Distributed Version #23
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I think that's the point - Google hasn't open sourced "scalable" version :) |
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Thanks for the question! To reiterate what I said here, we are working on making a distributed implementation available, it's currently not in the initial release. Please stay tuned, and take a look at the cifar multi-gpu tutorial for a flavor of how we handle multiple 'devices': http://tensorflow.org/tutorials/deep_cnn/index.md |
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would appreciate any insight on the availability of the distributed version. Is the distributed code that is being worked on in github? That is one place where some of us who are interested can contribute |
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👍 |
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Hello, After reading these plans and ideas, I'm somewhat surprised. According to http://static.googleusercontent.com/media/research.google.com/en//people/jeff/BayLearn2015.pdf, both data and model parallel are needed to train large and powerful models quickly. BTW, GPUs transferring data takes time as described in http://tensorflow.org/tutorials/deep_cnn/index.md. Then, how it's possible to efficiently support both model parallelism and heterogeneous multi-devices (of a single node) on distributed cluster? Could you please roughly explain how different it from DistBelief? Thanks! |
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P.S., GPU acceleration also could be limited by model partition strategies. |
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Our current internal distributed extensions are somewhat entangled with Google internal infrastructure, which is why we released the single-machine version first. The code is not yet in GitHub, because it has dependencies on other parts of the Google code base at the moment, most of which have been trimmed, but there are some remaining ones. We realize that distributed support is really important, and it's one of the top features we're prioritizing at the moment. |
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Awesome. After reading the whitepaper, I just realized that large neural network model can be partitioned into sub-graphs by layer (horizontal partitioning) and executed in a serial way. One thing not clear is the performance for fully connected network on multi node equipped with GPUs cluster .. |
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In theory, something like Dask could be layered on top for handling this - at least for the Python front-end. |
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Any update on timeline? |
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Dask looks interesting project but the drawback of the blocking algorithm is that it's not memory optimal. Since a large amount of memory is required for fully-connected layers, I was thought that Pregel-like model parallelism on CPUs w/ vertical partitioning is more attractive for fully connected layers (blocking mat-mult on GPU also appears to me slow and memory demanding). Of course, I maybe wrong but that's why I launched Apache Horn project recently. Since layers can be pipelined, I hope we can collaborate each projects in complementary way. |
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I don't know if you can give us some anticipation on the framework choice but.. |
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@bhack I wonder what their Java/Scala interface looks like... |
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We've started work on this using gRPC. We hopefully have something to show soon. |
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Really in desperate need for the distribute version.. Dream to see it to be released early and contribute my efforts in this great project.. |
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Any update on timeline? Can't wait any longer... @martinwicke |
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Bump. I'm really glad this is your top priority. Thanks for all your amazing work so far. |
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Sorry I completely missed the mention; have been heads down on various matters. This looked just like the other hundreds of commit notifications in my mailbox. Yes we'll be talking about a distributed implementation of TensorFlow on Spark, pyspark in particular. Some pretty interesting results on scaling and GPU vs CPU etc. I'll see you there if you'll be in NYC for SparkSummit, else on live streaming. The primary motivation is from the Spark perspective---e.g., easily adding another useful workload to an existing Spark deployment. For distributed TensorFlow in the abstract, Google will release a distributed implementation "soon". HTH.Update on the above (Distributed Tensorflow on Spark): |
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TensorFlow-serving released today, it seems networking in gRPC has been proved to be a mature solution, great news! |
Fixing issues tensorflow#23 and tensorflow#25
fix a bug in bazel version check (bazel-0.10 and later)
Create base Dockerfile
Add use_explicit_batch parameter available in OpConverterParams and other places Formatting and make const bool everywhere Enable use_explicit_batch for TRT 6.0 Revise validation checks to account for use_explicit_batch. Propagate flag to ConversionParams and TRTEngineOp Rename use_explicit_batch/use_implicit_batch Formatting Add simple activtion test for testing dynamic input shapes. Second test with None dims is disabled Update ConvertAxis to account for use_implicit batch fix use of use_implicit_batch (tensorflow#7) * fix use of use_implicit_batch * change order of parameters in ConvertAxis function fix build (tensorflow#8) Update converters for ResNet50 (except Binary ops) (tensorflow#9) * Update RN50 converters for use_implicit_batch: Conv2D, BiasAdd, Transpose, MaxPool, Squeeze, MatMul, Pad * Fix compilation errors * Fix tests Use TRT6 API's for dynamic shape (tensorflow#11) * adding changes for addnetworkv2 * add plugin utils header file in build * optimization profile api added * fix optimization profile * TRT 6.0 api changes + clang format * Return valid errors in trt_engine_op * add/fix comments * Changes to make sure activation test passes with TRT trunk * use HasStaticShape API, add new line at EOF Allow opt profiles to be set via env variables temporarily. Undo accidental change fix segfault by properly returning the status from OverwriteStaticDims function Update GetTrtBroadcastShapes for use_implicit_batch (tensorflow#14) * Update GetTrtBroadcastShapes for use_implicit_batch * Formatting Update activation test Fix merge errors Update converter for reshape (tensorflow#17) Allow INT32 for elementwise (tensorflow#18) Add Shape op (tensorflow#19) * Add Shape op * Add #if guards for Shape. Fix formatting Support dynamic shapes for strided slice (tensorflow#20) Support dynamic shapes for strided slice Support const scalars + Pack on constants (tensorflow#21) Support const scalars and pack with constants in TRT6 Fixes/improvements for BERT (tensorflow#22) * Support shrink_axis_mask for StridedSlice * Use a pointer for final_shape arg in ConvertStridedSliceHelper. Use final_shape for unpack/unstack * Support BatchMatMulV2. * Remove TODO and update comments * Remove unused include * Update Gather for TRT 6 * Update BatchMatMul for TRT6 - may need more changes * Update StridedSlice shrink_axis for TRT6 * Fix bugs with ConvertAxis, StridedSlice shrink_axis, Gather * Fix FC and broadcast * Compile issue and matmul fix * Use nullptr for empty weights * Update Slice * Fix matmul for TRT6 * Use enqueueV2. Don't limit to 1 input per engine Change INetworkConfig to IBuilderConfig Allow expand dims to work on dynamic inputs by slicing shape. Catch problems with DepthwiseConv. Don't try to verify dynamic shapes in CheckValidSize (tensorflow#24) Update CombinedNMS converter (tensorflow#23) * Support CombinedNMS in non implicit batch mode. The squeeze will not work if multiple dimensions are unknown * Fix compile error and formatting Support squeeze when input dims are unknown Support an additional case of StridedSlice where some dims aren't known Use new API for createNetworkV2 Fix flag type for createNetworkV2 Use tensor inputs for strided slice Allow squeeze to work on -1 dims Add TRT6 checks to new API spliting ConvertGraphDefToEngine (tensorflow#29) * spliting ConvertGraphDefToEngine into ConvertGraphDefToNetwork and BuildEngineFromNetwork * some compiler error * fix format Squeeze Helper function (tensorflow#31) * Add squeeze helper * Fix compile issues * Use squeeze helper for CombinedNMS Update Split & Unpack for dynamic shapes (tensorflow#32) * Update Unpack for dynamic shapes * Fix compilation error Temporary hack to fix bug in config while finding TRT library Fix errors from rebasing Remove GatherV2 limitations for TRT6 Fix BiasAdd elementwise for NCHW case with explicit batch mode (tensorflow#34) Update TRT6 headers, Make tests compile (tensorflow#35) * Change header files for TRT6 in configure script * Fix bug with size of scalars. Use implicit batch mode based on the converter flag when creating network * Fix compilation of tests and Broadcast tests Properly fix biasadd nchw (tensorflow#36) Revert tensorflow#29 to fix weight corruption (tensorflow#37) * Revert tensorflow#29 to fix weight corruption * Revert change in test Fix bug with converters and get all tests passing for TRT6 (tensorflow#39) Update DepthToSpace and SpaceToTest for TRT6 + dynamic shapes (tensorflow#40) Add new C++ tests for TRT6 converters (tensorflow#41) * Remove third shuffle layer since bug with transpose was fixed * Add new tests for TRT6 features * Update TRT6 headers list Fix compilation errors Remove bazel_build.sh Enable quantization mnist test back Disabled by mistake I believe Remove undesirable changes in quantization_mnist_test Add code back that was missed during rebase Fix bug: change "type" to type_key
Cubkernels v2
fixed new AllocatePersistentBuffer in pad op
and others
Is there any distributed version of TensorFlow that could work on multiple machines?
-Minjie
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