def client_from_config(config_path: str) -> cx.Client:
with open(config_path) as f:
config = yaml.safe_load(f)
cluster_name = config["cluster_name"]
return cx.client(f"{cluster_name}")
def main():
# parse args
if len(sys.argv) != 3:
print("usage: python submit.py <cortex-env> <dest-s3-dir>")
sys.exit(1)
env_name = sys.argv[1]
dest_s3_dir = sys.argv[2]
# read sample file
with open("sample.json") as f:
sample_items: List[str] = json.load(f)
# get batch endpoint
cx = cortex.client(env_name)
batch_endpoint = cx.get_api("image-classifier-alexnet")["endpoint"]
# submit job
job_spec = {
"workers": 1,
"item_list": {
"items": sample_items,
"batch_size": 1
},
"config": {
"dest_s3_dir": dest_s3_dir
},
}
response = requests.post(batch_endpoint, json=job_spec)
print(json.dumps(response.json(), indent=2))
def __init__(self,
db_connection_pool: ThreadedConnectionPool,
gc_interval_sec=30 * 60,
cortex_env="aws"):
self.db_connection_pool = db_connection_pool
self._init_garbage_api_collector(gc_interval_sec)
self.cortex_env = cortex_env
self.cortex_vanilla = cortex.client(self.cortex_env)
logger.info(f'Constructing CortexClient for {CORTEX_PATH}.')
def client(config: Dict):
env_name = config["gcp"]["env"]
if env_name:
return cx.client(env_name)
config_path = config["gcp"]["config"]
if config_path is not None:
return client_from_config(config_path)
pytest.skip("--gcp-env or --gcp-config must be passed to run gcp tests")
def client(config):
env_name = config["aws"]["env"]
if env_name:
return cx.client(env_name)
config_path = config["aws"]["config"]
if config_path is not None:
return client_from_config(config_path)
pytest.skip("--aws-env or --aws-config must be passed to run aws tests")
def main():
# parse args
if len(sys.argv) != 3:
print("usage: python submit.py <cortex-env> <dest-s3-dir>")
sys.exit(1)
env_name = sys.argv[1]
dest_s3_dir = sys.argv[2]
# get task endpoint
cx = cortex.client(env_name)
task_endpoint = cx.get_api("iris-classifier-trainer")["endpoint"]
# submit job
job_spec = {"config": {"dest_s3_dir": dest_s3_dir}}
response = requests.post(task_endpoint, json=job_spec)
print(json.dumps(response.json(), indent=2))
import cortex
import os
import sys
import requests
dir_path = os.path.dirname(os.path.realpath(__file__))
cx = cortex.client()
api_spec = {
"name": "text-generator",
"kind": "RealtimeAPI",
}
class PythonPredictor:
def __init__(self, config):
from transformers import pipeline
self.model = pipeline(task="text-generation")
def predict(self, payload):
return self.model(payload["text"])[0]
api = cx.create_api(
api_spec,
predictor=PythonPredictor,
requirements=["torch", "transformers"],
wait=True,
)
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# USAGE: python ./dev/deploy_test.py <env_name>
# e.g.: python ./dev/deploy_test.py aws
import os
import cortex
import sys
import requests
cx = cortex.client(sys.argv[1])
api_config = {
"name": "text-generator",
"kind": "RealtimeAPI",
}
class PythonPredictor:
def __init__(self, config):
from transformers import pipeline
self.model = pipeline(task="text-generation")
def predict(self, payload):
return self.model(payload["text"])[0]
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]):
"""Overrides the tfx_pusher_executor.
Args:
input_dict: Input dict from input key to a list of artifacts,
including:
- model_export: exported model from trainer.
- model_blessing: model blessing path from evaluator.
output_dict: Output dict from key to a list of artifacts, including:
- model_push: A list of 'ModelPushPath' artifact of size one. It
will
include the model in this push execution if the model was pushed.
exec_properties: Mostly a passthrough input dict for
tfx.components.Pusher.executor.custom_config
Raises:
ValueError: if custom config not present or not a dict.
RuntimeError: if
"""
self._log_startup(input_dict, output_dict, exec_properties)
# check model blessing
model_push = artifact_utils.get_single_instance(
output_dict[tfx_pusher_executor.PUSHED_MODEL_KEY])
if not self.CheckBlessing(input_dict):
self._MarkNotPushed(model_push)
return
model_export = artifact_utils.get_single_instance(
input_dict[tfx_pusher_executor.MODEL_KEY])
custom_config = json_utils.loads(
exec_properties.get(_CUSTOM_CONFIG_KEY, 'null'))
if custom_config is not None and not isinstance(custom_config, Dict):
raise ValueError(
'custom_config in execution properties needs to be a '
'dict.')
cortex_serving_args = custom_config.get(SERVING_ARGS_KEY)
if not cortex_serving_args:
raise ValueError(
'\'cortex_serving_args\' is missing in \'custom_config\'')
# Deploy the model.
io_utils.copy_dir(src=path_utils.serving_model_path(model_export.uri),
dst=model_push.uri)
model_path = model_push.uri
# Cortex implementation starts here
# pop the env and initialize client
cx = cortex.client(cortex_serving_args.pop('env'))
# load the predictor
predictor_path = cortex_serving_args.pop('predictor_path')
with tempfile.TemporaryDirectory() as tmp_dir_name:
temp_project_dir = tmp_dir_name
# predictor
p_dump_path = os.path.join(temp_project_dir, 'predictor.py')
io_utils.copy_file(predictor_path, p_dump_path)
# requirements.txt
reqs = cortex_serving_args.pop('requirements', [])
if reqs:
r_dump_path = os.path.join(temp_project_dir,
'requirements.txt')
io_utils.write_string_file(r_dump_path, '\n'.join(reqs))
# conda-packages.txt
c_reqs = cortex_serving_args.pop('conda_packages', [])
if c_reqs:
r_dump_path = os.path.join(temp_project_dir,
'conda-packages.txt')
io_utils.write_string_file(r_dump_path, '\n'.join(c_reqs))
# edit the api_config
api_config = cortex_serving_args.pop('api_config')
if 'config' not in api_config['predictor']:
api_config['predictor']['config'] = {}
api_config['predictor']['config']['model_artifact'] = model_path
# launch the api
api_config['predictor']['path'] = 'predictor.py'
# configure the model path
if 'models' not in api_config['predictor']:
api_config['predictor']['models'] = {}
api_config['predictor']['models'].update({'path': model_path})
cx.create_api(api_config,
project_dir=temp_project_dir,
**cortex_serving_args)
self._MarkPushed(model_push, pushed_destination=model_path)
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]):
"""Overrides the tfx_pusher_executor.
Args:
input_dict: Input dict from input key to a list of artifacts,
including:
- model_export: exported model from trainer.
- model_blessing: model blessing path from evaluator.
output_dict: Output dict from key to a list of artifacts, including:
- model_push: A list of 'ModelPushPath' artifact of size one. It
will
include the model in this push execution if the model was pushed.
exec_properties: Mostly a passthrough input dict for
tfx.components.Pusher.executor.custom_config
Raises:
ValueError: if custom config not present or not a dict.
RuntimeError: if
"""
self._log_startup(input_dict, output_dict, exec_properties)
# check model blessing
model_push = artifact_utils.get_single_instance(
output_dict[tfx_pusher_executor.PUSHED_MODEL_KEY])
if not self.CheckBlessing(input_dict):
self._MarkNotPushed(model_push)
return
model_export = artifact_utils.get_single_instance(
input_dict[tfx_pusher_executor.MODEL_KEY])
custom_config = json_utils.loads(
exec_properties.get(_CUSTOM_CONFIG_KEY, 'null'))
if custom_config is not None and not isinstance(custom_config, Dict):
raise ValueError(
'custom_config in execution properties needs to be a '
'dict.')
cortex_serving_args = custom_config.get(SERVING_ARGS_KEY)
if not cortex_serving_args:
raise ValueError(
'\'cortex_serving_args\' is missing in \'custom_config\'')
# Deploy the model.
io_utils.copy_dir(
src=path_utils.serving_model_path(model_export.uri),
dst=model_push.uri)
model_path = model_push.uri
# Cortex implementation starts here
# pop the env and initialize client
cx = cortex.client(cortex_serving_args.pop('env'))
# load the predictor
predictor_path = cortex_serving_args.pop('predictor_path')
predictor = load_source_path_class(predictor_path)
# edit the api_config
api_config = cortex_serving_args.pop('api_config')
if 'config' not in api_config['predictor']:
api_config['predictor']['config'] = {}
api_config['predictor']['config']['model_artifact'] = model_path
# launch the api
cx.create_api(
api_config=api_config, predictor=predictor, **cortex_serving_args)
self._MarkPushed(
model_push,
pushed_destination=model_path)
