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I'd appreciate any help on the below, thank you in advance. I made a copy of Google Bert's notebook on fine-tuning and trained the SQUAD dataset on it using Cloud TPU and Bucket. The predictions on the dev set are ok, so I downloaded the checkpoint, model.ckpt.meta, model.ckpt.index and model.ckpt.data files locally and tried to restore using code:

sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))

saver = tf.train.import_meta_graph(META_FILE) # META_FILE being path to .meta

saver.restore(sess, 'model.ckpt')

However, I got the error:

    op_def = op_dict[node.op]

KeyError: 'InfeedEnqueueTuple'

I assume it is part of Cloud TPU Tools and I should continue on Cloud TPU, so I tried the below (reference):

# code from cells before includes

...

tf.contrib.cloud.configure_gcs(session, credentials=auth_info)

...

tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(TPU_ADDRESS)

run_config = tf.contrib.tpu.RunConfig(

    cluster=tpu_cluster_resolver,

    model_dir=OUTPUT_DIR,

    save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS,

    tpu_config=tf.contrib.tpu.TPUConfig(

        iterations_per_loop=ITERATIONS_PER_LOOP,

        num_shards=NUM_TPU_CORES,

        per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2))

...

Problem cell:

"""

# not valid checkpoint error. <bucket> placeholder for cloud bucket name

sess = tf.Session()

META_FILE = "gs://<bucket>/bert/models/bertsquad/model.ckpt-10949.meta"

CKPT_FILE = "gs://<bucket>/bert/models/bertsquad/model.ckpt"

saver = tf.train.import_meta_graph(META_FILE)

saver.restore(sess, CKPT_FILE)

"""



from google.cloud import storage

from tensorflow import MetaGraphDef



client = storage.Client(project="agent-helper-4a014")

bucket = client.get_bucket(<bucket>)

metafile = "bert/models/bertsquad/model.ckpt-10949.meta"

# using full path gs://<bucket>/bert/models/bertsquad doesn't work



blob = bucket.get_blob(metafile)

#blob = bucket.blob(metafile)

#model_graph = blob.download_to_filename("model.ckpt")

model_graph = blob.download_as_string()



mgd = MetaGraphDef()

mgd.ParseFromString(model_graph)



with tf.Session() as sess:

    saver = tf.train.import_meta_graph(mgd, clear_devices=True)

    init_checkpoint = saver.restore(sess, 'model.ckpt')

That in turn gave the following error:

InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:



No OpKernel was registered to support Op 'InfeedEnqueueTuple' with these attrs.  Registered devices: [CPU,XLA_CPU], Registered kernels:

  <no registered kernels>



     [[node input_pipeline_task0/while/InfeedQueue/enqueue/0 (defined at <ipython-input-67-e4b52b7b5944>:21)  = InfeedEnqueueTuple[_class=["loc:@input_pipeline_task0/while/IteratorGetNext"], device_ordinal=0, dtypes=[DT_INT32, DT_INT32, DT_INT32, DT_INT32, DT_INT32, DT_INT32], shapes=[[2], [2,384], [2,384], [2,384], [2], [2]], _device="/job:worker/task:0/device:CPU:0"](input_pipeline_task0/while/IteratorGetNext, input_pipeline_task0/while/IteratorGetNext:1, input_pipeline_task0/while/IteratorGetNext:2, input_pipeline_task0/while/IteratorGetNext:3, input_pipeline_task0/while/IteratorGetNext:4, input_pipeline_task0/while/IteratorGetNext:5)]]

If your motive is prediction, then just give the model_dir location(must be a GCS bucket) where the checkpoints and meta file are saved. The code will not go for training again (as the checkpoint is saved for the number of training steps and there is no change in the model graph). It will directly jump to the prediction.

But if your use case really want to save the checkpoints, and restore it only for inference then follow the steps:

• Create the model network for each and every layer manually as that of original model or use saved .meta file to recreate the network using tf.train.import() function like this:

saver = tf.train.import_meta_graph('<filename>.meta')

• Now, restore the checkpoints using: saver.restore(sess, 'model.ckpt')
  

NOTE: Model graph into which checkpoints are being restored, should be exactly the same of that original graph for which those checkpoints are saved.

Hope this solves your issue.