...

You should see the lambda-stack image in the list, this is the one we will use for now.

To start a program in the a container using this image, use the docker run --rm command:

$ docker run --rm lambda-stack:20.04 ls
TODO add outputpwd
/root

The --rm flag ensures that the container is stopped and cleanup cleaned-up after usage. TODO clarify/check this

To run an interactive command like bash or python interpreter, add the -it flag:

$ docker run --rm -it lambda-stack:20.04 bash
TODO add outputroot@fa416f6b82f5:~# pwd
/root
root@fa416f6b82f5:~# exit
$

So far, the container does not have access to the GPUs. To give it access to them, you need to change the runtime to nvidia and explicitly specify a list of GPUs. The following example uses the first 2 GPUs4th and 5th GPUs (indices start at 0):

$ docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=03,14 --rm lambda-stack:20.04 nvidia-smi
TODO output
Thu Sep  1 22:02:25 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01    Driver Version: 515.65.01    CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-SXM...  On   | 00000000:4C:00.0 Off |                    0 |
| N/A   60C    P0   374W / 400W |  33549MiB / 81920MiB |     47%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA A100-SXM...  On   | 00000000:88:00.0 Off |                    0 |
| N/A   54C    P0   377W / 400W |  33549MiB / 81920MiB |     86%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

Note that GPU device numbers are re-mapped to start from 0 in the container.

An alternative method to access GPUs is the --gpus option:

$ docker run --gpus 2 --rm lambda-stack:20.04 nvidia-smi

Unless you are using the 8 GPUs, we strongly recommend not using this syntax, as it does not let you choose precisely which GPUs to select.

Access a folder from the container

A container is isolated from the host environment by default. Bind mounts allow you to mount a folder from the host machine into the container. TODO check terminology between bind mounts and volumes

Specify the path to the directory on host and the corresponding path inside the container using the -v flag:

...

$ docker run -v $HOME/my_project:/my_project --rm lambda-stack:20.04 ls /
TODO outputmy_project

Any program running in the container can then access and modify files in the /my_project folder.

...

Add packages to the container

The container image may not have all the packages you need. To add more packages, you can create a new container image based on the LambdaStack one.

To create a container image, you need a Dockerfile definition file. It contains the information about the base container image and the installations installation instructions for the additional packages.

...

FROM lambda-stack:20.04
RUN pip install pip install transformers[torch]

To build the corresponding container image, first create an empty folder and save the Dockerfile in it:

$ mkdir hugging_container
$ echo "FROM lambda-stack:20.04" > hugging_container/Dockerfile
$ echo "pip installRUN pip install transformers[torch]" >> hugging_container/Dockerfile

then use the docker build command to generate the new container image:

$ cd hugging_container
$ docker build -t pytorch-transformers .

The -t flag is used to tag the container image, making it easier to find and use it later.

...

$ docker image ls
TODO outputREPOSITORY             TAG                       IMAGE ID       CREATED          SIZE
pytorch-transformers   latest                    432c6be0a999   13 seconds ago   12.1GB
lambda-stack           20.04                     abe4a492cee1   6 days ago       12GB
ubuntu                 latest                    df5de72bdb3b   4 weeks ago      77.8MB
ubuntu                 20.04                     3bc6e9f30f51   4 weeks ago      72.8MB
debian                 latest                    07d9246c53a6   4 weeks ago      124MB
nvidia/cuda            11.0.3-base-ubuntu20.04   8017f5c31b74   6 weeks ago      122MB
hello-world            latest                    feb5d9fea6a5   11 months ago    13.3kB

You can now use it in place of the LambdaStack containerimage:

$ docker run --rm pytorch-transformers \
    python -c "import "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
TODO output

Next steps

...

No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).
Using a pipeline without specifying a model name and revision in production is not recommended.
Downloading config.json: 100%|██████████| 629/629 [00:00<00:00, 1.45MB/s]
Downloading pytorch_model.bin: 100%|██████████| 255M/255M [00:10<00:00, 24.5MB/s]
Downloading tokenizer_config.json: 100%|██████████| 48.0/48.0 [00:00<00:00, 72.8kB/s]
Downloading vocab.txt: 100%|██████████| 226k/226k [00:00<00:00, 295kB/s]
[{'label': 'POSITIVE', 'score': 0.9998656511306763}]

Container images can be deleted using the docker image rm command. For example, remove the pytorch-transformers container image as follows:

$ docker image rm pytorch-transformers
Untagged: pytorch-transformers:latest
Deleted: sha256:432c6be0a999484db090c5d9904e5c783454080d8ad8bc39e0499ace479c4559
Deleted: sha256:623ae3b33709c2fc4c40bc2c3959049345fee0087d39b4f53eb95aefd1c16f7d

Next steps

This document is a very short introduction to Docker to use LambdaStack. If you want to know more about Docker in general, we can recommend this workshop material and the associated recording on Youtube.