Category: Misc

I was trying to make a prediction from a loaded tensorflow
model. Though I’m not sure if it’s correct how I previously saved
it, specifically I have doubts about code inside serving_input_fn()
function (MAX_SEQ_LENGTH=128):

def serving_input_fn(): feature_spec = { "input_ids" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64), "input_mask" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64), "segment_ids" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64), "label_ids" : tf.FixedLenFeature([], tf.int64) } serialized_tf_example = tf.placeholder(dtype=tf.string,shape=[None],name='input_example_tensor') receiver_tensors = {'example': serialized_tf_example} features = tf.parse_example(serialized_tf_example, feature_spec) return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) estimator.export_saved_model('gs://bucket/trained_model, serving_input_receiver_fn=serving_input_fn) 

When I try to predict from loaded model:

from tensorflow.contrib import predictor predict_fn = predictor.from_saved_model(LOAD_PATH) input_features_test = convert_examples_to_features( test_examples,label_list, MAX_SEQ_LENGTH, tokenizer) predictions = predict_fn({'example':input_features_test[0]}) 

it returns this error:

ValueError: Cannot feed value of shape () for Tensor
‘input_example_tensor:0’, which has shape ‘(?,)’

How should I change serving_input_fn() method?

If you want to reproduce it: github_repo (you
should download variables from
here and put it in trained_model/1608370941/ folder)

This is the tutorial I followed to fine tune BERT model on
google cloud TPU.

I’m using Colab environment to fine-tune a BERT Model (for
reference this is the
Notebook_with_Cloud_TPU_Sentence_Classification_Tasks.ipynb)).
How can I export fine tuned model (it’s a TPUEstimator object) to
HDF5 format? I need to use the trained model locally on CPU.

I came across this issue in my own projects, and found the
issue
linked here on the TensorFlow github, but I feel like it isn’t
getting much traction for the potential severity of the
problem.

Basically there was a non-release push to TF between 1.14 and
1.15 that broke some functionality for the
tf.image.per_image_standarization routine when used on unsigned
integer inputs. The majority of information content in images ends
up getting lost because of the naïve type conversions done in
per_image_standardization after 1.14. This isn’t addressed in
documentation, and is pretty clearly a major change in behavior
befitting a major release, but was introduced before a major
release, likely pointing to an untested edge case.

I’m concerned that the issue isn’t getting much traction but
could potentially impact labs all over the place. The simple
solution is to convert your unsigned int images to float before
calling per_image_standardization, but that isn’t obvious from any
of the documentation, and used to be handled naturally by the
method.

Thoughts?

Edit: formatting.

i’ve geforce 840m. it is cuda 5.0.My project has dependencies as
tensorflow ,opencv ,cuda 7.5+ and cudnn 5.0+.(https://github.com/dvschultz/neural-style-tf)

i keep getting this error

“W tensorflow/stream_executor/platform/default/dso_loader.cc:59]
Could not load dynamic library ‘cudart64_101.dll’; dlerror:
cudart64_101.dll not found”

tensorflow doesnt see my gpu.

1-is it because i’ve higher cuda version than my gpu?

2-is it because my tensorflow version 2.3.1 ?

thx.

I posted
this as an issue on Github, maybe someone here will have a
magic solution:

• TensorFlow version: 2.4.0-rc4 (also tried with stable
  2.4.0)
• TensorFlow Git version: v2.4.0-rc3-20-g97c3fef64ba
• Python version: 3.8.5
• CUDA/cuDNN version: CUDA 11.0, cuDNN 8.0.4
• GPU model and memory: Nvidia RTX 3090, 24GB RAM

Model training regularly freezes for large models.

Sometimes the first batch or so works, but then just a few
batches later and training seems stuck in a loop. From my activity
monitor, I see GPU CUDA use hovering around 100%. This goes on for
minutes or more, with no more batches being trained.

I don’t see an OOM error, nor does it seem like I’m hitting
memory limits in activity monitor or nvidia-smi.

I would expect the first batch to take a bit longer, then any
subsequent batches to take less than <1s. Never have a random
batch take minutes or stall forever.

Run through all the cells in the notebook shared below to
initialize the model, then run the final cell just a few times.
Eventually it will hang and never finish.

https://github.com/not-Ian/tensorflow-bug-example/blob/main/tensorflow%20error%20example.ipynb

Smaller models train quickly as expected, however I think even
then they eventually stall out after training many, many batches. I
had another similar, small VAE like in my example that trained for
5k-10k batches overnight before stalling.

Someone suggested I set a hard memory limit on the GPU like
this:

gpus = tf.config.experimental.list_physical_devices('GPU') tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024 * 23)]) 

And finally, I’ve tried using the hacky ptxas.exe file from CUDA
11.1 in my CUDA 11.0 installation. This seems to remove a warning?
But still no change.

Open to any other ideas, thanks.