Leaderboard and Visualization for RLCard

Overview

RLCard Showdown

This is the GUI support for the RLCard project and DouZero project. RLCard-Showdown provides evaluation and visualization tools to help understand the performance of the agents. It includes a replay module, where you can analyze the replays, and a PvE module, where you can play with the AI interactively. Currently, we only support Leduc Hold'em and Dou Dizhu. The frontend is developed with React. The backend is based on Django and Flask. Have fun!

Cite this work

Zha, Daochen, et al. "RLCard: A Platform for Reinforcement Learning in Card Games." IJCAI. 2020.

@inproceedings{zha2020rlcard,
  title={RLCard: A Platform for Reinforcement Learning in Card Games},
  author={Zha, Daochen and Lai, Kwei-Herng and Huang, Songyi and Cao, Yuanpu and Reddy, Keerthana and Vargas, Juan and Nguyen, Alex and Wei, Ruzhe and Guo, Junyu and Hu, Xia},
  booktitle={IJCAI},
  year={2020}
}

Installation

RLCard-Showdown has separated frontend and backend. The frontend is built with React and the backend is based on Django and Flask.

Prerequisite

To set up the frontend, you should make sure you have Node.js and NPM installed. Normally you just need to manually install Node.js, and the NPM package would be automatically installed together with Node.js for you. Please refer to its official website for installation of Node.js.

You can run the following commands to verify the installation

node -v
npm -v

For backend, make sure that you have Python 3.6+ and pip installed.

Install Frontend and Backend

The frontend can be installed with the help of NPM:

git clone -b master --single-branch --depth=1 https://github.com/datamllab/rlcard-showdown.git
cd rlcard-showdown
npm install

The backend of leaderboard can be installed with

pip3 install -r requirements.txt
cd server
python3 manage.py migrate
cd ..

Run RLCard-Showdown

  1. Launch the backend of leaderboard with
cd server
python3 manage.py runserver
  1. Download the pre-trained models in Google Drive or 百度网盘 提取码: qh6s. Extract it in pve_server/pretrained.

In a new terminal, start the PvE server (i.e., human vs AI) of DouZero with

cd pve_server
python3 run_douzero.py

Alternatively, you can start the PvE server interfaced with RLCard:

cd pve_server
python3 run_dmc.py

They are conceptually the same with minor differences in state representation and training time of the pre-trained models (DouZero is fully trained with more than a month, while DMC in RLCard is only trained for hours).

  1. Run the following command in another new terminal under the project folder to start frontend:
npm start

You can view leaderboard at http://127.0.0.1:3000/ and PvE demo of Dou Dizhu at http://127.0.0.1:3000/pve/doudizhu-demo. The backend of leaderboard will run in http://127.0.0.1:8000/. The PvE backend will run in http://127.0.0.1:5000/.

Demos

leaderboards upload doudizhu-replay leduc-replay

Contact Us

If you have any questions or feedback, feel free to drop an email to Songyi Huang for the frontend or Daochen Zha for backend.

Acknowledgements

We would like to thank JJ World Network Technology Co., LTD for the generous support, Chieh-An Tsai for user interface design, and Lei Pan for the help in visualizations.

Issues
  • Error receiving prediction result, please try refresh the page

    Error receiving prediction result, please try refresh the page

    本地进入斗地主网页后,该模型做动作时,提示“Error receiving prediction result, please try refresh the page”

    opened by ZF4444 5
  • Error in getting replay data

    Error in getting replay data

    After Launching Tournaments in guide.md, I Click the replay button doudizhu-rule-v1 to watch the replay with doudizhu, BUT when i click start in http://localhost:3000/replay/doudizhu, Suddenly prompt me “Error in getting replay data”, Then prompted the following error “TypeError: Cannot read property 'length' of undefined”. The replay button of Texas Hold'em will not report an error, This is very confusing to me.

    opened by Maxwell2017 4
  • 在Rlcard showdown 中无法Upload model

    在Rlcard showdown 中无法Upload model

    您好!请问一下该如何添加其他的模型上去呢,我添加给的样例zip文件,却提示Only zip file can be uploaded

    image

    opened by 274927760 3
  • python3 manage.py migrate not working

    python3 manage.py migrate not working

    I am running this on Windows 10 in both Windows Poer Shell and Pycharm platform but when I run this

    python3 manage.py migrate

    I get these issues:

    System check identified some issues:
    
    WARNINGS:
    tournament.Game: (models.W042) Auto-created primary key used when not defining a primary key type, by default 'django.db.models.AutoField'.
            HINT: Configure the DEFAULT_AUTO_FIELD setting or the TournamentConfig.default_auto_field attribute to point to a subclass of AutoField, e.g. 'django.db.model
    s.BigAutoField'.
    tournament.Payoff: (models.W042) Auto-created primary key used when not defining a primary key type, by default 'django.db.models.AutoField'.
            HINT: Configure the DEFAULT_AUTO_FIELD setting or the TournamentConfig.default_auto_field attribute to point to a subclass of AutoField, e.g. 'django.db.model
    s.BigAutoField'.
    tournament.UploadedAgent: (models.W042) Auto-created primary key used when not defining a primary key type, by default 'django.db.models.AutoField'.
            HINT: Configure the DEFAULT_AUTO_FIELD setting or the TournamentConfig.default_auto_field attribute to point to a subclass of AutoField, e.g. 'django.db.model
    s.BigAutoField'.
    Operations to perform:
      Apply all migrations: admin, auth, contenttypes, sessions, tournament
    Running migrations:
      No migrations to apply.
    

    This causes the server to not run properly.

    Page not found (404)
    Request Method:	GET
    Request URL:	http://127.0.0.1:8000/
    Using the URLconf defined in server.urls, Django tried these URL patterns, in this order:
    
    tournament/
    admin/
    The empty path didn’t match any of these.
    
    You’re seeing this error because you have DEBUG = True in your Django settings file. Change that to False, and Django will display a standard 404 page.
    
    opened by amirfarazmand 3
  • cannot interact with django server when scripts are run in a remote server

    cannot interact with django server when scripts are run in a remote server

    Hi, I have run the following on my local computer to start a remote service:

    python manage.py runserver 0.0.0.0:8000
    

    I can visit remote_ip:8000 this way. However it shows a blank table with no records read from the database (i.e., db.sqlite3) after I type npm start. Things went perfectly fine when I serve both on my local computer (even a remote frontend and local backend combination could work).

    I wonder what configurations should be done to deal with it. Thanks for your work.

    opened by dennislblog 2
  • Bump tensorflow from 1.14 to 1.15.2

    Bump tensorflow from 1.14 to 1.15.2

    Bumps tensorflow from 1.14 to 1.15.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 1.15.2

    Release 1.15.2

    Note that this release no longer has a single pip package for GPU and CPU. Please see #36347 for history and details

    Bug Fixes and Other Changes

    TensorFlow 1.15.0

    Release 1.15.0

    This is the last 1.x release for TensorFlow. We do not expect to update the 1.x branch with features, although we will issue patch releases to fix vulnerabilities for at least one year.

    Major Features and Improvements

    • As announced, tensorflow pip package will by default include GPU support (same as tensorflow-gpu now) for the platforms we currently have GPU support (Linux and Windows). It will work on machines with and without Nvidia GPUs. tensorflow-gpu will still be available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
    • TensorFlow 1.15 contains a complete implementation of the 2.0 API in its compat.v2 module. It contains a copy of the 1.15 main module (without contrib) in the compat.v1 module. TensorFlow 1.15 is able to emulate 2.0 behavior using the enable_v2_behavior() function. This enables writing forward compatible code: by explicitly importing either tensorflow.compat.v1 or tensorflow.compat.v2, you can ensure that your code works without modifications against an installation of 1.15 or 2.0.
    • EagerTensor now supports numpy buffer interface for tensors.
    • Add toggles tf.enable_control_flow_v2() and tf.disable_control_flow_v2() for enabling/disabling v2 control flow.
    • Enable v2 control flow as part of tf.enable_v2_behavior() and TF2_BEHAVIOR=1.
    • AutoGraph translates Python control flow into TensorFlow expressions, allowing users to write regular Python inside tf.function-decorated functions. AutoGraph is also applied in functions used with tf.data, tf.distribute and tf.keras APIS.
    • Adds enable_tensor_equality(), which switches the behavior such that:
      • Tensors are no longer hashable.
      • Tensors can be compared with == and !=, yielding a Boolean Tensor with element-wise comparison results. This will be the default behavior in 2.0.
    • Auto Mixed-Precision graph optimizer simplifies converting models to float16 for acceleration on Volta and Turing Tensor Cores. This feature can be enabled by wrapping an optimizer class with tf.train.experimental.enable_mixed_precision_graph_rewrite().
    • Add environment variable TF_CUDNN_DETERMINISTIC. Setting to "true" or "1" forces the selection of deterministic cuDNN convolution and max-pooling algorithms. When this is enabled, the algorithm selection procedure itself is also deterministic.
    • TensorRT
      • Migrate TensorRT conversion sources from contrib to compiler directory in preparation for TF 2.0.
      • Add additional, user friendly TrtGraphConverter API for TensorRT conversion.
      • Expand support for TensorFlow operators in TensorRT conversion (e.g. Gather, Slice, Pack, Unpack, ArgMin, ArgMax,DepthSpaceShuffle).
      • Support TensorFlow operator CombinedNonMaxSuppression in TensorRT conversion which significantly accelerates object detection models.

    Breaking Changes

    • Tensorflow code now produces 2 different pip packages: tensorflow_core containing all the code (in the future it will contain only the private implementation) and tensorflow which is a virtual pip package doing forwarding to tensorflow_core (and in the future will contain only the public API of tensorflow). We don't expect this to be breaking, unless you were importing directly from the implementation.
    • TensorFlow 1.15 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow.
    • Deprecated the use of constraint= and .constraint with ResourceVariable.
    • tf.keras:
      • OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.
      • tf.keras.model.save_model and model.save now defaults to saving a TensorFlow SavedModel.
      • keras.backend.resize_images (and consequently, keras.layers.Upsampling2D) behavior has changed, a bug in the resizing implementation was fixed.
      • Layers now default to float32, and automatically cast their inputs to the layer's dtype. If you had a model that used float64, it will probably silently use float32 in TensorFlow2, and a warning will be issued that starts with Layer "layer-name" is casting an input tensor from dtype float64 to the layer's dtype of float32. To fix, either set the default dtype to float64 with tf.keras.backend.set_floatx('float64'), or pass dtype='float64' to each of the Layer constructors. See tf.keras.layers.Layer for more information.
      • Some tf.assert_* methods now raise assertions at operation creation time (i.e. when this Python line executes) if the input tensors' values are known at that time, not during the session.run(). When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).

    Bug Fixes and Other Changes

    • tf.estimator:
      • tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.
      • Fix tests in canned estimators.
    ... (truncated)
    Changelog

    Sourced from tensorflow's changelog.

    Release 1.15.2

    Bug Fixes and Other Changes

    Release 2.1.0

    TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support officially ends an January 1, 2020. As announced earlier, TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019.

    Major Features and Improvements

    • The tensorflow pip package now includes GPU support by default (same as tensorflow-gpu) for both Linux and Windows. This runs on machines with and without NVIDIA GPUs. tensorflow-gpu is still available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
    • Windows users: Officially-released tensorflow Pip packages are now built with Visual Studio 2019 version 16.4 in order to take advantage of the new /d2ReducedOptimizeHugeFunctions compiler flag. To use these new packages, you must install "Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019", available from Microsoft's website here.
      • This does not change the minimum required version for building TensorFlow from source on Windows, but builds enabling EIGEN_STRONG_INLINE can take over 48 hours to compile without this flag. Refer to configure.py for more information about EIGEN_STRONG_INLINE and /d2ReducedOptimizeHugeFunctions.
      • If either of the required DLLs, msvcp140.dll (old) or msvcp140_1.dll (new), are missing on your machine, import tensorflow will print a warning message.
    • The tensorflow pip package is built with CUDA 10.1 and cuDNN 7.6.
    • tf.keras
      • Experimental support for mixed precision is available on GPUs and Cloud TPUs. See usage guide.
      • Introduced the TextVectorization layer, which takes as input raw strings and takes care of text standardization, tokenization, n-gram generation, and vocabulary indexing. See this end-to-end text classification example.
      • Keras .compile .fit .evaluate and .predict are allowed to be outside of the DistributionStrategy scope, as long as the model was constructed inside of a scope.
      • Experimental support for Keras .compile, .fit, .evaluate, and .predict is available for Cloud TPUs, Cloud TPU, for all types of Keras models (sequential, functional and subclassing models).
      • Automatic outside compilation is now enabled for Cloud TPUs. This allows tf.summary to be used more conveniently with Cloud TPUs.
      • Dynamic batch sizes with DistributionStrategy and Keras are supported on Cloud TPUs.
      • Support for .fit, .evaluate, .predict on TPU using numpy data, in addition to tf.data.Dataset.
      • Keras reference implementations for many popular models are available in the TensorFlow Model Garden.
    • tf.data
      • Changes rebatching for tf.data datasets + DistributionStrategy for better performance. Note that the dataset also behaves slightly differently, in that the rebatched dataset cardinality will always be a multiple of the number of replicas.
      • tf.data.Dataset now supports automatic data distribution and sharding in distributed environments, including on TPU pods.
      • Distribution policies for tf.data.Dataset can now be tuned with 1. tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA) 2. tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)
    • tf.debugging
      • Add tf.debugging.enable_check_numerics() and tf.debugging.disable_check_numerics() to help debugging the root causes of issues involving infinities and NaNs.
    • tf.distribute
      • Custom training loop support on TPUs and TPU pods is avaiable through strategy.experimental_distribute_dataset, strategy.experimental_distribute_datasets_from_function, strategy.experimental_run_v2, strategy.reduce.
      • Support for a global distribution strategy through tf.distribute.experimental_set_strategy(), in addition to strategy.scope().
    • TensorRT
      • TensorRT 6.0 is now supported and enabled by default. This adds support for more TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D, MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the TensorFlow-TensorRT python conversion API is exported as tf.experimental.tensorrt.Converter.
    • Environment variable TF_DETERMINISTIC_OPS has been added. When set to "true" or "1", this environment variable makes tf.nn.bias_add operate deterministically (i.e. reproducibly), but currently only when XLA JIT compilation is not enabled. Setting TF_DETERMINISTIC_OPS to "true" or "1" also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv*D and MaxPool*D layers operate deterministically in both the forward and backward directions when running on a CUDA-enabled GPU.

    Breaking Changes

    • Deletes Operation.traceback_with_start_lines for which we know of no usages.
    • Removed id from tf.Tensor.__repr__() as id is not useful other than internal debugging.
    • Some tf.assert_* methods now raise assertions at operation creation time if the input tensors' values are known at that time, not during the session.run(). This only changes behavior when the graph execution would have resulted in an error. When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).
    • The following APIs are not longer experimental: tf.config.list_logical_devices, tf.config.list_physical_devices, tf.config.get_visible_devices, tf.config.set_visible_devices, tf.config.get_logical_device_configuration, tf.config.set_logical_device_configuration.
    • tf.config.experimentalVirtualDeviceConfiguration has been renamed to tf.config.LogicalDeviceConfiguration.
    • tf.config.experimental_list_devices has been removed, please use tf.config.list_logical_devices.

    Bug Fixes and Other Changes

    ... (truncated)
    Commits
    • 5d80e1e Merge pull request #36215 from tensorflow-jenkins/version-numbers-1.15.2-8214
    • 71e9d8f Update version numbers to 1.15.2
    • e50120e Merge pull request #36214 from tensorflow-jenkins/relnotes-1.15.2-2203
    • 1a7e9fb Releasing 1.15.2 instead of 1.15.1
    • 85f7aab Insert release notes place-fill
    • e75a6d6 Merge pull request #36190 from tensorflow/mm-r1.15-fix-v2-build
    • a6d8973 Use config=v1 as this is r1.15 branch.
    • fdb8589 Merge pull request #35912 from tensorflow-jenkins/relnotes-1.15.1-31298
    • a6051e8 Add CVE number for main patch
    • 360b2e3 Merge pull request #34532 from ROCmSoftwarePlatform/r1.15-rccl-upstream-patch
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.14 to 1.15.4

    Bump tensorflow from 1.14 to 1.15.4

    Bumps tensorflow from 1.14 to 1.15.4.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 1.15.4

    Release 1.15.4

    Bug Fixes and Other Changes

    TensorFlow 1.15.3

    Bug Fixes and Other Changes

    TensorFlow 1.15.2

    Release 1.15.2

    Note that this release no longer has a single pip package for GPU and CPU. Please see #36347 for history and details

    Bug Fixes and Other Changes

    TensorFlow 1.15.0

    Release 1.15.0

    This is the last 1.x release for TensorFlow. We do not expect to update the 1.x branch with features, although we will issue patch releases to fix vulnerabilities for at least one year.

    Major Features and Improvements

    • As announced, tensorflow pip package will by default include GPU support (same as tensorflow-gpu now) for the platforms we currently have GPU support (Linux and Windows). It will work on machines with and without Nvidia GPUs. tensorflow-gpu will still be available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
    • TensorFlow 1.15 contains a complete implementation of the 2.0 API in its compat.v2 module. It contains a copy of the 1.15 main module (without contrib) in the compat.v1 module. TensorFlow 1.15 is able to emulate 2.0 behavior using the enable_v2_behavior() function. This enables writing forward compatible code: by explicitly importing either tensorflow.compat.v1 or tensorflow.compat.v2, you can ensure that your code works without modifications against an installation of 1.15 or 2.0.
    • EagerTensor now supports numpy buffer interface for tensors.
    • Add toggles tf.enable_control_flow_v2() and tf.disable_control_flow_v2() for enabling/disabling v2 control flow.
    • Enable v2 control flow as part of tf.enable_v2_behavior() and TF2_BEHAVIOR=1.
    • AutoGraph translates Python control flow into TensorFlow expressions, allowing users to write regular Python inside tf.function-decorated functions. AutoGraph is also applied in functions used with tf.data, tf.distribute and tf.keras APIS.
    • Adds enable_tensor_equality(), which switches the behavior such that:
      • Tensors are no longer hashable.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 1.15.4

    Bug Fixes and Other Changes

    Release 2.3.0

    Major Features and Improvements

    • tf.data adds two new mechanisms to solve input pipeline bottlenecks and save resources:

    ... (truncated)

    Commits
    • df8c55c Merge pull request #43442 from tensorflow-jenkins/version-numbers-1.15.4-31571
    • 0e8cbcb Update version numbers to 1.15.4
    • 5b65bf2 Merge pull request #43437 from tensorflow-jenkins/relnotes-1.15.4-10691
    • 814e8d8 Update RELEASE.md
    • 757085e Insert release notes place-fill
    • e99e53d Merge pull request #43410 from tensorflow/mm-fix-1.15
    • bad36df Add missing import
    • f3f1835 No disable_tfrt present on this branch
    • 7ef5c62 Merge pull request #43406 from tensorflow/mihaimaruseac-patch-1
    • abbf34a Remove import that is not needed
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.14 to 2.3.1

    Bump tensorflow from 1.14 to 2.3.1

    Bumps tensorflow from 1.14 to 2.3.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.3.1

    Release 2.3.1

    Bug Fixes and Other Changes

    TensorFlow 2.3.0

    Release 2.3.0

    Major Features and Improvements

    • tf.data adds two new mechanisms to solve input pipeline bottlenecks and save resources:

    In addition checkout the detailed guide for analyzing input pipeline performance with TF Profiler.

    • tf.distribute.TPUStrategy is now a stable API and no longer considered experimental for TensorFlow. (earlier tf.distribute.experimental.TPUStrategy).

    • TF Profiler introduces two new tools: a memory profiler to visualize your model’s memory usage over time and a python tracer which allows you to trace python function calls in your model. Usability improvements include better diagnostic messages and profile options to customize the host and device trace verbosity level.

    • Introduces experimental support for Keras Preprocessing Layers API (tf.keras.layers.experimental.preprocessing.*) to handle data preprocessing operations, with support for composite tensor inputs. Please see below for additional details on these layers.

    • TFLite now properly supports dynamic shapes during conversion and inference. We’ve also added opt-in support on Android and iOS for XNNPACK, a highly optimized set of CPU kernels, as well as opt-in support for executing quantized models on the GPU.

    • Libtensorflow packages are available in GCS starting this release. We have also started to release a nightly version of these packages.

    • The experimental Python API tf.debugging.experimental.enable_dump_debug_info() now allows you to instrument a TensorFlow program and dump debugging information to a directory on the file system. The directory can be read and visualized by a new interactive dashboard in TensorBoard 2.3 called Debugger V2, which reveals the details of the TensorFlow program including graph structures, history of op executions at the Python (eager) and intra-graph levels, the runtime dtype, shape, and numerical composistion of tensors, as well as their code locations.

    Breaking Changes

    • Increases the minimum bazel version required to build TF to 3.1.0.
    • tf.data
      • Makes the following (breaking) changes to the tf.data.
      • C++ API: - IteratorBase::RestoreInternal, IteratorBase::SaveInternal, and DatasetBase::CheckExternalState become pure-virtual and subclasses are now expected to provide an implementation.
      • The deprecated DatasetBase::IsStateful method is removed in favor of DatasetBase::CheckExternalState.
      • Deprecated overrides of DatasetBase::MakeIterator and MakeIteratorFromInputElement are removed.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.3.1

    Bug Fixes and Other Changes

    Release 2.2.1

    ... (truncated)

    Commits
    • fcc4b96 Merge pull request #43446 from tensorflow-jenkins/version-numbers-2.3.1-16251
    • 4cf2230 Update version numbers to 2.3.1
    • eee8224 Merge pull request #43441 from tensorflow-jenkins/relnotes-2.3.1-24672
    • 0d41b1d Update RELEASE.md
    • d99bd63 Insert release notes place-fill
    • d71d3ce Merge pull request #43414 from tensorflow/mihaimaruseac-patch-1-1
    • 9c91596 Fix missing import
    • f9f12f6 Merge pull request #43391 from tensorflow/mihaimaruseac-patch-4
    • 3ed271b Solve leftover from merge conflict
    • 9cf3773 Merge pull request #43358 from tensorflow/mm-patch-r2.3
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.14 to 2.4.0

    Bump tensorflow from 1.14 to 2.4.0

    Bumps tensorflow from 1.14 to 2.4.0.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.4.0

    Release 2.4.0

    Major Features and Improvements

    • tf.distribute introduces experimental support for asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy API. Please see the tutorial to learn more.

    • MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. Some of the major improvements involve handling peer failure and many bug fixes. Please check out the detailed tutorial on Multi-worker training with Keras.

    • Introduces experimental support for a new module named tf.experimental.numpy which is a NumPy-compatible API for writing TF programs. See the detailed guide to learn more. Additional details below.

    • Adds Support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.

    • A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.

    • Keras mixed precision API tf.keras.mixed_precision is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. Please see below for additional details.

    • TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and tracing multiple workers using the sampling mode API.

    • TFLite Profiler for Android is available. See the detailed guide to learn more.

    • TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.

    Breaking Changes

    • TF Core:

      • Certain float32 ops run in lower precsion on Ampere based GPUs, including matmuls and convolutions, due to the use of TensorFloat-32. Specifically, inputs to such ops are rounded from 23 bits of precision to 10 bits of precision. This is unlikely to cause issues in practice for deep learning models. In some cases, TensorFloat-32 is also used for complex64 ops. TensorFloat-32 can be disabled by running tf.config.experimental.enable_tensor_float_32_execution(False).
      • The byte layout for string tensors across the C-API has been updated to match TF Core/C++; i.e., a contiguous array of tensorflow::tstring/TF_TStrings.
      • C-API functions TF_StringDecode, TF_StringEncode, and TF_StringEncodedSize are no longer relevant and have been removed; see core/platform/ctstring.h for string access/modification in C.
      • tensorflow.python, tensorflow.core and tensorflow.compiler modules are now hidden. These modules are not part of TensorFlow public API.
      • tf.raw_ops.Max and tf.raw_ops.Min no longer accept inputs of type tf.complex64 or tf.complex128, because the behavior of these ops is not well defined for complex types.
      • XLA:CPU and XLA:GPU devices are no longer registered by default. Use TF_XLA_FLAGS=--tf_xla_enable_xla_devices if you really need them, but this flag will eventually be removed in subsequent releases.
    • tf.keras:

      • The steps_per_execution argument in model.compile() is no longer experimental; if you were passing experimental_steps_per_execution, rename it to steps_per_execution in your code. This argument controls the number of batches to run during each tf.function call when calling model.fit(). Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.
      • A major refactoring of the internals of the Keras Functional API may affect code that is relying on certain internal details:
        • Code that uses isinstance(x, tf.Tensor) instead of tf.is_tensor when checking Keras symbolic inputs/outputs should switch to using tf.is_tensor.
        • Code that is overly dependent on the exact names attached to symbolic tensors (e.g. assumes there will be ":0" at the end of the inputs, treats names as unique identifiers instead of using tensor.ref(), etc.) may break.
        • Code that uses full path for get_concrete_function to trace Keras symbolic inputs directly should switch to building matching tf.TensorSpecs directly and tracing the TensorSpec objects.
        • Code that relies on the exact number and names of the op layers that TensorFlow operations were converted into may have changed.
        • Code that uses tf.map_fn/tf.cond/tf.while_loop/control flow as op layers and happens to work before TF 2.4. These will explicitly be unsupported now. Converting these ops to Functional API op layers was unreliable before TF 2.4, and prone to erroring incomprehensibly or being silently buggy.
        • Code that directly asserts on a Keras symbolic value in cases where ops like tf.rank used to return a static or symbolic value depending on if the input had a fully static shape or not. Now these ops always return symbolic values.
        • Code already susceptible to leaking tensors outside of graphs becomes slightly more likely to do so now.
        • Code that tries directly getting gradients with respect to symbolic Keras inputs/outputs. Use GradientTape on the actual Tensors passed to the already-constructed model instead.
        • Code that requires very tricky shape manipulation via converted op layers in order to work, where the Keras symbolic shape inference proves insufficient.
        • Code that tries manually walking a tf.keras.Model layer by layer and assumes layers only ever have one positional argument. This assumption doesn't hold true before TF 2.4 either, but is more likely to cause issues now.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.4.0

    Major Features and Improvements

    Breaking Changes

    • TF Core:
      • Certain float32 ops run in lower precsion on Ampere based GPUs, including

    ... (truncated)

    Commits

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    dependencies 
    opened by dependabot[bot] 1
  • Django administration Password??

    Django administration Password??

    http://127.0.0.1:8000/admin

    Django administration Username: Password:

    python manage.py makemigrations

    File "manage.py", line 16 ) from exc ^

    后台登陆,应该要怎么设置

    opened by monkeycc 1
  • 叫分版本

    叫分版本

    请问一下https://www.douzero.org/bid 这个网址叫分版本,我怎么在源代码里面没有找到

    opened by gexiao 1
  • PvE can't run

    PvE can't run

    Not Found The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again.

    opened by panqingyong 1
  • 请问如何修改代码使得根据当前手牌以及观察到的出牌得到出牌建议?

    请问如何修改代码使得根据当前手牌以及观察到的出牌得到出牌建议?

    想让两个机器人来搞对抗,请问该怎么修改?谢谢.

    opened by leolle 0
  • do you consider to release one software ?

    do you consider to release one software ?

    do you consider to release one software ?

    opened by jackylee1 0
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Data Analytics Lab at Texas A&M University
We develop automated and interpretable machine learning algorithms/systems with understanding of their theoretical properties.
Data Analytics Lab at Texas A&M University
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