I dived into the world of ML using scikit-learn and now I am learning the Tensorflow. I searched alot about the deployment of models, but I am having a hard times understanding the whole meachnaism. I really appreciate your effort, this is the best content on ML deployments on UA-cam 👍🏻
I made a model in Keras. Installed Keras and Tensorflow on AWS instance in Virtualenv and created single python instance listening to RabbitMQ with Pika and used Flask over WSGI to put messages to the queue. HTML client uploads a photo and is returned with ID than it should request id info from the server in one second intervals. Works fine and queuing is kind of bullet proof since it's running on a small cpu instance :)
Thank you, I have been wondering what to do with a model once trained. No one seems to be talking about this and it seems like a very important step. And yes, I have been searching furiously to figure it out. Thanks again.
Hey Siraj, Firstly, great video Request: A tutorial on how to build a deployed Deep learning model that learns from live data and updates itself to a new version.
just load the model and do model.fit(new data) and then overwrite the file using model.save() or whatever save function your are using. Incremental Learning is the best solution for continuously updating models with new data.
24:18 When I run the command: 'docker build --pull -t $USER/tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel .' I get an error: 'invalid argument "/tensorflow-serving-devel" for t: invalid reference format' Help? (On Windows 7, Docker Toolbox) UPDATE: The following does work: 'docker build --pull -t tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel .'
I barely ever comment on videos but I have got to show love for all I've learned on your channel. I've been recommending you to everyone I can find. What software do you use to do the screen background with you in the foreground by the way? I want to start a channel teaching atmospheric science and I like this style...
Siraj, could you please upload weights of models you trained as well, as neither online and personalized training of models is a viable option financially.
I honestly dont get how to employ the model. At what stage do we use the neural net to make decisions about actionable data, in this case see it decode the words?
Thank you for these. I've learned a lot already. The big question and use case that I'm interested in is using ML in blender. The goal would be to create a blender add on that could be trained on and manipulate mesh in a character model. With Blender and its add ons all written in python, this seems doable. The mesh data can be called within the blender python api pretty easily. My question is how to best set up a system that would take a character mesh (this would be in the thousands of vertex coordinates) and then train a model on with shape keys for happy in each one, then be able to make a shape key on a new character mesh that also produces a happy expression.
Hi Siraj, you mentioned at 28:00 that inception has 100s of layers. If I am not wrong presently it has only 48 layers. Please correct me if I am wrong or you are referring something else.
Hi Siraj I have trained the keras model using imagdedatagenerator and flow_from_directory. When I deploy in tensorflow servimg i got wrong class due to shuffle true in flow_from_directory. How can i resolve this problem ? Thanks
I was very surprised by the fact that bazel have been building my custom client for serving for a very long time ...Does it need to compile so many sources every time when I change the client code?
Thanks so much for this vid! Could you by chance in the future do the same thing, but for something custom like a tensorflow model that simply adds two floats and returns the response? Reason I ask is because I have been having a big problem trying to figure out how to setup a custom model for serving with regards to configuring the proto files and client.
If I use a saved scaler function from sklearn for the input data - can that be loaded to the server along with the model? Basically before model is called - i have to use that function first on every input. I had to use a scaler since i have many inputs and they are very different : one can be in a range of 1-3, another 50000-1000000. For that i used StandardScaler from sklearn and it does great. In case of getting right prediction i have to apply it on the new coming data.
To build the docker file use : sudo docker build --pull -t $USER/tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel . To run : sudo docker run --name=tensorflow_container -it $USER/tensorflow-serving-devel
@Siraj : Can we train the models and deploy them Incremental ?. Is TF Serving supports multiple smaller models. If yes, how can we do it. I cannot get any help in internet.
So sad this tutorial is broken now, they changed the github repository. Now you don’t have the tensorflow folder inside serving. If anybody knows of a tutorial please let me know.
Hey Siraj, Love the videos. Question: I am taking the Udacity DL course, and am getting more and more into it and plan to continue on to make a career out of this. Would you recommend I switch over to Ubuntu as my primary OS or is it feasible to stay in Windows?
Hi Siraj, nice video! But I'm a bit confused about classification_signature and predict_signature in MNIST example. Should I use both of them, is there any difference between them, why classification's input is a string, etc. Or it's just example that I can use number of signatures to query single model? Thank you.
I think an alternate would be deploy the models in your code directly rather than calling a rest API. I have a model running on my iPhone, I don't see performance issues. The new chipsets are getting more and more powerful.
There is no /tensorflow folder to do 'configure' as Google has taken it out. It is no longer required to do the configure according to Google latest issue response. Just do 'bazel build -c opt tensorflow_serving/...' at tensorflow-serving directory. (of course without the ')
Hey Siraj! Thanks a lot for all this amazing content. I am working on generative models for symbolic (MIDI) music sequences. Your videos on the topic have been very useful. However, I'm intending on running the inference / generation part on mobile device (iOS). I am using TensorFlow and things seem to gradually improve (more functions, more support, more documentation) but I still find it very tricky to port the model on device (strip the unused / unsupported nodes, optimize, porting the generation scripts etc.). Even porting the fairly simple RBM model you used for one of your videos is challenging. Any suggestion on that? Given that running inference on mobile devices is becoming a trend, would you care to make a video about it?
lol need this in about two weeks thanks for a dank upload siraj!!!! really hope I dont run into that docker problem you had, I have over 20 docker images I think. lol 27:00 building custom linux kernels amirite lol
I think once you have accumulated the data, you can wrap the phases of the model training steps in functions then those functions in a class and trigger the class to train the model, persist the model on the disk and save the path in db, atleast this is how I do it :)
after uncompress the inception model, do --> 'bazel-bin/tensorflow_serving/example/inception_saved_model --checkpoint_dir=inception-v3 --output_dir=inception-export' as the command on the tutorial is old and no longer works.
Hi Siraj, thank you so much for the videos. bazel build failed on some error and I am gonna try it tomorrow (it might be problem with not enough memory for docker). However I am kinda lost with docker and containers. Now when I shut it down, how do I get back to the step where I can write bazel bild etc..? Thank you.
How would one go from building tensorflow in docker on a local CPU without CUDA support and then deploying the container to a GPU instance in the cloud with CUDA support? Would I need to build tensorflow again when I deploy the docker container to the GPU and just enable CUDA support there? Or is there a way to have CUDA support on my CPU and maintain that when I deploy the container?
I am having issue while deploying my deep learning model in h5 format on flask. It works fine on local machine however, it has issues on my pythoneverywhere hosting server.
I am currently facing a problem to deploy my custom models where my images data is located on my local computer, can u make a video to how to deploy custom models where image data is located in the local computer, save models and deploy for in android devices.
Hello. I hope you will answer as it's really important for me. I'm currently working on a project and my task is to generate meaningful unique text from a set of keywords. It doesn't need to be large, at least a couple of sentences. I'm pretty sure I have to use LSTM but I can not find any good examples of generation of meaningful texts. I saw a few of randomly generated but that's all. I would be grateful for any advice. Thank you in advance.
Awesomeeeeee. Hello guys, do you know if is possible using: • Variational Autoencoders Neural Network (VAE) or • Generative adversarial networks (GANs) For structured data? I have seen some examples and it could be used but just for unstructured data such as images, audio, etc. Maybe do you have any example with structured data? Thanks a lot
Nice vid man ! Did you like C (didnt see any vids about it :D). I would like to know more about Tensorflow C API. Thanks alot for all knowledge you share
My understanding is the tensorflow C api wasnt designed to be used for production directly. If you want to deploy a model in C/C++ consider writing it from scratch, its not as bad as it sounds (inference is much simpler than training). Deep networks are mostly just a series of matrix multiplies.
Hey siraj, actually i want to know that i have created a image classifier in docker and now i want to integrate into system which is written in python, how i can do that?
the process crashes when i try to create the docker image, it goes on for about 10 minutes and then everything freezes. any idea why ? i have an intel i5 processor
If I have an existing tf model, and I trained my data with train_test_split, what to do if I want to see the predictions for my model on the entire dataset (including the original training and testing data)?
I was probably too excited when I typed that, hence the exaggeration ! You probably don't want to have suggestions from a crappy coder, like me However as much as I love your other tutorial videos, which are informative too, but are restricted to jupyter notebooks There is no way to send across the information processed from that to anywhere which a common person can use I started learning Django and rabbitMQ, with thoughts that only it can provide an interface to tensorflow
Also I am not sure if we have used the mnist - numerical recognition classifier in your docker Why did we not use that and instead use inception? Edit - no need to answer, got answered at 29:48
Something else that could be useful if you can make videos that showcase how to tailor out of the box tutorials (e.g. the MNIST tutorial) to a completely different use case where there model is still useful (e.g. something with a dataset we've built from scratch). Sometimes there's friction going from these templates to your own use case. Eventually I figure it out but I would be nice to have key things to consider when going from one use case to the next.
Hello Siraj, Firstly thank you for this great video. I train a model in Python, than i saved with the train.saver to generate my checkpoint. i want to load this model in C++ , is there a way to do that ????
Hey Siraj , you did'nt teach what I want I want to know how to deploy a model into web if you have that video please share me or Please Replay my how to do that
failed: gcc failed: error executing command /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -B/usr/bin -B/usr/bin -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG ... (remaining 106 argument(s) skipped): com.google.devtools.build.lib.shell.BadExitStatusException: Process exited with status 4. :( I can't get rid of this error, I'm new in ML/DL and that's what I get as my welcoming message, can anyone help please ?
You are likely running out of memory. Try reducing number of parallel builds by passing '--local_resources 2048,.5,1.0', which would instruct bazel to spawn no more than one compiler process at the time.
Hi Siraj, Thanks for great video. please help me out to fix the issue,I have my own model. here i am using faster_rcnn_inception_v2_pets.config architecture. currently i have trained check points. But when ever i am exporting checkpoints by using below command bazel-bin/tensorflow_serving/example/inception_saved_model --checkpoint_dir=my-model6 --export_dir=inception-export at that time i am getting below error DataLossError (see above for traceback): Unable to open table file my-model6/model.ckpt-21292: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator? [[Node: save/RestoreV2_34 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_34/tensor_names, save/RestoreV2_34/shape_and_slices)]] Here we have TF=1.4 and Bazel=0.5.4 while training i got checkpoints like model.ckpt-21292.data-00000-of-00001 model.ckpt-21292.meta model.ckpt-21292.index for the above checkpoints i was renamed like model.ckpt-21292. I was followed your video, your downloading pre-trained model. but my question is we both having the same type of checkpoints, then why am getting above error?? Thank you
Not really helping. Thought it would. I have seen SEVERAL videos online about training, how to train etc. BUT if I have a mini PC onboard a device that I want to use the trained data. HOW do I do that? What does training produce? There is NO CLARITY in any of the videos on that ACTUAL workflow of what is produced by training on how to re-purpose the results across platforms. Lets take cars for example. Small computers on board. HOW do I run a trained CNN for detection of road signs? I dont want to train it I just want to RUN it because it will go on thousands of cars. Do you have ANYTHING that helps to understand how to train create something and then LOAD and use the trained data on a machine? NOT a web connected thing
Has anyone got an error like this, at the bazel build step? (run on Macbook Pro, OSX 10.11.6, via Docker method. With bazel 0.5.4 in Dockerfile) ERROR: /root/.cache/bazel/_bazel_root/f8d1071c69ea316497c31e40fe01608c/external/org_tensorflow/tensorflow/core/kernels/BUILD:2904:1: C++ compilation of rule '@org_tensorflow//tensorflow/core/kernels:conv_ops' failed (Exit 4). gcc: internal compiler error: Killed (program cc1plus)
hi @Siraj, great video, Please help with if you can, I want to convert a tf model(only .pb file is obtained) into a tflite model Whlie writing this script: import tensorflow as tf converter = tf.lite.TFLiteConverter.from_saved_model("C:/tmp/") tfl_model = converter.convert() open("converted_model.lite", "wb").write(tfl_model) i get this error: Traceback (most recent call last): File "C:/Users/user/PycharmProjects/converter/convert.py", line 4, in tfl_model = converter.convert() File "C:\Users\user\PycharmProjects\converter\venv\lib\site-packages\tensorflow_core\lite\python\lite.py", line 400, in convert raise ValueError("This converter can only convert a single " ValueError: This converter can only convert a single ConcreteFunction. Converting multiple functions is under development. While writing a command line: tflite_convert --saved_model_dir=C:/tmp --output_file=saved-model.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --input_shape=1,416,416,3 --input_array=input \ --output_array=output \ --inference_type=FLOAT \ --input_data_type=FLOAT i get this error: RuntimeError: MetaGraphDef associated with tags serve could not be found in SavedModel. To inspect available tag-sets in the SavedModel, please use the SavedModel CLI: `saved_model_cli`
Does anyone have the github.com/tensorflow/serving/ repo when this video published as the latest version is deficit some files and its hard to follow form there. Thanks.
I dived into the world of ML using scikit-learn and now I am learning the Tensorflow. I searched alot about the deployment of models, but I am having a hard times understanding the whole meachnaism. I really appreciate your effort, this is the best content on ML deployments on UA-cam 👍🏻
I made a model in Keras. Installed Keras and Tensorflow on AWS instance in Virtualenv and created single python instance listening to RabbitMQ with Pika and used Flask over WSGI to put messages to the queue. HTML client uploads a photo and is returned with ID than it should request id info from the server in one second intervals. Works fine and queuing is kind of bullet proof since it's running on a small cpu instance :)
Hey rad, sounds very interesting. Where do I learn how to do this?
rad rabbitMQ. I have heard about it and thought of using it .
very cool
That's awesome!
Thank you, I have been wondering what to do with a model once trained. No one seems to be talking about this and it seems like a very important step. And yes, I have been searching furiously to figure it out. Thanks again.
np
Siraj... my dude, yours are the shoulders I am standing on in my job. Thank you so much for all the incredible tutorials and additional resources!!!
Hey Siraj, Firstly, great video
Request: A tutorial on how to build a deployed Deep learning model that learns from live data and updates itself to a new version.
just load the model and do model.fit(new data) and then overwrite the file using model.save() or whatever save function your are using.
Incremental Learning is the best solution for continuously updating models with new data.
@Shashwat don't you think it will take so much time to retrain with new data included and user won't be waiting till infinity for results.
Long story short siraj earned around 864.84 dollars for this month lol go to 35:40.. He deserves lot more.. Keep up the good work.
jijo john not bad for a side job, and well deserved!
He deserves a lot more - i wish him the best!
ha! that slipped through. cool. i'll keep it there. transparency ftw
i mean, does he not deserve it?
Damn, imagine what those youtubers with millions of followers earn...
I always wondered "Ok, I created a model, now what?". Thanks, Siraj!
Man, you are really underrated! You deserve a lot for these great videos about ML. A big thanks from Brazil for the awesome work!!!
His notebook is Untitled... He chose the dark path....
renamed it to demo now, so much more content coming
Siraj Raval Your videos are good! May the force be with you...
@@adamyatripathi2743 Naming notebooks is for noobs
Siraj, excellent tutorial - thanks for creating this.
Loved the video, love the energy, he clearly has a deep understanding
24:18 When I run the command:
'docker build --pull -t $USER/tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel .'
I get an error:
'invalid argument "/tensorflow-serving-devel" for t: invalid reference format'
Help? (On Windows 7, Docker Toolbox)
UPDATE: The following does work:
'docker build --pull -t tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel .'
just ommit $USER/
Love your teaching :) Keep it up☺
thx
Your explanation was on point!! Thank you Siraj
np
Hey Siraj, Firstly, great video
Request: Can u post a tutorial on tensorflow distributed training on GPUs and Kubernates.
Great topic! Thanks Siraj.
np
Very useful. Thanks Siraj!
np
Please rehearse more and these would be 10000% better
I barely ever comment on videos but I have got to show love for all I've learned on your channel. I've been recommending you to everyone I can find. What software do you use to do the screen background with you in the foreground by the way? I want to start a channel teaching atmospheric science and I like this style...
Thanks for end 2end tensor flow tutorial. World wows you for AI revolution
awesome thx
Siraj, could you please upload weights of models you trained as well, as neither online and personalized training of models is a viable option financially.
I honestly dont get how to employ the model. At what stage do we use the neural net to make decisions about actionable data, in this case see it decode the words?
Thank you for these. I've learned a lot already. The big question and use case that I'm interested in is using ML in blender. The goal would be to create a blender add on that could be trained on and manipulate mesh in a character model. With Blender and its add ons all written in python, this seems doable. The mesh data can be called within the blender python api pretty easily. My question is how to best set up a system that would take a character mesh (this would be in the thousands of vertex coordinates) and then train a model on with shape keys for happy in each one, then be able to make a shape key on a new character mesh that also produces a happy expression.
So cool! This is inspiring me to make some models. :)
Hi Siraj, you mentioned at 28:00 that inception has 100s of layers. If I am not wrong presently it has only 48 layers. Please correct me if I am wrong or you are referring something else.
Hi Siraj
I have trained the keras model using imagdedatagenerator and flow_from_directory. When I deploy in tensorflow servimg i got wrong class due to shuffle true in flow_from_directory. How can i resolve this problem ?
Thanks
I was very surprised by the fact that bazel have been building my custom client for serving for a very long time ...Does it need to compile so many sources every time when I change the client code?
This is amazing, thanks Siraj. Since we are talking about production level can you plz suggest server configuration required for Tensorflow serving?
Thanks for this. It works for me.
Thanks for sharing this. I've been wondering how to do this.
Thanks so much for this vid! Could you by chance in the future do the same thing, but for something custom like a tensorflow model that simply adds two floats and returns the response? Reason I ask is because I have been having a big problem trying to figure out how to setup a custom model for serving with regards to configuring the proto files and client.
very good video
i am working o n image classificattion and my model is in tflite, how do i deploy? do i need to change anything in your video tutorial?
Hi @siraj, Could you please tell How to Deploy a Keras Model to Production?
If I use a saved scaler function from sklearn for the input data - can that be loaded to the server along with the model?
Basically before model is called - i have to use that function first on every input.
I had to use a scaler since i have many inputs and they are very different : one can be in a range of 1-3, another 50000-1000000. For that i used StandardScaler from sklearn and it does great. In case of getting right prediction i have to apply it on the new coming data.
To build the docker file use :
sudo docker build --pull -t $USER/tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel .
To run :
sudo docker run --name=tensorflow_container -it $USER/tensorflow-serving-devel
@Siraj : Can we train the models and deploy them Incremental ?. Is TF Serving supports multiple smaller models. If yes, how can we do it. I cannot get any help in internet.
such a helpful video!
So sad this tutorial is broken now, they changed the github repository. Now you don’t have the tensorflow folder inside serving. If anybody knows of a tutorial please let me know.
There is no tensorflow folder inside of serving on github. What should I do?
hi what do you do in this case i face the same issue
try to build in serving directory
You can do 'bazel build -c opt tensorflow_serving/...' at tensorflow-serving directory in docker container.
According to github.com/tensorflow/serving/issues/755 , looks like we can just skip the cd tensorflow and ./configure steps
Great video, Siraj! Could you add resources on how to deploy Keras models?
Hey Siraj, Love the videos. Question: I am taking the Udacity DL course, and am getting more and more into it and plan to continue on to make a career out of this. Would you recommend I switch over to Ubuntu as my primary OS or is it feasible to stay in Windows?
Why we want to train the model.. I want deploy in website my chatbot.. Can you tell me
Hi Siraj, nice video! But I'm a bit confused about classification_signature and predict_signature in MNIST example. Should I use both of them, is there any difference between them, why classification's input is a string, etc. Or it's just example that I can use number of signatures to query single model?
Thank you.
I think an alternate would be deploy the models in your code directly rather than calling a rest API. I have a model running on my iPhone, I don't see performance issues. The new chipsets are getting more and more powerful.
Can you please share either blog or video on steps you took to deploy to iphone. Thank you
share github!
share, please!
super informative:)
I guess you have shared enough knowledge for someone to start a company :/
yup
There is no /tensorflow folder to do 'configure' as Google has taken it out. It is no longer required to do the configure according to Google latest issue response. Just do 'bazel build -c opt tensorflow_serving/...' at tensorflow-serving directory. (of course without the ')
Please share link of part 2 of this tutorial for pushing this to cloud.
Hey Siraj! Thanks a lot for all this amazing content.
I am working on generative models for symbolic (MIDI) music sequences. Your videos on the topic have been very useful.
However, I'm intending on running the inference / generation part on mobile device (iOS). I am using TensorFlow and things seem to gradually improve (more functions, more support, more documentation) but I still find it very tricky to port the model on device (strip the unused / unsupported nodes, optimize, porting the generation scripts etc.).
Even porting the fairly simple RBM model you used for one of your videos is challenging. Any suggestion on that?
Given that running inference on mobile devices is becoming a trend, would you care to make a video about it?
thank you for tensorflow video
np
lol need this in about two weeks thanks for a dank upload siraj!!!! really hope I dont run into that docker problem you had, I have over 20 docker images I think. lol 27:00 building custom linux kernels amirite lol
dope u will do fine
May the force be with you
Stupid question: Are we using the MNIST model? Since we're using the ImageNet model right?
What’s the advantage of serving model using TF serving compared to a rest api
Hi, How could we use this approach of deploying a TensorFlow model to production, for a re-trained inception model using transfer learning?
How can one do incremental training of models already deployed to serving?
I think once you have accumulated the data, you can wrap the phases of the model training steps in functions then those functions in a class and trigger the class to train the model, persist the model on the disk and save the path in db, atleast this is how I do it :)
after uncompress the inception model, do --> 'bazel-bin/tensorflow_serving/example/inception_saved_model --checkpoint_dir=inception-v3 --output_dir=inception-export' as the command on the tutorial is old and no longer works.
i am not able to find step by step procedure to how to work on tensorflow in android studio
Hi Siraj, thank you so much for the videos. bazel build failed on some error and I am gonna try it tomorrow (it might be problem with not enough memory for docker). However I am kinda lost with docker and containers. Now when I shut it down, how do I get back to the step where I can write bazel bild etc..? Thank you.
How would one go from building tensorflow in docker on a local CPU without CUDA support and then deploying the container to a GPU instance in the cloud with CUDA support? Would I need to build tensorflow again when I deploy the docker container to the GPU and just enable CUDA support there? Or is there a way to have CUDA support on my CPU and maintain that when I deploy the container?
what's the difference between a docker image and a simple executable file?
Hey Siraj, what software do you use to make those images like on 2:34 or 11:46 etc?
I am having issue while deploying my deep learning model in h5 format on flask. It works fine on local machine however, it has issues on my pythoneverywhere hosting server.
It basically asks for GPU.
I am currently facing a problem to deploy my custom models where my images data is located on my local computer, can u make a video to how to deploy custom models where image data is located in the local computer, save models and deploy for in android devices.
Hello. I hope you will answer as it's really important for me. I'm currently working on a project and my task is to generate meaningful unique text from a set of keywords. It doesn't need to be large, at least a couple of sentences. I'm pretty sure I have to use LSTM but I can not find any good examples of generation of meaningful texts. I saw a few of randomly generated but that's all. I would be grateful for any advice. Thank you in advance.
When I run "bazel build -c .." ,
I get "no targets found beneath' tensorflow_serving' ".
How can we take that JSON output and have it display bounding boxes in the browser?
Siraj, if I wanna write an ML algorithm and make a web app based on it, would learning Django be useful for this task?
Awesomeeeeee.
Hello guys, do you know if is possible using:
• Variational Autoencoders Neural Network (VAE) or
• Generative adversarial networks (GANs)
For structured data? I have seen some examples and it could be used but just for unstructured data such as images, audio, etc. Maybe do you have any example with structured data? Thanks a lot
Nice vid man ! Did you like C (didnt see any vids about it :D).
I would like to know more about Tensorflow C API.
Thanks alot for all knowledge you share
My understanding is the tensorflow C api wasnt designed to be used for production directly. If you want to deploy a model in C/C++ consider writing it from scratch, its not as bad as it sounds (inference is much simpler than training). Deep networks are mostly just a series of matrix multiplies.
more tf vids coming thx
ty for answer, world gonna change thanks to guys like you ;)
Hey siraj, actually i want to know that i have created a image classifier in docker and now i want to integrate into system which is written in python, how i can do that?
the process crashes when i try to create the docker image, it goes on for about 10 minutes and then everything freezes. any idea why ? i have an intel i5 processor
Can you please explain how I can make and serve CNN model using deeplearning 4j in java ?
Does this mean that in order to make an ML web app I don't have to learn Django or any other python web framework, I could just use tensorflow?
If I have an existing tf model, and I trained my data with train_test_split, what to do if I want to see the predictions for my model on the entire dataset (including the original training and testing data)?
Siraj, I wonder if a similar strategy can be used to deploy a sci-kit learn model?
absolutely loads.pickle.me.uk/2016/04/04/deploying-a-scikit-learn-classifier-to-production/
I don't get this part: 32:08 How that bazel command worked out of the docker?
same question...the bazel command didnt work in my docker too..How did he do ..could you please tell us siraj
the only useful video that you have uploaded till date!
thx what else would be useful?
I was probably too excited when I typed that, hence the exaggeration !
You probably don't want to have suggestions from a crappy coder, like me
However as much as I love your other tutorial videos, which are informative too, but are restricted to jupyter notebooks
There is no way to send across the information processed from that to anywhere which a common person can use
I started learning Django and rabbitMQ, with thoughts that only it can provide an interface to tensorflow
Also I am not sure if we have used the mnist - numerical recognition classifier in your docker
Why did we not use that and instead use inception?
Edit - no need to answer, got answered at 29:48
Something else that could be useful if you can make videos that showcase how to tailor out of the box tutorials (e.g. the MNIST tutorial) to a completely different use case where there model is still useful (e.g. something with a dataset we've built from scratch). Sometimes there's friction going from these templates to your own use case. Eventually I figure it out but I would be nice to have key things to consider when going from one use case to the next.
If you want more detailed tutorials, look at Melvin L. He's really good with step-by-step solutions
Siraj, Please, put together a video tutorial on how to generate an Image from Text with TensorFlow. (Text to Image)
Hello Siraj, Firstly thank you for this great video. I train a model in Python, than i saved with the train.saver to generate my checkpoint. i want to load this model in C++ , is there a way to do that ????
"we have to deal with C++" count me in :DD
Come back buddy, we miss you!
Was waiting for this
dope
Siraj can you please please recommend me a book to get start on ML, your videos are great but I am having hard time in grasping the concept
deep learning by bengio
Any video on image localisation
Hey Siraj , you did'nt teach what I want I want to know how to deploy a model into web if you have that video please share me or Please Replay my how to do that
pls can i have a copy of your pipeline or pls how do i do mine?
failed: gcc failed: error executing command /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -B/usr/bin -B/usr/bin -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG ... (remaining 106 argument(s) skipped): com.google.devtools.build.lib.shell.BadExitStatusException: Process exited with status 4.
:( I can't get rid of this error, I'm new in ML/DL and that's what I get as my welcoming message, can anyone help please ?
You are likely running out of memory. Try reducing number of parallel builds by passing '--local_resources 2048,.5,1.0', which would instruct bazel to spawn no more than one compiler process at the time.
bazel-bin/tensorflow_serving/example/inception_export: No such file or directory
Now what?
how can you upload new models at runtime?
#update
The tensorflow submodule has been removed. You should no longer have to run TensorFlow's configure script manually
Hi Siraj, Thanks for great video.
please help me out to fix the issue,I have my own model. here i am using faster_rcnn_inception_v2_pets.config architecture. currently i have trained check points.
But when ever i am exporting checkpoints by using below command
bazel-bin/tensorflow_serving/example/inception_saved_model --checkpoint_dir=my-model6 --export_dir=inception-export
at that time i am getting below error
DataLossError (see above for traceback): Unable to open table file my-model6/model.ckpt-21292: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?
[[Node: save/RestoreV2_34 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_34/tensor_names, save/RestoreV2_34/shape_and_slices)]]
Here we have TF=1.4 and Bazel=0.5.4
while training i got checkpoints like
model.ckpt-21292.data-00000-of-00001
model.ckpt-21292.meta
model.ckpt-21292.index
for the above checkpoints i was renamed like model.ckpt-21292.
I was followed your video, your downloading pre-trained model.
but my question is we both having the same type of checkpoints, then why am getting above error??
Thank you
I found solution.,.,.,.
Hello all, just follow the below video and export your own model with in a 10 seconds
ua-cam.com/video/w0Ebsbz7HYA/v-deo.html
Not really helping. Thought it would.
I have seen SEVERAL videos online about training, how to train etc. BUT if I have a mini PC onboard a device that I want to use the trained data. HOW do I do that?
What does training produce? There is NO CLARITY in any of the videos on that ACTUAL workflow of what is produced by training on how to re-purpose the results across platforms. Lets take cars for example. Small computers on board. HOW do I run a trained CNN for detection of road signs? I dont want to train it I just want to RUN it because it will go on thousands of cars.
Do you have ANYTHING that helps to understand how to train create something and then LOAD and use the trained data on a machine?
NOT a web connected thing
"If Google can use it, we can use it." is so 2017.
cardigan lol. inception network is savage
Has anyone got an error like this, at the bazel build step? (run on Macbook Pro, OSX 10.11.6, via Docker method. With bazel 0.5.4 in Dockerfile)
ERROR: /root/.cache/bazel/_bazel_root/f8d1071c69ea316497c31e40fe01608c/external/org_tensorflow/tensorflow/core/kernels/BUILD:2904:1: C++ compilation of rule '@org_tensorflow//tensorflow/core/kernels:conv_ops' failed (Exit 4).
gcc: internal compiler error: Killed (program cc1plus)
Solved! See this GitHub issue thread -
scroll down. github.com/tensorflow/serving/issues/227
Thanks a lot!
hi @Siraj, great video,
Please help with if you can,
I want to convert a tf model(only .pb file is obtained) into a tflite model
Whlie writing this script:
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model("C:/tmp/")
tfl_model = converter.convert()
open("converted_model.lite", "wb").write(tfl_model)
i get this error:
Traceback (most recent call last):
File "C:/Users/user/PycharmProjects/converter/convert.py", line 4, in
tfl_model = converter.convert()
File "C:\Users\user\PycharmProjects\converter\venv\lib\site-packages\tensorflow_core\lite\python\lite.py", line 400, in convert
raise ValueError("This converter can only convert a single "
ValueError: This converter can only convert a single ConcreteFunction. Converting multiple functions is under development.
While writing a command line:
tflite_convert --saved_model_dir=C:/tmp --output_file=saved-model.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --input_shape=1,416,416,3 --input_array=input \ --output_array=output \ --inference_type=FLOAT \ --input_data_type=FLOAT
i get this error:
RuntimeError: MetaGraphDef associated with tags serve could not be found in SavedModel. To inspect available tag-sets in the SavedModel, please use the SavedModel CLI: `saved_model_cli`
Where would i be without you?
love u
on docker build --pull -t tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel i get docker build requires exactly 1 argument
Does anyone have the github.com/tensorflow/serving/ repo when this video published as the latest version is deficit some files and its hard to follow form there.
Thanks.
You can do 'bazel build -c opt tensorflow_serving/...' at tensorflow-serving directory in docker container.