_FEMODEL_DIR).joinpath("fmap")
hdfs_fmap_dir = Path(config.HDFS_FEMODEL_DIR).joinpath("fmap")
ckpt_dir = "ckpt_{}".format(config.uuid)
tmp_ckpt_dir = Path(config.LOCAL_TMP_DIR).joinpath(ckpt_dir)
local_ckpt_link = Path(config.LOCAL_MODEL_DIR).joinpath("ckpt")
local_ckpt_dir = Path(config.LOCAL_MODEL_DIR).joinpath(ckpt_dir)
hdfs_ckpt_dir = Path(config.HDFS_MODEL_DIR).joinpath("ckpt")
if hdfs.exists(hdfs_fmap_dir.joinpath("fmap.meta")):
logging.info(
i18n("Using HDFS fmap: {}").format(hdfs_fmap_dir))
hdfs.get(hdfs_fmap_dir, tmp_fmap_dir)
elif local_fmap_dir.joinpath("fmap.meta").exists():
logging.info(
i18n("Using local fmap: {}").format(local_fmap_dir))
shutil.copytree(local_fmap_dir, tmp_fmap_dir)
else:
raise FmapNotExists(
i18n("No available fmap found, pleas "
"process feature encoding first."))
if hdfs.exists(hdfs_ckpt_dir):
logging.info(
i18n("Getting ckpt from HDFS: {}").format(hdfs_ckpt_dir))
hdfs.get(hdfs_ckpt_dir, tmp_ckpt_dir)
elif local_ckpt_link.resolve().exists():
logging.info(
i18n("Using local ckpt: {}").format(local_ckpt_link))
shutil.copytree(local_ckpt_link.resolve(), tmp_ckpt_dir)
else:
logging.info(
i18n("No available ckpt found, reinitializing..."))
config._build_dirs = {
"fmap_dir": fmap_dir,
"ckpt_dir": ckpt_dir,
"hdfs_fmap_dir": hdfs_fmap_dir,
"hdfs_ckpt_dir": hdfs_ckpt_dir,
"hdfs_static_dir": hdfs_static_dir,
"tmp_fmap_dir": tmp_fmap_dir,
"tmp_ckpt_dir": tmp_ckpt_dir,
"local_fmap_dir": local_fmap_dir,
"local_ckpt_dir": local_ckpt_dir,
"local_static_dir": local_static_dir,
"local_ckpt_link": local_ckpt_link
}
class DNNBinaryClassifier(ModelBase):
cfg = config.setting(config.req("MODEL.layers"),
config.opt("MODEL.learning_rate", 0.001),
config.opt("MODEL.batch_size", 128))
def __init__(self, fmap):
super(DNNBinaryClassifier, self).__init__(fmap)
self.optimizer = tf.keras.optimizers.Adam(
learning_rate=config.MODEL.learning_rate)
self.compute_loss = tf.keras.losses.BinaryCrossentropy(
from_logits=True)
self.mean_loss = tf.keras.metrics.Mean()
self.acc = tf.keras.metrics.BinaryAccuracy()
self.auc = tf.keras.metrics.AUC()
self.metrics = {
"mean_loss": self.mean_loss,
"acc": self.acc,
"auc": self.auc
}
def build(self):
concat_list = self.get_inputs(tp="nums")
for ctg_inp, depth in self.get_inputs(tp="ctgs", with_depth=True):
_emb = _Embedding(depth, 6)(ctg_inp)
concat_list.append(_emb)
net = tf.concat(concat_list, axis=1)
for size in config.MODEL.layers:
net = tf.keras.layers.Dense(size, activation=tf.nn.relu)(net)
logits = tf.keras.layers.Dense(1)(net)
sigmoid = tf.nn.sigmoid(logits)
self.set_output(logits, "logits")
self.set_output(sigmoid, "sigmoid")
@tf.function
def train(self, feature, label):
_label = label["label"]
with tf.GradientTape() as tape:
logits, sigmoid = self.model(feature)
loss = self.compute_loss(_label, logits)
grads = tape.gradient(loss, self.model.trainable_variables)
self.optimizer.apply_gradients(
zip(grads, self.model.trainable_variables))
self.mean_loss(loss)
self.acc(_label, sigmoid)
self.auc(_label, sigmoid)
@tf.function
def evaluate(self, feature, label):
_label = label["label"]
logits, sigmoid = self.model(feature)
loss = self.compute_loss(_label, logits)
self.mean_loss(loss)
self.acc(_label, sigmoid)
self.auc(_label, sigmoid)
@tf.function
def predict(self, feature):
pred = self.model(feature)
return pred
class MyModel(ModelBase):
cfg = config.setting(config.req("MODEL.learning_rate"),
config.req("MODEL.classes"),
config.req("MODEL.layers"),
config.opt("MODEL.batch_size", 8))
def __init__(self, fmap):
super(MyModel, self).__init__(fmap)
self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
self.compute_loss = tf.keras.losses.SparseCategoricalCrossentropy()
self.mean_loss = tf.keras.metrics.Mean()
self.acc = tf.keras.metrics.SparseCategoricalAccuracy()
self.metrics = {"mean_loss": self.mean_loss, "acc": self.acc}
self.msg_frac = 10
def build(self):
concat_list = self.get_inputs(tp="nums")
for ctg_inp, depth in self.get_inputs(tp="ctgs", with_depth=True):
_emb = _Embedding(depth, 6)(ctg_inp)
concat_list.append(_emb)
net = tf.concat(concat_list, axis=1)
for size in config.MODEL.layers:
net = tf.keras.layers.Dense(size, activation=tf.nn.relu)(net)
output = tf.keras.layers.Dense(config.MODEL.classes,
activation=tf.nn.softmax)(net)
arg_max = tf.argmax(output, axis=1)
self.set_output(output, "softmax")
self.set_output(arg_max, "argmax")
@tf.function
def train(self, feature, label):
_label = label["species"]
with tf.GradientTape() as tape:
output, _ = self.model(feature)
loss = self.compute_loss(_label, output)
grads = tape.gradient(loss, self.model.trainable_variables)
self.optimizer.apply_gradients(
zip(grads, self.model.trainable_variables))
self.mean_loss(loss)
self.acc(_label, output)
@tf.function
def evaluate(self, feature, label):
_label = label["species"]
output, _ = self.model(feature)
loss = self.compute_loss(_label, output)
self.mean_loss(loss)
self.acc(_label, output)
@tf.function
def predict(self, feature):
pred = self.model(feature)
return pred