What Docker can do that Anaconda can't, and why you should use it for your ML/DL projects
Deep learning projects on GitHub can be hard to set up because they have a lot of dependencies, aren't always compatible, need specific hardware, and don't have a lot of documentation. In this post, I'll show you how Docker and Anaconda are different, how they can deal with these problems, and why Docker is better.
It can be hard to set up an environment for deep learning projects on GitHub for a few reasons:
- Dependency management: Many deep learning projects need specific versions of Python, libraries, and frameworks, among other things. Managing and installing all of these dependencies can be hard, especially if you are working on multiple projects that all have different dependencies.
- Compatibility problems: Different versions of libraries and frameworks may not work well together, making it hard to set up an environment that works for all of your projects.
- Requirements for the hardware: Some deep learning projects may need specific hardware, like a GPU with a certain amount of memory. Setting up an environment that meets all of these requirements can be hard, especially if you don't have the right hardware.
- Lack of documentation: Not all deep learning projects have complete documentation, which can make it hard to understand how to set up and use the code.
Anaconda is a distribution of Python and R that comes with a lot of packages for scientific computing, data science, and machine learning. The conda package manager that comes with Anaconda makes it easy to install and update packages.
One of the best things about Anaconda for deep learning projects is that you can set up different environments for each project. You can install different versions of packages and libraries in different Anaconda environments. This can be helpful when working on projects that depend on different things.
You can use the following command in Anaconda to set up an environment:
conda create --name myenv
This will create a new environment called "myenv" that you can use for your deep learning project. The next step is to activate this newly created environment.
To switch between environments, you can use the
conda activate command. For example:
conda activate myenv
You can then install the necessary packages and libraries in this environment using the
conda install command. For example:
conda install tensorflow numpy scipy
It's a good tool for setting up different environments for deep learning projects because it lets you manage and install packages, make separate environments, and easily switch between environments.
Docker is a platform for containerization that lets you put an application and all of its dependencies into a single container. One of the best things about Docker is that it lets you put your application and all of its dependencies in its own container.
This means that you can install different versions of libraries and packages in different containers and switch between them as needed. This can be especially helpful for machine learning, where different models and libraries may need different versions of CUDA and other dependencies.
On the other hand, Anaconda is a distribution of packages that are added to an operating system that is already running. This means that you can only use packages and libraries that are compatible with the operating system that is running in the background. Even though Anaconda lets you create different environments and install different versions of packages, you are still limited to packages that are compatible with the operating system.
To install CUDA 10.1 in a Docker container using the command line, you will need to have Nvidia Docker installed on your system. If you don't already have Docker installed, you can follow the instructions here: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
Once you have Docker installed, you can follow these steps to create a Docker container with CUDA 10.1 installed:
Pull the CUDA 10.1, PyTorch 1.4, and cudnn7 Docker image from the NVIDIA Docker Registry and then create a new Docker container from the CUDA 10.2 image. You can do this using the
docker run command, as follows:
nvidia-docker run -it --name YourContainerName \ -p 9527:9527 -p 5008:5008 -p 8522:8522 \ -e DISPLAY=unix$DISPLAY -e USER=root \ --mount type=bind,source=/path/to/source/path/,target=/path/to/target/container \ pytorch/pytorch:1.4-cuda10.1-cudnn7-devel
Once you are inside the container, you can verify that CUDA is installed by running the
This should print the version of CUDA that is installed in the container.
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