I am trying to build Tensorflow Serving on Ubuntu. I have compiled Tensorflow and Tensorflow serving from source without error, but when I run:
bazel test -c opt tensorflow_serving/...
I get the following error:
//tensorflow_serving/model_servers:tensorflow_model_server_test
TIMEOUT in 315.1s
All of the other tests pass.
When I look in the log file, I only see the following:
exec ${PAGER:-/usr/bin/less} "$0" || exit 1
I don't know what the error is, or why the model server test is failing.
It could simply be that the test is running slow in your environment and would pass if configured with a more generous timeout. Try editing the BUILD file to set the test size to enormous and see if it passes then.
This was an issue in TensorFlow Serving. Please refer to this Github issue for more information.
Related
I was trying to run a command to test local predict in my computer. However, the command failed every time with this error.
ERROR: gcloud crashed (PermissionError): [WinError 5] Access is denied
This is the command:
gcloud ai-platform local predict --model-dir model_final --json-instances image_b64.json --framework tensorflow
I am positive 101% positive that I have followed everything in the doc by Google.
First, the command required a model file to be saved in TensorFlow SavedModel format, which, since I use Keras, I can just do model.save("model_final").
If you have used Keras for training, use tf.keras.Model.save to export a SavedModel
So I did, at it only output a single file, so I can only assume it's the file to be placed in the --model-dir parameter. I admit doing model.save("model_final") created a file, not a dir, which is a bit weird but the document for Keras just said use that so there is no way I could be wrong.
And also:
If you export your SavedModel using tf.keras.Model.save, then you do not need to specify a serving input function.
If you export a SavedModel from tf.keras or from a TensorFlow estimator, the exported graph is ready for serving by default.
The "image_b64.json" file follows this format:
{"image_bytes":{"b64": base64_jpeg_data )}}
So after 3 hours and having followed everything required by Google, and somehow the gloud still throws me that error. And, yes, of course I have run the command line under Administrator Mode. I also tried it in two of my computers, and I got the same error. I am using Windows, Tensorflow 1.15.
Can anyone point out what is the problem with my implementation, or Google Doc/Keras is just lack lustering. Thank you.
I installed Uiniversal Sentence Encoder (Tensorflow 2) in 2 virtual environment with Ananconda. One is on Mac, anther is on Ubuntu.
All worked with following:
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
Installed with:
conda create -n my-tf2-env python=3.6 tensorflow
conda init bash
conda activate my-tf2-env
conda install -c conda-forge tensorflow-hub
But, for unknown reason after 3 weeks, Mac does not work with following error which fails at:
model = hub.load(module_url)
Error: SavedModel file does not exist at: /var/folders/99/8rwn_9hx3jj9x3qz6yf0j2f00000gp/T/tfhub_modules/063d866c06683311b44b4992fd46003be952409c/{saved_model.pbtxt|saved_model.pb}
On Mac, I recreated new env with same procedure but has same error.
On Ubuntu, all works well.
I want to know how to fix Mac. Thank you for help.
What I attempted on Mac is that I tried to download "https://tfhub.dev/google/universal-sentence-encoder/4" to local drive and load it from local drive in future, not from web url. This process was not finished and not successful yet. I don't remember if there is anything downloaded to Mac with this attempt, that might corrupted Tensorflow-hub on login user account of my Mac.
This error usually occurs when the saved_model.pb is not present in the path specified in the module_url.
For example, if we consider the Folder structure as shown in the screenshot below,
The code,
import tensorflow_hub as hub
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
and
import tensorflow_hub as hub
module_url = "/home/mothukuru/Downloads/Hub"
model = hub.load(module_url)
work successfully.
But if saved_model.pb is not present in that Folder as shown below,
Executing the code,
import tensorflow_hub as hub
module_url = "/home/mothukuru/Downloads/Hub"
model = hub.load(module_url)
results in the below error,
OSError: SavedModel file does not exist at: /home/mothukuru/Downloads/Hub/{saved_model.pbtxt|saved_model.pb}
In your specific case, executing the code while the Download of the Model was in progress might have resulted in the error.
As stated in the comment, deleting the Downloaded File can fix the problem.
Please let me know if this answer has not resolved your issue and I will be happy to modify it accordingly.
TF Published some additional guidelines on caching models apparently in response to questions about this issue.
In my case, I was running this locally on Mac via a jupyter notebook.
I was not sure how to "Delete the download file" as suggest in the other answer, but I found this resolved my issue:
https://www.tensorflow.org/hub/caching#reading_from_remote_storage
Reading from remote storage
Users can instruct the tensorflow_hub
library to directly read models from remote storage (GCS) instead of
downloading the models locally with
os.environ["TFHUB_MODEL_LOAD_FORMAT"] = "UNCOMPRESSED"
or by setting the command-line flag --tfhub_model_load_format to UNCOMPRESSED. This way, no caching directory is needed, which is especially helpful in environments that provide little disk space but a fast internet connection.
I ran that command in my notebook, and then the error was immediately resolved.
Note: I assume this is slower, especially if you do not have a fast internet connection, since what you are doing is telling the program to not locally cache (store) a copy and to just download it on demand.
I am attempting to compile the code at this Coral example on Colab to run on runtime version 10, since I have a Coral USB Accelerator connected to a customized build for Raspberry Pi Zero W.
The command I'd like to get working is
edgetpu_compiler --min_runtime_version 10 [.TFLITE file]
It always ends with an internal error; unknown to me why that would be...? The error is:
Edge TPU Compiler version 2.1.302470888
Internal compiler error. Aborting!
To reproduce this, you should do the import, preparation, build, and first training steps. No need to fine-tune: results are the same.
I understand that certain operations are not available for lower runtimes, but I am at a loss at what exactly would need to change in the demo so as to compile it successfully.
Does anyone know what might be missing, or otherwise provide guidance?
just got a chance to check this out, looks like there is actually a bug preventing compilation at older runtime version...
This is fixed as I'm able to compile this model with -m 10 form the code base, it'll be fixed for you by next release. For now here is a work around (essentially checking out an older compiler version to compile the model):
$ git clone https://github.com/google-coral/edgetpu.git && cd edgetpu
$ git checkout 657d2b6
$ ./compiler/x86_64/edgetpu_compiler -s -m 10 /path/to/model
This should works, although with an older runtime, many ops weren't supported then so you may not see the performance increase that you would with the current runtime version!
I am trying to follow this tutorial:
https://medium.com/#natu.neeraj/training-a-keras-model-on-google-cloud-ml-cb831341c196
to upload and train a Keras model on Google Cloud Platform, but I can't get it to work.
Right now I have downloaded the package from GitHub, and I have created a cloud environment with AI-Platform and a bucket for storage.
I am uploading the files (with the suggested folder structure) to my Cloud Storage bucket (basically to the root of my storage), and then trying the following command in the cloud terminal:
gcloud ai-platform jobs submit training JOB1
--module-name=trainer.cnn_with_keras
--package-path=./trainer
--job-dir=gs://mykerasstorage
--region=europe-north1
--config=gs://mykerasstorage/trainer/cloudml-gpu.yaml
But I get errors, first the cloudml-gpu.yaml file can't be found, it says "no such folder or file", and trying to just remove it, I get errors because it says the --init--.py file is missing, but it isn't, even if it is empty (which it was when I downloaded from the tutorial GitHub). I am Guessing I haven't uploaded it the right way.
Any suggestions of how I should do this? There is really no info on this in the tutorial itself.
I have read in another guide that it is possible to let gcloud package and upload the job directly, but I am not sure how to do this or where to write the commands, in my terminal with gcloud command? Or in the Cloud Shell in the browser? And how do I define the path where my python files are located?
Should mention that I am working with Mac, and pretty new to using Keras and Python.
I was able to follow the tutorial you mentioned successfully, with some modifications along the way.
I will mention all the steps although you made it halfway as you mentioned.
First of all create a Cloud Storage Bucket for the job:
gsutil mb -l europe-north1 gs://keras-cloud-tutorial
To answer your question on where you should write these commands, depends on where you want to store the files that you will download from GitHub. In the tutorial you posted, the writer is using his own computer to run the commands and that's why he initializes the gcloud command with gcloud init. However, you can submit the job from the Cloud Shell too, if you download the needed files there.
The only files we need from the repository are the trainer folder and the setup.py file. So, if we put them in a folder named keras-cloud-tutorial we will have this file structure:
keras-cloud-tutorial/
├── setup.py
└── trainer
├── __init__.py
├── cloudml-gpu.yaml
└── cnn_with_keras.py
Now, a possible reason for the ImportError: No module named eager error is that you might have changed the runtimeVersion inside the cloudml-gpu.yaml file. As we can read here, eager was introduced in Tensorflow 1.5. If you have specified an earlier version, it is expected to experience this error. So the structure of cloudml-gpu.yaml should be like this:
trainingInput:
scaleTier: CUSTOM
# standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs
masterType: standard_gpu
runtimeVersion: "1.5"
Note: "standard_gpu" is a legacy machine type.
Also, the setup.py file should look like this:
from setuptools import setup, find_packages
setup(name='trainer',
version='0.1',
packages=find_packages(),
description='Example on how to run keras on gcloud ml-engine',
author='Username',
author_email='user#gmail.com',
install_requires=[
'keras==2.1.5',
'h5py'
],
zip_safe=False)
Attention: As you can see, I have specified that I want version 2.1.5 of keras. This is because if I don't do that, the latest version is used which has compatibility issues with versions of Tensorflow earlier than 2.0.
If everything is set, you can submit the job by running the following command inside the folder keras-cloud-tutorial:
gcloud ai-platform jobs submit training test_job --module-name=trainer.cnn_with_keras --package-path=./trainer --job-dir=gs://keras-cloud-tutorial --region=europe-west1 --config=trainer/cloudml-gpu.yaml
Note: I used gcloud ai-platform instead of gcloud ml-engine command although both will work. At some point in the future though, gcloud ml-engine will be deprecated.
Attention: Be careful when choosing the region in which the job will be submitted. Some regions do not support GPUs and will throw an error if chosen. For example, if in my command I set the region parameter to europe-north1 instead of europe-west1, I will receive the following error:
ERROR: (gcloud.ai-platform.jobs.submit.training) RESOURCE_EXHAUSTED:
Quota failure for project . The request for 1 K80
accelerators exceeds the allowed maximum of 0 K80, 0 P100, 0 P4, 0 T4,
0 TPU_V2, 0 TPU_V3, 0 V100. To read more about Cloud ML Engine quota,
see https://cloud.google.com/ml-engine/quotas.
- '#type': type.googleapis.com/google.rpc.QuotaFailure violations:
- description: The request for 1 K80 accelerators exceeds the allowed maximum of
0 K80, 0 P100, 0 P4, 0 T4, 0 TPU_V2, 0 TPU_V3, 0 V100.
subject:
You can read more about the features of each region here and here.
EDIT:
After the completion of the training job, there should be 3 folders in the bucket that you specified: logs/, model/ and packages/. The model is saved on the model/ folder a an .h5 file. Have in mind that if you set a specific folder for the destination you should include the '/' at the end. For example, you should set gs://my-bucket/output/ instead of gs://mybucket/output. If you do the latter you will end up with folders output, outputlogs and outputmodel. Inside output there should be packages. The job page link should direct to output folder so make sure to check the rest of the bucket too!
In addition, in the AI-Platform job page you should be able to see information regarding CPU, GPU and Network utilization:
Also, I would like to clarify something as I saw that you posted some related questions as an answer:
Your local environment, either it is your personal Mac or the Cloud Shell has nothing to do with the actual training job. You don't need to install any specific package or framework locally. You just need to have the Google Cloud SDK installed (in Cloud Shell is of course already installed) to run the appropriate gcloud and gsutil commands. You can read more on how exactly training jobs on the AI-Platform work here.
I hope that you will find my answer helpful.
I got it to work halfway now by not uploading the files but just running the upload commands from cloud at my local terminal... however there was an error during it running ending in "job failed"
Seems it was trying to import something from the TensorFlow backend called "from tensorflow.python.eager import context" but there was an ImportError: No module named eager
I have tried "pip install tf-nightly" which was suggested at another place, but it says I don't have permission or I am loosing the connection to cloud shell(exactly when I try to run the command).
I have also tried making a virtual environment locally to match that on gcloud (with Conda), and have made an environment with Conda with Python=3.5, Tensorflow=1.14.0 and Keras=2.2.5, which should be supported for gcloud.
The python program works fine in this environment locally, but I still get the (ImportError: No module named eager) when trying to run the job on gcloud.
I am putting the flag --python-version 3.5 when submitting the job, but when I write the command "Python -V" in the google cloud shell, it says Python=2.7. Could this be the issue? I have not fins a way to update the python version with the cloud shell prompt, but it says google cloud should support python 3.5. If this is anyway the issue, any suggestions on how to upgrade python version on google cloud?
It is also possible to manually there a new job in the google cloud web interface, doing this, I get a different error message: ERROR: Could not find a version that satisfies the requirement cnn_with_keras.py (from versions: none) and No matching distribution found for cnn_with_keras.py. Where cnn_with_keras.py is my python code from the tutorial, which runs fine locally.
Really don't know what to do next. Any suggestions or tips would be very helpful!
The issue with the GPU is solved now, it was something so simple as, my google cloud account had GPU settings disabled and needed to be upgraded.
I've downloaded the newest Nsight Compute profiling tool and I want to use it to benchmark Tensorflow applications. The code I'm using is here. It runs perfectly fine when I execute it and when I benchmark it with nvprof ./mnist.py it had no problem at all. However, when I try to run it with command sudo ./nv-nsight-cu-cli [path to the file] I get the following error:
ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory
I suspect that nv-nsight-cu-cli somehow didn't recognized the environment variable at all. Is there any fix around?
You need to search for differences in both environments:
env variables
LD_LIBRARY_PATH
/etc/ld.so.conf
/etc/ld.so.conf.d/*
cuBLAS
Is installation complete/not broken?
Is it installed at the same location on both machines?
Versions
...
You can start with locate libcublas.so on both machines to see if there's a difference. Alternatively, you can strace -f -e open the program to check where it tries to libcublas.so from.
Your error has (for now) nothing to do with GPUs: libcublas.so.9.0 can just not be found. Find it, find why Tensorflow can not find it and your problem will be solved.
It appears that GP100 is not supported by the tool at this moment.
The answer is found here:
Nsight Compute only supports Pascal (other than GP100) and later GPUs.