def create_dir_test():
"""Verifies file_io directory handling methods ."""
starttime = int(round(time.time() * 1000))
dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime)
print("Creating dir %s" % dir_name)
file_io.create_dir(dir_name)
elapsed = int(round(time.time() * 1000)) - starttime
print("Created directory in: %d milliseconds" % elapsed)
# Check that the directory exists.
dir_exists = file_io.is_directory(dir_name)
print("%s directory exists: %s" % (dir_name, dir_exists))
# List contents of just created directory.
print("Listing directory %s." % dir_name)
starttime = int(round(time.time() * 1000))
print(file_io.list_directory(dir_name))
elapsed = int(round(time.time() * 1000)) - starttime
print("Listed directory %s in %s milliseconds" % (dir_name, elapsed))
# Delete directory.
print("Deleting directory %s." % dir_name)
starttime = int(round(time.time() * 1000))
file_io.delete_recursively(dir_name)
elapsed = int(round(time.time() * 1000)) - starttime
print("Deleted directory %s in %s milliseconds" % (dir_name, elapsed))
def testListDirectory(self):
dir_path = os.path.join(self._base_dir, "test_dir")
file_io.create_dir(dir_path)
files = ["file1.txt", "file2.txt", "file3.txt"]
for name in files:
file_path = os.path.join(dir_path, name)
file_io.FileIO(file_path, mode="w").write("testing")
subdir_path = os.path.join(dir_path, "sub_dir")
file_io.create_dir(subdir_path)
subdir_file_path = os.path.join(subdir_path, "file4.txt")
file_io.FileIO(subdir_file_path, mode="w").write("testing")
dir_list = file_io.list_directory(dir_path)
self.assertItemsEqual(files + ["sub_dir"], dir_list)
def testListDirectory(self):
dir_path = os.path.join(self._base_dir, "test_dir")
file_io.create_dir(dir_path)
files = [b"file1.txt", b"file2.txt", b"file3.txt"]
for name in files:
file_path = os.path.join(dir_path, compat.as_str_any(name))
file_io.write_string_to_file(file_path, "testing")
subdir_path = os.path.join(dir_path, "sub_dir")
file_io.create_dir(subdir_path)
subdir_file_path = os.path.join(subdir_path, "file4.txt")
file_io.write_string_to_file(subdir_file_path, "testing")
dir_list = file_io.list_directory(dir_path)
self.assertItemsEqual(files + [b"sub_dir"], dir_list)
def testListDirectory(self, join):
dir_path = join(self._base_dir, "test_dir")
file_io.create_dir(dir_path)
files = ["file1.txt", "file2.txt", "file3.txt"]
for name in files:
file_path = join(str(dir_path), name)
file_io.FileIO(file_path, mode="w").write("testing")
subdir_path = join(str(dir_path), "sub_dir")
file_io.create_dir(subdir_path)
subdir_file_path = join(str(subdir_path), "file4.txt")
file_io.FileIO(subdir_file_path, mode="w").write("testing")
dir_list = file_io.list_directory(dir_path)
self.assertItemsEqual(files + ["sub_dir"], dir_list)
def _get_tfrecord_filenames(dir_path):
assert isinstance(dir_path,
str), "dir_path is not a String: %r" % dir_path
assert file_io.file_exists(
dir_path), "directory `%s` does not exist" % dir_path
assert file_io.is_directory(
dir_path), "`%s` is not a directory" % dir_path
flist = file_io.list_directory(dir_path)
input_files = [
pjoin(dir_path, x)
for x in filter(lambda f: not f.startswith("_"), flist)
]
filenames = tf.placeholder_with_default(input_files, shape=[None])
return filenames
def get_record_id_map(brats_tfrecords_dir):
"""
Get the mapping from patient_id --> TFRecord file
:param brats_tfrecords_dir: directory containing all TFRecords
:return: Dictionary mapping patient_id to TFRecord file
"""
tfrecord_filenames = file_io.list_directory(brats_tfrecords_dir)
id_record_map = {}
for file_name in tfrecord_filenames:
patient_id = get_id_of_TFRecord(file_name)
id_record_map[patient_id] = os.path.join(brats_tfrecords_dir,
file_name)
return id_record_map
def testWriteTransformFnIsRetryable(self):
tft.test_case.skip_if_external_environment(
'Retries are currently not available on this environment.')
original_copy_tree_to_unique_temp_dir = (
transform_fn_io._copy_tree_to_unique_temp_dir)
def mock_copy_tree_to_unique_temp_dir(source, base_temp_dir_path):
"""Mocks transform_fn_io._copy_tree to fail the first time it is called by this test, thus forcing a retry which should succeed."""
global _COPY_TREE_TO_UNIQUE_TEMP_DIR_CALLED
if not _COPY_TREE_TO_UNIQUE_TEMP_DIR_CALLED:
_COPY_TREE_TO_UNIQUE_TEMP_DIR_CALLED = True
original_copy_tree_to_unique_temp_dir(source,
base_temp_dir_path)
raise ArithmeticError('Some error')
return original_copy_tree_to_unique_temp_dir(
source, base_temp_dir_path)
with self._makeTestPipeline() as pipeline:
transform_output_dir = os.path.join(self.get_temp_dir(), 'output')
# Create an empty directory for the source saved model dir.
saved_model_dir = os.path.join(self.get_temp_dir(), 'source')
file_io.recursive_create_dir(saved_model_dir)
saved_model_path = os.path.join(saved_model_dir, 'saved_model')
with file_io.FileIO(saved_model_path, mode='w') as f:
f.write('some content')
saved_model_dir_pcoll = (
pipeline
| 'CreateSavedModelDir' >> beam.Create([saved_model_dir]))
# Combine test metadata with a dict of PCollections resolving futures.
deferred_metadata = pipeline | 'CreateDeferredMetadata' >> beam.Create(
[test_metadata.COMPLETE_METADATA])
metadata = beam_metadata_io.BeamDatasetMetadata(
test_metadata.INCOMPLETE_METADATA, deferred_metadata)
with mock.patch.object(transform_fn_io,
'_copy_tree_to_unique_temp_dir',
mock_copy_tree_to_unique_temp_dir):
_ = ((saved_model_dir_pcoll, metadata)
| transform_fn_io.WriteTransformFn(transform_output_dir))
# Test reading with TFTransformOutput
tf_transform_output = tft.TFTransformOutput(transform_output_dir)
metadata = tf_transform_output.transformed_metadata
self.assertEqual(metadata, test_metadata.COMPLETE_METADATA)
transform_fn_dir = tf_transform_output.transform_savedmodel_dir
self.assertTrue(file_io.file_exists(transform_fn_dir))
self.assertTrue(file_io.is_directory(transform_fn_dir))
# Check temp directory created by failed run was cleaned up.
self.assertEqual(2, len(file_io.list_directory(transform_output_dir)))
def proc_tensorboard_works_with_same_file_path(test_obj,
saving_filepath):
model, _, train_ds, steps = _model_setup(test_obj, file_format='')
num_epoch = 2
# The saving_filepath shouldn't exist at the beginning (as it's unique).
test_obj.assertFalse(file_io.file_exists(saving_filepath))
model.fit(
x=train_ds,
epochs=num_epoch,
steps_per_epoch=steps,
callbacks=[callbacks.TensorBoard(log_dir=saving_filepath)])
test_obj.assertTrue(file_io.list_directory(saving_filepath))
def recursive_copy(src_dir, dest_dir):
"""Copy the contents of src_dir into the folder dest_dir.
Args:
src_dir: gsc or local path.
dest_dir: gcs or local path.
"""
file_io.recursive_create_dir(dest_dir)
for file_name in file_io.list_directory(src_dir):
old_path = os.path.join(src_dir, file_name)
new_path = os.path.join(dest_dir, file_name)
if file_io.is_directory(old_path):
recursive_copy(old_path, new_path)
else:
file_io.copy(old_path, new_path, overwrite=True)
def recursive_copy(src_dir, dest_dir):
"""Copy the contents of src_dir into the folder dest_dir.
Args:
src_dir: gsc or local path.
dest_dir: gcs or local path.
"""
file_io.recursive_create_dir(dest_dir)
for file_name in file_io.list_directory(src_dir):
old_path = os.path.join(src_dir, file_name)
new_path = os.path.join(dest_dir, file_name)
if file_io.is_directory(old_path):
recursive_copy(old_path, new_path)
else:
file_io.copy(old_path, new_path, overwrite=True)
def main():
tf.logging.set_verbosity(tf.logging.INFO)
args = parse_arguments()
args.slice_columns = [
column
for column in column_group.split(',')
for column_group in args.slice_columns
]
schema = json.loads(file_io.read_file_to_string(args.schema))
eval_model_parent_dir = os.path.join(args.model, 'tfma_eval_model_dir')
model_export_dir = os.path.join(eval_model_parent_dir, file_io.list_directory(eval_model_parent_dir)[0])
run_analysis(args.output, model_export_dir, args.eval, schema,
args.project, args.mode, args.slice_columns)
generate_static_html_output(args.output, args.slice_columns)
with open('/output.txt', 'w') as f:
f.write(args.output)
def list_odps_table_oss_files(table_name):
table_data_meta = os.path.join(FLAGS.buckets, table_name, '.odps/.meta')
print(table_data_meta)
meta_str = file_io.read_file_to_string(table_data_meta)
print('meta_str:', meta_str)
meta_dict = json.loads(meta_str)
table_data_dirs = meta_dict['dirs']
table_data_dirs = [os.path.join(FLAGS.buckets, table_name, '.odps', d) for d in table_data_dirs]
all_files = []
for data_dir in table_data_dirs:
print("data_dir:", data_dir)
tmp_files = file_io.list_directory(data_dir)
for fname in tmp_files:
all_files.append(os.path.join(data_dir, fname))
return all_files
def _recursive_copy(src_dir, dest_dir):
"""Copy the contents of src_dir into the folder dest_dir.
Args:
src_dir: gsc or local path.
dest_dir: gcs or local path.
When called, dest_dir should exist.
"""
src_dir = python_portable_string(src_dir)
dest_dir = python_portable_string(dest_dir)
file_io.recursive_create_dir(dest_dir)
for file_name in file_io.list_directory(src_dir):
old_path = os.path.join(src_dir, file_name)
new_path = os.path.join(dest_dir, file_name)
if file_io.is_directory(old_path):
_recursive_copy(old_path, new_path)
else:
file_io.copy(old_path, new_path, overwrite=True)
def load_paths_only(path):
print("Parsing data...")
gui_paths = []
img_paths = []
for f in file_io.list_directory(path):
if f.find(".gui") != -1:
path_gui = "{}/{}".format(path, f)
gui_paths.append(path_gui)
file_name = f[:f.find(".gui")]
if file_io.file_exists("{}/{}.png".format(path, file_name)):
path_img = "{}/{}.png".format(path, file_name)
img_paths.append(path_img)
elif file_io.file_exists("{}/{}.npz".format(path, file_name)):
path_img = "{}/{}.npz".format(path, file_name)
img_paths.append(path_img)
assert len(gui_paths) == len(img_paths)
return gui_paths, img_paths
def _recursive_copy(src_dir, dest_dir):
"""Copy the contents of src_dir into the folder dest_dir.
Args:
src_dir: gsc or local path.
dest_dir: gcs or local path.
When called, dest_dir should exist.
"""
src_dir = python_portable_string(src_dir)
dest_dir = python_portable_string(dest_dir)
file_io.recursive_create_dir(dest_dir)
for file_name in file_io.list_directory(src_dir):
old_path = os.path.join(src_dir, file_name)
new_path = os.path.join(dest_dir, file_name)
if file_io.is_directory(old_path):
_recursive_copy(old_path, new_path)
else:
file_io.copy(old_path, new_path, overwrite=True)
def generator(subdir, batch_size):
desired_size = 224
file_names = [(desired_size, subdir, fn) for fn in file_io.list_directory(subdir)]
np.random.shuffle(file_names)
i = 0
batch = np.zeros((batch_size, desired_size, desired_size, 3))
labels = np.zeros((batch_size, LABELS))
p = Pool()
while True:
for im, label in p.imap_unordered(read_image, file_names):
if im == -1:
continue
batch[i], labels[i] = im, label
if i == batch_size-1:
yield batch, labels
i = 0
else:
i += 1
def create_dir_test():
"""Verifies file_io directory handling methods ."""
starttime = int(round(time.time() * 1000))
dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime)
print("Creating dir %s" % dir_name)
file_io.create_dir(dir_name)
elapsed = int(round(time.time() * 1000)) - starttime
print("Created directory in: %d milliseconds" % elapsed)
# Check that the directory exists.
dir_exists = file_io.is_directory(dir_name)
print("%s directory exists: %s" % (dir_name, dir_exists))
# List contents of just created directory.
starttime = int(round(time.time() * 1000))
print("Listing directory %s." % dir_name)
print(file_io.list_directory(dir_name))
elapsed = int(round(time.time() * 1000)) - starttime
print("Listed directory %s in %s milliseconds" % (dir_name, elapsed))
def load(self) -> T5ForConditionalGeneration:
try:
if not self.flush_cache:
return self._fix_t5_model(
T5ForConditionalGeneration.from_pretrained(
str(self.model_cache_dir),
from_tf=True,
force_download=False))
except (RuntimeError, OSError):
logging.info('T5 model weights not in cache.')
m = re.search(r'model_checkpoint_path: "(.+?)"', self.ckpt_prefix)
assert m is not None, 'checkpoint file malformed'
# Copy over checkpoint data
ckpt_patt = re.compile(
rf'^{m.group(1)}\.(data-\d+-of-\d+|index|meta)$')
for name in file_io.list_directory(self.url):
if not ckpt_patt.match(name):
continue
url = os.path.join(self.url, name)
url_stat = file_io.stat(url)
cache_file_path = self.model_cache_dir / ckpt_patt.sub(
rf'{TRANSFO_PREFIX}.\1', name)
try:
cs = os.stat(str(cache_file_path))
if cs.st_size == url_stat.length and cs.st_mtime_ns > url_stat.mtime_nsec and not self.flush_cache:
logging.info(f'Skipping {name}...')
continue
except FileNotFoundError:
pass
logging.info(f'Caching {name}...')
file_io.copy(url, str(cache_file_path), overwrite=True)
# Transformers expects a model config.json
config = T5Config.from_pretrained(self.model_type)
with open(str(self.model_cache_dir / 'config.json'), 'w') as f:
json.dump(config.__dict__, f, indent=4)
return self._fix_t5_model(
T5ForConditionalGeneration.from_pretrained(str(
self.model_cache_dir),
from_tf=True,
force_download=False))
def dataset(data_path, batch_size=1, shuffle=False, repeat=False):
names = file_io.list_directory(data_path)
_paths = []
for name in names:
_paths.append(os.path.join(data_path, name))
ds = tf.data.TFRecordDataset(_paths)
ds = ds.map(__parse_proto)
ds = ds.map(
lambda article, abstract: tuple(tf.py_func(
__preprocess_article_and_abstract,
[article, abstract],
[tf.string, tf.string],
name='preprocess_article_and_abstract'
)))
if shuffle:
ds = ds.shuffle(buffer_size=100)
ds = ds.batch(batch_size, drop_remainder=True)
if repeat:
ds = ds.repeat()
return ds
def load(self, path, generate_binary_sequences=False):
print("Loading data...")
for f in file_io.list_directory(path):
if f.find(".gui") != -1:
gui = file_io.FileIO("{}/{}".format(path, f), 'r')
file_name = f[:f.find(".gui")]
if file_io.file_exists("{}/{}.png".format(path, file_name)):
img = Utils.get_preprocessed_img(
"{}/{}.png".format(path, file_name), IMAGE_SIZE)
self.append(file_name, gui, img)
elif file_io.file_exists("{}/{}.npz".format(path, file_name)):
f_str = StringIO(
file_io.read_file_to_string("{}/{}.npz".format(
path, file_name)))
img = np.load(f_str)["features"]
self.append(file_name, gui, img)
print("Generating sparse vectors...")
self.voc.create_binary_representation()
self.next_words = self.sparsify_labels(self.next_words, self.voc)
if generate_binary_sequences:
self.partial_sequences = self.binarize(self.partial_sequences,
self.voc)
else:
self.partial_sequences = self.indexify(self.partial_sequences,
self.voc)
self.size = len(self.ids)
assert self.size == len(self.input_images) == len(
self.partial_sequences) == len(self.next_words)
assert self.voc.size == len(self.voc.vocabulary)
print("Dataset size: {}".format(self.size))
print("Vocabulary size: {}".format(self.voc.size))
self.input_shape = self.input_images[0].shape
self.output_size = self.voc.size
print("Input shape: {}".format(self.input_shape))
print("Output size: {}".format(self.output_size))
def __init__(self, export_dir):
self._saved_model = saved_model_pb2.SavedModel()
self._saved_model.saved_model_schema_version = (
constants.SAVED_MODEL_SCHEMA_VERSION)
self._export_dir = export_dir
if file_io.file_exists(export_dir):
if file_io.list_directory(export_dir):
raise AssertionError(
"Export directory already exists, and isn't empty. Please choose "
"a different export directory, or delete all the contents of the "
"specified directory: %s" % export_dir)
else:
file_io.recursive_create_dir(self._export_dir)
# Boolean to track whether variables and assets corresponding to the
# SavedModel have been saved. Specifically, the first meta graph to be added
# MUST use the add_meta_graph_and_variables() API. Subsequent add operations
# on the SavedModel MUST use the add_meta_graph() API which does not save
# weights.
self._has_saved_variables = False
def leer_imagenes_etiquetas(ruta):
## variables
x = []
y = []
ruta = ruta + "/"
print('LA RUTA ES...' + ruta)
## obtenemos la lista de ficneros para la ruta pasada por parámentro
filelist = sorted(file_io.list_directory(ruta))
random.seed(42)
random.shuffle(filelist)
print('TAMAÑO DIRECTORIO...' + str(len(filelist)) + " primero..." +
filelist[1])
## obtenemos las imágenes y las etiquetas de cada elemento de la lista
for imgPath in filelist:
imagen = leer_una_imagen(ruta + imgPath)
imagen = img_to_array(imagen) # pasa a array
x.append(imagen)
# lee, redimensiona y lo añade al array
#imagePath = ruta + imgPath
#imagen = cv2.imread(imagePath) # lee
#imagen = cv2.resize(imagen, (img_width, img_height)) # redimentsiona
# extraemos la etiqueta del nombre de cada fichero
#ruta_partes = imagePath.split("/") # lo dividimos en las partes de la ruta
#nombreimagen = ruta_partes[len(ruta_partes)-1] # nos quedamos con el nombre del fichero, que es el último
label = imgPath[imgPath.find("_") +
1:imgPath.find(".")] # la etiqueta está entre _ y .
label = int(
label
) - 1 # las etiquetas empiezan en 1, y les restamos 1 para que empiecen en 0
y.append(label)
print('REGISTROS LEIDOS X...' + str(len(x)))
print('REGISTROS LEIDOS Y...' + str(len(y)))
return (x, y)
def load_library(library_location):
"""Loads a TensorFlow plugin.
"library_location" can be a path to a specific shared object, or a folder.
If it is a folder, all sahred objects that are named "libtfkernel*" will be
loaded. When the library is loaded, kernels registered in the library via the
`REGISTER_*` macros are made available in the TensorFlow process.
Args:
library_location: Path to the plugin or the folder of plugins.
Relative or absolute filesystem path to a dynamic library file or folder.
Returns:
None
Raises:
OSError: When the file to be loaded is not found.
RuntimeError: when unable to load the library.
"""
if file_io.file_exists(library_location):
if file_io.is_directory(library_location):
directory_contents = file_io.list_directory(library_location)
kernel_libraries = [
os.path.join(library_location, f) for f in directory_contents
if _is_shared_object(f)
]
else:
kernel_libraries = [library_location]
for lib in kernel_libraries:
py_tf.TF_LoadLibrary(lib)
else:
raise OSError(
errno.ENOENT,
'The file or folder to load kernel libraries from does not exist.',
library_location)
def leer_imagenes_etiquetas(ruta):
## variables
x = []
y = []
## obtenemos la lista de ficneros para la ruta pasada por parámentro
ruta = ruta + "/"
print('LA RUTA ES...' + ruta)
filelist = sorted(file_io.list_directory(ruta))
random.seed(42)
random.shuffle(filelist)
## obtenemos las imágenes y las etiquetas de cada elemento de la lista
print("EMPIEZA A LEER ", time.time())
with tqdm(total=len(filelist)) as t:
for imgPath in filelist:
imagen = leer_una_imagen(ruta + imgPath)
if not imagen:
continue # si ha fallado la lectura, y por tanto viene vacÃo, nos saltamos la imagen
try:
imagen = img_to_array(
imagen) # pasa a array, si falla nos saltamos la imagen
except:
continue
x.append(imagen)
label = imgPath[imgPath.find("_") + 1:imgPath.find(
".")] # la etiqueta está entre _ y .
label = int(
label
) - 1 # las etiquetas empiezan en 1, y les restamos 1 para que empiecen en 0
y.append(label)
t.update(1)
print('REGISTROS LEIDOS X...' + str(len(x)))
print('REGISTROS LEIDOS Y...' + str(len(y)))
return (x, y)
def load_library(library_location):
"""Loads a TensorFlow plugin.
"library_location" can be a path to a specific shared object, or a folder.
If it is a folder, all sahred objects that are named "libtfkernel*" will be
loaded. When the library is loaded, kernels registered in the library via the
`REGISTER_*` macros are made available in the TensorFlow process.
Args:
library_location: Path to the plugin or the folder of plugins.
Relative or absolute filesystem path to a dynamic library file or folder.
Returns:
None
Raises:
OSError: When the file to be loaded is not found.
RuntimeError: when unable to load the library.
"""
if file_io.file_exists(library_location):
if file_io.is_directory(library_location):
directory_contents = file_io.list_directory(library_location)
kernel_libraries = [
os.path.join(library_location, f) for f in directory_contents
if _is_shared_object(f)]
else:
kernel_libraries = [library_location]
for lib in kernel_libraries:
py_tf.TF_LoadLibrary(lib)
else:
raise OSError(
errno.ENOENT,
'The file or folder to load kernel libraries from does not exist.',
library_location)
def main():
logging.getLogger().setLevel(logging.INFO)
args = parse_arguments()
# Models trained with estimator are exported to base/export/export/123456781 directory.
# Our trainer export only one model.
export_parent_dir = os.path.join(args.model, 'export', 'export')
model_export_dir = os.path.join(export_parent_dir, file_io.list_directory(export_parent_dir)[0])
schema = json.loads(file_io.read_file_to_string(args.schema))
run_predict(args.output, args.data, schema, args.target, model_export_dir,
args.project, args.mode, args.batchsize)
prediction_results = os.path.join(args.output, 'prediction_results-*')
with open('/output.txt', 'w') as f:
f.write(prediction_results)
tmp = os.path.join(args.output, 'prediction_results-00000-of-00001')
with open('/prediction-output.txt', 'w') as f:
f.write(open(tmp,"r").read())
with file_io.FileIO(os.path.join(args.output, 'schema.json'), 'r') as f:
schema = json.load(f)
BUCKET_PATH = "/".join(args.output.split('/')[3:])
upload_to_minio('/prediction-output.txt', BUCKET_PATH)
metadata = {
'outputs' : [{
'type': 'table',
'storage': 'minio',
'format': 'csv',
'header': [x['name'] for x in schema],
'source': "{}://{}/{}/{}".format("minio",BUCKET_NAME,BUCKET_PATH,"prediction-output.txt")
}]
}
with open('/mlpipeline-ui-metadata.json', 'w') as f:
json.dump(metadata, f)
def testListDirectoryFailure(self):
dir_path = os.path.join(self._base_dir, "test_dir")
with self.assertRaises(errors.NotFoundError):
file_io.list_directory(dir_path)
def load_session_bundle_from_path(export_dir, target="", config=None):
"""Load session bundle from the given path.
The function reads input from the export_dir, constructs the graph data to the
default graph and restores the parameters for the session created.
Args:
export_dir: the directory that contains files exported by exporter.
target: The execution engine to connect to. See target in tf.Session()
config: A ConfigProto proto with configuration options. See config in
tf.Session()
Returns:
session: a tensorflow session created from the variable files.
meta_graph: a meta graph proto saved in the exporter directory.
Raises:
RuntimeError: if the required files are missing or contain unrecognizable
fields, i.e. the exported model is invalid.
"""
if hasattr(tf, "GIT_VERSION"):
logging.info("tf.GIT_VERSION=%s", tf.GIT_VERSION)
else:
logging.info("tf.GIT_VERSION=unknown")
meta_graph_filename = os.path.join(export_dir, META_GRAPH_DEF_FILENAME)
if not file_io.file_exists(meta_graph_filename):
raise RuntimeError("Expected meta graph file missing %s" %
meta_graph_filename)
variables_filename = ""
variables_filename_list = []
additional_files_to_copy = []
checkpoint_sharded = False
variables_index_filename = os.path.join(export_dir,
VARIABLES_INDEX_FILENAME_V2)
checkpoint_v2 = file_io.file_exists(variables_index_filename)
if checkpoint_v2:
# The checkpoint is in v2 format.
variables_filename = os.path.join(export_dir, VARIABLES_FILENAME_V2)
# Check to see if the file "export" exists or not.
if file_io.file_exists(variables_filename):
variables_filename_list = [variables_filename]
else:
# Check to see if the sharded file named "export-?????-of-?????" exists.
variables_filename_list = fnmatch.filter(
file_io.list_directory(export_dir),
VARIABLES_FILENAME_PATTERN_V2)
checkpoint_sharded = True
# If the checkpoint is not local, we need to copy export.index locally too.
additional_files_to_copy = [variables_index_filename]
else:
variables_filename = os.path.join(export_dir, VARIABLES_FILENAME)
if file_io.file_exists(variables_filename):
variables_filename_list = [variables_filename]
else:
variables_filename_list = fnmatch.filter(
file_io.list_directory(export_dir), VARIABLES_FILENAME_PATTERN)
checkpoint_sharded = True
if not variables_filename_list or not variables_filename:
raise RuntimeError("No or bad checkpoint files found in %s" %
export_dir)
# Prepare the files to restore a session.
restore_files = ""
if checkpoint_v2 or not checkpoint_sharded:
# For checkpoint v2 or v1 with non-sharded files, use "export" to restore
# the session.
restore_files = VARIABLES_FILENAME
else:
restore_files = VARIABLES_FILENAME_PATTERN
# Reads meta graph file.
meta_graph_def = meta_graph_pb2.MetaGraphDef()
with file_io.FileIO(meta_graph_filename, "r") as f:
logging.info("Reading metagraph from %s", meta_graph_filename)
meta_graph_def.ParseFromString(f.read())
collection_def = meta_graph_def.collection_def
graph_def = tf.GraphDef()
if GRAPH_KEY in collection_def:
logging.info("Using value of collection %s for the graph.", GRAPH_KEY)
# Use serving graph_def in MetaGraphDef collection_def if exists
graph_def_any = collection_def[GRAPH_KEY].any_list.value
if len(graph_def_any) != 1:
raise RuntimeError(
"Expected exactly one serving GraphDef in : %s" %
meta_graph_def)
else:
graph_def_any[0].Unpack(graph_def)
# Replace the graph def in meta graph proto.
meta_graph_def.graph_def.CopyFrom(graph_def)
# TODO(b/36055868): If we don't clear the collections then
# import_meta_graph fails.
#
# We can't delete all the collections because some of them are used
# by prediction to get the names of the input/output tensors.
keys_to_delete = (set(meta_graph_def.collection_def.keys()) -
set(keys_used_for_serving()))
for k in keys_to_delete:
del meta_graph_def.collection_def[k]
else:
logging.info(
"No %s found in metagraph. Using metagraph as serving graph",
GRAPH_KEY)
tf.reset_default_graph()
sess = tf.Session(target, graph=None, config=config)
# Import the graph.
saver = tf.train.import_meta_graph(meta_graph_def)
# Restore the session.
if variables_filename_list[0].startswith("gs://"):
# Make copy from GCS files.
# TODO(b/36052034): Retire this once tensorflow can access GCS.
try:
temp_dir_path = tempfile.mkdtemp("local_variable_files")
for f in variables_filename_list + additional_files_to_copy:
file_io.copy(f, os.path.join(temp_dir_path,
os.path.basename(f)))
saver.restore(sess, os.path.join(temp_dir_path, restore_files))
finally:
try:
shutil.rmtree(temp_dir_path)
except OSError as e:
if e.message == "Cannot call rmtree on a symbolic link":
# Interesting synthetic exception made up by shutil.rmtree.
# Means we received a symlink from mkdtemp.
# Also means must clean up the symlink instead.
os.unlink(temp_dir_path)
else:
raise
else:
saver.restore(sess, os.path.join(export_dir, restore_files))
init_op_tensor = None
if INIT_OP_KEY in collection_def:
init_ops = collection_def[INIT_OP_KEY].node_list.value
if len(init_ops) != 1:
raise RuntimeError("Expected exactly one serving init op in : %s" %
meta_graph_def)
init_op_tensor = tf.get_collection(INIT_OP_KEY)[0]
if init_op_tensor:
# Run the init op.
sess.run(fetches=[init_op_tensor])
return sess, meta_graph_def
def testListDirectoryFailure(self):
dir_path = os.path.join(self._base_dir, "test_dir")
with self.assertRaises(errors.NotFoundError):
file_io.list_directory(dir_path)
def create_dir_test():
"""Verifies file_io directory handling methods."""
# Test directory creation.
starttime_ms = int(round(time.time() * 1000))
dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms)
print("Creating dir %s" % dir_name)
file_io.create_dir(dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Created directory in: %d milliseconds" % elapsed_ms)
# Check that the directory exists.
dir_exists = file_io.is_directory(dir_name)
assert dir_exists
print("%s directory exists: %s" % (dir_name, dir_exists))
# Test recursive directory creation.
starttime_ms = int(round(time.time() * 1000))
recursive_dir_name = "%s/%s/%s" % (dir_name,
"nested_dir1",
"nested_dir2")
print("Creating recursive dir %s" % recursive_dir_name)
file_io.recursive_create_dir(recursive_dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Created directory recursively in: %d milliseconds" % elapsed_ms)
# Check that the directory exists.
recursive_dir_exists = file_io.is_directory(recursive_dir_name)
assert recursive_dir_exists
print("%s directory exists: %s" % (recursive_dir_name, recursive_dir_exists))
# Create some contents in the just created directory and list the contents.
num_files = 10
files_to_create = ["file_%d.txt" % n for n in range(num_files)]
for file_num in files_to_create:
file_name = "%s/%s" % (dir_name, file_num)
print("Creating file %s." % file_name)
file_io.write_string_to_file(file_name, "test file.")
print("Listing directory %s." % dir_name)
starttime_ms = int(round(time.time() * 1000))
directory_contents = file_io.list_directory(dir_name)
print(directory_contents)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Listed directory %s in %s milliseconds" % (dir_name, elapsed_ms))
assert set(directory_contents) == set(files_to_create + ["nested_dir1/"])
# Test directory renaming.
dir_to_rename = "%s/old_dir" % dir_name
new_dir_name = "%s/new_dir" % dir_name
file_io.create_dir(dir_to_rename)
assert file_io.is_directory(dir_to_rename)
assert not file_io.is_directory(new_dir_name)
starttime_ms = int(round(time.time() * 1000))
print("Will try renaming directory %s to %s" % (dir_to_rename, new_dir_name))
file_io.rename(dir_to_rename, new_dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Renamed directory %s to %s in %s milliseconds" % (
dir_to_rename, new_dir_name, elapsed_ms))
assert not file_io.is_directory(dir_to_rename)
assert file_io.is_directory(new_dir_name)
# Test Delete directory recursively.
print("Deleting directory recursively %s." % dir_name)
starttime_ms = int(round(time.time() * 1000))
file_io.delete_recursively(dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
dir_exists = file_io.is_directory(dir_name)
assert not dir_exists
print("Deleted directory recursively %s in %s milliseconds" % (
dir_name, elapsed_ms))
def run_tfma(slice_spec,
eval_model_base_dir,
tfma_run_dir,
input_csv,
working_dir,
mode,
project,
setup_file,
add_metrics_callbacks=None):
"""Does model analysis, using the given spec of how to 'slice', and returns an
EvalResult that can be used with TFMA visualization functions.
"""
print("eval model base dir: %s" % eval_model_base_dir)
# Make sure the model dir exists before proceeding, as sometimes it takes a few seconds to become
# available after training completes.
retries = 0
sleeptime = 5
while retries < 20:
try:
eval_model_dir = os.path.join(
eval_model_base_dir,
file_io.list_directory(eval_model_base_dir)[0])
print("eval model dir: %s" % eval_model_dir)
if 'temp' not in eval_model_dir:
break
else:
print("Sleeping %s seconds to sync with GCS..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
except Exception as e:
print(e)
print("Sleeping %s seconds to sync with GCS..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
schema = taxi.read_schema('schema.pbtxt')
temp_dir = os.path.join(working_dir, 'tmp')
if mode == 'local':
print("mode == local")
options = {'project': project}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
runner = 'DirectRunner'
elif mode == 'cloud':
print("mode == cloud")
options = {
'job_name': 'tfma-' + str(uuid.uuid4()),
'temp_location': temp_dir,
'project': project,
'save_main_session': True,
'setup_file': setup_file
}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
runner = 'DataFlowRunner'
else:
raise ValueError("Invalid mode %s." % mode)
display_only_data_location = input_csv
with beam.Pipeline(runner, options=pipeline_options) as pipeline:
with beam_impl.Context(temp_dir=temp_dir):
csv_coder = taxi.make_csv_coder(schema)
raw_data = (
pipeline
| 'ReadFromText' >> beam.io.ReadFromText(
input_csv,
# coder=beam.coders.BytesCoder(),
skip_header_lines=1)
| 'ParseCSV' >> beam.Map(csv_coder.decode))
# Examples must be in clean tf-example format.
coder = taxi.make_proto_coder(schema)
raw_data = (
raw_data
# | 'CleanData' >> beam.Map(taxi.clean_raw_data_dict)
| 'ToSerializedTFExample' >> beam.Map(coder.encode))
_ = raw_data | 'EvaluateAndWriteResults' >> tfma.EvaluateAndWriteResults(
eval_saved_model_path=eval_model_dir,
slice_spec=slice_spec,
output_path=tfma_run_dir,
add_metrics_callbacks=add_metrics_callbacks,
display_only_data_location=input_csv)
return tfma.load_eval_result(output_path=tfma_run_dir)
def main():
parser = argparse.ArgumentParser(description='ML Trainer')
parser.add_argument(
'--model_name',
help='...',
required=True)
parser.add_argument(
'--model_path',
help='...',
required=True)
parser.add_argument('--cluster', type=str,
help='GKE cluster set up for kubeflow. If set, zone must be provided. ' +
'If not set, assuming this runs in a GKE container and current ' +
'cluster is used.')
parser.add_argument('--zone', type=str, help='zone of the kubeflow cluster.')
args = parser.parse_args()
KUBEFLOW_NAMESPACE = 'kubeflow'
# Make sure model dir exists before proceeding
retries = 0
sleeptime = 5
while retries < 20:
try:
model_dir = os.path.join(args.model_path, file_io.list_directory(args.model_path)[-1])
print("model subdir: %s" % model_dir)
break
except Exception as e: #pylint: disable=broad-except
print(e)
print("Sleeping %s seconds to sync with GCS..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
if retries >= 20:
print("could not get model subdir from %s, exiting" % args.model_path)
exit(1)
logging.getLogger().setLevel(logging.INFO)
args_dict = vars(args)
if args.cluster and args.zone:
cluster = args_dict.pop('cluster') #pylint: disable=unused-variable
zone = args_dict.pop('zone') #pylint: disable=unused-variable
else:
# Get cluster name and zone from metadata
metadata_server = "http://metadata/computeMetadata/v1/instance/"
metadata_flavor = {'Metadata-Flavor' : 'Google'}
cluster = requests.get(metadata_server + "attributes/cluster-name",
headers=metadata_flavor).text
zone = requests.get(metadata_server + "zone",
headers=metadata_flavor).text.split('/')[-1]
# logging.info('Getting credentials for GKE cluster %s.' % cluster)
# subprocess.call(['gcloud', 'container', 'clusters', 'get-credentials', cluster,
# '--zone', zone])
logging.info('Generating training template.')
template_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'tf-serve-template.yaml')
target_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'tf-serve.yaml')
with open(template_file, 'r') as f:
with open(target_file, "w") as target:
data = f.read()
changed = data.replace('MODEL_NAME', args.model_name)
changed1 = changed.replace('KUBEFLOW_NAMESPACE', KUBEFLOW_NAMESPACE)
changed2 = changed1.replace('MODEL_PATH', args.model_path)
target.write(changed2)
logging.info('deploying model serving.')
subprocess.call(['kubectl', 'create', '-f', '/ml/tf-serve.yaml'])
def train(args):
def get_dataset():
images = []
labels = []
local_zip = IO_utils.load_data(args.training_dir, LOCAL_TRAIN_FILE)
print("zip path = ", local_zip)
with file_io.FileIO(args.training_dir, 'r') as f:
with ZipFile(f, 'r') as archive:
file_list = archive.infolist()
random.shuffle(file_list)
print("image number", len(file_list))
for entry in file_list:
with archive.open(entry) as file:
try:
open_img = Image.open(file)
images.append(np.array(open_img))
label = np.zeros(num_classes)
label = define_label_one_hot(file.name, label)
labels.append(label)
except Exception as error:
print(error)
images = np.array(images)
labels = np.array(labels)
x_train, x_test, y_train, y_test = train_test_split(images,
labels,
test_size=0.1,
random_state=42)
x_train = x_train / 255.0
x_test = x_test / 255.0
return x_train, x_test, y_train, y_test
def define_label_one_hot(img, label):
for c in class_names:
if c in os.path.basename(img):
label[class_names.index(c)] = 1
return label
def define_label(img, label):
for c in class_names:
if c in os.path.basename(img):
label = class_names.index(c)
return label
with file_io.FileIO(args.label_file, 'r') as f:
class_names = f.read().split(",")
num_classes = len(class_names)
input_shape = (args.img_size, args.img_size, args.channel)
print("labels", class_names)
print("num_class", num_classes)
X_train, X_test, y_train, y_test = get_dataset()
print("Xtrain", X_train.shape)
print("Ytrain", y_train.shape)
model = model_helper.init_cnn_model(input_shape, num_classes)
model.summary()
# checkpoint
checkpoint = ModelCheckpoint(LOCAL_MODEL_FILE,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
model.fit(
X_train,
y_train,
batch_size=args.batch_size,
epochs=args.epochs,
validation_data=(X_test, y_test),
callbacks=[checkpoint,
model_helper.get_tensorboard_config("logs")],
verbose=1)
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print("Predict :", model.predict(X_test[:4]))
model.save("last-" + LOCAL_MODEL_FILE)
# predict first 4 images in the test set
print("Predict :", model.predict(X_test[:4]))
logs = file_io.list_directory("logs")
print("logs = ", logs)
IO_utils.save_file_in_cloud("last-" + LOCAL_MODEL_FILE,
args.job_dir + "/" + 'last-' + args.job_name)
IO_utils.save_file_in_cloud(LOCAL_MODEL_FILE,
args.job_dir + "/" + args.job_name)
for entry in logs:
IO_utils.save_file_in_cloud("logs/" + entry,
args.job_dir + "/logs/" + entry)
def create_dir_test():
"""Verifies file_io directory handling methods."""
# Test directory creation.
starttime_ms = int(round(time.time() * 1000))
dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms)
print("Creating dir %s" % dir_name)
file_io.create_dir(dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Created directory in: %d milliseconds" % elapsed_ms)
# Check that the directory exists.
dir_exists = file_io.is_directory(dir_name)
assert dir_exists
print("%s directory exists: %s" % (dir_name, dir_exists))
# Test recursive directory creation.
starttime_ms = int(round(time.time() * 1000))
recursive_dir_name = "%s/%s/%s" % (dir_name,
"nested_dir1",
"nested_dir2")
print("Creating recursive dir %s" % recursive_dir_name)
file_io.recursive_create_dir(recursive_dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Created directory recursively in: %d milliseconds" % elapsed_ms)
# Check that the directory exists.
recursive_dir_exists = file_io.is_directory(recursive_dir_name)
assert recursive_dir_exists
print("%s directory exists: %s" % (recursive_dir_name, recursive_dir_exists))
# Create some contents in the just created directory and list the contents.
num_files = 10
files_to_create = ["file_%d.txt" % n for n in range(num_files)]
for file_num in files_to_create:
file_name = "%s/%s" % (dir_name, file_num)
print("Creating file %s." % file_name)
file_io.write_string_to_file(file_name, "test file.")
print("Listing directory %s." % dir_name)
starttime_ms = int(round(time.time() * 1000))
directory_contents = file_io.list_directory(dir_name)
print(directory_contents)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Listed directory %s in %s milliseconds" % (dir_name, elapsed_ms))
assert set(directory_contents) == set(files_to_create + ["nested_dir1/"])
# Test directory renaming.
dir_to_rename = "%s/old_dir" % dir_name
new_dir_name = "%s/new_dir" % dir_name
file_io.create_dir(dir_to_rename)
assert file_io.is_directory(dir_to_rename)
assert not file_io.is_directory(new_dir_name)
starttime_ms = int(round(time.time() * 1000))
print("Will try renaming directory %s to %s" % (dir_to_rename, new_dir_name))
file_io.rename(dir_to_rename, new_dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
print("Renamed directory %s to %s in %s milliseconds" % (
dir_to_rename, new_dir_name, elapsed_ms))
assert not file_io.is_directory(dir_to_rename)
assert file_io.is_directory(new_dir_name)
# Test Delete directory recursively.
print("Deleting directory recursively %s." % dir_name)
starttime_ms = int(round(time.time() * 1000))
file_io.delete_recursively(dir_name)
elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
dir_exists = file_io.is_directory(dir_name)
assert not dir_exists
print("Deleted directory recursively %s in %s milliseconds" % (
dir_name, elapsed_ms))
def main(argv=None):
parser = argparse.ArgumentParser(description='ML Trainer')
parser.add_argument('--model_name', help='...', required=True)
parser.add_argument('--model_path', help='...', required=True)
parser.add_argument('--aws_secret', help='...', required=False)
parser.add_argument('--s3-endpoint', help="""...""", required=False)
parser.add_argument(
'--cluster',
type=str,
help='GKE cluster set up for kubeflow. If set, zone must be provided. '
+ 'If not set, assuming this runs in a GKE container and current ' +
'cluster is used.')
parser.add_argument('--zone',
type=str,
help='zone of the kubeflow cluster.')
args = parser.parse_args()
# KUBEFLOW_NAMESPACE = 'default'
KUBEFLOW_NAMESPACE = 'kubeflow'
# Make sure model dir exists before proceeding
retries = 0
sleeptime = 5
while retries < 20:
try:
model_dir = os.path.join(
args.model_path,
file_io.list_directory(args.model_path)[-1])
print("model subdir: %s" % model_dir)
break
except Exception as e:
print(e)
print("Sleeping %s seconds to sync with GCS..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
if retries >= 20:
print("could not get model subdir from %s, exiting" % args.model_path)
exit(1)
logging.getLogger().setLevel(logging.INFO)
logging.info('Running deploy-tf-serve.py')
logging.info('Generating training template.')
if args.aws_secret:
template_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'tf-serve-creds-template.yaml')
else:
template_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'tf-serve-template.yaml')
target_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'tf-serve.yaml')
aws_region = os.environ.get('AWS_REGION', 'us-east-1')
# o = urlparse(args.model_path)
# if o.scheme == 's3':
# client = s3_client(args.s3_endpoint)
# override model s3 location region and endpoint
# so tensorflow could access it without troubles
# aws_region = client.get_bucket_location(
# Bucket=o.netloc
# ).get('LocationConstraint', 'us-east-1')
with open(template_file, 'r') as f:
with open(target_file, "w") as target:
data = f.read()
changed = data.replace('MODEL_NAME', args.model_name)
changed = changed.replace('KUBEFLOW_NAMESPACE', KUBEFLOW_NAMESPACE)
changed = changed.replace('MODEL_PATH', args.model_path)
changed = changed.replace('BUCKET_REGION', aws_region)
if args.aws_secret:
changed = changed.replace('AWS_SECRET_NAME', args.aws_secret)
target.write(changed)
logging.info('deploying model serving.')
subprocess.call(['kubectl', 'create', '-f', '/ml/tf-serve.yaml'])
def read_image_file_names(dir_path):
"""Returns a list of absolute file paths for relative dir input with all relevant file names."""
return [
os.path.join(dir_path, p) for p in file_io.list_directory(dir_path)
]
def run_tfma(slice_spec, eval_model_base_dir, tfma_run_dir, input_csv,
working_dir, mode, project, setup_file, add_metrics_callbacks=None):
"""Does model analysis, using the given spec of how to 'slice', and returns an
EvalResult that can be used with TFMA visualization functions.
"""
print("eval model base dir: %s" % eval_model_base_dir)
# Make sure the model dir exists before proceeding, as sometimes it takes a few seconds to become
# available after training completes.
retries = 0
sleeptime = 5
while retries < 20:
try:
eval_model_dir = os.path.join(
eval_model_base_dir, file_io.list_directory(eval_model_base_dir)[0])
print("eval model dir: %s" % eval_model_dir)
if 'temp' not in eval_model_dir:
break
else:
print("Sleeping %s seconds to sync with GCS..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
except Exception as e:
print(e)
print("Sleeping %s seconds to sync with GCS..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
schema = taxi.read_schema('schema.pbtxt')
temp_dir = os.path.join(working_dir, 'tmp')
if mode == 'local':
print("mode == local")
options = {
'project': project}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
runner = 'DirectRunner'
elif mode == 'cloud':
print("mode == cloud")
options = {
'job_name': 'tfma-' + str(uuid.uuid4()),
'temp_location': temp_dir,
'project': project,
'save_main_session': True,
'setup_file': setup_file
}
pipeline_options = beam.pipeline.PipelineOptions(flags=[], **options)
runner = 'DataFlowRunner'
else:
raise ValueError("Invalid mode %s." % mode)
display_only_data_location = input_csv
with beam.Pipeline(runner, options=pipeline_options) as pipeline:
with beam_impl.Context(temp_dir=temp_dir):
csv_coder = taxi.make_csv_coder(schema)
raw_data = (
pipeline
| 'ReadFromText' >> beam.io.ReadFromText(
input_csv,
# coder=beam.coders.BytesCoder(),
skip_header_lines=1)
| 'ParseCSV' >> beam.Map(csv_coder.decode))
# Examples must be in clean tf-example format.
coder = taxi.make_proto_coder(schema)
raw_data = (
raw_data
# | 'CleanData' >> beam.Map(taxi.clean_raw_data_dict)
| 'ToSerializedTFExample' >> beam.Map(coder.encode))
_ = raw_data | 'EvaluateAndWriteResults' >> tfma.EvaluateAndWriteResults(
eval_saved_model_path=eval_model_dir,
slice_spec=slice_spec,
output_path=tfma_run_dir,
add_metrics_callbacks=add_metrics_callbacks,
display_only_data_location=input_csv)
return tfma.load_eval_result(output_path=tfma_run_dir)
def main():
parser = argparse.ArgumentParser(description='ML Trainer')
parser.add_argument(
'--model_name',
help='...',
required=True)
parser.add_argument(
'--model_path',
help='...',
required=True)
parser.add_argument('--cluster', type=str,
help='GKE cluster set up for kubeflow. If set, zone must be provided. ' +
'If not set, assuming this runs in a GKE container and current ' +
'cluster is used.')
parser.add_argument('--zone', type=str, help='zone of the kubeflow cluster.')
args = parser.parse_args()
KUBEFLOW_NAMESPACE = 'kubeflow'
ts = str(int(time.time()))
# Make sure model dir exists before proceeding
retries = 0
sleeptime = 5
while retries < 20:
try:
model_dir = os.path.join(args.model_path, file_io.list_directory(args.model_path)[-1])
print("model subdir: %s" % model_dir)
break
except Exception as e: #pylint: disable=broad-except
print(e)
print("Sleeping %s seconds to sync with GCS..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
if retries >= 20:
print("could not get model subdir from %s, exiting" % args.model_path)
exit(1)
logging.getLogger().setLevel(logging.INFO)
args_dict = vars(args)
if args.cluster and args.zone:
cluster = args_dict.pop('cluster') #pylint: disable=unused-variable
zone = args_dict.pop('zone') #pylint: disable=unused-variable
else:
# Get cluster name and zone from metadata
metadata_server = "http://metadata/computeMetadata/v1/instance/"
metadata_flavor = {'Metadata-Flavor' : 'Google'}
cluster = requests.get(metadata_server + "attributes/cluster-name",
headers=metadata_flavor).text
zone = requests.get(metadata_server + "zone",
headers=metadata_flavor).text.split('/')[-1]
# logging.info('Getting credentials for GKE cluster %s.' % cluster)
# subprocess.call(['gcloud', 'container', 'clusters', 'get-credentials', cluster,
# '--zone', zone])
logging.info('Generating training template.')
template_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'tf-serve-template.yaml')
target_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'tf-serve.yaml')
with open(template_file, 'r') as f:
with open(target_file, "w") as target:
data = f.read()
changed = data.replace('MODEL_NAME', args.model_name)
changed1 = changed.replace('KUBEFLOW_NAMESPACE', KUBEFLOW_NAMESPACE)
changed2 = changed1.replace('MODEL_PATH', args.model_path)
changed3 = changed2.replace('SERVICE_NAME', args.model_name + ts)
target.write(changed3)
logging.info("template: %s", changed3)
logging.info('deploying model serving.')
subprocess.call(['kubectl', 'create', '-f', '/ml/tf-serve.yaml'])
def main(argv=None):
parser = argparse.ArgumentParser(description='ML Trainer')
parser.add_argument('--project',
help='The GCS project to use',
required=True)
parser.add_argument(
'--gcs-path',
help=
'The GCS path to the trained model. The path should end with "../export/<model-name>".',
required=True)
parser.add_argument('--version-name',
help='The model version name.',
required=True)
parser.add_argument('--model-name',
help='The model name.',
default='taxifare')
parser.add_argument('--region',
help='The model region.',
default='us-central1')
args = parser.parse_args()
# Make sure the model dir exists before proceeding, as sometimes it takes a few seconds to become
# available after training completes.
retries = 0
sleeptime = 5
while retries < 20:
try:
model_location = os.path.join(
args.gcs_path,
file_io.list_directory(args.gcs_path)[-1])
print("model location: %s" % model_location)
break
except Exception as e:
print(e)
print("Sleeping %s seconds to wait for GCS files..." % sleeptime)
time.sleep(sleeptime)
retries += 1
sleeptime *= 2
if retries >= 20:
print("could not get model location subdir from %s, exiting" %
args.gcs_path)
exit(1)
model_create_command = [
'gcloud', 'ml-engine', 'models', 'create', args.model_name,
'--regions', args.region, '--project', args.project
]
print(model_create_command)
result = subprocess.call(model_create_command)
print(result)
proper_version_name = args.version_name.replace('-', '_')
print("using version name: %s" % proper_version_name)
model_deploy_command = [
'gcloud', 'ml-engine', 'versions', 'create', proper_version_name,
'--model', args.model_name, '--runtime-version', '1.6', '--project',
args.project, '--origin', model_location
]
print(model_deploy_command)
result2 = subprocess.call(model_deploy_command)
print(result2)
type=int)
parser.add_argument(
'--eval-steps',
help='Number of steps to run evalution for at each checkpoint',
default=100,
type=int)
args = parser.parse_args()
# Set python level verbosity
tf.logging.set_verbosity(args.verbosity)
# Set C++ Graph Execution level verbosity
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(
tf.logging.__dict__[args.verbosity] / 10)
train_files = []
tflist = file_io.list_directory(args.train_files_dir)
for x in tflist:
if args.train_files_prefix in x:
train_files.append(os.path.join(args.train_files_dir, x))
print("train files list: %s" % train_files)
eval_files = []
eflist = file_io.list_directory(args.eval_files_dir)
for x in eflist:
if args.eval_files_prefix in x:
eval_files.append(os.path.join(args.eval_files_dir, x))
print("eval files list: %s" % eval_files)
# Run the training job
hparams = tf.contrib.training.HParams(**args.__dict__)
run_experiment(train_files, eval_files, hparams)