https://github.com/abhishekkrthakur/ml_dev_env

Machine Learning / Deep Learning Environment. Everywhere. Anywhere.
https://github.com/abhishekkrthakur/ml_dev_env

code deep-learning docker docker-compose jupyterlab machine-learning

Last synced: 14 days ago
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Machine Learning / Deep Learning Environment. Everywhere. Anywhere.

• Host: GitHub
• URL: https://github.com/abhishekkrthakur/ml_dev_env
• Owner: abhishekkrthakur
• Created: 2020-08-11T22:53:59.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2020-08-11T23:31:47.000Z (about 4 years ago)
• Last Synced: 2024-10-03T12:19:31.885Z (about 1 month ago)
• Topics: code, deep-learning, docker, docker-compose, jupyterlab, machine-learning
• Language: Dockerfile
• Homepage:
• Size: 5.86 KB
• Stars: 50
• Watchers: 3
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ML Development Environment

A fully fledged development environment for OSX, Windows, Linux

### Step - 1: Install docker 

You need docker! Check out https://docs.docker.com/get-docker/ on information on how to install docker for your system.

### Step - 2: NVIDIA docker runtime (not needed if you don't want to use GPUs)

If you have NVIDIA drivers installed, you need the NVIDIA runtime to use GPUs in the development environment.

Run the following commands if you are on Ubuntu to set up the NVIDIA runtimes.

```

# Add the package repositories

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -

curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit

sudo systemctl restart docker

```

For more information about the NVIDIA docker runtime, take a look here: https://github.com/NVIDIA/nvidia-docker

### Step - 3: Build the container

```

make build

```

### Step - 4: Start the coding environment

```

WORKSPACE=[PATH_TO_YOUR_CODEBASE] CPORT=[PORT] make code

```

Where ```PATH_TO_YOUR_CODEBASE``` is the path to your code base where all the scripts/notebooks are located and ```PORT``` is the port you want to run the IDE on

e.g. ```WORKSPACE=/home/abhishek/workspace/bert-sentiment CPORT=10012 make code```

### Step - 5: Open the URL in broswer

```http://127.0.0.1:10012/```

And have fun coding!