Comments (3)
For CPU, simply add the new framework options and pull the images in lcm/container_helper_extensions.go. Then we need to add some unit test and example jobs and should be good to go.
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For GPU, they are on the same boat with Caffe and TensorFlow where we need to implement a trigger in LCM to switch jobs between CPU and GPU seamlessly.
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Fixed in #24
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Related Issues (20)
- FfDL v0.1.1 model training error HOT 4
- FfDL CLI output is not properly machine parsable
- [Documentation] Update IBM Cloud CLI instructions in /etc/converter/train-deploy-wml.md
- dind-port-forward.sh -> invalid resource name ? HOT 5
- Grafana charts shows no data points HOT 1
- Unable to mount volumes for pod Learner HOT 8
- Learner pod stuck at training step 100 using custom image with TF Object Detection HOT 5
- / FfDL/demos/fashion-mnist-adversarial/README.md references internal repository HOT 1
- how to use pytorch and caffe built by ourselves? HOT 2
- kubectl get pods :lcm ContainerCreating,prometheus trainer and trainingdata STATUS CrashLoopBackOff HOT 26
- tiller-deploy is in status CrashLoopBackOff HOT 2
- Confused about manifest.yml HOT 2
- learner pod failed HOT 19
- caffe training speed is very slow HOT 4
- pytorch training issue: insufficient shared memory HOT 2
- distributed training questions HOT 2
- why pytorch distributed training on two servers is slower than training on one server HOT 21
- .travis.yml: The 'sudo' tag is now deprecated in Travis CI
- ssh permission denied when deploying FfDL on public cloud
- fail to install
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