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115 lines
5.6 KiB
Markdown
115 lines
5.6 KiB
Markdown
# icefall dockerfile
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2 sets of configuration are provided - (a) Ubuntu18.04-pytorch1.12.1-cuda11.3-cudnn8, and (b) Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8.
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If your NVIDIA driver supports CUDA Version: 11.3, please go for case (a) Ubuntu18.04-pytorch1.12.1-cuda11.3-cudnn8.
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Otherwise, since the older PyTorch images are not updated with the [apt-key rotation by NVIDIA](https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key), you have to go for case (b) Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8. Ensure that your NVDIA driver supports at least CUDA 11.0.
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You can check the highest CUDA version within your NVIDIA driver's support with the `nvidia-smi` command below. In this example, the highest CUDA version is 11.0, i.e. case (b) Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8.
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```bash
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$ nvidia-smi
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Tue Sep 20 00:26:13 2022
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 450.119.03 Driver Version: 450.119.03 CUDA Version: 11.0 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|===============================+======================+======================|
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| 0 TITAN RTX On | 00000000:03:00.0 Off | N/A |
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| 41% 31C P8 4W / 280W | 16MiB / 24219MiB | 0% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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| 1 TITAN RTX On | 00000000:04:00.0 Off | N/A |
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| 41% 30C P8 11W / 280W | 6MiB / 24220MiB | 0% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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+-----------------------------------------------------------------------------+
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| Processes: |
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| GPU GI CI PID Type Process name GPU Memory |
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| ID ID Usage |
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|=============================================================================|
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| 0 N/A N/A 2085 G /usr/lib/xorg/Xorg 9MiB |
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| 0 N/A N/A 2240 G /usr/bin/gnome-shell 4MiB |
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| 1 N/A N/A 2085 G /usr/lib/xorg/Xorg 4MiB |
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+-----------------------------------------------------------------------------+
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```
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## Building images locally
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If your environment requires a proxy to access the Internet, remember to add those information into the Dockerfile directly.
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For most cases, you can uncomment these lines in the Dockerfile and add in your proxy details.
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```dockerfile
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ENV http_proxy=http://aaa.bb.cc.net:8080 \
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https_proxy=http://aaa.bb.cc.net:8080
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```
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Then, proceed with these commands.
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### If you are case (a), i.e. your NVIDIA driver supports CUDA version >= 11.3:
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```bash
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cd docker/Ubuntu18.04-pytorch1.12.1-cuda11.3-cudnn8
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docker build -t icefall/pytorch1.12.1 .
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```
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### If you are case (b), i.e. your NVIDIA driver can only support CUDA versions 11.0 <= x < 11.3:
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```bash
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cd docker/Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8
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docker build -t icefall/pytorch1.7.1 .
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```
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## Running your built local image
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Sample usage of the GPU based images. These commands are written with case (a) in mind, so please make the necessary changes to your image name if you are case (b).
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Note: use [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) to run the GPU images.
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```bash
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docker run -it --runtime=nvidia --shm-size=2gb --name=icefall --gpus all icefall/pytorch1.12.1
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```
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### Tips:
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1. Since your data and models most probably won't be in the docker, you must use the -v flag to access the host machine. Do this by specifying `-v {/path/in/host/machine}:{/path/in/docker}`.
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2. Also, if your environment requires a proxy, this would be a good time to add it in too: `-e http_proxy=http://aaa.bb.cc.net:8080 -e https_proxy=http://aaa.bb.cc.net:8080`.
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Overall, your docker run command should look like this.
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```bash
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docker run -it --runtime=nvidia --shm-size=2gb --name=icefall --gpus all -v {/path/in/host/machine}:{/path/in/docker} -e http_proxy=http://aaa.bb.cc.net:8080 -e https_proxy=http://aaa.bb.cc.net:8080 icefall/pytorch1.12.1
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```
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You can explore more docker run options [here](https://docs.docker.com/engine/reference/commandline/run/) to suit your environment.
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### Linking to icefall in your host machine
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If you already have icefall downloaded onto your host machine, you can use that repository instead so that changes in your code are visible inside and outside of the container.
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Note: Remember to set the -v flag above during the first run of the container, as that is the only way for your container to access your host machine.
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Warning: Check that the icefall in your host machine is visible from within your container before proceeding to the commands below.
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Use these commands once you are inside the container.
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```bash
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rm -r /workspace/icefall
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ln -s {/path/in/docker/to/icefall} /workspace/icefall
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```
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## Starting another session in the same running container.
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```bash
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docker exec -it icefall /bin/bash
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```
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## Restarting a killed container that has been run before.
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```bash
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docker start -ai icefall
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```
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## Sample usage of the CPU based images:
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```bash
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docker run -it icefall /bin/bash
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```
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