本文介绍了使用TPU模式时，如何从Google Colaboratory保存Tensorflow Checkpoint文件?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
当我使用saver = tf.train.Saver()和save_path = saver.save(session, "checkpointsFolder/checkpoint.ckpt")
我收到UnimplementedError (see above for traceback): File system scheme '[local]' not implemented错误
这是完整的错误
---------------------------------------------------------------------------
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)]]
查找此错误，发现以下内容:
Looking up this error, I found the following:
摘自Google官方TPU调试指南
From Google official TPU debugging guide
https://cloud.google.com/tpu/docs/troubleshooting
InvalidArgumentError:未实现:文件系统方案'[local]'不是 已实施
InvalidArgumentError: Unimplemented: File system scheme '[local]' not implemented
详细信息
所有输入文件和模型目录必须使用云存储 存储桶路径(gs://bucket-name/...)，并且此存储桶必须可访问 从TPU服务器.注意所有数据处理和模型 检查点是在TPU服务器上执行的，而不是在本地计算机上执行的. 有关如何正确配置云存储以供使用的信息 有关TPU的信息，请参阅《连接到云存储桶》指南.
All input files and the model directory must use a cloud storage bucket path (gs://bucket-name/...), and this bucket must be accessible from the TPU server. Note that all data processing and model checkpointing is performed on the TPU server, not the local machine. For information on how to properly configure cloud storage for use with the TPU, see the guide Connecting to Cloud Storage Buckets.
遇到类似问题的其他人
更多详细信息在这里
https://cloud.google.com/tpu/docs/storage-buckets
但是，我没有Google Cloud服务，我只是在使用Google Colab.在TPU模式下，是否可以保存Tensorflow检查点?
However, I do not have a Google Cloud service, I am just using Google Colab. Is there a way to save a Tensorflow checkpoint when in TPU mode?
推荐答案
另一种方法是使用Keras重写模型，并使用tf.contrib.tpu.keras_to_tpu_model(..)和tf.contrib.tpu.TPUDistributionStrategy (...).这是为此的小代码段:
Another way to do this is to rewrite the model using Keras and use tf.contrib.tpu.keras_to_tpu_model(..) with tf.contrib.tpu.TPUDistributionStrategy(...). Here is small code snippet for this:
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)
这篇关于使用TPU模式时，如何从Google Colaboratory保存Tensorflow Checkpoint文件?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！