Comments (1)
Hello,
Thank you for your interest in the repository.
The reason behind that decision was the fact that users need to specify 2 port numbers one for the training api and one for tensorboard.
In order to avoid selecting already used ports and cause errors there is a small script inside tf_docker_sdk that checks open ports so user can only select from then hence minimizing user input error.
Since tf_docker_sdk is now required to see the host ports, the network mode has been set to host.
from bmw-tensorflow-training-gui.
Related Issues (20)
- Couldn't choose Type of Labels sometimes HOT 1
- Error while building docker HOT 3
- The button doesn't respond.
- The button doesn't respond. HOT 1
- Support for IBM Power Architecture (ppc64le) HOT 1
- URL malformed on RHEL HOT 8
- API down or unknown error HOT 4
- Performance issues in training_api/research/object_detection/utils/ops.py HOT 2
- Performance issues in training_api/research/ (by P3) HOT 2
- Can I use this project without nvidia gpu? HOT 1
- Unsupported config option for services.docker_sdk: 'runtime HOT 1
- colab HOT 3
- Issue with image name in docker_sdk : "tf2_docker_sdk_api:latest" for docker compose in run_gpu.yml HOT 4
- training weights for multiple classes one by one. HOT 1
- unpack requires a buffer of 2 bytes HOT 2
- Training job crash if classes are digits HOT 4
- Creating job crashing between step 2 and 3 HOT 5
- Docker-Py 4.3.1 needs requests<2.2.29 HOT 2
- when starting the test training i get unknown error HOT 4
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