def run_simple_replication(**kwargs):
"""Helper function to run replication tensorflow python script with
command line arguments"""
cwd = os.path.dirname(os.path.abspath(inspect.stack()[0][1]))
out = test_util.run_python_script_helper(cwd, "simple_replication.py",
**kwargs)
return out
def run_ipu_estimator_cnn(**kwargs):
"""Helper function to run ipu_estimator_cnn tensorflow python script with
command line arguments"""
out = run_python_script_helper(
os.path.dirname(__file__), "ipu_estimator_cnn.py", **kwargs
)
return out
def run_simple_replication(**kwargs):
"""Helper function to run replication tensorflow python script with
command line arguments"""
out = run_python_script_helper(
os.path.dirname(__file__), "simple_replication.py", **kwargs
)
return out
def run_tensorflow_nmt(**kwargs):
"""Helper function to run nmt tensorflow python script with
command line arguments"""
out = run_python_script_helper(os.path.dirname(__file__),
"nmt-tf.py",
**kwargs)
return out
def generate_data(path_to_generation_script):
"""Runs the data generation scripts assumes path"""
files_to_generate = ["training.csv", "validation.csv"]
if test_util.check_data_exists(path_to_generation_script,
files_to_generate):
print("Data already generated, skipping...")
return
cwd = os.path.dirname(os.path.abspath(inspect.stack()[0][1]))
test_util.run_python_script_helper(cwd, "data_gen/generate.py")
if not test_util.check_data_exists(path_to_generation_script,
files_to_generate):
raise Exception("Dataset generation failed")
print("Successfully generated datasets")
def run_connection_type(connection_type):
"""Helper to run connect_type.py with specific connection type,
capture the output, and parse the result."""
kwargs = {"--connection_type": connection_type}
out = run_python_script_helper(os.path.dirname(__file__),
"connection_type.py",
want_std_err=True,
**kwargs)
result = parse_output(out)
print("result {}".format(result))
return result
def generate_data(path_to_generation_script):
"""Runs the data generation scripts assumes path"""
files_to_generate = ["training.csv", "validation.csv"]
if check_data_exists(
path_to_generation_script,
files_to_generate
):
print("Data already generated, skipping...")
return
run_python_script_helper(os.path.dirname(__file__), "data_gen/generate.py")
if not check_data_exists(
path_to_generation_script,
files_to_generate
):
raise Exception("Dataset generation failed")
print("Successfully generated datasets")
def test_tf_code(self):
"""Run the python script and check the result"""
# Run the script and capture the output
out = run_python_script_helper(self.path, "tf_code.py")
# Get the first and the second line of output
ipu_res, target_res = out.split("\n")[:-1]
# Convert these lines to arrays, in turn
list_regex = r"\[.*\]$"
match = re.search(list_regex, ipu_res)
string_vals = match.group()[1:-1].split()
ipu_arr = np.array([float(val) for val in string_vals],
dtype=np.float32)
match = re.search(list_regex, target_res)
string_vals = match.group()[1:-1].split()
target_arr = np.array([float(val) for val in string_vals],
dtype=np.float32)
# Finally, check that the results are reasonably close
assert np.allclose(ipu_arr,
target_arr), ("Output value {} does not "
"equal expected value {}".format(
ipu_arr, target_arr))
# Clean up
subprocess.run(["make", "clean"], cwd=self.path)
def run_pytorch_mnist(**kwargs):
cwd = os.path.dirname(os.path.abspath(inspect.stack()[0][1]))
return test_util.run_python_script_helper(cwd, "pytorch_popart_mnist.py",
**kwargs)
def run_pytorch_mnist(**kwargs):
return run_python_script_helper(os.path.dirname(__file__),
"pytorch_popart_mnist.py", **kwargs)
def run_popart_mnist_training(**kwargs):
out = test_util.run_python_script_helper(os.path.dirname(__file__),
"popart_mnist_conv.py", **kwargs)
return out
def run_report_generation(**kwargs):
"""Helper function to run report generation python script"""
out = run_python_script_helper(os.path.dirname(__file__),
"report_generation_example.py", **kwargs)
return out
def run_pytorch_Torchvision(**kwargs):
return run_python_script_helper(os.path.dirname(__file__),
"torchvision_examples.py", **kwargs)
def run_popart_mnist_training(**kwargs):
"""Helper function to run popart mnist linear model python script with
command line arguments"""
out = run_python_script_helper(os.path.dirname(__file__),
"popart_mnist.py", **kwargs)
return out
def run_report_generation():
"""Helper function to run report generation python script"""
cwd = os.path.dirname(os.path.abspath(inspect.stack()[0][1]))
out = test_util.run_python_script_helper(cwd,
"report_generation_example.py")
return out
