def fixture_single_dataset():
"""A pytest fixture that creates a temporary working directory, a config file to match, creates the schema,
and populates the environment component repository.
Class scope modifier attached
"""
# Create temp dir
config_file, temp_dir = _create_temp_work_dir()
# Create user identity
insert_cached_identity(temp_dir)
# Create test client
schema = graphene.Schema(query=LabbookQuery, mutation=LabbookMutations)
# Create a bunch of lab books
im = InventoryManager(config_file)
ds = im.create_dataset('default',
'default',
"test-dataset",
storage_type="gigantum_object_v1",
description="Cats 2")
m = Manifest(ds, 'default')
cm_class = get_cache_manager_class(ds.client_config)
cache_mgr = cm_class(ds, 'default')
revision = ds.git.repo.head.commit.hexsha
os.makedirs(os.path.join(cache_mgr.cache_root, revision, "other_dir"))
helper_append_file(cache_mgr.cache_root, revision, "test1.txt", "asdfasdf")
helper_append_file(cache_mgr.cache_root, revision, "test2.txt", "rtg")
helper_append_file(cache_mgr.cache_root, revision, "test3.txt", "wer")
helper_append_file(cache_mgr.cache_root, revision, "other_dir/test4.txt",
"dfasdfhfgjhg")
helper_append_file(cache_mgr.cache_root, revision, "other_dir/test5.txt",
"fdghdfgsa")
m.update()
with patch.object(Configuration, 'find_default_config',
lambda self: config_file):
# Load User identity into app context
app = Flask("lmsrvlabbook")
app.config["LABMGR_CONFIG"] = Configuration()
app.config["LABMGR_ID_MGR"] = get_identity_manager(Configuration())
with app.app_context():
# within this block, current_app points to app. Set current user explicitly (this is done in the middleware)
flask.g.user_obj = app.config["LABMGR_ID_MGR"].get_user_profile()
# Create a test client
client = Client(schema,
middleware=[DataloaderMiddleware()],
context_value=ContextMock())
yield config_file, temp_dir, client, ds, cache_mgr
# Remove the temp_dir
shutil.rmtree(temp_dir)
def test_get_cache_manager_class(self, mock_dataset_with_cache_dir):
ds = mock_dataset_with_cache_dir[0]
cm = get_cache_manager_class(ds.client_config)
hfc = cm(ds, "tester")
assert isinstance(hfc, HostFilesystemCache)
assert hfc.cache_root == os.path.join(ds.client_config.config.get('git')['working_directory'], '.labmanager',
'datasets', 'tester', 'tester', ds.name)
rev = ds.git.repo.head.commit.hexsha
assert hfc.current_revision_dir == os.path.join(ds.client_config.config.get('git')['working_directory'],
'.labmanager', 'datasets', 'tester', 'tester', ds.name, rev)
def test_get_cache_manager_class_no_config(self, mock_dataset_with_cache_dir):
ds = mock_dataset_with_cache_dir[0]
config = ds.client_config
assert 'datasets' in config.config
del config.config['datasets']
assert 'datasets' not in config.config
cm = get_cache_manager_class(config)
hfc = cm(ds, "tester")
assert isinstance(hfc, HostFilesystemCache)
assert hfc.cache_root == os.path.join(ds.client_config.config.get('git')['working_directory'], '.labmanager',
'datasets', 'tester', 'tester', ds.name)
def __init__(self,
dataset: Dataset,
logged_in_username: Optional[str] = None) -> None:
self.dataset = dataset
cache_mgr_class = get_cache_manager_class(self.dataset.client_config)
self.cache_mgr: CacheManager = cache_mgr_class(self.dataset,
logged_in_username)
self.hasher = SmartHash(dataset.root_dir, self.cache_mgr.cache_root,
self.dataset.git.repo.head.commit.hexsha)
self.ignore_file = os.path.join(dataset.root_dir, ".gigantumignore")
self.manifest = self._load_manifest()
def __init__(self, dataset: 'Dataset', logged_in_username: Optional[str] = None) -> None:
self.dataset = dataset
self.logged_in_username = logged_in_username
cache_mgr_class = get_cache_manager_class(self.dataset.client_config)
self.cache_mgr: CacheManager = cache_mgr_class(self.dataset, logged_in_username)
self.hasher = SmartHash(dataset.root_dir, self.cache_mgr.cache_root,
self.dataset.git.repo.head.commit.hexsha)
self._manifest_io = ManifestFileCache(dataset, logged_in_username)
# TODO: Support ignoring files
# self.ignore_file = os.path.join(dataset.root_dir, ".gigantumignore")
# self.ignored = self._load_ignored()
self._legacy_manifest_file = os.path.join(self.dataset.root_dir, 'manifest', 'manifest0')
def start_labbook_container(labbook_root: str,
config_path: str,
username: str,
override_image_id: Optional[str] = None) -> str:
""" Start a Docker container from a given image_name.
Args:
labbook_root: Root dir of labbook
config_path: Path to LabBook configuration file.
override_image_id: Optional explicit docker image id (do not infer).
username: Username of active user. Do not use with override_image_id.
Returns:
Tuple containing docker container id, dict mapping of exposed ports.
Raises:
"""
if username and override_image_id:
raise ValueError(
'Argument username and override_image_id cannot both be set')
lb = InventoryManager(
config_file=config_path).load_labbook_from_directory(labbook_root)
if not override_image_id:
owner = InventoryManager().query_owner(lb)
tag = infer_docker_image_name(lb.name, owner, username)
else:
tag = override_image_id
mnt_point = labbook_root.replace('/mnt/gigantum',
os.environ['HOST_WORK_DIR'])
volumes_dict = {
mnt_point: {
'bind': '/mnt/labbook',
'mode': 'cached'
},
'labmanager_share_vol': {
'bind': '/mnt/share',
'mode': 'rw'
}
}
# Set up additional bind mounts for datasets if needed.
submodules = lb.git.list_submodules()
for submodule in submodules:
try:
namespace, dataset_name = submodule['name'].split("&")
submodule_dir = os.path.join(lb.root_dir, '.gigantum', 'datasets',
namespace, dataset_name)
ds = InventoryManager().load_dataset_from_directory(submodule_dir)
ds.namespace = namespace
cm_class = get_cache_manager_class(ds.client_config)
cm = cm_class(ds, username)
ds_cache_dir = cm.current_revision_dir.replace(
'/mnt/gigantum', os.environ['HOST_WORK_DIR'])
volumes_dict[ds_cache_dir] = {
'bind': f'/mnt/labbook/input/{ds.name}',
'mode': 'ro'
}
except InventoryException:
continue
# If re-mapping permissions, be sure to configure the container
if 'LOCAL_USER_ID' in os.environ:
env_var = [f"LOCAL_USER_ID={os.environ['LOCAL_USER_ID']}"]
else:
env_var = ["WINDOWS_HOST=1"]
# Get resource limits
resource_args = dict()
memory_limit = lb.client_config.config['container']['memory']
cpu_limit = lb.client_config.config['container']['cpu']
gpu_shared_mem = lb.client_config.config['container']['gpu_shared_mem']
if memory_limit:
# If memory_limit not None, pass to Docker to limit memory allocation to container
resource_args["mem_limit"] = memory_limit
if cpu_limit:
# If cpu_limit not None, pass to Docker to limit CPU allocation to container
# "nano_cpus" is an integer in factional parts of a CPU
resource_args["nano_cpus"] = round(cpu_limit * 1e9)
docker_client = get_docker_client()
# run with nvidia-docker if we have GPU support on the Host compatible with the project
should_run_nvidia, reason = should_launch_with_cuda_support(
lb.cuda_version)
if should_run_nvidia:
logger.info(f"Launching container with GPU support:{reason}")
if gpu_shared_mem:
resource_args["shm_size"] = gpu_shared_mem
container_id = docker_client.containers.run(tag,
detach=True,
init=True,
name=tag,
environment=env_var,
volumes=volumes_dict,
runtime='nvidia',
**resource_args).id
else:
logger.info(f"Launching container without GPU support. {reason}")
container_id = docker_client.containers.run(tag,
detach=True,
init=True,
name=tag,
environment=env_var,
volumes=volumes_dict,
**resource_args).id
labmanager_ip = ""
try:
labmanager_ip = get_labmanager_ip() or ""
except IndexError:
logger.warning("Cannot find labmanager IP")
labmanager_ip = labmanager_ip.strip()
cmd = f"echo {labmanager_ip} > /home/giguser/labmanager_ip"
for timeout in range(20):
time.sleep(0.5)
if docker_client.containers.get(container_id).status == 'running':
r = docker_client.containers.get(container_id).exec_run(
f'sh -c "{cmd}"')
logger.info(f"Response to write labmanager_ip in {tag}: {r}")
break
else:
logger.error(
"After 10 seconds could not write IP to labmanager container."
f" Container status = {docker_client.containers.get(container_id).status}"
)
return container_id
