Как сохранить файл контрольной точки Tensorflow из Google Colab Laboratory в режиме TPU?
Когда я использую saver = tf.train.Saver() а также save_path = saver.save(session, "checkpointsFolder/checkpoint.ckpt")
Я получаю UnimplementedError (see above for traceback): File system scheme '[local]' not implemented ошибка
Вот полная ошибка
---------------------------------------------------------------------------
UnimplementedError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1333 try:
-> 1334 return fn(*args)
1335 except errors.OpError as e:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1318 return self._call_tf_sessionrun(
-> 1319 options, feed_dict, fetch_list, target_list, run_metadata)
1320
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1406 self._session, options, feed_dict, fetch_list, target_list,
-> 1407 run_metadata)
1408
UnimplementedError: File system scheme '[local]' not implemented (file: 'checkpointsBook2Vec5Inputs')
[[{{node save/SaveV2}} = SaveV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:tpu_worker/replica:0/task:0/device:CPU:0"](_recv_save/Const_0, save/SaveV2/tensor_names, save/SaveV2/shape_and_slices, embeddings, embeddings/Shampoo, embeddings/Shampoo_1, embeddings/Shampoo_2, epochCount, softmax_biases, softmax_weights, softmax_weights/Shampoo, softmax_weights/Shampoo_1, softmax_weights/Shampoo_2)]]
During handling of the above exception, another exception occurred:
UnimplementedError Traceback (most recent call last)
<ipython-input-22-ca87cd5e5739> in <module>()
48 print('recEpoch_indexA is', recEpoch_indexA)
49
---> 50 save_path = saver.save(session, "checkpointsBook2Vec5Inputs/Research2VecCS4.ckpt") #Save checkpoint
51 print( 'epochCount.eval() is ', epochCount.eval() )
52
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py in save(self, sess, save_path, global_step, latest_filename, meta_graph_suffix, write_meta_graph, write_state, strip_default_attrs)
1439 model_checkpoint_path = sess.run(
1440 self.saver_def.save_tensor_name,
-> 1441 {self.saver_def.filename_tensor_name: checkpoint_file})
1442
1443 model_checkpoint_path = compat.as_str(model_checkpoint_path)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1150 if final_fetches or final_targets or (handle and feed_dict_tensor):
1151 results = self._do_run(handle, final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata)
1153 else:
1154 results = []
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1326 if handle is None:
1327 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata)
1329 else:
1330 return self._do_call(_prun_fn, handle, feeds, fetches)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1346 pass
1347 message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message)
1349
1350 def _extend_graph(self):
UnimplementedError: File system scheme '[local]' not implemented (file: 'checkpointsBook2Vec5Inputs')
[[node save/SaveV2 (defined at <ipython-input-15-c14caac2081d>:45) = SaveV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:tpu_worker/replica:0/task:0/device:CPU:0"](_recv_save/Const_0, save/SaveV2/tensor_names, save/SaveV2/shape_and_slices, embeddings, embeddings/Shampoo, embeddings/Shampoo_1, embeddings/Shampoo_2, epochCount, softmax_biases, softmax_weights, softmax_weights/Shampoo, softmax_weights/Shampoo_1, softmax_weights/Shampoo_2)]]
Caused by op 'save/SaveV2', defined at:
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.6/dist-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/usr/local/lib/python3.6/dist-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.6/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.6/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-15-c14caac2081d>", line 45, in <module>
saver = tf.train.Saver()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1102, in __init__
self.build()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1114, in build
self._build(self._filename, build_save=True, build_restore=True)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1151, in _build
build_save=build_save, build_restore=build_restore)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 792, in _build_internal
save_tensor = self._AddSaveOps(filename_tensor, saveables)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 284, in _AddSaveOps
save = self.save_op(filename_tensor, saveables)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 202, in save_op
tensors)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1690, in save_v2
shape_and_slices=shape_and_slices, tensors=tensors, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
self._traceback = tf_stack.extract_stack()
UnimplementedError (see above for traceback): File system scheme '[local]' not implemented (file: 'checkpointsBook2Vec5Inputs')
[[node save/SaveV2 (defined at <ipython-input-15-c14caac2081d>:45) = SaveV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:tpu_worker/replica:0/task:0/device:CPU:0"](_recv_save/Const_0, save/SaveV2/tensor_names, save/SaveV2/shape_and_slices, embeddings, embeddings/Shampoo, embeddings/Shampoo_1, embeddings/Shampoo_2, epochCount, softmax_biases, softmax_weights, softmax_weights/Shampoo, softmax_weights/Shampoo_1, softmax_weights/Shampoo_2)]]
Посмотрев эту ошибку, я обнаружил следующее:
Из Google официального руководства по отладке TPU
https://cloud.google.com/tpu/docs/troubleshooting
Сообщение об ошибке
InvalidArgumentError: Не реализовано: схема файловой системы '[local]' не реализована
подробности
Все входные файлы и каталог модели должны использовать путь сегмента облачного хранилища (gs://bucket-name/...), и этот сегмент должен быть доступен с сервера TPU. Обратите внимание, что вся обработка данных и контрольные точки модели выполняются на сервере TPU, а не на локальном компьютере. Информацию о том, как правильно настроить облачное хранилище для использования с TPU, см. В руководстве "Подключение к облачным хранилищам".
Кто-то еще с похожей проблемой
Локальная файловая система ТПУ не существует?
Локальная файловая система недоступна в облачных TPU. Каталоги моделей (контрольные точки и т. Д.) И входные данные должны храниться в облачном хранилище Google (с префиксом "gs://").
Подробнее здесь
Однако у меня нет службы Google Cloud, я просто использую Google Colab. Есть ли способ сохранить контрольную точку Tensorflow в режиме TPU?
2 ответа
Решение
Другой способ сделать это - переписать модель с помощью Keras и использовать tf.contrib.tpu.keras_to_tpu_model(..) с tf.contrib.tpu.TPUDistributionStrategy(...). Вот небольшой фрагмент кода для этого:
def get_model():
return keras.Sequential([
keras.layers.Dense(10, input_shape=(4,), activation=tf.nn.relu, name = "Dense_1"),
keras.layers.Dense(10, activation=tf.nn.relu, name = "Dense_2"),
keras.layers.Dense(3, activation=None, name = "logits"),
keras.layers.Dense(3, activation=tf.nn.softmax, name = "softmax")
])
dnn_model = get_model()
dnn_model.compile(optimizer=tf.train.AdagradOptimizer(learning_rate=0.1),
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_crossentropy'])
tpu_model = tf.contrib.tpu.keras_to_tpu_model(
dnn_model,
strategy=tf.contrib.tpu.TPUDistributionStrategy(
tf.contrib.cluster_resolver.TPUClusterResolver(TPU_ADDRESS)))
# Train the model
tpu_model.fit(
train_x, train_y,
steps_per_epoch = steps_per_epoch,
epochs=epochs,
)
tpu_model.save_weights('./saved_weights.h5', overwrite=True)
Вы можете создать учетную запись Google Cloud в рамках бесплатного уровня, а затем создать корзину GCS. После этого вы можете аутентифицировать себя в Colab, чтобы получить доступ на запись в ваше ведро GCS из Colab, выполнив следующие действия:
from google.colab import auth
auth.authenticate_user()
Вот пример ноутбука Colab, который использует облачные TPU и GCS.