def run_job(args):
should_ingest, cols_to_validate, cols_to_aggregate, cols_to_transform, training_datasets = parse_args(
args)
resource_id_list = cols_to_validate + cols_to_aggregate + cols_to_transform + training_datasets
try:
ctx = Context(s3_path=args.context,
cache_dir=args.cache_dir,
workload_id=args.workload_id)
except Exception as e:
logger.exception("An error occurred, see the logs for more details.")
sys.exit(1)
try:
spark = None # For the finally clause
spark = get_spark_session(ctx.workload_id)
spark.sparkContext.parallelize(
[1, 2, 3, 4, 5]).count() # test that executors are allocated
raw_df = ingest_raw_dataset(spark, ctx, cols_to_validate,
should_ingest)
if len(cols_to_aggregate) > 0:
run_aggregators(spark, ctx, cols_to_aggregate, raw_df)
if len(cols_to_transform) > 0:
validate_transformers(spark, ctx, cols_to_transform, raw_df)
create_training_datasets(spark, ctx, training_datasets, raw_df)
util.log_job_finished(ctx.workload_id)
except CortexException as e:
e.wrap("error")
logger.error(str(e))
logger.exception(
"An error occurred, see `cortex logs -v {} {}` for more details.".
format(
ctx.id_map[resource_id_list[0]]["resource_type"],
ctx.id_map[resource_id_list[0]]["name"],
))
sys.exit(1)
except Exception as e:
logger.exception(
"An error occurred, see `cortex logs -v {} {}` for more details.".
format(
ctx.id_map[resource_id_list[0]]["resource_type"],
ctx.id_map[resource_id_list[0]]["name"],
))
sys.exit(1)
finally:
if spark is not None:
spark.stop()
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 build(args):
ctx = Context(s3_path=args.context,
cache_dir=args.cache_dir,
workload_id=args.workload_id)
python_packages_list = [
ctx.pp_id_map[id] for id in args.python_packages.split(",")
]
python_packages = {
python_package["name"]: python_package
for python_package in python_packages_list
}
ctx.upload_resource_status_start(*python_packages_list)
try:
build_packages(python_packages, ctx.bucket)
util.log_job_finished(ctx.workload_id)
except Exception as e:
logger.exception(e)
ctx.upload_resource_status_failed(*python_packages_list)
else:
ctx.upload_resource_status_success(*python_packages_list)
