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First check that the container image is available using the docker image ls command:
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$ docker image ls
REPOSITORY TAG IMAGE ID CREATED SIZE
lambda-stack 20.04 abe4a492cee1 6 hours ago 12GB
ubuntu latest df5de72bdb3b 3 weeks ago 77.8MB
ubuntu 20.04 3bc6e9f30f51 3 weeks ago 72.8MB
debian latest 07d9246c53a6 3 weeks ago 124MB
nvidia/cuda 11.0.3-base-ubuntu20.04 8017f5c31b74 5 weeks ago 122MB
hello-world latest feb5d9fea6a5 11 months ago 13.3kB |
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To start a program in the container, use the docker run --rm command:
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$ docker run --rm lambda-stack:20.04 ls
TODO add output |
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To run an interactive command like bash or python interpreter, add the -it flag:
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$ docker run --rm -it lambda-stack:20.04 bash
TODO add output |
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| breakoutMode | wide |
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| language | bashnone |
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$ docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0,1 --rm lambda-stack:20.04 nvidia-smi
TODO output |
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For example, assuming you have a project folder in $HOME/my_project and want to access it as /my_project in the container, you would use:
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$ docker run -v $HOME/my_project:/my_project --rm lambda-stack:20.04 ls /
TODO output |
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In the following Dockerfile example, the Transformers library (PyTorch version) from Hugging Face is added to the LambdaStack container:
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FROM lambda-stack:20.04
RUN pip install pip install transformers[torch] |
To build the corresponding container, first create an empty folder and save the Dockerfile in it:
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$ mkdir hugging_container
$ echo "FROM lambda-stack:20.04" > hugging_container/Dockerfile
$ echo "pip install pip install transformers[torch]" >> hugging_container/Dockerfile |
then use the docker build command to generate the new container:
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$ cd hugging_container
$ docker build -t $USER/huggingpytorch-transformers . |
TODO list imageThe -t flag is used to tag the container, making it easier to find and use it later.
Use docker image ls to check the availability of the image:
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$ docker image ls
TODO output |
You can now use it in place of the LambdaStack container:
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$ docker run --rm pytorch-transformers \
python -c "import "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
TODO output |
Next steps
TODO list references to go beyond the basics