mirror of
https://github.com/helm/charts.git
synced 2026-08-23 22:37:45 +00:00
Szymon Piotr Krasuski
19b4b54a39
* Add custom variable to set number of trainign steps. By default script run in cheyang/distributed-tensorflow container runs with flag train_steps set to 0 which means that script will be running inifitely, However, it's to possible to set this flag to fixed number which will make script to run only passed number of training steps and stop. This change adds custom variable in hyperparams that lets user set custom value for training steps. Signed-off-by: Szymon <krasuski.szymon.piotr@gmail.com> * Bump chart version Signed-off-by: Szymon <krasuski.szymon.piotr@gmail.com>
87 lines
2.5 KiB
Markdown
87 lines
2.5 KiB
Markdown
# Distributed TensorFlow
|
|
|
|
TensorFlow is an open source software library for numerical computation using data flow graphs, and it supports distributed computing, allowing 'data parallel' or 'model parallel' on different servers. This means data scientists can now scale out distributed training to 100s of GPUs using TensorFlow.
|
|
|
|
- https://www.tensorflow.org
|
|
- https://www.tensorflow.org/deploy/distributed
|
|
|
|
|
|
|
|
## Prerequisites
|
|
|
|
- Kubernetes cluster v1.8+
|
|
|
|
## Chart Details
|
|
|
|
This chart will create a TensorFlow cluster, and distribute a computation graph across that cluster.
|
|
|
|
## Installing the Chart
|
|
|
|
* To install the chart with the release name `mnist`:
|
|
|
|
```bash
|
|
$ helm install --name mnist incubator/distributed-tensorflow
|
|
```
|
|
|
|
* To install with custom values via file:
|
|
|
|
```
|
|
$ helm install --values values.yaml --name mnist incubator/distributed-tensorflow
|
|
```
|
|
|
|
Below is an example of the custom value file values.yaml with GPU support.
|
|
|
|
```
|
|
worker:
|
|
number: 2
|
|
podManagementPolicy: Parallel
|
|
image:
|
|
repository: cheyang/distributed-tf
|
|
tag: 1.6.0-gpu
|
|
port: 9090
|
|
gpuCount: 1
|
|
|
|
ps:
|
|
number: 2
|
|
podManagementPolicy: Parallel
|
|
image:
|
|
repository: cheyang/distributed-tf
|
|
tag: 1.6.0
|
|
pullPolicy: IfNotPresent
|
|
port: 8080
|
|
|
|
# optimize for training
|
|
hyperparams:
|
|
batchsize: 20
|
|
learningrate: 0.001
|
|
trainsteps: 10000
|
|
```
|
|
|
|
> Notice: you can check the details of docker image from [Docker hub](https://hub.docker.com/r/cheyang/distributed-tf/)
|
|
|
|
## Uninstalling the Chart
|
|
|
|
* To uninstall/delete the `mnist` deployment:
|
|
|
|
```bash
|
|
$ helm delete mnist
|
|
```
|
|
|
|
The command removes all the Kubernetes components associated with the chart and deletes the release.
|
|
|
|
## Configuration
|
|
|
|
The following table lists the configurable parameters of the Service Distributed Tensorflow
|
|
chart and their default values.
|
|
|
|
| Parameter | Description | Default |
|
|
|-----------|-------------|---------|
|
|
| `worker.image.repository` | TensorFlow Worker Server's image repository | `cheyang/distributed-tf` |
|
|
| `worker.image.tag` | TensorFlow Worker Server's image tag | `gpu` |
|
|
| `worker.image.pullPolicy` | image pullPolicy for the worker | `IfNotPresent` |
|
|
| `worker.gpuCount` | Set the gpu to be allocated and allowed for the Pods | `0` |
|
|
| `ps.image.repository` | TensorFlow Parameter Server's image repository | `cheyang/distributed-tf` |
|
|
| `ps.image.tag` | TensorFlow Parameter Server's image tag | `1.6.0-gpu` |
|
|
| `ps.image.pullPolicy` | image pullPolicy for the ps | `IfNotPresent` |
|
|
|