Comments (2)
I recommend that you look up some Docker tutorials and familiarise yourself with how it works, since this is a very basic question.
One way of achieving what you want is to create your own Dockerfile based on mine using the FROM
line. So with this approach you will have your own custom image with the additional packages you need.
Another option is to run a container, but not remove it when it stops (no --rm
option). Then you can start/stop it by its name/ID whenever you want, maintaining its state. This can be useful for testing stuff out, but makes it more difficult to create more containers later.
from docker-pytorch.
To the other newbies like me this is what I did:
- download the dockerfile from here and
- added requirements.txt near the end of the file as suggested here
COPY requirements.txt /tmp/
RUN pip install --requirement /tmp/requirements.txt
COPY . /tmp/
- listed my packages in requirements like so
pandas
....
I used this tutorial to get oriented.
from docker-pytorch.
Related Issues (20)
- this is the issue which it's showing while running the docker file HOT 2
- Cuda not found after pulling docker image at docker pull anibali/pytorch:1.4.0-cuda10.1 HOT 2
- can we have a docker image with torch 1.4+ cuda 10.2 and ubuntu 16.04 HOT 1
- Any ideas regarding reducing Image size? HOT 1
- pytorch 1.3 with cuda 10 on ubuntu 18.04 image HOT 1
- could not install software HOT 5
- Is CUDA being downloaded twice? HOT 4
- python3: can't open file 'script.py': [Errno 2] No such file or directory HOT 4
- CUDA Version Problem HOT 1
- Disable user prompt when building dockerfile HOT 1
- install HOT 1
- docker image failed to build HOT 2
- why do we use miniconda with pytorch cuda image? HOT 2
- Images to work with for Google Cloud SDK HOT 1
- About the permissions of docekr images HOT 1
- Key errors when deriving from 2.0 images & installing packages HOT 2
- How to enter the container with su mode HOT 1
- Size to big: use multi stage docker files HOT 4
- ERROR: failed to solve -> 1.7.0-cuda11.0-ubuntu20.04 HOT 1
- Support arm64 platform image HOT 1
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from docker-pytorch.