Run a TensorFlow jupyter notebook on docker

 This is getting started guide for ML beginners who wants to use Jupyter Notebook on Docker environment.


Docker is a way to use different development environments and frameworks without physically install them on your PC or workstation. For more information, please visit docker website.

TensorFlow is Machine Learning library where you can train your own ML model and develop AI modules

This guide based on Windows version

Install the Docker client

Install Docker desktop client, then we need to run a docker command to download the TensorFlow docker image. 

docker pull tensorflow/tensorflow:latest  # Download latest stable image

Open the docker client and visit the image tab, will show the image.

Running the Tensor container

To work with the image, we need to run container. On the command prompt run the docker run command.

docker run -it -p 8888:8888 tensorflow/tensorflow:latest-jupyter  # Start Jupyter server

It will start the Jupiter notebook on the browser. Try creating a new Notebook and run the cell. Is it working.

All the Notebook you created are stored in tf folder inside the container. Go to the docker client - > click on the container tab - > click container Name -> Files -> tf, all your files were stored in this location.

Store Notebook on Local drive using Volume

Is it a way to sync the notebooks to a local drive? Yes.

We have to chain the volume option in our docker run command. Use full path instead of relative path while specifying the local path.

docker run -it -p 8888:8888 -v d:/src/tensor:/tf/ tensorflow/tensorflow:latest-jupyter  # Start Jupyter server

The first argument to volume option (-v) is the target and the second is the source. All the files were sync between the container and the local drive.

Let's run the command and try to print tensor version by creating new jupyter notebook with following code

import tensorflow as tf;print(tf.__version__)

See the src\tensor\ folder, the synced notebooks were stored here. Try to change the file which will automatically update the container version too.

I hope this will helps. 

Comments

Post a Comment

Popular posts from this blog

Zustland : a minimal data store to consider for the Next React Project

 I have been depending on Context for passing state through components in React and eventually things complicated when data is become complex. Move on to beautiful Redux but it is much complex to digest. For learning Redux I have spent lots of time and Redux Toolkit, easy to use Redux, sav ed me a lot. Even though RTK is ok, it is not a simple library. I am fan of Svelte store and Vuex , Pinia store. Is there any such library for React ? I recently found Zustand a minimal store for React. Love it. 😍 Create a store Binding Updating the state Create a store Create a store using the create an d export the custom hook. <pre class='language-javascript'> <code> import create from 'zustand' const useBearStore = create((set) => ({ bears: 0, increasePopulation: () => set((state) => ({ bears: state.bears + 1 })), removeAllBears: () => set({ bears: 0 }), })) </code></pre> Binding In your component use the ho
Read more

How to run HugginFace models in Python

  Hugging Face Hugging face is a machine learning community where you can share pre trained models and explore other trained models, which are suited for many use cases. Many of the models already trained well and ready to use. Without delay get started! Python setup Open your Jupiter notebook or for easy startup without python installation on local machine, use Colab . In this example we are using mistralai/Mistral-7B-v0.1 model. Let's install the transformers module which is the primary dependency for HuggingFace.   conda install transformers Following script will work like a charm . # mistrel model test from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mistral-7B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.d
Read more

Build a Flask-Python web App

Image
Building a dynamic website made so easy with Python -Flask, a Micro FrameWork which helps us to create the websites like Twitter and even more. Forget about the complicated web programming languages, start learning Python. So where you start, Python or Flask ?. I should say, you have to learn basics of how Python first, just have a look at Python.org
Read more