def download_file(self, key, local_path):
try:
util.mkdir_p(os.path.dirname(local_path))
self.s3.download_file(self.bucket, key, local_path)
return local_path
except Exception as e:
raise CortexException("bucket " + self.bucket, "key " + key) from e
def _get_dir(self, prefix, local_dir):
prefix = util.add_suffix_unless_present(prefix, "/")
util.mkdir_p(local_dir)
for key in self._get_matching_s3_keys_generator(prefix):
rel_path = util.remove_prefix_if_present(key, prefix)
local_dest_path = os.path.join(local_dir, rel_path)
self.download_file(key, local_dest_path)
def download_dir_from_s3(prefix, local_dir, bucket, client_config={}):
prefix = util.add_suffix_unless_present(prefix, "/")
util.mkdir_p(local_dir)
for key in get_matching_s3_keys_generator(bucket, prefix, client_config=client_config):
rel_path = util.remove_prefix_if_present(key, prefix)
local_dest_path = os.path.join(local_dir, rel_path)
download_file_from_s3(key, local_dest_path, bucket, client_config=client_config)
def download_file_from_s3(key, local_path, bucket, client_config={}):
try:
util.mkdir_p(os.path.dirname(local_path))
s3 = s3_client(client_config)
s3.download_file(bucket, key, local_path)
return local_path
except Exception as e:
raise CortexException("bucket " + bucket, "key " + key) from e
def download_file(self, key, local_path):
util.mkdir_p(os.path.dirname(local_path))
try:
self.s3.download_file(self.bucket, key, local_path)
return local_path
except Exception as e:
raise CortexException(
'key "{}" in bucket "{}" could not be accessed; '.format(
key, bucket) +
"it may not exist, or you may not have suffienct permissions"
) from e
def write_files(self, files, base_path):
if os.path.isdir(base_path):
shutil.rmtree(base_path)
for filepath, contents in files.iteritems():
filename = os.path.join(base_path, filepath)
file_dir = os.path.dirname(filename)
util.mkdir_p(file_dir)
util.logger.debug("Writing out file %s" % filepath)
f = open(filename, 'w')
f.write(contents)
f.close()
util.logger.info("All done writing out input data")
def write_files(self, files, base_path):
if os.path.isdir(base_path):
shutil.rmtree(base_path)
for filepath, contents in files.iteritems():
filename = os.path.join(base_path, filepath)
file_dir = os.path.dirname(filename)
util.mkdir_p(file_dir)
util.logger.debug("Writing out file %s" % filepath)
f = open(filename, 'w')
f.write(contents)
f.close()
util.logger.info("All done writing out input data")
def __init__(self, ids_or_urls=[], options={}):
util.mkdir_p(self.test_cases_path())
if not os.path.isfile(http_client.certs_path()):
msg = ("You seem to have deleted the file of certificates "
"that shipped with this repo. It should exist "
"at %s" % http_client.certs_path())
raise error.StripeError(msg)
if ids_or_urls == []:
util.logger.info('No test case supplied. Randomly choosing among defaults.')
ids_or_urls = [SystemRandom().choice(self.DEFAULT_TEST_CASES)]
self.test_cases = map(lambda token: TestCase(self, token), ids_or_urls)
self.options = options
headers = {
'User-Agent': 'Stripe TestHarness/%s' % (self.VERSION,),
}
self.http_client = http_client.new_default_http_client(headers=headers, verify_ssl_certs=True)
def train(model_name, model_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")
eval_input_fn = generate_input_fn(model_name, ctx, "evaluation")
serving_input_fn = generate_json_serving_input_fn(model_name, ctx)
exporter = tf.estimator.FinalExporter("estimator",
serving_input_fn,
as_text=False)
dataset_metadata = aws.read_json_from_s3(model["dataset"]["metadata_key"],
ctx.bucket)
train_num_steps = model["training"]["num_steps"]
if model["training"]["num_epochs"]:
train_num_steps = (
math.ceil(dataset_metadata["dataset_size"] *
model["data_partition_ratio"]["training"] /
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["dataset_size"] *
model["data_partition_ratio"]["evaluation"] /
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"])
tf_lib.add_tf_types(model_config)
try:
estimator = model_impl.create_estimator(run_config, model_config)
except Exception as e:
raise UserRuntimeException("model " + model_name) from e
if model["type"] == "regression":
estimator = tf.contrib.estimator.add_metrics(
estimator, get_regression_eval_metrics)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
return model_dir
def download_and_extract_zip(key, local_dir, bucket, client_config={}):
util.mkdir_p(local_dir)
local_zip = os.path.join(local_dir, "zip.zip")
download_file_from_s3(key, local_zip, bucket, client_config=client_config)
util.extract_zip(local_zip, delete_zip_file=True)
def download_and_unzip(self, key, local_dir):
util.mkdir_p(local_dir)
local_zip = os.path.join(local_dir, "zip.zip")
self.download_file(key, local_zip)
util.extract_zip(local_zip, delete_zip_file=True)
def __init__(self, **kwargs):
if "cache_dir" in kwargs:
self.cache_dir = kwargs["cache_dir"]
elif "local_path" in kwargs:
local_path_dir = os.path.dirname(
os.path.abspath(kwargs["local_path"]))
self.cache_dir = os.path.join(local_path_dir, "cache")
else:
raise ValueError(
"cache_dir must be specified (or inferred from local_path)")
util.mkdir_p(self.cache_dir)
if "local_path" in kwargs:
ctx_raw = util.read_msgpack(kwargs["local_path"])
self.ctx = _deserialize_raw_ctx(ctx_raw)
elif "obj" in kwargs:
self.ctx = kwargs["obj"]
elif "raw_obj" in kwargs:
ctx_raw = kwargs["raw_obj"]
self.ctx = _deserialize_raw_ctx(ctx_raw)
elif "s3_path":
local_ctx_path = os.path.join(self.cache_dir, "context.msgpack")
bucket, key = S3.deconstruct_s3_path(kwargs["s3_path"])
S3(bucket, client_config={}).download_file(key, local_ctx_path)
ctx_raw = util.read_msgpack(local_ctx_path)
self.ctx = _deserialize_raw_ctx(ctx_raw)
else:
raise ValueError("invalid context args: " + kwargs)
self.workload_id = kwargs.get("workload_id")
self.id = self.ctx["id"]
self.key = self.ctx["key"]
self.cortex_config = self.ctx["cortex_config"]
self.dataset_version = self.ctx["dataset_version"]
self.root = self.ctx["root"]
self.raw_dataset = self.ctx["raw_dataset"]
self.status_prefix = self.ctx["status_prefix"]
self.app = self.ctx["app"]
self.environment = self.ctx["environment"]
self.python_packages = self.ctx["python_packages"]
self.raw_columns = self.ctx["raw_columns"]
self.transformed_columns = self.ctx["transformed_columns"]
self.transformers = self.ctx["transformers"]
self.aggregators = self.ctx["aggregators"]
self.aggregates = self.ctx["aggregates"]
self.constants = self.ctx["constants"]
self.models = self.ctx["models"]
self.apis = self.ctx["apis"]
self.training_datasets = {
k: v["dataset"]
for k, v in self.models.items()
}
self.api_version = self.cortex_config["api_version"]
if "local_storage_path" in kwargs:
self.storage = LocalStorage(base_dir=kwargs["local_storage_path"])
else:
self.storage = S3(
bucket=self.cortex_config["bucket"],
region=self.cortex_config["region"],
client_config={},
)
if self.api_version != consts.CORTEX_VERSION:
raise ValueError(
"API version mismatch (Context: {}, Image: {})".format(
self.api_version, consts.CORTEX_VERSION))
self.columns = util.merge_dicts_overwrite(
self.raw_columns,
self.transformed_columns # self.aggregates
)
self.values = util.merge_dicts_overwrite(self.aggregates,
self.constants)
self.raw_column_names = list(self.raw_columns.keys())
self.transformed_column_names = list(self.transformed_columns.keys())
self.column_names = list(self.columns.keys())
# Internal caches
self._transformer_impls = {}
self._aggregator_impls = {}
self._model_impls = {}
# This affects Tensorflow S3 access
os.environ["AWS_REGION"] = self.cortex_config.get("region", "")
# Id map
self.pp_id_map = ResourceMap(self.python_packages)
self.rf_id_map = ResourceMap(self.raw_columns)
self.ag_id_map = ResourceMap(self.aggregates)
self.tf_id_map = ResourceMap(self.transformed_columns)
self.td_id_map = ResourceMap(self.training_datasets)
self.models_id_map = ResourceMap(self.models)
self.apis_id_map = ResourceMap(self.apis)
self.constants_id_map = ResourceMap(self.constants)
self.id_map = util.merge_dicts_overwrite(
self.pp_id_map,
self.rf_id_map,
self.ag_id_map,
self.tf_id_map,
self.td_id_map,
self.models_id_map,
self.apis_id_map,
self.constants_id_map,
)
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