def wait_training_finish(timeout: int, wait: bool, mt_id: str,
mt_client: ModelTrainingClient):
"""
Wait for training to finish according to command line arguments
:param wait:
:param timeout:
:param mt_id: Model Training name
:param mt_client: Model Training Client
"""
if not wait:
return
start = time.time()
if timeout <= 0:
raise Exception(
'Invalid --timeout argument: should be positive integer')
# We create a separate client for logs because it has the different timeout settings
log_mt_client = ModelTrainingClient.construct_from_other(mt_client)
log_mt_client.timeout = mt_client.timeout, LOG_READ_TIMEOUT_SECONDS
click.echo("Logs streaming...")
while True:
elapsed = time.time() - start
if elapsed > timeout:
raise Exception(TIMEOUT_ERROR_MESSAGE)
try:
mt = mt_client.get(mt_id)
if mt.status.state == TRAINING_SUCCESS_STATE:
click.echo(
f'Model {mt_id} was trained. Training took {round(time.time() - start)} seconds'
)
return
elif mt.status.state == TRAINING_FAILED_STATE:
raise Exception(f'Model training {mt_id} was failed.')
elif mt.status.state == "":
click.echo(
f"Can't determine the state of {mt.id}. Sleeping...")
else:
for msg in log_mt_client.log(mt.id, follow=True):
print_logs(msg)
except (WrongHttpStatusCode, HTTPException, RequestException,
APIConnectionException) as e:
LOGGER.info(
'Callback have not confirmed completion of the operation. Exception: %s',
str(e))
LOGGER.debug('Sleep before next request')
time.sleep(DEFAULT_WAIT_TIMEOUT)
def run(client: ModelTrainingClient, train_id: str, manifest_file: List[str],
manifest_dir: List[str], output_dir: str):
"""
\b
Start a training process locally.
\b
Usage example:
* odahuflowctl local train run --id examples-git
\f
"""
entities: List[OdahuflowCloudResourceUpdatePair] = []
for file_path in manifest_file:
entities.extend(parse_resources_file(file_path).changes)
for dir_path in manifest_dir:
entities.extend(parse_resources_dir(dir_path))
mt: Optional[ModelTraining] = None
# find a training
toolchains: Dict[str, ToolchainIntegration] = {}
for entity in map(lambda x: x.resource, entities):
if isinstance(entity, ToolchainIntegration):
toolchains[entity.id] = entity
elif isinstance(entity, ModelTraining) and entity.id == train_id:
mt = entity
if not mt:
click.echo(
f'{train_id} training not found. Trying to retrieve it from API server'
)
mt = client.get(train_id)
toolchain = toolchains.get(mt.spec.toolchain)
if not toolchain:
click.echo(
f'{toolchain} toolchain not found. Trying to retrieve it from API server'
)
toolchain = ToolchainIntegrationClient.construct_from_other(
client).get(mt.spec.toolchain)
trainer = K8sTrainer(
model_training=mt,
toolchain_integration=toolchain,
)
start_train(trainer, output_dir)
def get(client: ModelTrainingClient, train_id: str, output_format: str):
"""
\b
Get trainings.
The command without id argument retrieve all trainings.
\b
Get all trainings in json format:
odahuflowctl train get --output-format json
\b
Get training with "git-repo" id:
odahuflowctl train get --id git-repo
\b
Using jsonpath:
odahuflowctl train get -o 'jsonpath=[*].spec.reference'
\f
:param client: Model training HTTP client
:param train_id: Model training ID
:param output_format: Output format
:return:
"""
trains = [client.get(train_id)] if train_id else client.get_all()
format_output(trains, output_format)
