Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
Hi Sheng, I suggest to put down a reason for such actions later. It may confuse other contributors, e.g., Steffen raised his concern in a private thread. Best Mu > On Jun 10, 2018, at 7:42 PM, Sheng Zha wrote: > > Thanks, Henri. I was reverting the commit on a PR that another committer > didn't intend to merge but only realized afterwards. Given that it wasn't > convenient for him to revert and the negative effect, I committed the > revert and cc'd the original committer in the PR, both as notification and > as a proof of the claim. > > -sz > >> On Sun, Jun 10, 2018 at 10:21 PM, Hen wrote: >> >> It wasn't clear why this was commit was reverted. Things that stood out as >> odd: >> >> * I didn't see an email to dev@ on the topic of a revert. >> * Rather than reverting, if there is a minor item requiring a fix it could >> simply be fixed; if a major item then it should be raised on dev@. >> * I didn't see a reason to revert in the revert PR (11154). >> * The original PR has github:szha asking for github:piiswrong to review >> with no context; I'm concerned that it was implied that the commit could >> not go in without this review. >> * I don't see anything in the original PR to earn a revert. At best >> 'github:john-andrilla' being asked if "a flexible, scalable, >> multi-framework serving solution" was okay. >> * I find it odd that github:lupesko is a reviewer. >> >> Hen >> >> >> >>> On Tue, Jun 5, 2018 at 5:08 PM, GitBox wrote: >>> >>> szha closed pull request #11154: Revert "[MXNET-503] Website landing page >>> for MMS (#11037)" >>> URL: https://github.com/apache/incubator-mxnet/pull/11154 >>> >>> >>> >>> >>> This is a PR merged from a forked repository. >>> As GitHub hides the original diff on merge, it is displayed below for >>> the sake of provenance: >>> >>> As this is a foreign pull request (from a fork), the diff is supplied >>> below (as it won't show otherwise due to GitHub magic): >>> >>> diff --git a/docs/mms/index.md b/docs/mms/index.md >>> deleted file mode 100644 >>> index ff6edae414b..000 >>> --- a/docs/mms/index.md >>> +++ /dev/null >>> @@ -1,114 +0,0 @@ >>> -# Model Server for Apache MXNet (incubating) >>> - >>> -[Model Server for Apache MXNet (incubating)](https://github. >>> com/awslabs/mxnet-model-server), otherwise known as MXNet Model Server >>> (MMS), is an open source project aimed at providing a simple yet scalable >>> solution for model inference. It is a set of command line tools for >>> packaging model archives and serving them. The tools are written in >> Python, >>> and have been extended to support containers for easy deployment and >>> scaling. MMS also supports basic logging and advanced metrics with Amazon >>> CloudWatch integration. >>> - >>> - >>> -## Multi-Framework Model Support with ONNX >>> - >>> -MMS supports both *symbolic* MXNet and *imperative* Gluon models. While >>> the name implies that MMS is just for MXNet, it is in fact much more >>> flexible, as it can support models in the [ONNX](https://onnx.ai) >> format. >>> This means that models created and trained in PyTorch, Caffe2, or other >>> ONNX-supporting frameworks can be served with MMS. >>> - >>> -To find out more about MXNet's support for ONNX models and using ONNX >>> with MMS, refer to the following resources: >>> - >>> -* [MXNet-ONNX Docs](../api/python/contrib/onnx.md) >>> -* [Export an ONNX Model to Serve with MMS](https://github.com/ >>> awslabs/mxnet-model-server/docs/export_from_onnx.md) >>> - >>> -## Getting Started >>> - >>> -To install MMS with ONNX support, make sure you have Python installed, >>> then for Ubuntu run: >>> - >>> -```bash >>> -sudo apt-get install protobuf-compiler libprotoc-dev >>> -pip install mxnet-model-server >>> -``` >>> - >>> -Or for Mac run: >>> - >>> -```bash >>> -conda install -c conda-forge protobuf >>> -pip install mxnet-model-server >>> -``` >>> - >>> - >>> -## Serving a Model >>> - >>> -To serve a model you must first create or download a model archive. >> Visit >>> the [model zoo](https://github.com/awslabs/mxnet-model-server/ >>> docs/model_zoo.md) to browse the models. MMS options can be explored as >>> follows: >>> - >>> -```bash >>> -mxnet-model-server --help >>> -``` >>> - >>> -Here is an easy example for serving an object classification model. You >>> can use any URI and the model will be downloaded first, then served from >>> that location: >>> - >>> -```bash >>> -mxnet-model-server \ >>> - --models squeezenet=https://s3.amazonaws.com/model-server/ >>> models/squeezenet_v1.1/squeezenet_v1.1.model >>> -``` >>> - >>> - >>> -### Test Inference on a Model >>> - >>> -Assuming you have run the previous `mxnet-model-server` command to start >>> serving the object classification model, you can now upload an image to >> its >>> `predict` REST API endpoint. The following will download a picture of a >>> kitten, then upload it to the prediction endpoint. >>> - >>> -```bash >>> -curl -O https://s3.amazonaws.com/model-server/inputs/kitten.
Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
Thanks, Henri. I was reverting the commit on a PR that another committer didn't intend to merge but only realized afterwards. Given that it wasn't convenient for him to revert and the negative effect, I committed the revert and cc'd the original committer in the PR, both as notification and as a proof of the claim. -sz On Sun, Jun 10, 2018 at 10:21 PM, Hen wrote: > It wasn't clear why this was commit was reverted. Things that stood out as > odd: > > * I didn't see an email to dev@ on the topic of a revert. > * Rather than reverting, if there is a minor item requiring a fix it could > simply be fixed; if a major item then it should be raised on dev@. > * I didn't see a reason to revert in the revert PR (11154). > * The original PR has github:szha asking for github:piiswrong to review > with no context; I'm concerned that it was implied that the commit could > not go in without this review. > * I don't see anything in the original PR to earn a revert. At best > 'github:john-andrilla' being asked if "a flexible, scalable, > multi-framework serving solution" was okay. > * I find it odd that github:lupesko is a reviewer. > > Hen > > > > On Tue, Jun 5, 2018 at 5:08 PM, GitBox wrote: > > > szha closed pull request #11154: Revert "[MXNET-503] Website landing page > > for MMS (#11037)" > > URL: https://github.com/apache/incubator-mxnet/pull/11154 > > > > > > > > > > This is a PR merged from a forked repository. > > As GitHub hides the original diff on merge, it is displayed below for > > the sake of provenance: > > > > As this is a foreign pull request (from a fork), the diff is supplied > > below (as it won't show otherwise due to GitHub magic): > > > > diff --git a/docs/mms/index.md b/docs/mms/index.md > > deleted file mode 100644 > > index ff6edae414b..000 > > --- a/docs/mms/index.md > > +++ /dev/null > > @@ -1,114 +0,0 @@ > > -# Model Server for Apache MXNet (incubating) > > - > > -[Model Server for Apache MXNet (incubating)](https://github. > > com/awslabs/mxnet-model-server), otherwise known as MXNet Model Server > > (MMS), is an open source project aimed at providing a simple yet scalable > > solution for model inference. It is a set of command line tools for > > packaging model archives and serving them. The tools are written in > Python, > > and have been extended to support containers for easy deployment and > > scaling. MMS also supports basic logging and advanced metrics with Amazon > > CloudWatch integration. > > - > > - > > -## Multi-Framework Model Support with ONNX > > - > > -MMS supports both *symbolic* MXNet and *imperative* Gluon models. While > > the name implies that MMS is just for MXNet, it is in fact much more > > flexible, as it can support models in the [ONNX](https://onnx.ai) > format. > > This means that models created and trained in PyTorch, Caffe2, or other > > ONNX-supporting frameworks can be served with MMS. > > - > > -To find out more about MXNet's support for ONNX models and using ONNX > > with MMS, refer to the following resources: > > - > > -* [MXNet-ONNX Docs](../api/python/contrib/onnx.md) > > -* [Export an ONNX Model to Serve with MMS](https://github.com/ > > awslabs/mxnet-model-server/docs/export_from_onnx.md) > > - > > -## Getting Started > > - > > -To install MMS with ONNX support, make sure you have Python installed, > > then for Ubuntu run: > > - > > -```bash > > -sudo apt-get install protobuf-compiler libprotoc-dev > > -pip install mxnet-model-server > > -``` > > - > > -Or for Mac run: > > - > > -```bash > > -conda install -c conda-forge protobuf > > -pip install mxnet-model-server > > -``` > > - > > - > > -## Serving a Model > > - > > -To serve a model you must first create or download a model archive. > Visit > > the [model zoo](https://github.com/awslabs/mxnet-model-server/ > > docs/model_zoo.md) to browse the models. MMS options can be explored as > > follows: > > - > > -```bash > > -mxnet-model-server --help > > -``` > > - > > -Here is an easy example for serving an object classification model. You > > can use any URI and the model will be downloaded first, then served from > > that location: > > - > > -```bash > > -mxnet-model-server \ > > - --models squeezenet=https://s3.amazonaws.com/model-server/ > > models/squeezenet_v1.1/squeezenet_v1.1.model > > -``` > > - > > - > > -### Test Inference on a Model > > - > > -Assuming you have run the previous `mxnet-model-server` command to start > > serving the object classification model, you can now upload an image to > its > > `predict` REST API endpoint. The following will download a picture of a > > kitten, then upload it to the prediction endpoint. > > - > > -```bash > > -curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg > > -curl -X POST http://127.0.0.1:8080/squeezenet/predict -F > > "data=@kitten.jpg" > > -``` > > - > > -The predict endpoint will return a prediction response in JSON. It will > > look something like the following result: > > - > > -``` > > -{ > > - "prediction": [ > > -
Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
It wasn't clear why this was commit was reverted. Things that stood out as odd: * I didn't see an email to dev@ on the topic of a revert. * Rather than reverting, if there is a minor item requiring a fix it could simply be fixed; if a major item then it should be raised on dev@. * I didn't see a reason to revert in the revert PR (11154). * The original PR has github:szha asking for github:piiswrong to review with no context; I'm concerned that it was implied that the commit could not go in without this review. * I don't see anything in the original PR to earn a revert. At best 'github:john-andrilla' being asked if "a flexible, scalable, multi-framework serving solution" was okay. * I find it odd that github:lupesko is a reviewer. Hen On Tue, Jun 5, 2018 at 5:08 PM, GitBox wrote: > szha closed pull request #11154: Revert "[MXNET-503] Website landing page > for MMS (#11037)" > URL: https://github.com/apache/incubator-mxnet/pull/11154 > > > > > This is a PR merged from a forked repository. > As GitHub hides the original diff on merge, it is displayed below for > the sake of provenance: > > As this is a foreign pull request (from a fork), the diff is supplied > below (as it won't show otherwise due to GitHub magic): > > diff --git a/docs/mms/index.md b/docs/mms/index.md > deleted file mode 100644 > index ff6edae414b..000 > --- a/docs/mms/index.md > +++ /dev/null > @@ -1,114 +0,0 @@ > -# Model Server for Apache MXNet (incubating) > - > -[Model Server for Apache MXNet (incubating)](https://github. > com/awslabs/mxnet-model-server), otherwise known as MXNet Model Server > (MMS), is an open source project aimed at providing a simple yet scalable > solution for model inference. It is a set of command line tools for > packaging model archives and serving them. The tools are written in Python, > and have been extended to support containers for easy deployment and > scaling. MMS also supports basic logging and advanced metrics with Amazon > CloudWatch integration. > - > - > -## Multi-Framework Model Support with ONNX > - > -MMS supports both *symbolic* MXNet and *imperative* Gluon models. While > the name implies that MMS is just for MXNet, it is in fact much more > flexible, as it can support models in the [ONNX](https://onnx.ai) format. > This means that models created and trained in PyTorch, Caffe2, or other > ONNX-supporting frameworks can be served with MMS. > - > -To find out more about MXNet's support for ONNX models and using ONNX > with MMS, refer to the following resources: > - > -* [MXNet-ONNX Docs](../api/python/contrib/onnx.md) > -* [Export an ONNX Model to Serve with MMS](https://github.com/ > awslabs/mxnet-model-server/docs/export_from_onnx.md) > - > -## Getting Started > - > -To install MMS with ONNX support, make sure you have Python installed, > then for Ubuntu run: > - > -```bash > -sudo apt-get install protobuf-compiler libprotoc-dev > -pip install mxnet-model-server > -``` > - > -Or for Mac run: > - > -```bash > -conda install -c conda-forge protobuf > -pip install mxnet-model-server > -``` > - > - > -## Serving a Model > - > -To serve a model you must first create or download a model archive. Visit > the [model zoo](https://github.com/awslabs/mxnet-model-server/ > docs/model_zoo.md) to browse the models. MMS options can be explored as > follows: > - > -```bash > -mxnet-model-server --help > -``` > - > -Here is an easy example for serving an object classification model. You > can use any URI and the model will be downloaded first, then served from > that location: > - > -```bash > -mxnet-model-server \ > - --models squeezenet=https://s3.amazonaws.com/model-server/ > models/squeezenet_v1.1/squeezenet_v1.1.model > -``` > - > - > -### Test Inference on a Model > - > -Assuming you have run the previous `mxnet-model-server` command to start > serving the object classification model, you can now upload an image to its > `predict` REST API endpoint. The following will download a picture of a > kitten, then upload it to the prediction endpoint. > - > -```bash > -curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg > -curl -X POST http://127.0.0.1:8080/squeezenet/predict -F > "data=@kitten.jpg" > -``` > - > -The predict endpoint will return a prediction response in JSON. It will > look something like the following result: > - > -``` > -{ > - "prediction": [ > -[ > - { > -"class": "n02124075 Egyptian cat", > -"probability": 0.9408261179924011 > - }, > -... > -``` > - > -For more examples of serving models visit the following resources: > - > -* [Quickstart: Model Serving](https://github.com/ > awslabs/mxnet-model-server/README.md#serve-a-model) > -* [Running the Model Server](https://github.com/ > awslabs/mxnet-model-server/docs/server.md) > - > - > -## Create a Model Archive > - > -Creating a model archive involves rounding up the required model > artifacts, then using the `mxnet-model-export` command line interface. The > process for creating archi