def test_dax_with_categories(tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"home_dir":
str(tmp_path),
})
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
multiply_job_category = "arithmetic"
run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
category=multiply_job_category,
)
# Check that the multiply job has the appropriate category set in the DAX file
dax_file = write_workflow_description()
assert dax_file.exists()
assert _job_in_dax_has_category(dax_file, multiply_job_name,
multiply_job_category)
assert not _job_in_dax_has_category(dax_file, multiply_job_name,
"an-arbitrary-category")
def test_not_clearing_ckpts(monkeypatch, tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"scavenge",
"home_dir":
str(tmp_path),
})
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
multiple_dir = directory_for(multiply_job_name)
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
run_python_on_parameters(multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[])
monkeypatch.setattr("builtins.input", lambda _: "n")
write_workflow_description()
assert checkpointed_multiply_file.exists()
def test_composed_key_value_transform(tmp_path):
kvs = {"doc1": 5, "doc2": 10}
def add1(values, **kwargs): # pylint:disable=unused-argument
return {key: val + 1 for key, val in values.items()}
def subtract2(values, **kwargs): # pylint:disable=unused-argument
return {key: val - 2 for key, val in values.items()}
composed_transforms = compose_key_value_store_transforms(
transforms=[add1, subtract2])
params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"home_dir":
str(tmp_path),
})
initialize_vista_pegasus_wrapper(params)
transformed_kvs = transform_key_value_store(kvs,
composed_transforms,
output_locator=Locator([]),
parallelism=1)
expected_kvs = {"doc1": 4, "doc2": 9}
assert expected_kvs == transformed_kvs
def example_workflow(params: Parameters): # pragma: no cover
"""
An example script to generate a container workflow for submission to Pegasus.
"""
tmp_path = params.creatable_directory("example_root_dir")
docker_tar = params.creatable_file("docker_tar")
docker_build_dir = params.existing_directory("docker_build_dir")
docker_image_name = params.string(
"docker_image_name", default="pegasus_wrapper_container_demo"
)
docker_image_tag = params.string("docker_image_tag", default="0.2")
mongo_db_tar = params.string(
"mongo_db_tar", default="/nas/gaia/shared/cluster/docker/mongo-4.4.tar"
)
monogo_db_data = "/scratch/dockermount/pegasus_wrapper_tmp/data"
mongo_db_config = "/scratch/dockermount/pegasus_wrapper_tmp/config"
# Generating parameters for initializing a workflow
# We recommend making workflow directory, site, and partition parameters
# in an research workflow
workflow_params = Parameters.from_mapping(
{
"workflow_name": "Test",
"workflow_created": "Testing",
"workflow_log_dir": str(tmp_path / "log"),
"workflow_directory": str(tmp_path / "working"),
"site": "saga",
"namespace": "test",
"home_dir": str(tmp_path),
"partition": "scavenge",
}
)
saga31_request = SlurmResourceRequest.from_parameters(
Parameters.from_mapping({"run_on_single_node": "saga31", "partition": "gaia"})
)
workflow_params = workflow_params.unify(params)
# Our source input for the sample jobs
input_file = tmp_path / "raw_nums.txt"
add_y_output_file_nas = tmp_path / "nums_y.txt"
sorted_output_file_nas = tmp_path / "sorted.txt"
random = Random()
random.seed(0)
nums = [int(random.random() * 100) for _ in range(0, 25)]
# Base Job Locator
job_locator = Locator(("jobs",))
docker_python_root = Path("/home/app/")
job_profile = PegasusProfile(
namespace="pegasus", key="transfer.bypass.input.staging", value="True"
)
# Write a list of numbers out to be able to run the workflow
with input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
initialize_vista_pegasus_wrapper(workflow_params)
build_container = run_bash(
job_locator / "build_docker",
command=[
"mkdir -p /scratch/dockermount/pegasus_wrapper_tmp",
f"cd {docker_build_dir}",
f"docker build . -t {docker_image_name}:{docker_image_tag}",
f"docker save -o /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_image_name}:{docker_image_tag}",
f"cp /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_tar.absolute()}",
f"chmod go+r {docker_tar.absolute()}",
f"docker load --input {mongo_db_tar}",
f"mkdir -p {monogo_db_data}",
f"mkdir -p {mongo_db_config}",
],
depends_on=[],
resource_request=saga31_request,
)
python36 = add_container(
f"{docker_image_name}:{docker_image_tag}",
"docker",
str(docker_tar.absolute()),
image_site="saga",
bypass_staging=True,
)
mongo4_4 = add_container(
"mongo:4.4", "docker", mongo_db_tar, image_site="saga", bypass_staging=True
)
start_mongo = start_docker_as_service(
mongo4_4,
depends_on=[build_container],
mounts=[f"{monogo_db_data}:/data/db", f"{mongo_db_config}/etc/custom"],
docker_args=f"-p 27017:27017",
resource_request=saga31_request,
)
add_y_job = run_python_on_args(
job_locator / "add",
docker_python_root / "add_y.py",
set_args=f"{input_file} {add_y_output_file_nas} --y 10",
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file],
output_file_paths=[add_y_output_file_nas],
)
sort_job = run_python_on_parameters(
job_locator / "sort",
sort_nums_in_file,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
_ = stop_docker_as_service(
mongo4_4, depends_on=[start_mongo, sort_job], resource_request=saga31_request
)
# Generate the Pegasus DAX file & a Submit Script
write_workflow_description(tmp_path)
def example_workflow(params: Parameters):
"""
An example script to generate a workflow for submission to Pegasus.
"""
tmp_path = params.creatable_directory("example_root_dir")
# Generating parameters for initializing a workflow
# We recommend making workflow directory, site, and partition parameters
# in an research workflow
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
})
workflow_params = workflow_params.unify(params)
# Our source input for the sample jobs
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = [int(random.random() * 100) for _ in range(0, 25)]
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
# Base Job Locator
job_locator = Locator(("jobs", ))
# Write a list of numbers out to be able to run the workflow
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
initialize_vista_pegasus_wrapper(workflow_params)
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(
job_locator / "multiply",
multiply_by_x,
{
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4,
"logfile": str(tmp_path / "multiply_log.txt"),
},
depends_on=[],
),
locator=Locator("multiply"),
)
run_python_on_parameters(
job_locator / "sort",
sort_nums_in_file,
{
"input_file": multiply_output_file,
"output_file": sorted_output_file
},
depends_on=[multiply_artifact],
# if you want to use a different resource for some task, you can do this way
# resource_request=SlurmResourceRequest.from_parameters(slurm_params),
)
# Generate the Pegasus DAX file
dax_file = write_workflow_description(tmp_path)
submit_script = tmp_path / "submit_script.sh"
# Our attempt at an easy submit file, it MAY NOT be accurate for more complicated
# workflows but it
# does work for this simple example.
# See https://github.com/isi-vista/vista-pegasus-wrapper/issues/27
build_submit_script(
submit_script,
str(dax_file),
experiment_directory(), # pylint:disable=protected-access
)
def example_workflow(params: Parameters):
"""
An example script to generate a workflow for submission to Pegasus.
"""
tmp_path = params.creatable_directory("example_root_dir")
# Generating parameters for initializing a workflow
# We recommend making workflow directory, site, and partition parameters
# in an research workflow
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"home_dir":
str(tmp_path),
"partition":
"scavenge",
})
workflow_params = workflow_params.unify(params)
# Our source input for the sample jobs
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = [int(random.random() * 100) for _ in range(0, 25)]
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
add_output_file = tmp_path / "add_nums.txt"
# Base Job Locator
job_locator = Locator(("jobs", ))
# Write a list of numbers out to be able to run the workflow
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
initialize_vista_pegasus_wrapper(workflow_params)
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(
job_locator / "multiply",
multiply_by_x,
{
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4,
"logfile": str(tmp_path / "multiply_log.txt"),
},
depends_on=[],
),
locator=Locator("multiply"),
)
# You can also just track the dep node itself to pass to a future job if you don't
# need the value portion of an artifacy
mul_dep = run_python_on_parameters(
job_locator / "sort",
sort_nums_in_file,
{
"input_file": multiply_output_file,
"output_file": sorted_output_file
},
depends_on=[multiply_artifact],
# if you want to use a different resource for some task, you can do this way
# resource_request=SlurmResourceRequest.from_parameters(slurm_params),
)
run_python_on_args(
job_locator / "add",
add_y,
set_args=f"{sorted_output_file} {add_output_file} --y 10",
depends_on=[mul_dep],
category="add", # Can be used as a custom category for job limits
)
# If you want to limit the number of active jobs in a category use the following
# limit_jobs_for_category("scavenge", 1)
# Generate the Pegasus DAX file & a Submit Script
write_workflow_description(tmp_path)
def test_dax_with_python_into_container_jobs(tmp_path):
docker_tar = Path(f"{tmp_path}/docker/tar.tar")
docker_build_dir = tmp_path
docker_image_name = "pegasus_wrapper_container_demo"
docker_image_tag = "0.2"
# Generating parameters for initializing a workflow
# We recommend making workflow directory, site, and partition parameters
# in an research workflow
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"home_dir":
str(tmp_path),
"partition":
"scavenge",
})
saga31_request = SlurmResourceRequest.from_parameters(
Parameters.from_mapping({
"run_on_single_node": "saga31",
"partition": "gaia"
}))
# Our source input for the sample jobs
input_file = tmp_path / "raw_nums.txt"
add_y_output_file_nas = tmp_path / "nums_y.txt"
sorted_output_file_nas = tmp_path / "sorted.txt"
random = Random()
random.seed(0)
nums = [int(random.random() * 100) for _ in range(0, 25)]
# Base Job Locator
job_locator = Locator(("jobs", ))
docker_python_root = Path("/home/app/")
# Write a list of numbers out to be able to run the workflow
with input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
initialize_vista_pegasus_wrapper(workflow_params)
build_container_locator = job_locator / "build_docker"
build_container = run_bash(
build_container_locator,
command=[
"mkdir -p /scratch/dockermount/pegasus_wrapper_tmp",
f"cd {docker_build_dir}",
f"docker build . -t {docker_image_name}:{docker_image_tag}",
f"docker save -o /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_image_name}:{docker_image_tag}",
f"cp /scratch/dockermount/pegasus_wrapper_tmp/{docker_tar.name} {docker_tar.absolute()}",
f"chmod go+r {docker_tar.absolute()}",
],
depends_on=[],
resource_request=saga31_request,
)
build_container_dir = directory_for(build_container_locator)
assert (build_container_dir / "script.sh").exists()
python36 = add_container(
f"{docker_image_name}:{docker_image_tag}",
"docker",
str(docker_tar.absolute()),
image_site="saga",
bypass_staging=True,
)
job_profile = PegasusProfile(namespace="pegasus",
key="transfer.bypass.input.staging",
value="True")
mongo4_4 = add_container("mongo:4.4",
"docker",
"path/to/tar.tar",
image_site="saga",
bypass_staging=True)
with pytest.raises(RuntimeError):
_ = stop_docker_as_service(mongo4_4,
depends_on=[],
resource_request=saga31_request)
start_mongo = start_docker_as_service(
mongo4_4,
depends_on=[build_container],
docker_args=f"-v /scratch/mongo/data/db:/data/db",
resource_request=saga31_request,
)
mongo4_4_dir = directory_for(Locator(("containers", mongo4_4.name)))
assert (mongo4_4_dir / "start.sh").exists()
assert (mongo4_4_dir / "stop.sh").exists()
add_y_locator = job_locator / "add"
add_y_job = run_python_on_args(
add_y_locator,
docker_python_root / "add_y.py",
set_args=f"{input_file} {add_y_output_file_nas} --y 10",
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file],
output_file_paths=[add_y_output_file_nas],
)
add_y_dir = directory_for(add_y_locator)
assert (add_y_dir / "___run.sh").exists()
with pytest.raises(RuntimeError):
_ = run_python_on_args(
add_y_locator,
docker_python_root / "add_y.py",
set_args=f"{input_file} {add_y_output_file_nas} --y 10",
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file, input_file],
output_file_paths=[add_y_output_file_nas],
)
sort_job_locator = job_locator / "sort"
sort_job = run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{
"input_file": add_y_output_file_nas,
"output_file": sorted_output_file_nas
},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
assert sort_job == run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{
"input_file": add_y_output_file_nas,
"output_file": sorted_output_file_nas
},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
sort_job_dir = directory_for(sort_job_locator)
assert (sort_job_dir / "___run.sh").exists()
assert (sort_job_dir / "____params.params").exists()
with pytest.raises(RuntimeError):
_ = run_python_on_parameters(
sort_job_locator,
sort_nums_main,
{
"input_file": add_y_output_file_nas,
"output_file": sorted_output_file_nas
},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=[sorted_output_file_nas, sorted_output_file_nas],
)
celebration_bash_locator = job_locator / "celebrate"
celebration_bash = run_bash(
celebration_bash_locator,
'echo "Jobs Runs Successfully"',
depends_on=[sort_job],
job_profiles=[job_profile],
)
assert celebration_bash == run_bash(
celebration_bash_locator,
'echo "Jobs Runs Successfully"',
depends_on=[sort_job],
job_profiles=[job_profile],
)
celebration_bash_dir = directory_for(celebration_bash_locator)
assert (celebration_bash_dir / "script.sh").exists()
_ = stop_docker_as_service(mongo4_4,
depends_on=[start_mongo, sort_job],
resource_request=saga31_request)
# Generate the Pegasus DAX file & a Submit Script
dax_file_one = write_workflow_description(tmp_path)
assert dax_file_one.exists()
submit_script_one = tmp_path / "submit.sh"
assert submit_script_one.exists()
def test_dax_with_job_on_saga_with_dict_as_params(tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"experiment_name":
"fred",
"home_dir":
str(tmp_path),
})
slurm_params = Parameters.from_mapping({
"partition": "gaia",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G"
})
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
add_output_file = tmp_path / "add_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = {
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4,
}
sort_params = {
"input_file": multiply_output_file,
"output_file": sorted_output_file
}
add_args = f"{sorted_output_file} {add_output_file} --y 10"
job_profile = PegasusProfile(namespace="pegasus",
key="transfer.bypass.input.staging",
value="True")
resources = SlurmResourceRequest.from_parameters(slurm_params)
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
job_profiles=[job_profile],
),
locator=Locator("multiply"),
)
multiple_dir = directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = directory_for(sort_job_name)
sort_artifact = run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
category="add",
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
add_job_name = Locator(_parse_parts("jobs/add"))
add_dir = directory_for(add_job_name)
run_python_on_args(add_job_name,
"add_job_main.py",
add_args,
depends_on=[sort_artifact])
assert (add_dir / "___run.sh").exists()
dax_file_one = write_workflow_description(tmp_path)
dax_file_two = write_workflow_description()
assert dax_file_one.exists()
assert dax_file_two.exists()
submit_script_one = tmp_path / "submit_script_one.sh"
submit_script_two = tmp_path / "submit_script_two.sh"
build_submit_script(submit_script_one, str(dax_file_one),
experiment_directory())
build_submit_script(submit_script_two, str(dax_file_two),
experiment_directory())
assert submit_script_one.exists()
assert submit_script_two.exists()
site_catalog = workflow_params.existing_directory(
"workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") /
"replicas.yml")
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") /
"transformations.yml")
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") /
"pegasus.properties")
assert properties_file.exists()
def test_dax_with_job_in_container(tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"experiment_name":
"fred",
"home_dir":
str(tmp_path),
})
slurm_params = Parameters.from_mapping({
"partition": "gaia",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G"
})
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
sort_params = Parameters.from_mapping({
"input_file": multiply_output_file,
"output_file": sorted_output_file
})
resources = SlurmResourceRequest.from_parameters(slurm_params)
initialize_vista_pegasus_wrapper(workflow_params)
# Add Container
example_docker = add_container("example_container", "docker",
tmp_path / "docker.img")
with pytest.raises(ValueError):
_ = add_container("fake_container", "invalid",
tmp_path / "invalid_docker.img")
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
container=example_docker,
),
locator=Locator("multiply"),
)
multiple_dir = directory_for(multiply_job_name)
assert (multiple_dir / "___run.sh").exists()
assert (multiple_dir / "____params.params").exists()
sort_job_name = Locator(_parse_parts("jobs/sort"))
sort_dir = directory_for(sort_job_name)
run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
container=example_docker,
)
assert (sort_dir / "___run.sh").exists()
assert (sort_dir / "____params.params").exists()
dax_file_one = write_workflow_description()
assert dax_file_one.exists()
site_catalog = workflow_params.existing_directory(
"workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") /
"replicas.yml")
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") /
"transformations.yml")
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") /
"pegasus.properties")
assert properties_file.exists()
def object_language_ablation_runner_entry_point(params: Parameters) -> None:
"""This function creates all possible object language ablation param files within a given range"""
initialize_vista_pegasus_wrapper(params)
baseline_parameters = params.namespace("object_language_ablation")
pursuit_resource_request_params = params.namespace(
"pursuit_resource_request")
# get the minimum and maximum number of objects in a scene
min_num_objects = params.integer("min_num_objects", default=1)
max_num_objects = params.integer("max_num_objects", default=7)
# get the minimum and maximum accuracy of the language with the situation
min_language_accuracy = params.floating_point("min_language_accuracy",
default=0.1)
max_language_accuracy = params.floating_point("max_language_accuracy",
default=0.5)
num_language_accuracy_increment = params.integer(
"num_language_accuracy_increment", default=5)
values_for_accuracy = np.linspace(min_language_accuracy,
max_language_accuracy,
num_language_accuracy_increment)
limit_jobs_for_category(
"pursuit", params.integer("num_pursuit_learners_active", default=8))
for num_objects in range(min_num_objects, max_num_objects + 1):
for language_accuracy in values_for_accuracy:
for learner_type in LEARNER_VALUES_TO_PARAMS:
for params_str, learner_params in LEARNER_VALUES_TO_PARAMS[
learner_type]:
experiment_name_string = EXPERIMENT_NAME_FORMAT.format(
num_objects=num_objects,
language_accuracy=language_accuracy,
learner_type=learner_type,
learner_params=params_str,
)
experiment_name = Locator(
experiment_name_string.split("-"))
# Note that the input parameters should include the root params and
# anything else we want.
experiment_params = baseline_parameters.unify(
FIXED_PARAMETERS
).unify({
"experiment":
experiment_name_string,
"experiment_group_dir":
directory_for(experiment_name),
"hypothesis_log_dir":
directory_for(experiment_name) / "hypotheses",
"learner_logging_path":
directory_for(experiment_name),
"log_learner_state":
True,
"resume_from_latest_logged_state":
True,
"train_curriculum": {
"accurate_language_percentage":
float(language_accuracy)
},
"object_learner_type":
learner_type,
"object_learner":
learner_params,
# We subtract one because the target object is a given
"num_noise_objects":
num_objects - 1,
})
run_python_on_parameters(
experiment_name,
log_experiment_script,
experiment_params,
depends_on=[],
resource_request=SlurmResourceRequest.from_parameters(
pursuit_resource_request_params)
if learner_type == "pursuit" else None,
category=learner_type,
)
write_workflow_description()
def test_category_max_jobs(tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"home_dir":
str(tmp_path),
})
multiply_slurm_params = Parameters.from_mapping({
"partition": "gaia",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G"
})
multiply_resources = SlurmResourceRequest.from_parameters(
multiply_slurm_params)
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
resource_request=multiply_resources,
),
locator=Locator("multiply"),
)
sort_slurm_params = Parameters.from_mapping({
"partition": "ephemeral",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G",
"job_time_in_minutes": 120,
})
sort_resources = SlurmResourceRequest.from_parameters(sort_slurm_params)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping({
"input_file": multiply_output_file,
"output_file": sorted_output_file
})
run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=sort_resources,
)
limit_jobs_for_category("gaia", 1)
write_workflow_description()
site_catalog = workflow_params.existing_directory(
"workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") /
"replicas.yml")
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") /
"transformations.yml")
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") /
"pegasus.properties")
assert properties_file.exists()
# Make sure the config contains the appropriate maxjobs lines and no inappropriate maxjobs lines
with properties_file.open("r") as f:
lines = f.readlines()
for line in lines:
print(line)
assert any("dagman.gaia.maxjobs = 1" in line for line in lines)
assert all("dagman.ephemeral.maxjobs =" not in line for line in lines)
def test_dax_with_saga_categories(tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"home_dir":
str(tmp_path),
})
multiply_partition = "gaia"
multiply_slurm_params = Parameters.from_mapping({
"partition": multiply_partition,
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G"
})
multiply_resources = SlurmResourceRequest.from_parameters(
multiply_slurm_params)
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(
multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[],
resource_request=multiply_resources,
),
locator=Locator("multiply"),
)
sort_partition = "lestat"
sort_slurm_params = Parameters.from_mapping({
"partition": sort_partition,
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G"
})
sort_resources = SlurmResourceRequest.from_parameters(sort_slurm_params)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping({
"input_file": multiply_output_file,
"output_file": sorted_output_file
})
run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=sort_resources,
)
dax_file = write_workflow_description()
assert dax_file.exists()
# Check that the multiply and sort jobs have the appropriate partition-defined categories set in
# the DAX file
assert _job_in_dax_has_category(dax_file, multiply_job_name,
multiply_partition)
assert not _job_in_dax_has_category(dax_file, multiply_job_name,
sort_partition)
assert _job_in_dax_has_category(dax_file, sort_job_name, sort_partition)
assert not _job_in_dax_has_category(dax_file, sort_job_name,
multiply_partition)
def test_dax_with_checkpointed_jobs_on_saga(tmp_path):
workflow_params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"home_dir":
str(tmp_path),
})
slurm_params = Parameters.from_mapping({
"partition": "gaia",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G"
})
resources = SlurmResourceRequest.from_parameters(slurm_params)
initialize_vista_pegasus_wrapper(workflow_params)
multiply_job_name = Locator(_parse_parts("jobs/multiply"))
multiply_output_file = tmp_path / "multiplied_nums.txt"
multiply_input_file = tmp_path / "raw_nums.txt"
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
multiple_dir = directory_for(multiply_job_name)
# Create checkpointed file so that when trying to create the job again,
# Pegasus just adds the file to the Replica Catalog
checkpointed_multiply_file = multiple_dir / "___ckpt"
checkpointed_multiply_file.touch()
multiply_output_file.touch()
assert checkpointed_multiply_file.exists()
assert multiply_output_file.exists()
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(multiply_job_name,
multiply_by_x_main,
multiply_params,
depends_on=[]),
locator=Locator("multiply"),
)
sort_job_name = Locator(_parse_parts("jobs/sort"))
sorted_output_file = tmp_path / "sorted_nums.txt"
sort_params = Parameters.from_mapping({
"input_file": multiply_output_file,
"output_file": sorted_output_file
})
run_python_on_parameters(
sort_job_name,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
)
write_workflow_description()
site_catalog = workflow_params.existing_directory(
"workflow_directory") / "sites.yml"
assert site_catalog.exists()
replica_catalog = (
workflow_params.existing_directory("workflow_directory") /
"replicas.yml")
assert replica_catalog.exists()
transformations_catalog = (
workflow_params.existing_directory("workflow_directory") /
"transformations.yml")
assert transformations_catalog.exists()
properties_file = (
workflow_params.existing_directory("workflow_directory") /
"pegasus.properties")
assert properties_file.exists()
# Make sure the Replica Catalog is not empty
assert replica_catalog.stat().st_size > 0
def integrated_experiment_entry_point(params: Parameters) -> None:
initialize_vista_pegasus_wrapper(params)
baseline_parameters = params.namespace("integrated_learners_experiment")
pursuit_resource_request_params = params.namespace(
"pursuit_resource_request")
# This code is commented out but may be used in the near future to add language ablation
# Capabilities to this curriculum.
# get the minimum and maximum accuracy of the language with the situation
# min_language_accuracy = params.floating_point("min_language_accuracy", default=0.1)
# max_language_accuracy = params.floating_point("max_language_accuracy", default=0.5)
# num_language_accuracy_increment = params.integer(
# "num_language_accuracy_increment", default=5
# )
# values_for_accuracy = np.linspace(
# min_language_accuracy, max_language_accuracy, num_language_accuracy_increment
# )
# Get if attributes or relations should be included
include_attributes = params.boolean("include_attributes", default=True)
include_relations = params.boolean("include_relations", default=True)
limit_jobs_for_category(
"pursuit_job_limit",
params.integer("num_pursuit_learners_active", default=8))
curriculum_repository_path = params.creatable_directory(
"curriculum_repository_path")
# Job to build desired curriculum(s) which our learners use
curriculum_dependencies = immutableset((
CURRICULUM_NAME_FORMAT.format(
noise=add_noise,
shuffled=shuffle,
relations=include_relations,
attributes=include_attributes,
),
run_python_on_parameters(
Locator(
CURRICULUM_NAME_FORMAT.format(
noise=add_noise,
shuffled=shuffle,
relations=include_relations,
attributes=include_attributes,
).split("-")),
generate_curriculum_script,
baseline_parameters.unify({
"train_curriculum":
Parameters.from_mapping(CURRICULUM_PARAMS).unify(
{
"add_noise": add_noise,
"shuffled": shuffle,
"include_attributes": include_attributes,
"include_relations": include_relations,
}).as_mapping()
}).unify(FIXED_PARAMETERS).unify(
{"curriculum_repository_path": curriculum_repository_path}),
depends_on=[],
),
Parameters.from_mapping(CURRICULUM_PARAMS).unify(
{
"add_noise": add_noise,
"shuffled": shuffle,
"include_attributes": include_attributes,
"include_relations": include_relations,
}),
) for add_noise in (True, False) for shuffle in (True, False))
# jobs to build experiment
for (curriculum_str, curriculum_dep,
curr_params) in curriculum_dependencies:
object_learner_type = params.string(
"object_learner.learner_type",
valid_options=["pursuit", "subset", "pbv"],
default="pursuit",
)
attribute_learner_type = params.string(
"attribute_learner.learner__type",
valid_options=["none", "pursuit", "subset"],
default="pursuit",
)
relation_learner_type = params.string(
"relation_learner.learner_type",
valid_options=["none", "pursuit", "subset"],
default="pursuit",
)
experiment_name_string = EXPERIMENT_NAME_FORMAT.format(
curriculum_name=curriculum_str.replace("-", "+"),
object_learner=object_learner_type,
attribute_learner=attribute_learner_type,
relation_learner=relation_learner_type,
)
experiment_name = Locator(experiment_name_string.split("-"))
# Note that the input parameters should include the root params and
# anything else we want.
experiment_params = baseline_parameters.unify(FIXED_PARAMETERS).unify({
"experiment":
experiment_name_string,
"experiment_group_dir":
directory_for(experiment_name),
"hypothesis_log_dir":
directory_for(experiment_name) / "hypotheses",
"learner_logging_path":
directory_for(experiment_name),
"log_learner_state":
True,
"resume_from_latest_logged_state":
True,
"load_from_curriculum_repository":
curriculum_repository_path,
"train_curriculum":
curr_params,
})
run_python_on_parameters(
experiment_name,
log_experiment_script,
experiment_params,
depends_on=[curriculum_dep],
resource_request=SlurmResourceRequest.from_parameters(
pursuit_resource_request_params) if "pursuit" in [
object_learner_type, attribute_learner_type,
relation_learner_type
] else None,
category="pursuit" if "pursuit" in [
object_learner_type, attribute_learner_type,
relation_learner_type
] else "subset",
use_pypy=True,
)
write_workflow_description()
def main(params: Parameters):
adam_root = params.existing_directory("adam_root")
m13_experiments_dir = adam_root / "parameters" / "experiments" / "m13"
use_pegasus = params.boolean("use_pegasus", default=False)
if use_pegasus:
initialize_vista_pegasus_wrapper(params)
param_files: List[Path] = []
if params.boolean("include_objects", default=True):
param_files.append(m13_experiments_dir / "objects.params")
if params.boolean("include_imprecise_size", default=True):
param_files.append(m13_experiments_dir / "imprecise_size.params")
if params.boolean("include_imprecise_temporal", default=True):
param_files.append(m13_experiments_dir / "imprecise_temporal.params")
if params.boolean("include_subtle_verb", default=True):
param_files.append(m13_experiments_dir / "subtle_verb.params")
if params.boolean("include_object_restrictions", default=True):
param_files.append(m13_experiments_dir / "object_restrictions.params")
if params.boolean("include_functionally_defined_objects", default=True):
param_files.append(m13_experiments_dir / "functionally_defined_objects.params")
if params.boolean("include_relations", default=True):
param_files.append(m13_experiments_dir / "relations.params")
if params.boolean("include_generics", default=True):
param_files.append(m13_experiments_dir / "generics.params")
if params.boolean("include_verbs_with_dynamic_prepositions", default=True):
param_files.append(
m13_experiments_dir / "events_with_dynamic_prepositions.params"
)
if params.boolean("include_m9_complete", default=False):
param_files.append(m13_experiments_dir / "m9_complete.params")
if params.boolean("include_m13_complete", default=False):
param_files.append(m13_experiments_dir / "m13_complete.params")
if params.boolean("include_m13_shuffled", default=False):
param_files.append(m13_experiments_dir / "m13_shuffled.params")
# This activates a special "debug" curriculum,
# which is meant to be edited in the code by a developer to do fine-grained debugging.
if params.boolean("include_debug", default=False):
param_files.append(m13_experiments_dir / "debug.params")
# If any of the param files don't exist, bail out earlier instead of making the user
# wait for the error.
for param_file in param_files:
if not param_file.exists():
raise RuntimeError(f"Expected param file {param_file} does not exist")
for param_file in param_files:
logging.info("Running %s", param_file)
experiment_params = YAMLParametersLoader().load(param_file)
if not use_pegasus:
log_experiment_entry_point(experiment_params)
else:
experiment_name = Locator(experiment_params.string("experiment"))
experiment_params = experiment_params.unify(
{
"experiment_group_dir": directory_for(experiment_name) / "output",
"hypothesis_log_dir": directory_for(experiment_name) / "hypotheses",
# State pickles will go under experiment_name/learner_state
"learner_logging_path": directory_for(experiment_name),
"log_learner_state": True,
"resume_from_latest_logged_state": True,
"log_hypothesis_every_n_steps": params.integer(
"save_state_every_n_steps"
),
"debug_learner_pickling": params.boolean(
"debug_learner_pickling", default=False
),
}
)
run_python_on_parameters(
experiment_name, log_experiment_script, experiment_params, depends_on=[]
)
if use_pegasus:
write_workflow_description()
def gaze_ablation_runner_entry_point(params: Parameters) -> None:
"""This function creates all possible gaze ablation param files within a given range"""
initialize_vista_pegasus_wrapper(params)
# Get the baseline experiment parameters for gaze ablation -- these are things common to all of
# the experiments, like:
#
# include_image_links: true
# sort_learner_descriptions_by_length: True
# num_pretty_descriptions: 5
baseline_parameters = params.namespace("gaze_ablation")
# get the minimum and maximum number of objects in a scene
min_num_objects = params.integer("min_num_objects", default=1)
max_num_objects = params.integer("max_num_objects", default=7)
# this gets the number of different accuracies to try; default = increment by 0.1
num_accuracy_increments = params.integer("num_increments", default=11)
values_for_accuracy = np.linspace(0, 1, num_accuracy_increments)
# the number of noise instances to be included
min_num_noise_instances = params.integer("min_num_noise", default=0)
max_num_noise_instances = params.integer("max_num_noise", default=0)
# get the number of instances in the entire curriculum
min_num_instances_in_curriculum = params.integer("min_instances",
default=10)
max_num_instances_in_curriculum = params.integer("max_instances",
default=20)
# all possible numbers of noise instances
for num_noise_instances in range(min_num_noise_instances,
max_num_noise_instances + 1):
# all possible numbers of instances in the curriculum
for num_instances in range(min_num_instances_in_curriculum,
max_num_instances_in_curriculum + 1):
# all possible numbers of instances
for num_objects_in_instance in range(min_num_objects,
max_num_objects + 1):
# all possible accuracies
for prob_given in values_for_accuracy:
for prob_not_given in values_for_accuracy:
# both ignoring and perceiving gaze
for add_gaze in [True, False]:
# Define the experiment name, which is used both as a job name and to
# choose a directory in which to store the experiment results.
experiment_name_string = EXPERIMENT_NAME_FORMAT.format(
num_instances=num_instances,
num_noise_instances=num_noise_instances,
num_objects_in_instance=num_objects_in_instance,
prob_given=prob_given,
prob_not_given=prob_not_given,
add_gaze=add_gaze,
)
experiment_name = Locator(
experiment_name_string.split("-"))
# Note that the input parameters should include the root params and
# anything else we want.
experiment_params = baseline_parameters.unify(
FIXED_PARAMETERS).unify({
"experiment":
experiment_name_string,
"experiment_group_dir":
directory_for(experiment_name),
"hypothesis_log_dir":
directory_for(experiment_name) /
"hypotheses",
"learner_logging_path":
directory_for(experiment_name),
"log_learner_state":
True,
"resume_from_latest_logged_state":
True,
"pursuit-curriculum-params": {
"num_instances": num_instances,
"num_noise_instances":
num_noise_instances,
"num_objects_in_instance":
num_objects_in_instance,
"add_gaze": add_gaze,
"prob_given": float(prob_given),
"prob_not_given":
float(prob_not_given),
},
})
run_python_on_parameters(
experiment_name,
log_experiment_script,
experiment_params,
depends_on=[],
)
write_workflow_description()
def test_dax_test_exclude_nodes_on_saga(tmp_path):
sample_exclude = "saga01,saga03,saga21,saga05"
sample_include = "saga06"
params = Parameters.from_mapping({
"workflow_name":
"Test",
"workflow_created":
"Testing",
"workflow_log_dir":
str(tmp_path / "log"),
"workflow_directory":
str(tmp_path / "working"),
"site":
"saga",
"namespace":
"test",
"partition":
"gaia",
"exclude_list":
sample_exclude,
"home_dir":
str(tmp_path),
})
slurm_params = Parameters.from_mapping({
"partition": "gaia",
"num_cpus": 1,
"num_gpus": 0,
"memory": "4G"
})
multiply_input_file = tmp_path / "raw_nums.txt"
random = Random()
random.seed(0)
nums = immutableset(int(random.random() * 100) for _ in range(0, 25))
multiply_output_file = tmp_path / "multiplied_nums.txt"
sorted_output_file = tmp_path / "sorted_nums.txt"
with multiply_input_file.open("w") as mult_file:
mult_file.writelines(f"{num}\n" for num in nums)
multiply_params = Parameters.from_mapping({
"input_file": multiply_input_file,
"output_file": multiply_output_file,
"x": 4
})
sort_params = Parameters.from_mapping({
"input_file": multiply_output_file,
"output_file": sorted_output_file
})
resources = SlurmResourceRequest.from_parameters(
slurm_params.unify({"run_on_single_node": sample_include}))
initialize_vista_pegasus_wrapper(params)
multiply_job_locator = Locator(_parse_parts("jobs/multiply"))
multiply_artifact = ValueArtifact(
multiply_output_file,
depends_on=run_python_on_parameters(multiply_job_locator,
multiply_by_x_main,
multiply_params,
depends_on=[]),
locator=Locator("multiply"),
)
sort_job_locator = Locator(_parse_parts("jobs/sort"))
run_python_on_parameters(
sort_job_locator,
sort_nums_main,
sort_params,
depends_on=[multiply_artifact],
resource_request=resources,
)
dax_file = write_workflow_description(tmp_path)
with dax_file.open("r") as dax:
dax_yaml = load(dax, Loader=SafeLoader)
root = dax_yaml["jobs"]
for item in root:
if item["type"] == "job":
if "pegasus" in item["profiles"]:
if item["name"] == "jobs_multiply":
assert (f"--exclude={sample_exclude}"
in item["profiles"]["pegasus"]["glite.arguments"])
elif item["name"] == "jobs_sort":
assert "--exclude=" in item["profiles"]["pegasus"][
"glite.arguments"]
assert (f"--nodelist={sample_include}"
in item["profiles"]["pegasus"]["glite.arguments"])
else:
assert False
