I have been having issues setting up Darknet. I will be using yolov2 to detect cerebral microbleeds for a neuroscience project. After battling Darknet for a few days, I managed to install it and successfully download the train, test and validation Pascal VOC data by using the below general configuration/set up:
Cmake-GUI 3.2 (binary distributions, not source)
MSVS 2019
CUDA 11.1
cuDNN 8.0.5
OpenCV 4.2.0
I always get an error when running darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23 in cmd:
'darknet.exe' is not recognized as an internal or external command,
operable program or batch file
I cannot seem to understand the reason why.
In addition, when following the pjreddie instructions to modify cfg for Pascal Data:
classes= 20
train = /train.txt
valid = 2007_test.txt
names = data/voc.names
backup = backup
I change the Notepad file and all / to backslash, does that make a difference?
Could anyone shed some light as to how to successfully train the data?
that's a generic error when you are trying to execute a program that is not in your current directory or not defined in PATH variable.
try adding the path to the darknet.exe file in your path variable:
\darknet\build\darknet\x64\
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 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’m using a python notebook in gGloud datalab to re-train a neural network.
From the notebook I call retrain.py with
!python -m retrain --bottleneck_dir=../tf_files/bottlenecks --how_many_training_steps=500 --model_dir=../tf_files/models/ --summaries_dir=../tf_files/training_summaries/'mobilenet_1.0_224' --output_graph=../tf_files/retrained_graph.pb --output_labels=../tf_files/retrained_labels.txt --architecture='mobilenet_1.0_224' --image_dir=../tf_files/flower_photos
Within retrain.py I import the Tensorboard with
from google.datalab.ml import TensorBoard as tb
followed by the main function that does a bunch of things for the training process followed by:
inti=tf.global_variables_initializer()
sess.run(init)
tb.start('./tmp/retrain_logs’)
Executing retrain.py, a neural network will be trained, and TensorBoard will be activated (as stated in the output of my notebook copied below)
TensorBoard 1.8.0 at http://3439c553be9b:59199 (Press CTRL+C to quit)
{'text/html':TensorBoard was started successfully with pid 7707. Click here to access it.}
I tried to see the TensorBoard by:
clicking on the link provided (http://3439c553be9b:59199). A site on my web-brower opens but is empty.
I used gCloud Shell to connect with 'datalab connect --port=59199 .' This brings me to my files on gCloud, but not to a TensorBoard.
Can someone tell me how to access the TensorBoard please?
Thank you,
Julia
Just clicking on the link should work.
Could you check to see if you have a firewall rule that is preventing this?
I think the first string "TensorBoard 1.8.0 at http://3439c553be9b:59199 (Press CTRL+C to quit) " was output by the new tensorboard version. The direct link won't work.
The second string "TensorBoard was started successfully with pid 7707. Click here to access it.", the word "here" should be backed up by a hyperlink. See code https://github.com/googledatalab/pydatalab/blob/master/google/datalab/ml/_tensorboard.py#L73. That link should work. Do you get that link?
I want to use TensorBoard to visualize results stored on an S3 server, without downloading them to my machine. Ideally, this would work:
$ tensorboard --logdir s3://mybucket/summary
Assuming the tfevents files are stored under summary. However this does not work and returns UnimplementedError: File system scheme s3 not implemented.
Is there some workaround to enable TensorBoard to access the data on the server?
The S3 File system plugin for tensorflow was released in Version 1.4 in early October. You'll need to make sure your tensorflow-tensorboard version is at least pip install tensorflow-tensorboard==0.4.0-rc1
Then you can start the server:
tensorboard --logdir=s3://root-bucket/jobs/4/train