Hub: How to use TF Hub on a distributed setting?

[cgarciae]

I want to use the ResNet-101-v2 feature vectors to do some transfer learning. I am training with the Estimator API on GCP, I call the hub Module at the beggining of the model_fn.

module_url = "https://tfhub.dev/google/imagenet/resnet_v2_101/feature_vector/1"
module = hub.Module(module_url)
height, width = hub.get_expected_image_size(module)
images = tf.image.resize_images(input_tensor, [height, width])
feature_vectors = module(images)

When I run in a single node ("basic-gpu") all is well, however, when I run the same code in distributed mode ("standard-1") I get this error:

The replica master 0 exited with a non-zero status of 1. Termination reason: Error. Traceback (most recent call last): [...] File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/checkpoint_utils.py", line 337, in _set_variable_or_list_initializer _set_checkpoint_initializer(variable_or_list, ckpt_file, tensor_name, "") File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/checkpoint_utils.py", line 299, in _set_checkpoint_initializer ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1458, in restore_v2 shape_and_slices=shape_and_slices, dtypes=dtypes, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3290, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1654, in __init__ self._traceback = self._graph._extract_stack() # pylint: disable=protected-access InvalidArgumentError (see above for traceback): Unsuccessful TensorSliceReader constructor: Failed to get matching files on /tmp/tfhub_modules/e0c607f95a3d67bc8928a5c20d09d1915322cfcb/variables/variables: Not found: /tmp/tfhub_modules/e0c607f95a3d67bc8928a5c20d09d1915322cfcb/variables; No such file or directory [[Node: checkpoint_initializer_537 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:ps/replica:0/task:1/device:CPU:0"](checkpoint_initializer_537/prefix, checkpoint_initializer_537/tensor_names, checkpoint_initializer_537/shape_and_slices)]] [[Node: init/NoOp_3_S22 = _Recv[client_terminated=false, recv_device="/job:master/replica:0/task:0/device:CPU:0", send_device="/job:ps/replica:0/task:1/device:CPU:0", send_device_incarnation=-7983147897712139617, tensor_name="edge_3296_init/NoOp_3", tensor_type=DT_FLOAT, _device="/job:master/replica:0/task:0/device:CPU:0"]()]] To find out more about why your job exited please check the logs: ....

How should I structure my code for TF Hub to work with the Estimator API for distributed training?

[svsgoogle]

The issue seems to be that the module is cached in a directory that is not accessible to all the machines that initialize variables.

Can you try to specify a location for module caching that all the jobs can read from? You can look at https://www.tensorflow.org/hub/basics section "Caching Modules" for instructions.

[cgarciae]

@svsgoogle thanks! Would a GS bucket be a valid location?

[svsgoogle]

It should be :)

[bhack]

It should not :) https://github.com/tensorflow/hub/issues/50

[yu-iskw]

I have the same issue with Google ML Engine. In my opinion, the issue is caused by tensorflow-hub it self with a distributed training, not Google ML Engine.

I have made a repository to reproduce the issue.
https://github.com/yu-iskw/tensorflow-hub-with-ml-engine

As well as, we are discussing it on the google issue tracker.
https://issuetracker.google.com/issues/78898344

[cgarciae]

For those interested, the issue can be solved by saving the module into GCP, you can do it with the following steps:

On some local machine

1. [optional] clear all modules: rm -fr /tmp/tfhub_modules
2. Run some code that downloads the module: hub.Module(...)
3. Upload the module to some gcp bucket:

gsutil -m cp -R /tmp/tfhub_modules/{module_hash} gs://bucket/some/path/to/module

Now on your code you can use pass the location of the bucket to TF Hub

hub.Module("gs://bucket/some/path/to/module")

[andresusanopinto]

This was fixed in https://github.com/tensorflow/hub/commit/68606210c33fa2c473963281a073be3277968191, but users will have to wait for a new pypi release + picked up by cloud.

[bhack]

I suppose.. is it working also on s3?

[akhorlin]

It should though I haven't explicitly tested on S3, so please let us know
if you see any issues.

On Mon, Jun 18, 2018 at 6:11 PM bhack [e-mail] wrote:

I suppose.. is it working also on s3?

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[bhack]

Probably is it a little bit incorrect https://github.com/tensorflow/hub/commit/68606210c33fa2c473963281a073be3277968191#diff-781a53e648f3df8d16a08ec083b04bf4R18? Cause we are using tensorflow file:// as I see in https://github.com/tensorflow/hub/commit/68606210c33fa2c473963281a073be3277968191#diff-cd783e402e7064fee42578c5b35d1c3c

[akhorlin]

What error are you observing with the fix in question? The test uses
file:// because we cannot use GCS or S3 in a unit test, but in offline
testing, we did test gs://. The breakage that was address by the fix
applies to all custom filesystem in TF (file://, gs://. etc).

On Mon, Jun 18, 2018 at 6:23 PM bhack [e-mail] wrote:

Probably is a little bit incorrect 6860621

diff-781a53e648f3df8d16a08ec083b04bf4R18

https://github.com/tensorflow/hub/commit/68606210c33fa2c473963281a073be3277968191#diff-781a53e648f3df8d16a08ec083b04bf4R18?
Cause we are using tensorflow file:// as I see in 6860621

diff-cd783e402e7064fee42578c5b35d1c3c

https://github.com/tensorflow/hub/commit/68606210c33fa2c473963281a073be3277968191#diff-cd783e402e7064fee42578c5b35d1c3c

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[bhack]

Is that it claims gcs in the chabgelog but file:// is not gcs only.

[akhorlin]

Yep, we will be more clear with the cl description next time ))

On Mon, Jun 18, 2018 at 7:38 PM bhack [e-mail] wrote:

Is that it claims gcs in the chabgelog but file:// is not gcs only.

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[budzianowski]

For those interested, the issue can be solved by saving the module into GCP, you can do it with the following steps:

On some local machine

1. [optional] clear all modules: `rm -fr /tmp/tfhub_modules`

2. Run some code that downloads the module: `hub.Module(...)`

3. Upload the module to some gcp bucket:

gsutil -m cp -R /tmp/tfhub_modules/{module_hash} gs://bucket/some/path/to/module

Now on your code you can use pass the location of the bucket to TF Hub

hub.Module("gs://bucket/some/path/to/module")

This approach does not work for me? Did you manage to make it run? I have version 0.1.1

[akhorlin]

What error are you seeing?

On Wed, Nov 14, 2018, 19:21 Paweł Budzianowski [e-mail]
wrote:

For those interested, the issue can be solved by saving the module into
GCP, you can do it with the following steps:

On some local machine

1. [optional] clear all modules: rm -fr /tmp/tfhub_modules

2. Run some code that downloads the module: hub.Module(...)

3. Upload the module to some gcp bucket:

gsutil -m cp -R /tmp/tfhub_modules/{module_hash} gs://bucket/some/path/to/module

Now on your code you can use pass the location of the bucket to TF Hub

hub.Module("gs://bucket/some/path/to/module")

This approach does not work for me? Did you manage to make it run?

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[budzianowski]

I'm trying do the caching through gs but model gets stuck at loading the module as in #50.

[akhorlin]

One option for further debugging is to try to run a small test program that
reads/write to the GCS bucket in question using tf.gfile
https://www.tensorflow.org/api_docs/python/tf/gfile/GFile. This will make
sure that the bucket and the local machine are set up correctly.

On Thu, Nov 15, 2018 at 1:01 AM Paweł Budzianowski [e-mail]
wrote:

I'm trying do the caching through gs but model gets stuck at loading the
module as in #50 https://github.com/tensorflow/hub/issues/50.

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[edumotya]

For those interested, the issue can be solved by saving the module into GCP, you can do it with the following steps:

On some local machine

1. [optional] clear all modules: rm -fr /tmp/tfhub_modules
2. Run some code that downloads the module: hub.Module(...)
3. Upload the module to some gcp bucket:

gsutil -m cp -R /tmp/tfhub_modules/{module_hash} gs://bucket/some/path/to/module

Now on your code you can use pass the location of the bucket to TF Hub

hub.Module("gs://bucket/some/path/to/module")

This approach does not work for me neither. I am seeing the following error:

File "/root/.local/lib/python3.5/site-packages/tensorflow_hub/module.py", line 58, in load_module_spec return registry.loader(path) File "/root/.local/lib/python3.5/site-packages/tensorflow_hub/registry.py", line 45, in __call__ self._name, args, kwargs)) RuntimeError: Missing implementation that supports: loader(*('gs://xxx/07Nasnet',), **{})

-------------------- EDITED

It actually works, I just added the inner module hash to the gs path. I mean this:

hub.Module("gs://bucket/some/path/to/module/")

hub.Module("gs://bucket/some/path/to/module/{module_hash}")

[rmothukuru]

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!