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6 changes: 3 additions & 3 deletions docs/user-guide.md
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Expand Up @@ -25,15 +25,15 @@ Currently, Fabric for Deep Learning supports following community frameworks

| Framework | Versions | Processing Unit |
| ------------- | ------------- | --------------- |
| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0, 1.3.0-py3, 1.4.0, 1.4.0-py3, 1.5.0, 1.5.0-py3, 1.5.1, 1.5.1-py3, 1.6.0, 1.6.0-py3, 1.7.0, 1.7.0-py3, 1.8.0, 1.8.0-py3, latest, latest-py3 | CPU |
| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0-gpu, 1.3.0-gpu-py3, 1.4.0-gpu, 1.4.0-gpu-py3, 1.5.0-gpu, 1.5.0-gpu-py3, 1.5.1-gpu, 1.5.1-gpu-py3, 1.6.0-gpu, 1.6.0-gpu-py3, 1.7.0-gpu, 1.7.0-gpu-py3, 1.8.0-gpu, 1.8.0-gpu-py3, latest-gpu, latest-gpu-py3 | GPU |
| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.4.0, 1.4.0-py3, 1.5.0, 1.5.0-py3, 1.5.1, 1.5.1-py3, 1.6.0, 1.6.0-py3, 1.7.0, 1.7.0-py3, 1.8.0, 1.8.0-py3, 1.9.0, 1.9.0-py3, latest, latest-py3 | CPU |
| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.4.0-gpu, 1.4.0-gpu-py3, 1.5.0-gpu, 1.5.0-gpu-py3, 1.5.1-gpu, 1.5.1-gpu-py3, 1.6.0-gpu, 1.6.0-gpu-py3, 1.7.0-gpu, 1.7.0-gpu-py3, 1.8.0-gpu, 1.8.0-gpu-py3, 1.9.0-gpu, 1.9.0-gpu-py3, latest-gpu, latest-gpu-py3 | GPU |
| [caffe](https://hub.docker.com/r/bvlc/caffe/) | cpu, intel | CPU |
| [caffe](https://hub.docker.com/r/bvlc/caffe/) | gpu | GPU |
| [pytorch](https://hub.docker.com/r/pytorch/pytorch/) | v0.2, latest | CPU, GPU |
| [caffe2](https://hub.docker.com/r/caffe2ai/caffe2/) | c2v0.8.1.cpu.full.ubuntu14.04, c2v0.8.0.cpu.full.ubuntu16.04 | CPU |
| [caffe2](https://hub.docker.com/r/caffe2ai/caffe2/) | c2v0.8.1.cuda8.cudnn7.ubuntu16.04, latest | GPU |
| [h2o3](https://hub.docker.com/r/opsh2oai/h2o3-ffdl/) | latest | CPU |
| [horovod](https://hub.docker.com/r/uber/horovod/) | 0.13.4-tf1.8.0-torch0.4.0-py3.5, 0.13.4-tf1.8.0-torch0.4.0-py2.7 | CPU, GPU |
| [horovod](https://hub.docker.com/r/uber/horovod/) | 0.13.10-tf1.9.0-torch0.4.0-py2.7, 0.13.10-tf1.9.0-torch0.4.0-py3.5 | CPU, GPU |

You can deploy models based on these frameworks and then train your models using the FfDL CLI or FfDL UI.

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2 changes: 1 addition & 1 deletion etc/examples/horovod/manifest_pytorchmnist.yml
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Expand Up @@ -20,5 +20,5 @@ data_stores:

framework:
name: horovod
version: "0.13.4-tf1.8.0-torch0.4.0-py3.5"
version: "0.13.10-tf1.9.0-torch0.4.0-py3.5"
command: python pytorch_mnist.py
2 changes: 1 addition & 1 deletion etc/examples/horovod/manifest_tfmnist.yml
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Expand Up @@ -20,6 +20,6 @@ data_stores:

framework:
name: horovod
version: "0.13.4-tf1.8.0-torch0.4.0-py3.5"
version: "0.13.10-tf1.9.0-torch0.4.0-py3.5"
command: python tensorflow_mnist.py
# the command is basically running the above command via openmpi, feel free to remove -x NCCL_DEBUG=INFO
12 changes: 6 additions & 6 deletions templates/services/learner-configmap.yml
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Expand Up @@ -31,10 +31,10 @@ data:
tensorflow_gpu_1.4.0-gpu_CURRENT: manual
tensorflow_cpu_1.4.0_CURRENT: manual
tensorflow_cpu_1.4.0-py3_CURRENT: manual
tensorflow_gpu_1.3.0-gpu-py3_CURRENT: manual
tensorflow_gpu_1.3.0-gpu_CURRENT: manual
tensorflow_cpu_1.3.0_CURRENT: manual
tensorflow_cpu_1.3.0-py3_CURRENT: manual
tensorflow_gpu_1.9.0-gpu-py3_CURRENT: manual
tensorflow_gpu_1.9.0-gpu_CURRENT: manual
tensorflow_cpu_1.9.0_CURRENT: manual
tensorflow_cpu_1.9.0-py3_CURRENT: manual
h2o3_cpu_latest_CURRENT: manual
caffe_cpu_cpu_CURRENT: master-39
caffe_gpu_gpu_CURRENT: master-39
Expand All @@ -45,8 +45,8 @@ data:
caffe2_gpu_c2v0.8.1.cuda8.cudnn7.ubuntu16.04_CURRENT: master-39
caffe2_cpu_c2v0.8.0.cpu.full.ubuntu16.04_CURRENT: master-39
caffe2_gpu_latest_CURRENT: master-39
horovod_gpu_0.13.4-tf1.8.0-torch0.4.0-py3.5_CURRENT: manual
horovod_gpu_0.13.4-tf1.8.0-torch0.4.0-py2.7_CURRENT: manual
horovod_gpu_0.13.10-tf1.9.0-torch0.4.0-py3.5_CURRENT: manual
horovod_gpu_0.13.10-tf1.9.0-torch0.4.0-py2.7_CURRENT: manual
---

apiVersion: v1
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