def model_config(self, model_name):
model = self.models[model_name]
if model is None:
return None
estimator = self.estimators[model["estimator"]]
target_column = self.columns[util.get_resource_ref(model["target_column"])]
if estimator.get("target_column") is not None:
target_col_type = self.get_inferred_column_type(target_column["name"])
if target_col_type not in estimator["target_column"]:
raise UserException(
"model " + model_name,
"target_column",
target_column["name"],
"unsupported type (expected type {}, got type {})".format(
util.data_type_str(estimator["target_column"]),
util.data_type_str(target_col_type),
),
)
model_config = deepcopy(model)
config_keys = [
"name",
"estimator"
"estimator_path"
"target_column"
"input"
"training_input"
"hparams"
"prediction_key"
"data_partition_ratio"
"training"
"evaluation"
"tags",
]
util.keep_dict_keys(model_config, config_keys)
model_config["target_column"] = target_column["name"]
model_config["input"] = self.populate_values(
model["input"], estimator["input"], preserve_column_refs=False
)
if model.get("training_input") is not None:
model_config["training_input"] = self.populate_values(
model["training_input"], estimator["training_input"], preserve_column_refs=False
)
if model.get("hparams") is not None:
model_config["hparams"] = self.populate_values(
model["hparams"], estimator["hparams"], preserve_column_refs=False
)
return model_config
def create_transformer_inputs_from_map(input, col_value_map):
if util.is_str(input):
if util.is_resource_ref(input):
res_name = util.get_resource_ref(input)
return col_value_map[res_name]
return input
if util.is_list(input):
replaced = []
for item in input:
replaced.append(create_transformer_inputs_from_map(item, col_value_map))
return replaced
if util.is_dict(input):
replaced = {}
for key, val in input.items():
key_replaced = create_transformer_inputs_from_map(key, col_value_map)
val_replaced = create_transformer_inputs_from_map(val, col_value_map)
replaced[key_replaced] = val_replaced
return replaced
return input
def read_parquet(ctx, spark):
parquet_config = ctx.environment["data"]
df = spark.read.parquet(parquet_config["path"])
alias_map = {}
for parquet_col_config in parquet_config["schema"]:
col_name = util.get_resource_ref(parquet_col_config["raw_column"])
if col_name in ctx.raw_columns:
alias_map[col_name] = parquet_col_config["parquet_column_name"]
missing_cols = set(alias_map.keys()) - set(df.columns)
if len(missing_cols) > 0:
logger.error("found schema:")
log_df_schema(df, logger.error)
raise UserException("missing column(s) in input dataset",
str(missing_cols))
selectExprs = [
"{} as {}".format(parq_name, col_name)
for col_name, parq_name in alias_map.items()
]
return df.selectExpr(*selectExprs)
def read_csv(ctx, spark):
data_config = ctx.environment["data"]
csv_config = {
util.snake_to_camel(param_name): val
for param_name, val in data_config.get("csv_config", {}).items()
if val is not None
}
df = spark.read.csv(data_config["path"],
inferSchema=True,
mode="FAILFAST",
**csv_config)
if len(data_config["schema"]) != len(df.columns):
raise UserException("expected " + len(data_config["schema"]) +
" column(s) but got " + len(df.columns))
col_names = [
util.get_resource_ref(col_ref) for col_ref in data_config["schema"]
]
renamed_cols = [
F.col(c).alias(col_names[idx]) for idx, c in enumerate(df.columns)
]
return df.select(*renamed_cols)
def populate_values(self, input, input_schema, preserve_column_refs):
if input is None:
if input_schema is None:
return None
if input_schema.get("_allow_null") == True:
return None
raise UserException("Null value is not allowed")
if util.is_resource_ref(input):
res_name = util.get_resource_ref(input)
if res_name in self.constants:
if self.constants[res_name].get("value") is not None:
const_val = self.constants[res_name]["value"]
elif self.constants[res_name].get("path") is not None:
const_val = self.storage.get_json_external(self.constants[res_name]["path"])
try:
return self.populate_values(const_val, input_schema, preserve_column_refs)
except CortexException as e:
e.wrap("constant " + res_name)
raise
if res_name in self.aggregates:
agg_val = self.get_obj(self.aggregates[res_name]["key"])
try:
return self.populate_values(agg_val, input_schema, preserve_column_refs)
except CortexException as e:
e.wrap("aggregate " + res_name)
raise
if res_name in self.columns:
if input_schema is not None:
col_type = self.get_inferred_column_type(res_name)
if col_type not in input_schema["_type"]:
raise UserException(
"column {}: unsupported input type (expected type {}, got type {})".format(
res_name,
util.data_type_str(input_schema["_type"]),
util.data_type_str(col_type),
)
)
if preserve_column_refs:
return input
else:
return res_name
if util.is_list(input):
elem_schema = None
if input_schema is not None:
if not util.is_list(input_schema["_type"]):
raise UserException(
"unsupported input type (expected type {}, got {})".format(
util.data_type_str(input_schema["_type"]), util.user_obj_str(input)
)
)
elem_schema = input_schema["_type"][0]
min_count = input_schema.get("_min_count")
if min_count is not None and len(input) < min_count:
raise UserException(
"list has length {}, but the minimum allowed length is {}".format(
len(input), min_count
)
)
max_count = input_schema.get("_max_count")
if max_count is not None and len(input) > max_count:
raise UserException(
"list has length {}, but the maximum allowed length is {}".format(
len(input), max_count
)
)
casted = []
for i, elem in enumerate(input):
try:
casted.append(self.populate_values(elem, elem_schema, preserve_column_refs))
except CortexException as e:
e.wrap("index " + i)
raise
return casted
if util.is_dict(input):
if input_schema is None:
casted = {}
for key, val in input.items():
key_casted = self.populate_values(key, None, preserve_column_refs)
try:
val_casted = self.populate_values(val, None, preserve_column_refs)
except CortexException as e:
e.wrap(util.user_obj_str(key))
raise
casted[key_casted] = val_casted
return casted
if not util.is_dict(input_schema["_type"]):
raise UserException(
"unsupported input type (expected type {}, got {})".format(
util.data_type_str(input_schema["_type"]), util.user_obj_str(input)
)
)
min_count = input_schema.get("_min_count")
if min_count is not None and len(input) < min_count:
raise UserException(
"map has length {}, but the minimum allowed length is {}".format(
len(input), min_count
)
)
max_count = input_schema.get("_max_count")
if max_count is not None and len(input) > max_count:
raise UserException(
"map has length {}, but the maximum allowed length is {}".format(
len(input), max_count
)
)
is_generic_map = False
if len(input_schema["_type"]) == 1:
input_type_key = next(iter(input_schema["_type"].keys()))
if is_compound_type(input_type_key):
is_generic_map = True
generic_map_key_schema = input_schema_from_type_schema(input_type_key)
generic_map_value = input_schema["_type"][input_type_key]
if is_generic_map:
casted = {}
for key, val in input.items():
key_casted = self.populate_values(
key, generic_map_key_schema, preserve_column_refs
)
try:
val_casted = self.populate_values(
val, generic_map_value, preserve_column_refs
)
except CortexException as e:
e.wrap(util.user_obj_str(key))
raise
casted[key_casted] = val_casted
return casted
# fixed map
casted = {}
for key, val_schema in input_schema["_type"].items():
if key in input:
val = input[key]
else:
if val_schema.get("_optional") is not True:
raise UserException("missing key: " + util.user_obj_str(key))
if val_schema.get("_default") is None:
continue
val = val_schema["_default"]
try:
val_casted = self.populate_values(val, val_schema, preserve_column_refs)
except CortexException as e:
e.wrap(util.user_obj_str(key))
raise
casted[key] = val_casted
return casted
if input_schema is None:
return input
if not util.is_str(input_schema["_type"]):
raise UserException(
"unsupported input type (expected type {}, got {})".format(
util.data_type_str(input_schema["_type"]), util.user_obj_str(input)
)
)
return cast_compound_type(input, input_schema["_type"])
def start(args):
ctx = Context(s3_path=args.context,
cache_dir=args.cache_dir,
workload_id=args.workload_id)
api = ctx.apis_id_map[args.api]
local_cache["api"] = api
local_cache["ctx"] = ctx
if api.get("request_handler_impl_key") is not None:
local_cache["request_handler"] = ctx.get_request_handler_impl(
api["name"])
if not util.is_resource_ref(api["model"]):
if api.get("request_handler") is not None:
package.install_packages(ctx.python_packages, ctx.storage)
if not os.path.isdir(args.model_dir):
ctx.storage.download_and_unzip_external(api["model"],
args.model_dir)
else:
package.install_packages(ctx.python_packages, ctx.storage)
model_name = util.get_resource_ref(api["model"])
model = ctx.models[model_name]
estimator = ctx.estimators[model["estimator"]]
local_cache["model"] = model
local_cache["estimator"] = estimator
local_cache["target_col"] = ctx.columns[util.get_resource_ref(
model["target_column"])]
local_cache["target_col_type"] = ctx.get_inferred_column_type(
util.get_resource_ref(model["target_column"]))
log_level = "DEBUG"
if ctx.environment is not None and ctx.environment.get(
"log_level") is not None:
log_level = ctx.environment["log_level"].get("tensorflow", "DEBUG")
tf_lib.set_logging_verbosity(log_level)
if not os.path.isdir(args.model_dir):
ctx.storage.download_and_unzip(model["key"], args.model_dir)
for column_name in ctx.extract_column_names(
[model["input"], model["target_column"]]):
if ctx.is_transformed_column(column_name):
trans_impl, _ = ctx.get_transformer_impl(column_name)
local_cache["trans_impls"][column_name] = trans_impl
transformed_column = ctx.transformed_columns[column_name]
# cache aggregate values
for resource_name in util.extract_resource_refs(
transformed_column["input"]):
if resource_name in ctx.aggregates:
ctx.get_obj(ctx.aggregates[resource_name]["key"])
local_cache["required_inputs"] = tf_lib.get_base_input_columns(
model["name"], ctx)
if util.is_dict(model["input"]) and model["input"].get(
"target_vocab") is not None:
local_cache["target_vocab_populated"] = ctx.populate_values(
model["input"]["target_vocab"], None, False)
try:
validate_model_dir(args.model_dir)
except Exception as e:
logger.exception(e)
sys.exit(1)
channel = grpc.insecure_channel("localhost:" + str(args.tf_serve_port))
local_cache["stub"] = prediction_service_pb2_grpc.PredictionServiceStub(
channel)
# wait a bit for tf serving to start before querying metadata
limit = 300
for i in range(limit):
try:
local_cache["metadata"] = run_get_model_metadata()
break
except Exception as e:
if i == limit - 1:
logger.exception(
"An error occurred, see `cortex logs -v api {}` for more details."
.format(api["name"]))
sys.exit(1)
time.sleep(1)
logger.info("Serving model: {}".format(
util.remove_resource_ref(api["model"])))
serve(app, listen="*:{}".format(args.port))
def _parse_example(example_proto):
features = tf.parse_single_example(serialized=example_proto, features=feature_spec)
target = features.pop(util.get_resource_ref(model["target_column"]), None)
return features, target
def get_label_placeholder(model_name, ctx):
model = ctx.models[model_name]
target_column_name = util.get_resource_ref(model["target_column"])
column_type = tf_lib.CORTEX_TYPE_TO_TF_TYPE[ctx.columns[target_column_name]["type"]]
return tf.placeholder(shape=[None], dtype=column_type)
def train(model_name, estimator_impl, ctx, model_dir):
model = ctx.models[model_name]
util.mkdir_p(model_dir)
util.rm_dir(model_dir)
tf_lib.set_logging_verbosity(ctx.environment["log_level"]["tensorflow"])
run_config = tf.estimator.RunConfig(
tf_random_seed=model["training"]["tf_random_seed"],
save_summary_steps=model["training"]["save_summary_steps"],
save_checkpoints_secs=model["training"]["save_checkpoints_secs"],
save_checkpoints_steps=model["training"]["save_checkpoints_steps"],
log_step_count_steps=model["training"]["log_step_count_steps"],
keep_checkpoint_max=model["training"]["keep_checkpoint_max"],
keep_checkpoint_every_n_hours=model["training"]["keep_checkpoint_every_n_hours"],
model_dir=model_dir,
)
train_input_fn = generate_input_fn(model_name, ctx, "training", estimator_impl)
eval_input_fn = generate_input_fn(model_name, ctx, "evaluation", estimator_impl)
serving_input_fn = generate_json_serving_input_fn(model_name, ctx, estimator_impl)
exporter = tf.estimator.FinalExporter("estimator", serving_input_fn, as_text=False)
train_num_steps = model["training"]["num_steps"]
dataset_metadata = ctx.get_metadata(model["dataset"]["id"])
if model["training"]["num_epochs"]:
train_num_steps = (
math.ceil(dataset_metadata["training_size"] / float(model["training"]["batch_size"]))
* model["training"]["num_epochs"]
)
train_spec = tf.estimator.TrainSpec(train_input_fn, max_steps=train_num_steps)
eval_num_steps = model["evaluation"]["num_steps"]
if model["evaluation"]["num_epochs"]:
eval_num_steps = (
math.ceil(dataset_metadata["eval_size"] / float(model["evaluation"]["batch_size"]))
* model["evaluation"]["num_epochs"]
)
eval_spec = tf.estimator.EvalSpec(
eval_input_fn,
steps=eval_num_steps,
exporters=[exporter],
name="estimator-eval",
start_delay_secs=model["evaluation"]["start_delay_secs"],
throttle_secs=model["evaluation"]["throttle_secs"],
)
model_config = ctx.model_config(model_name)
try:
tf_estimator = estimator_impl.create_estimator(run_config, model_config)
except Exception as e:
raise UserRuntimeException("model " + model_name) from e
target_col_name = util.get_resource_ref(model["target_column"])
if ctx.get_inferred_column_type(target_col_name) == consts.COLUMN_TYPE_FLOAT:
tf_estimator = tf.contrib.estimator.add_metrics(tf_estimator, get_regression_eval_metrics)
tf.estimator.train_and_evaluate(tf_estimator, train_spec, eval_spec)
return model_dir