schuetoliver
/
handson-ml
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Simplify and organize the instructions

main
Aurélien Geron 2021-03-05 23:03:23 +13:00
parent 931c2c9558
commit b59bd8c745
1 changed files with 19 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
docker/README.md
View File

@ -74,6 +74,8 @@ To be precise, the output will tell you *what modifications should be re-played
## GPU Support on Linux (experimental)
### Prerequisites
If you're running on Linux, and you have a TensorFlow-compatible GPU card (NVidia card with Compute Capability ≥ 3.5) that you would like TensorFlow to use inside the Docker container, then you should download and install the latest driver for your card from [nvidia.com](https://www.nvidia.com/Download/index.aspx?lang=en-us). You will also need to install [NVidia Docker support](https://github.com/NVIDIA/nvidia-docker): if you are using Docker 19.03 or above, you must install the `nvidia-container-toolkit` package, and for earlier versions, you must install `nvidia-docker2`.
Next, edit the `docker-compose.yml` file:
@ -87,7 +89,9 @@ $ edit environment.yml # use your favorite editor
* Replace `image: ageron/handson-ml2:latest` with `image: ageron/handson-ml2:latest-gpu`
* If you want to use `docker-compose`, you will need version 1.28 or above for GPU support, and you must uncomment the whole `deploy` section in `docker-compose.yml`.
Next, if you want to pull the prebuilt image from Docker Hub (this will download over 4 GB of data):
### Prepare the image (once)
If you want to pull the prebuilt image from Docker Hub (this will download over 4 GB of data):
```bash
$ docker pull ageron/handson-ml2:latest-gpu
@ -100,7 +104,9 @@ $ cd /path/to/project/handson-ml2/docker
$ docker-compose build
```
To run the image, it depends. If you have `docker-compose` version 1.28 or above, that's great! You can simply run:
### Run the notebooks with `docker-compose` (version 1.28 or above)
If you have `docker-compose` version 1.28 or above, that's great! You can simply run:
```bash
$ cd /path/to/project/handson-ml2/docker
@ -117,36 +123,29 @@ import tensorflow as tf
tf.config.list_physical_devices("GPU")
```
To stop and remove the container, just run:
To stop the server, just press Ctrl-C.
```bash
$ docker-compose stop
```
### Run the notebooks without `docker-compose`
However, if you have a version of `docker-compose` earlier than 1.28, you will have to use `docker run` directly. If you are using Docker 19.03 or above, you can run:
If you have a version of `docker-compose` earlier than 1.28, you will have to use `docker run` directly.
If you are using Docker 19.03 or above, you can run:
```bash
$ cd /path/to/project/handson-ml2
$ docker run --name handson-ml2 --gpus all -p 8888:8888 -p 6006:6006 --log-opt mode=non-blocking --log-opt max-buffer-size=50m -d -v `pwd`:/home/devel/handson-ml2 ageron/handson-ml2:latest-gpu /opt/conda/envs/tf2/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
$ docker run --name handson-ml2 --gpus all -p 8888:8888 -p 6006:6006 --log-opt mode=non-blocking --log-opt max-buffer-size=50m -v `pwd`:/home/devel/handson-ml2 ageron/handson-ml2:latest-gpu /opt/conda/envs/tf2/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
```
If you are using an older version of Docker, then replace `--gpus all` with `--runtime=nvidia`.
Then, after a second or two, display the container's logs like this:
Now point your browser to the displayed URL: Jupyter should appear, and you can open a notebook and run `import tensorflow as tf` and `tf.config.list_physical_devices("GPU)` as above to confirm that TensorFlow does indeed see your GPU device(s).
Lastly, to interrupt the server, press Ctrl-C, then run:
```bash
$ docker logs handson-ml2
[...]
or http://127.0.0.1:8888/?token=[...]
```
And point your browser to the displayed URL. Again, Jupyter should appear, and you can run the `tf.config.list_physical_devices("GPU)` code as above to confirm that TensorFlow does indeed see your GPU device(s).
To stop and destroy the container (but not the image), run:
```bash
$ docker stop handson-ml2
$ docker rm handson-ml2
```
This will remove the container so you can start a new one later (but it will not remove the image or the notebooks, don't worry!).
Have fun!