Comments (10)
got it, thanks! I will test the distributed training and computational graph to support RNN, also check the corresponding documentation.
update on 9/1: submitted some fix to #785 , some test result of the distributed training is pasted on that PR
update on 9/3: worked with Rulin @XJDKC to fix the graph operation in #787
update on 9/8: updated the documentation of dist train and comp graph on PR apache/singa-doc#27
update on 9/27: added the documentation of Optimizer, Time Profiling function, and Model Checkpoint Save Load Function on PR apache/singa-doc#31
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The workflow for coverage is ready in #788 and Travis CI can be removed now as discussed in #790.
More workflows can be made to automate packaging (pypi, debian, etc.) and to automate website updates.
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Please add details about the changes that you have made since v3.0.0. Follow the release note
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-
BiLSTM model on InsuranceQA example training script. The baseline model is inspired by Tan, Ming, et al. "Lstm-based deep learning models for non-factoid answer selection." arXiv preprint arXiv:1511.04108 (2015).
-
Tensor Refactoring and Enhancement
- Tensor transformation (reshape, transpose) supports up to 6 dimensions.
- Implemented traverse_unary_transform in Cuda backend, which is similar to CPP backend one.
- Added tensor operation erf, rounde (round even).
- Fix Tensor operation Mult on Broadcasting use cases.
- Gaussian function on Tensor now can run on Tensor with odd size.
-
autograd
- Added some sanity check on autograd input to prevent fatal error caused by unexpected input shape.
- Updated a testing helper function gradients() in autograd to lookup param gradient by param python object id for testing purpose.
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12 new operators are added into the autograd module: CosSim, DepthToSpace, Embedding, Erf, Expand, Floor, Pad, Round, Rounde, SpaceToDepth, UpSample, Where.
-
2 layers are added into layer module: Embedding, Gemm.
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9 new operators are added to sonnx module for both backend and frontend:
DepthToSpace,
Erf,
Expand,
Floor,
Pad,
Round,
SpaceToDepth,
UpSample,
Where,
Their tests are added as well. -
Some ONNX models are imported into SINGA, including
DenseNet121,
ShuffleNetv1,
ShuffleNetv2,
SqueezeNet,
VGG19,
GPT2,
RoBERTa, -
Reconstruct soonx,
- Support creating operators from both
layer
andautograd
. - Re-write
SingaRep
to provide a more powerful intermediate representation of SINGA. - Add a
SONNXModel
which implements fromModel
to provide uniform API and features.
- Support creating operators from both
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3.1 already released
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3.1 already released
I see that there is vote result email in singa-dev mail list. However, I don't see the Publish the release information.
step.
An email "[ANNOUNCE] Apache SINGA X.Y.Z released" should be sent to [email protected], [email protected] as given above in the check list of release tasks. This step is required to complete the release process.
Note also that the email body says "The release is available at: http://singa.apache.org/downloads.html", however this page does not exist in the new website. We need to have downloads.html page because it is like a standard in Apache projects to have ProjectName.apache.org/downloads.html . The page http://singa.apache.org/docs/download-singa/
does not follow the standard URLs of the Apache projects, and it also does not contain the 3.1 release.
Finally, it will be nice to update the wikipedia page of singa with the new version information.
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Now I added a redirect link so that http://singa.apache.org/downloads.html is working now. It also has the 3.1 release. Does it look better now?
Note also that the email body says "The release is available at: http://singa.apache.org/downloads.html", however this page does not exist in the new website. We need to have downloads.html page because it is like a standard in Apache projects to have ProjectName.apache.org/downloads.html . The page http://singa.apache.org/docs/download-singa/ does not follow the standard URLs of the Apache projects, and it also does not contain the 3.1 release.
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I have sent the announcement email.
Thanks for the reminder.
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Great. I updated the infobox of the wikipedia page. I think we can close the issue now.
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Related Issues (20)
- Switch between CPU and GPU devices for cnn example HOT 4
- Save the downloaded datasets to local directory HOT 2
- Add running scripts for cnn and cifar_distributed_cnn examples HOT 4
- Intermediate information printing HOT 3
- Adding arguments for weight decay and momentum HOT 2
- Increase max epoch for cnn example for better convergence HOT 2
- Update CMakeLists.txt for release 4.0.0 HOT 1
- Check Apache license header for release 4.0.0
- OpenCL Compilation Fails
- Upload Release 4.0.0 Package to SVN HOT 1
- Update the NOTICE file for images HOT 1
- gitignore and gitmodules should be removed from the release tar file HOT 2
- Create a new branch dev-postgresql HOT 2
- Create the SumError New Loss Function HOT 1
- Dynamic Creation of Models HOT 2
- Need to return the gradients from optimizer HOT 4
- Maximum recursion depth exceeded in comparison for string HOT 1
- can sparse all-reduce keep efficiency with large number of gpu workers?
- Python 3.11, Model, ImportError HOT 9
- Update documentation for distributed training HOT 1
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