AIME ML Containers
AIME machine learning container management system.
Easily install, run and manage Docker containers for the most common deep learning frameworks.
- Setup and run a specific version of Tensorflow, Pytorch or Mxnet with one simple command
- Run different versions of machine learning frameworks and required libraries in parallel
- manages required libraries (CUDA, CUDNN, CUBLAS, etc.) in containers, without compromising the host installation
- Clear separation of user code and framework installation, test your code with a different framework version in minutes
- multi session: open and run many shell session on a single container simultaneously
- multi user: separate container space for each user
- multi GPU: allocate GPUs per user, container or session
- Runs with the same performance as a bare metal installation
- Repository of all major deep learning framework versions as containers
Create a machine learning container
mlc-create container_name framework version [-w=workspace_dir] [-d=data_dir]
Create a new machine learning container
Tensorflow, MXNet, Pytorch
Available versions for NVIDIA Ampere based GPUs (RTX 30x0, RTX A5000/A6000, A100):
Tensorflow: 2.9.0, 2.8.0, 2.7.0, 2.6.1, 2.5.0, 2.4.1, 2.4.0, 2.3.1-nvidia, 1.15.4-nvidia
Pytorch: 1.11.0, 1.10.2-aime, 1.10.0, 1.9.0, 1.8.0, 1.7.1, 1.7.0, 1.7.0-nvidia
Example to create a container with the name 'my-container' as Tensorflow 1.15.4 with mounted user home directory as workspace use:
> mlc-create my-container Tensorflow 1.15.4 -w=/home/admin
Open a machine learning container
To open the created machine learning container "my-container"
> mlc-open my-container
[my-container] starting container [my-container] opening shell to container ________ _______________ ___ __/__________________________________ ____/__ /________ __ __ / _ _ \_ __ \_ ___/ __ \_ ___/_ /_ __ /_ __ \_ | /| / / _ / / __/ / / /(__ )/ /_/ / / _ __/ _ / / /_/ /_ |/ |/ / /_/ \___//_/ /_//____/ \____//_/ /_/ /_/ \____/____/|__/ You are running this container as user with ID 1000 and group 1000, which should map to the ID and group for your user on the Docker host. Great! [my-container] admin@aime01:/workspace$
The container is run with the access rights of the user. To use privileged rights like for installing packages with 'apt' within the container use 'sudo'. The default is that no password is needed for sudo, to change this behaviour set a password with 'passwd'.
Multiple instances of a container can be opened with mlc-open. Each instance runs in its own process.
To exit an opened shell to the container type 'exit' on the command line. The last exited shell will automatically stop the container.
List available machine learning containers
mlc-list will list all available containers for the current user
will output for example:
Available ml-containers are: CONTAINER FRAMEWORK STATUS [torch-vid2vid] Pytorch-1.2.0 Up 2 days [tf1.15.0] Tensorflow-1.15.0 Up 8 minutes [mx-container] Mxnet-1.5.0 Exited (137) 1 day ago [tf1-nvidia] Tensorflow-1.14.0_nvidia Exited (137) 1 week ago [tf1.13.2] Tensorflow-1.13.2 Exited (137) 2 weeks ago [torch1.3] Pytorch-1.3.0 Exited (137) 3 weeks ago [tf2-gpt2] Tensorflow-2.0.0 Exited (137) 7 hours ago
List active machine learning containers
mlc-stats show all current running ml containers and their CPU and memory usage
> mlc-stats Running ml-containers are: CONTAINER CPU % MEM USAGE / LIMIT [torch-vid2vid] 4.93% 8.516GiB / 63.36GiB [tf1.15.0] 7.26% 9.242GiB / 63.36GiB
Start machine learning containers
mlc-start container_name to explicitly start a container
mlc-start is a way to start the container to run installed background processes, like an installed web server, on the container without the need to open an interactive shell to it.
For opening a shell to the container just use 'mlc-open', which will automatically start the container if the container is not already running.
Stop machine learning containers
mlc-stop container_name [-Y] to explicitly stop a container.
mlc-stop on a container is comparable to a shutdown of a computer, all activate processes and open shells to the container will be terminated.
To force a stop on a container use:
mlc-stop my-container -Y
Remove/Delete a machine learning container
mlc-remove container_name to remove the container.
Warning: the container will be unrecoverable deleted only data stored in the /workspace directory will be kept. Only use to clean up containers which are not needed any more.
Update ML Containers
mlc-update-sys to update the container managment system to latest version.
The container system and container repo will be updated to latest version. Run this command to check if new framework versions are available. On most systems privileged access (sudo password) is required to do so.
AIME machines come pre installed with AIME machine learning container management system for more information see: https://www.aime.info/blog/deep-learning-framework-container-management/