def start(args):
ctx = Context(s3_path=args.context,
cache_dir=args.cache_dir,
workload_id=args.workload_id)
package.install_packages(ctx.python_packages, ctx.bucket)
api = ctx.apis_id_map[args.api]
model = ctx.models[api["model_name"]]
tf_lib.set_logging_verbosity(ctx.environment["log_level"]["tensorflow"])
local_cache["ctx"] = ctx
local_cache["api"] = api
local_cache["model"] = model
if not os.path.isdir(args.model_dir):
aws.download_and_extract_zip(model["key"], args.model_dir, ctx.bucket)
for column_name in model["feature_columns"] + [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]
input_args_schema = transformed_column["inputs"]["args"]
# cache aggregates and constants in memory
if input_args_schema is not None:
local_cache["transform_args_cache"][
column_name] = ctx.populate_args(input_args_schema)
channel = implementations.insecure_channel("localhost", args.tf_serve_port)
local_cache[
"stub"] = prediction_service_pb2.beta_create_PredictionService_stub(
channel)
local_cache["required_inputs"] = tf_lib.get_base_input_columns(
model["name"], ctx)
# wait a bit for tf serving to start before querying metadata
limit = 600
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 `cx logs api {}` for more details."
.format(api["name"]))
sys.exit(1)
time.sleep(1)
logger.info("Serving model: {}".format(model["name"]))
serve(app, listen="*:{}".format(args.port))
def train(args):
ctx = Context(s3_path=args.context,
cache_dir=args.cache_dir,
workload_id=args.workload_id)
package.install_packages(ctx.python_packages, ctx.bucket)
model = ctx.models_id_map[args.model]
logger.info("Training")
with util.Tempdir(ctx.cache_dir) as temp_dir:
model_dir = os.path.join(temp_dir, "model_dir")
ctx.upload_resource_status_start(model)
try:
model_impl = ctx.get_model_impl(model["name"])
train_util.train(model["name"], model_impl, ctx, model_dir)
ctx.upload_resource_status_success(model)
logger.info("Caching")
logger.info("Caching model " + model["name"])
model_export_dir = os.path.join(model_dir, "export", "estimator")
model_zip_path = os.path.join(temp_dir, "model.zip")
util.zip_dir(model_export_dir, model_zip_path)
aws.upload_file_to_s3(local_path=model_zip_path,
key=model["key"],
bucket=ctx.bucket)
util.log_job_finished(ctx.workload_id)
except CortexException as e:
ctx.upload_resource_status_failed(model)
e.wrap("error")
logger.error(str(e))
logger.exception(
"An error occurred, see `cx logs model {}` for more details.".
format(model["name"]))
sys.exit(1)
except Exception as e:
ctx.upload_resource_status_failed(model)
logger.exception(
"An error occurred, see `cx logs model {}` for more details.".
format(model["name"]))
sys.exit(1)
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:
package.install_packages(ctx.python_packages, ctx.storage)
local_cache["request_handler"] = ctx.get_request_handler_impl(
api["name"])
model_cache_path = os.path.join(args.model_dir, args.api)
if not os.path.exists(model_cache_path):
ctx.storage.download_file_external(api["model"], model_cache_path)
sess = rt.InferenceSession(model_cache_path)
local_cache["sess"] = sess
local_cache["input_metadata"] = sess.get_inputs()
local_cache["output_metadata"] = sess.get_outputs()
logger.info("Serving model: {}".format(
util.remove_resource_ref(api["model"])))
serve(app, listen="*:{}".format(args.port))
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))