I am learning transfer learning according to How to Retrain Inception's Final Layer for New Categories however, when I build 'retrain.py' using bazel, the following error ocures:
The error message is:
python configuration error:'PYTHON_BIN_PATH' environment variable is not set and referenced by '//third_party/py/numpy:headers'
I am so sorry, I have done my best to display the error image.unfortunately, I failed.
I use python2.7, anaconda2 and bazel0.6.1, tensorflow1.3.
appreciate for your any reply !
Related
Recently, I started one of the tutorial for GANs created by TensorFlow.
See link: https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter4-gan/dcgan-mnist-4.2.1.py
But in the initializer (i.e. if name == 'main') I am having to issues to run it, since I run it
from Colab and is a .py program. So that, it says that error:
usage: ipykernel_launcher.py [-h] [-g GENERATOR]
ipykernel_launcher.py: error: unrecognized arguments: -f
/root/.local/share/jupyter/runtime/kernel-bcd12960-b051-4b8d-b6b0-3ff02367dbcc.json
An exception has occurred, use %tb to see the full traceback.
Would anyone know how to debug it for .ipynb files?
Thank you in advance
I created a model through AI Platform UI that uses a global endpoint. I am trying to deploy a basic tensorflow 1.15.0 model I exported using the Saved Model builder. When I try to deploy this model I get a Create Version Failed. Bad model detected with error: "Error loading the model" error in the UI and the I see the following in the logs:
ERROR:root:Failed to import GA GRPC module. This is OK if the runtime version is 1.x
Failure: Could not reach metadata service: Internal Server Error.
ERROR:root:Command '['/tools/google-cloud-sdk/bin/gsutil', '-o', 'GoogleCompute:service_account=default', 'cp', '-R', 'gs://cml-365057443918-1608667078774578/models/xsqr_global/v6/7349456410861999293/model/*', '/tmp/model/0001']' returned non-zero exit status 1.
ERROR:root:Error loading model: 'generator' object has no attribute 'next'
ERROR:root:Error loading the model
Framework/ML runtime version: Tensorflow 1.15.0
Python: 3.7.3
What is strange is that the gcloud ai-platform local predict works correctly with this exported model, and I can deploy this exact same model on a regional endpoint with no issues. It only gives this error if I try to use a global endpoint model. But I need the global endpoint because I plan on using a custom prediction routine (if I can get this basic model working first).
The logs seem to suggest an issue with copying the model from storage? I've tried giving various IAM roles additional viewer permissions, but I still get the same errors.
Thanks for the help.
I think it's the same issue as https://issuetracker.google.com/issues/175316320
The comment in the issue says the fix is now rolling out.
Today I faced the same error (ERROR: (gcloud.ai-platform.versions.create) Create Version failed. Bad model detected with error: "Error loading the model") & for those who wants a summary:
The recommendation is to use n1* machine types (for example: n1-standard-4) via regional endpoints (for example: us-central1) instead of mls1* machines while deploying version. Also I made sure to mention the same region (us-central1) while creating the model itself using the below command, thereby resolving the above mentioned error.
!gcloud ai-platform models create $model_name
--region=$REGION
I am trying to run the tensorflow DeepLab tutorial on the cityscapes dataset. I downloaded the gtFine dataset and cloned into the cityscapesScripts folder, and set up the directories as recommended in the tutorial. When I ran the following line from the tutorial,
sh convert_cityscapes.sh,
I received an error message stating "Failed to find all Cityscapes modules".
I checked the cityscapesScripts documentation and I think I am missing the labels module, which is likely causing the error. Where can I clone or download the missing module(s)?
In the dependencies for sh convert_cityscapes.sh, there's a file with invalid syntax.
You can get it to work on Python3 by commenting out the line
print type(obj).name
from cityscapeScripts/helpers/annotation.py line 238
I’m trying to deploy a model trained using Tensorflow 1.7 onto Google Cloud Platform. I get the following error:
Create Version failed. Bad model detected with error: "Failed to load model: Loading servable: {name: default version: 1} failed: Not found: Op type not registered 'SparseFillEmptyRows'\n\n (Error code: 0)"
I know Cloudml runtime prediction only supports Tensorflow 1.6 so I tried specifying:
REQUIRED_PACKAGES = [
'tensorflow==1.6',
]
in setup.py but I still get the same message
Any help gratefully appreciated
You need to rebuild your model using TensorFlow 1.6. You can't deploy a model created with TensorFlow 1.7 to the ML Engine.
Also, you can set the version of the engine's runtime to one of the versions listed here. If you're using gcloud ml-engine jobs submit training, you can set the version with the --runtime-version flag. The documentation is here.
Rebuilding with 1.6 and deploying with --runtime-version=1.6 worked.
I installed the object detection API correctly using this https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md and I checked by running model_builder_test.py
This gave me a OK result.
Then I moved on to running the train.py on my dataset using the following command
python train.py --logtostderr --pipeline_config_path=pipeline.config --train_dir=train_file
And I am getting the error ImportError: cannot import name 'preprocessor_pb2'
This particular preprocessor_pb2.py exists in the path it is looking for i.e
C:\Users\SP-TestMc\Anaconda3\envs\tensorflow\models-master\models-master\research\object_detection\protos
What could be the reason for this error then?
See https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md#protobuf-compilation. Sounds like you need to compile the protos before using the Python scripts.