Proposal for Conversion from FP32 to Mixed Precision Models

Hi all, I have created a doc for conversion from FP32 to Mixed Precision Models: https://cwiki.apache.org/confluence/display/MXNET/Conversion+from+FP32+to+Mixed+Precision+Models I look forward to your feedback on the same. Thanks, Anirudh

Re: Proposal for Conversion from FP32 to Mixed Precision Models

Hi Tao, The APIs proposed: "convert_model" and "convert_block" are mainly for inference use cases, where customers bring a FP32 model to convert it to a mixed precision model to get improved performance while not losing out on the accuracy. The PR:

RE: Proposal for Conversion from FP32 to Mixed Precision Models

Thank you for sharing this, Anirudh. Curious to know: - what will be saved in a training checkpoint or snapshot? Can it be resumed on another platform which might not support the lower precision the previous one used? - what will be saved in the final symbol.json and params file when training

RE: Proposal for Conversion from FP32 to Mixed Precision Models

Thank you for the explanation. Sorry I didn't realize the proposal is for inference only. Then how do you think the amp_cast and amp_multicast in this proposal can work with the existing INT8 quantization workflow which I think should also be considered as 'mixed precision'. -Original

Re: [RFC] Support for creation of Large Tensors in MXNet

Tao, - what's the max size of dimensionality? Which data type is used to define dimensionality (ndims)? We assume the max size of dimensionality is relatively small. Hence `int` data type is used to define ndim - what's the max size of each dimension? Which data type is used to define dimension

Re: Clojure MXNet Monthly Update

Thanks for the feedback. I remember a wiki page for the monthly updates but I can't remember the exact location. Does anyone have a link handy? I would be happy to add the updates to a Clojure section of the "main" one. I will most likely continue to put out a targeted one for the Clojure

Re: Proposal for Conversion from FP32 to Mixed Precision Models

Hi Zach, You raise an interesting point. Thank you for the pointer! Incorporating CSE pass comes with its own cost, and the advantage it brings is to make the ReducePrecision nnvm pass more lightweight. Since the amortized cost of the ReducePrecision pass is O(1) it shouldn't matter much whether

Re: MXNet 1.4.1 Release Proposal

Dear community, We would love to follow up to remind that our release candidate 0 for Apache MXNet 1.4.1 will be cut by the end of the day. We will open a voting thread for this release tonight. Thanks, Junru On Mon, Apr 8, 2019 at 6:57 PM Junru Shao wrote: > Thanks for the great opportunity!

[Announcement] New Committer - Zhennan Qin

Please join me in welcoming Zhennan Qin (https://github.com/ZhennanQin) from Intel as a new committer. Zhennan is the main author of accelerating MXNet/MKLDNN inference through operator fusion and model quantization. His work has placed MXNet in an advantageous place for inference workloads on

[Announcement] New Committer - Hao Jin

Please join me in welcoming Hao Jin (https://github.com/haojin2) from AWS as a new committer. Hao has designed and implemented many sophisticated algorithms for tensor operations. His work has greatly expanded the coverage of MXNet operator inventory and enhanced the performance of many operators

RE: [RFC] Support for creation of Large Tensors in MXNet

Thank you Lin! I would expect the current MKL-DNN implementation already supports the scenario you mentioned here. Can be verified by this issue: https://github.com/apache/incubator-mxnet/issues/13451 But as I said before, since we support flatten or reshape operators, so it's possible for

Invitation to join the #mxnet slack channel

Hi, I'd like to be a part of the MXNet Slack channel. Please send me an invitation for the same. Thanks for your time. -- Regards, -Nikhil Kulkarni