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Adding --gpu={gpu index} flag to docker option in tutorials. #1212

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Sep 23, 2020
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Adding --gpu={gpu index} flag to docker option in tutorials. #1212

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40c00a3
Adding `--gpu={gpu index}` flag to docker option in tutorials.
ananno Sep 16, 2020
8d4ff29
Merge branch 'master' into adding_gpu_flag_to_docs
ananno Sep 16, 2020
0026beb
Changing parameter description `GPU Index` -> `number of gpus`
ananno Sep 16, 2020
24d25b5
Removing `CUDA_VISIBLE_DEVICES` and replaced with `--gpus devices=0` …
ananno Sep 16, 2020
02b429b
Adding instruction for older docker version
ananno Sep 16, 2020
aa98022
Revert "Adding instruction for older docker version"
ananno Sep 16, 2020
1ce3780
Fixing typos
ananno Sep 16, 2020
66cd894
Merge branch 'master' into adding_gpu_flag_to_docs
ananno Sep 16, 2020
394db01
Fixing feature description for `--gpus` parameter.
ananno Sep 16, 2020
5de78ad
Merge branch 'master' into adding_gpu_flag_to_docs
bo-mergebot[bot] Sep 23, 2020
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5 changes: 3 additions & 2 deletions docs/tutorial/image_cls.md
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Original file line number Diff line number Diff line change
Expand Up @@ -106,15 +106,16 @@ If configuration finishes, the configuration file is generated in the `cifar10_t
Train your model by running `blueoil train` with model configuration.

$ docker run --rm \
-e CUDA_VISIBLE_DEVICES=0 \
--gpus '"device=0"' \
-v $(pwd)/cifar:/home/blueoil/cifar \
-v $(pwd)/config:/home/blueoil/config \
-v $(pwd)/saved:/home/blueoil/saved \
blueoil_$(id -un):{TAG} \
blueoil train -c config/cifar10_test.py

Just like init, set the value of `{TAG}` to the value obtained by `docker images`.
Change the value of `CUDA_VISIBLE_DEVICES` according to your environment.
Change the value of `--gpus '"device=<gpu indices>"'` according to your environment.
For detail check the official [documentation](https://docs.docker.com/config/containers/resource_constraints/#gpu).

When training has started, the training log and checkpoints are generated under `./saved/{MODEL_NAME}`.
The value of `{MODEL_NAME}` will be `{Configuration file}_{TIMESTAMP}`.
Expand Down
5 changes: 3 additions & 2 deletions docs/tutorial/image_det.md
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Expand Up @@ -67,15 +67,16 @@ If configuration finishes, the configuration file is generated in the `objectdet
Train your model by running `blueoil train` with a model configuration.

$ docker run --rm \
-e CUDA_VISIBLE_DEVICES=0 \
--gpus '"device=0"' \
-v $(pwd)/openimages_face:/home/blueoil/openimages_face \
-v $(pwd)/config:/home/blueoil/config \
-v $(pwd)/saved:/home/blueoil/saved \
blueoil_$(id -un):{TAG} \
blueoil train -c config/objectdetection.py

Just like init, set the value of `{TAG}` to the value obtained by `docker images`.
Change the value of `CUDA_VISIBLE_DEVICES` according to your environment.
Change the value of `--gpus '"device=<gpu indices>"'` according to your environment.
For detail check the official [documentation](https://docs.docker.com/config/containers/resource_constraints/#gpu).

When training has started, the training log and checkpoints are generated under `./saved/{MODEL_NAME}`.
The value of `{MODEL_NAME}` will be `{Configuration file}_{TIMESTAMP}`.
Expand Down
5 changes: 3 additions & 2 deletions docs/tutorial/image_keypoint_detection.md
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Expand Up @@ -61,7 +61,7 @@ If configuration finishes, the configuration file is generated in the `keypoint_
Train your model by running `blueoil train` with a model configuration.

$ docker run --rm \
-e CUDA_VISIBLE_DEVICES=0 \
--gpus '"device=0"' \
-e DATA_DIR=/home/blueoil \
-e OUTPUT_DIR=/home/blueoil/saved \
-v /storage/dataset/MSCOCO_2017:/home/blueoil/MSCOCO_2017 \
Expand All @@ -71,7 +71,8 @@ Train your model by running `blueoil train` with a model configuration.
blueoil train -c config/keypoint_detection_demo.py

Just like init, set the value of `{TAG}` to the value obtained by `docker images`.
Change the value of `CUDA_VISIBLE_DEVICES` according to your environment.
Change the value of `--gpus '"device=<gpu indices>"'` according to your environment.
For detail check the official [documentation](https://docs.docker.com/config/containers/resource_constraints/#gpu).

When training has started, the training log and checkpoints are generated under `./saved/{MODEL_NAME}`.
The value of `{MODEL_NAME}` will be `{Configuration file}_{TIMESTAMP}`.
Expand Down
5 changes: 3 additions & 2 deletions docs/tutorial/image_seg.md
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Original file line number Diff line number Diff line change
Expand Up @@ -97,15 +97,16 @@ If configuration finishes, the configuration file is generated in the `camvid.py
Train your model by running `blueoil train` command with model configuration.

$ docker run --rm \
-e CUDA_VISIBLE_DEVICES=0 \
--gpus '"device=0"' \
-v $(pwd)/CamVid:/home/blueoil/CamVid \
-v $(pwd)/config:/home/blueoil/config \
-v $(pwd)/saved:/home/blueoil/saved \
blueoil_$(id -un):{TAG} \
blueoil train -c config/camvid.py

Just like init, set the value of `{TAG}` to the value obtained by `docker images`.
Change the value of `CUDA_VISIBLE_DEVICES` according to your environment.
Change the value of `--gpus '"device=<gpu indices>"'` according to your environment.
For detail check the official [documentation](https://docs.docker.com/config/containers/resource_constraints/#gpu).

When training has started, the training log and checkpoints are generated under `./saved/{MODEL_NAME}`.
The value of `{MODEL_NAME}` will be `{Configuration file}_{TIMESTAMP}`.
Expand Down