Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

Last update: Dec 28, 2022

Related tags

Machine Learning python scala apache-spark pytorch keras-tensorflow bigdl distributed-deep-learning deep-neural-network analytics-zoo

Overview

A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray

What is Analytics Zoo?

Analytics Zoo seamless scales TensorFlow, Keras and PyTorch to distributed big data (using Spark, Flink & Ray).

End-to-end pipeline for applying AI models (TensorFlow, PyTorch, OpenVINO, etc.) to distributed big data
- Write TensorFlow or PyTorch inline with Spark code for distributed training and inference.
- Native deep learning (TensorFlow/Keras/PyTorch/BigDL) support in Spark ML Pipelines.
- Directly run Ray programs on big data cluster through RayOnSpark.
- Plain Java/Python APIs for (TensorFlow/PyTorch/BigDL/OpenVINO) Model Inference.
High-level ML workflow for automating machine learning tasks
- Cluster Serving for automatically distributed (TensorFlow/PyTorch/Caffe/OpenVINO) model inference .
- Scalable AutoML for time series prediction.
Built-in models for Recommendation, Time Series, Computer Vision and NLP applications.

Why use Analytics Zoo?

You may want to develop your AI solutions using Analytics Zoo if:

You want to easily apply AI models (e.g., TensorFlow, Keras, PyTorch, BigDL, OpenVINO, etc.) to distributed big data.
You want to transparently scale your AI applications from a single laptop to large clusters with "zero" code changes.
You want to deploy your AI pipelines to existing YARN or K8S clusters WITHOUT any modifications to the clusters.
You want to automate the process of applying machine learning (such as feature engineering, hyperparameter tuning, model selection, distributed inference, etc.).

How to use Analytics Zoo?

Check out the Getting Started page for a quick overview of how to use Analytics Zoo.
Refer to the Python, Scala and Docker guides to install Analytics Zoo.
Visit the Document Website (mirror in China) for more information on Analytics Zoo.
Check the Powered By & Presentations pages for real-world applications using Analytics Zoo.
Join the Google Group (or subscribe to the Mail List) for more questions and discussions on Analytics Zoo.

Comments

Bump tensorflow from 1.15.2 to 2.4.0 in /readthedocs
Bumps tensorflow from 1.15.2 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

tf.distribute introduces experimental support for asynchronous training of models via the [tf.distribute.experimental.ParameterServerStrategy] (https://www.tensorflow.org/api_docs/python/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] (https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras).

Introduces experimental support for a new module named [tf.experimental.numpy] (https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) which is a NumPy-compatible API for writing TF programs. See the [detailed guide] (https://www.tensorflow.org/guide/tf_numpy) 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] (https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision?version=nightly) 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] (https://www.tensorflow.org/guide/profiler#profiling_apis).

TFLite Profiler for Android is available. See the detailed [guide] (https://www.tensorflow.org/lite/performance/measurement#trace_tensorflow_lite_internals_in_android) 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 precision on Ampere based GPUs, including

... (truncated)

Commits

582c8d2 Merge pull request #44220 from tensorflow-jenkins/relnotes-2.4.0rc0-18048

c16387f Update RELEASE.md

4cf406c Update RELEASE.md

3f35ef2 Update RELEASE.md

3647e8e Update RELEASE.md

281c7d5 Update RELEASE.md

91ec75f Update RELEASE.md

ed5ad82 Update RELEASE.md

1267bba Update RELEASE.md

13a4067 Update RELEASE.md

Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

@dependabot rebase will rebase this PR

@dependabot recreate will recreate this PR, overwriting any edits that have been made to it

@dependabot merge will merge this PR after your CI passes on it

@dependabot squash and merge will squash and merge this PR after your CI passes on it

@dependabot cancel merge will cancel a previously requested merge and block automerging

@dependabot reopen will reopen this PR if it is closed

@dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually

@dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)

@dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

@dependabot use these labels will set the current labels as the default for future PRs for this repo and language

@dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language

@dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language

@dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

python dependencies
opened by dependabot[bot] 83
Running spark standalone mode in AZ with random master node yields "/bin/sh: 1: ray: not found"

Start to launch ray on cluster Traceback (most recent call last):
File "fashion_mnist.py", line 175, in main() File "fashion_mnist.py", line 161, in main backend="torch_distributed") File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/orca/learn/pytorch/estimator.py", line 92, in from_torch backend=backend) File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/orca/learn/pytorch/estimator.py", line 137, in init workers_per_node=workers_per_node) File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/orca/learn/pytorch/pytorch_ray_estimator.py", line 107, in init ray_ctx = RayContext.get() File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/ray/raycontext.py", line 390, in get ray_ctx.init() File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/ray/raycontext.py", line 473, in init redis_address = self._start_cluster() File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/ray/raycontext.py", line 502, in _start_cluster verbose=self.verbose) File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/ray/process.py", line 113, in init self.print_ray_remote_err_out() File "/home/yifan/anaconda3/envs/zoo/lib/python3.7/site-packages/zoo/ray/process.py", line 117, in print_ray_remote_err_out raise Exception(str(self.master)) Exception: node_ip: 192.168.65.130 tag: ray-master, pgid: 3180, pids: [], returncode: 127, master_addr: 192.168.65.130:32350,
/bin/sh: 1: ray: not found Stopping orca context stopping org.apache.spark.deploy.worker.Worker stopping org.apache.spark.deploy.master.Master
user issue

opened by Yifanzhou-0713 31
Run the latest version 'NYC'_ taxi_ dataset.ipynb 'error

When I running the latest version 'NYC_ taxi_ dataset.ipynb',the following error occurred:

from zoo.automl.common.util import train_val_test_split train_df, val_df, test_df = train_val_test_split(df, val_ratio=0.1, test_ratio=0.1)

Prepending /home/wxy/anaconda3/envs/ZooAutoml/lib/python3.6/site-packages/bigdl/share/conf/spark-bigdl.conf to sys.path Adding /home/wxy/anaconda3/envs/ZooAutoml/lib/python3.6/site-packages/zoo/share/lib/analytics-zoo-bigdl_0.10.0-spark_2.4.3-0.8.1-jar-with-dependencies.jar to BIGDL_JARS Prepending /home/wxy/anaconda3/envs/ZooAutoml/lib/python3.6/site-packages/zoo/share/conf/spark-analytics-zoo.conf to sys.path

ImportError Traceback (most recent call last) in ----> 1 from zoo.automl.common.util import train_val_test_split 2 train_df, val_df, test_df = train_val_test_split(df, val_ratio=0.1, test_ratio=0.1)

ImportError: cannot import name 'train_val_test_split'

opened by 2017wxyzwxyz 19
[BigDL 2.0] examples on k8s integration tests client mode on new image

| Module | Example | Client Mode | | ---- | ---- | ---- | | nnframes | ImageInferenceExample.py | Succeed | | nnframes | ImageTransferLearningExample.py | Succeed | | pytorch | learn/pytorch/cifar10/cifar10.py | Succeed | | pytorch | learn/pytorch/fashion_mnist/fashion_mnist.py | Succeed | | pytorch | learn/pytorch/super_resolution/super_resolution.py | Succeed | | tf | learn/tf/basic_text_classification/basic_text_classification.py | Succeed | | tf | learn/tf/transfer_learning/transfer_learning.py | Succeed | | tf | learn/tf/inception/inception.py | Succeed | | tf | learn/tf/image_segmentation/image_segmentation.py | Succeed | | tf2 | learn/tf2/yolov3/yoloV3.py | Succeed | | torchmodel | torchmodel/train/imagenet/main.py | Succeed | | torchmodel | torchmodel/train/mnist/main.py | Succeed | | torchmodel | torchmodel/train/resnet_finetune/resnet_finetune.py | Succeed |

opened by piaolaidelangman 17

Cannot successfuly pass test code for orca image

I installed the development environment according to the official developer guide Run in IDE. But I get an error when I run test_write_parquet.py::test_write_mnist in Pycharm.

/root/anaconda3/envs/zoo-dev/bin/python /root/app/pycharm-community-2020.3/plugins/python-ce/helpers/pycharm/_jb_pytest_runner.py --target test_write_parquet.py::test_write_mnist
Testing started at 上午9:29 ...
Launching pytest with arguments test_write_parquet.py::test_write_mnist in /root/zoo-project/analytics-zoo/pyzoo/test/zoo/orca/data

============================= test session starts ==============================
platform linux -- Python 3.6.12, pytest-6.2.1, py-1.10.0, pluggy-0.13.1 -- /root/anaconda3/envs/zoo-dev/bin/python
cachedir: .pytest_cache
rootdir: /root/zoo-project/analytics-zoo/pyzoo
collecting ... collected 1 item

test_write_parquet.py::test_write_mnist ERROR                            [100%]Initializing orca context
Current pyspark location is : /root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/pyspark/__init__.py
Start to getOrCreate SparkContext
pyspark_submit_args is:  --driver-class-path /root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/share/lib/bigdl-0.12.1-jar-with-dependencies.jar pyspark-shell 
2020-12-23 09:29:24 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

test setup failed
request = <SubRequest 'orca_context_fixture' for <Function test_write_mnist>>

    @pytest.fixture(autouse=True, scope='package')
    def orca_context_fixture(request):
        import os
        from zoo.orca import OrcaContext, init_orca_context, stop_orca_context
        OrcaContext._eager_mode = True
        access_key_id = os.getenv("AWS_ACCESS_KEY_ID")
        secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY")
        if access_key_id is not None and secret_access_key is not None:
            env = {"AWS_ACCESS_KEY_ID": access_key_id,
                   "AWS_SECRET_ACCESS_KEY": secret_access_key}
        else:
            env = None
        sc = init_orca_context(cores=4, spark_log_level="INFO",
                               env=env, object_store_memory="1g",
>                              init_ray_on_spark=True)

conftest.py:34: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../../../zoo/orca/common.py:135: in init_orca_context
    sc = init_spark_on_local(cores, **spark_args)
../../../../zoo/common/nncontext.py:53: in init_spark_on_local
    python_location=python_location)
../../../../zoo/util/spark.py:56: in init_spark_on_local
    redirect_spark_log=self.redirect_spark_log)
../../../../zoo/common/nncontext.py:387: in init_nncontext
    redire_spark_logs()
/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:449: in redire_spark_logs
    callBigDlFunc(bigdl_type, "redirectSparkLogs", log_path)
/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:592: in callBigDlFunc
    for jinvoker in JavaCreator.instance(bigdl_type, gateway).value:
/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:56: in instance
    cls._instance = cls(bigdl_type, *args)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <bigdl.util.common.JavaCreator object at 0x7f39c12ed9e8>
bigdl_type = 'float'
gateway = <py4j.java_gateway.JavaGateway object at 0x7f39c1331978>

    def __init__(self, bigdl_type, gateway):
        self.value = []
        for creator_class in JavaCreator.get_creator_class():
            jclass = getattr(gateway.jvm, creator_class)
            if bigdl_type == "float":
>               self.value.append(getattr(jclass, "ofFloat")())
E               TypeError: 'JavaPackage' object is not callable

/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:96: TypeError


Assertion failed


Assertion failed


Assertion failed


Assertion failed


==================================== ERRORS ====================================
______________________ ERROR at setup of test_write_mnist ______________________

request = <SubRequest 'orca_context_fixture' for <Function test_write_mnist>>

    @pytest.fixture(autouse=True, scope='package')
    def orca_context_fixture(request):
        import os
        from zoo.orca import OrcaContext, init_orca_context, stop_orca_context
        OrcaContext._eager_mode = True
        access_key_id = os.getenv("AWS_ACCESS_KEY_ID")
        secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY")
        if access_key_id is not None and secret_access_key is not None:
            env = {"AWS_ACCESS_KEY_ID": access_key_id,
                   "AWS_SECRET_ACCESS_KEY": secret_access_key}
        else:
            env = None
        sc = init_orca_context(cores=4, spark_log_level="INFO",
                               env=env, object_store_memory="1g",
>                              init_ray_on_spark=True)

conftest.py:34: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../../../zoo/orca/common.py:135: in init_orca_context
    sc = init_spark_on_local(cores, **spark_args)
../../../../zoo/common/nncontext.py:53: in init_spark_on_local
    python_location=python_location)
../../../../zoo/util/spark.py:56: in init_spark_on_local
    redirect_spark_log=self.redirect_spark_log)
../../../../zoo/common/nncontext.py:387: in init_nncontext
    redire_spark_logs()
/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:449: in redire_spark_logs
    callBigDlFunc(bigdl_type, "redirectSparkLogs", log_path)
/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:592: in callBigDlFunc
    for jinvoker in JavaCreator.instance(bigdl_type, gateway).value:
/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:56: in instance
    cls._instance = cls(bigdl_type, *args)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <bigdl.util.common.JavaCreator object at 0x7f39c12ed9e8>
bigdl_type = 'float'
gateway = <py4j.java_gateway.JavaGateway object at 0x7f39c1331978>

    def __init__(self, bigdl_type, gateway):
        self.value = []
        for creator_class in JavaCreator.get_creator_class():
            jclass = getattr(gateway.jvm, creator_class)
            if bigdl_type == "float":
>               self.value.append(getattr(jclass, "ofFloat")())
E               TypeError: 'JavaPackage' object is not callable

/root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/util/common.py:96: TypeError
---------------------------- Captured stdout setup -----------------------------
Initializing orca context
Current pyspark location is : /root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/pyspark/__init__.py
Start to getOrCreate SparkContext
pyspark_submit_args is:  --driver-class-path /root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/bigdl/share/lib/bigdl-0.12.1-jar-with-dependencies.jar pyspark-shell 
2020-12-23 09:29:24 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
---------------------------- Captured stderr setup -----------------------------
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
=============================== warnings summary ===============================
../../../../../../../anaconda3/envs/zoo-dev/lib/python3.6/site-packages/pyspark/cloudpickle.py:47
  /root/anaconda3/envs/zoo-dev/lib/python3.6/site-packages/pyspark/cloudpickle.py:47: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
    import imp

-- Docs: https://docs.pytest.org/en/stable/warnings.html
=========================== short test summary info ============================
ERROR test_write_parquet.py::test_write_mnist - TypeError: 'JavaPackage' obje...
========================= 1 warning, 1 error in 4.99s ==========================
Stopping orca context
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
  File "/root/zoo-project/analytics-zoo/pyzoo/zoo/orca/common.py", line 210, in stop_orca_context
    ray_ctx = RayContext.get(initialize=False)
  File "/root/zoo-project/analytics-zoo/pyzoo/zoo/ray/raycontext.py", line 393, in get
    raise Exception("No active RayContext. Please create a RayContext and init it first")
Exception: No active RayContext. Please create a RayContext and init it first

Process finished with exit code 1


Assertion failed

Assertion failed

user issue

opened by GitEasonXu 17

Support pyspark.sql.types.ArrayType with pyspark.sql.types.StringType elements
This modification has been tested using pyzoo/test/zoo/orca/learn/ray/tf/test_tf_ray_estimator.py adding test_array_string_input(). (I also modified test_string_input() to adapt a vocabulary.)

results are: test_array_string_input():

[Row(id=0, input=['foo', 'qux', 'bar'], prediction=DenseVector([4.0, 1.0, 2.0])), Row(id=1, input=['qux', 'baz'], prediction=DenseVector([1.0, 3.0]))]

test_string_input():

[Row(input='foo qux bar', prediction=DenseVector([3.0, 2.0, 5.0, 0.0])), Row(input='qux baz', prediction=DenseVector([2.0, 4.0, 0.0, 0.0]))]

test env:

python3.7

tensorflow==2.7.0

ray==1.9.2
opened by nyamashi 16
Showing 'Exception in thread "main" java.lang.IllegalStateException: Cannot find any build directories' when excecuting 'sc = init_nncontext()'

Could you please fix this issue as it is showing error just after calling init_nncontext() at the very beginning of the code. As well as its showing error 'Exception: Java gateway process exited before sending its port number'. Could you please explain me what the problem is and how it can be solved?
user issue

opened by dhannya34 16
AutoML Installation error, help me

I installed "automl" tool kit according to the steps in the webpage,'https://github.com/intel-analytics/analytics-zoo/tree/automl/apps/automl' but the following error occurred, asking for help,

(1) Win10 Linux subsystem (Ubuntu18.04onWindows) (2) Anaconda3-2020.02-Linux-x86_64 (3)Details： (base) wxy@SC-202007040719:/$ conda activate zoo_automl (zoo_automl) wxy@SC-202007040719:/$ pip install analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl[automl] WARNING: Requirement 'analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl[automl]' looks like a filename, but the file does not exist Processing /analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl ERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: '/analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl'

(zoo_automl) wxy@SC-202007040719:/$ quit

Command 'quit' not found, did you mean:

command 'luit' from deb x11-utils command 'quot' from deb quota command 'qgit' from deb qgit command 'quilt' from deb quilt command 'quiz' from deb bsdgames

Try: sudo apt install

(zoo_automl) wxy@SC-202007040719:/$ conda deactivate (base) wxy@SC-202007040719:/$ source activate zoo_automl (zoo_automl) wxy@SC-202007040719:/$ pip install analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl[automl] WARNING: Requirement 'analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl[automl]' looks like a filename, but the file does not exist Processing /analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl ERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: '/analytics-zoo/pyzoo/dist/analytics_zoo-0.8.1-py2.py3-none-manylinux1_x86_64.whl'

(zoo_automl) wxy@SC-202007040719:/$

I am a beginner, please take care of me. Thank you very much！！！！！！！

opened by 2017wxyzwxyz 15
Error while running the openvino example

Run by downloading the prebuilt package. https://github.com/intel-analytics/analytics-zoo/tree/master/pyzoo/zoo/examples/openvino

Traceback (most recent call last): File "/tmp/1558587157102-0/model-optimizer/mo_tf.py", line 28, in from mo.main import main File "/tmp/1558587157102-0/model-optimizer/mo/main.py", line 28, in from mo.utils.cli_parser import get_placeholder_shapes, get_tuple_values, get_model_name,
File "/tmp/1558587157102-0/model-optimizer/mo/utils/cli_parser.py", line 26, in from mo.front.extractor import split_node_in_port File "/tmp/1558587157102-0/model-optimizer/mo/front/extractor.py", line 21, in import networkx as nx ModuleNotFoundError: No module named 'networkx'
high priority

opened by zhichao-li 15
FL server and client
Mainly modification is:

Use a FLServer to start all services, same as a FLClient

move some test client and utils to test directory

some useless code are not deleted because I do not know whether they will be used in future.
opened by Litchilitchy 14

Some random errors when running TF2Estimator on recsys full dataset

When converting SparkXShards to RayXShards:

(raylet, ip=172.16.0.113) [2021-07-22 11:06:06,984 C 134880 134880] service_based_gcs_client.cc:235: Couldn't reconnect to GCS server. The last attempted
[Stage 504:==========================================>       (849 + 158) / 1007]2021-07-22 11:07:33 ERROR DAGScheduler:91 - Failed to update accumulator 0
 (org.apache.spark.api.python.PythonAccumulatorV2) for task 717
java.net.SocketException: Connection reset

2021-07-22 11:07:33 ERROR DAGScheduler:91 - Failed to update accumulator 0 (org.apache.spark.api.python.PythonAccumulatorV2) for task 885
java.net.SocketException: Broken pipe (Write failed)
        at java.net.SocketOutputStream.socketWrite0(Native Method)
        at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:111)
        at java.net.SocketOutputStream.write(SocketOutputStream.java:155)
        at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
        at java.io.BufferedOutputStream.flush(BufferedOutputStream.java:140)
        at java.io.DataOutputStream.flush(DataOutputStream.java:123)
        at org.apache.spark.api.python.PythonAccumulatorV2.merge(PythonRDD.scala:650)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$updateAccumulators$1.apply(DAGScheduler.scala:1257)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$updateAccumulators$1.apply(DAGScheduler.scala:1248)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.updateAccumulators(DAGScheduler.scala:1248)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:1338)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2107)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)

http://172.16.0.107:7777/history/application_1626654036089_0216/jobs/ http://172.16.0.107:7777/history/application_1626654036089_0518/jobs/ http://172.16.0.107:7777/history/application_1626654036089_0563/jobs/

high priority orca friesian

opened by hkvision 14

TuneError: ('Trials did not complete', [train_func_19925_00004])

I'm trying to implement auto_ts on my multivariate time series data using Lstm , while fitting it is giving the following error.

Data is in this format

== Status ==
Memory usage on this node: 2.7/12.7 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/4 CPUs, 0/0 GPUs, 0.0/6.79 GiB heap, 0.0/2.34 GiB objects
Current best trial: 19925_00005 with mse=0.15181996493457617 and parameters={'hidden_dim': 64, 'layer_num': 2, 'lr': 0.0010343663029423226, 'dropout': 0.09671240437800133, 'input_feature_num': None, 'output_feature_num': 1, 'past_seq_len': 4, 'future_seq_len': 1, 'selected_features': ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'MINUTE', 'DAY', 'DAYOFYEAR', 'HOUR', 'WEEKDAY', 'WEEKOFYEAR', 'MONTH', 'YEAR', 'IS_AWAKE', 'IS_BUSY_HOURS', 'IS_WEEKEND'], 'batch_size': 32}
Result logdir: /tmp/autots_estimator/autots_estimator
Number of trials: 6/6 (1 ERROR, 5 TERMINATED)
Number of errored trials: 1

--------------------------------------------------------------------------- pre-wrap; display: inline;">TuneError                                 Traceback (most recent call last) pre-wrap; display: inline;"><ipython-input-17-c1017f49fcaa> in <module>() 2 ts_pipeline = auto_estimator.fit(data=tsdata_train, # train dataset 3                                  validation_data=tsdata_val, # validation dataset var(--ansi-green);">----> 4                                  epochs=5) # number of epochs to train in each trial cursor: pointer; display: flex;">
4 frames
/usr/local/lib/python3.7/dist-packages/zoo/chronos/autots/autotsestimator.py in fit(self, data, epochs, batch_size, validation_data, metric_threshold, n_sampling, search_alg, search_alg_params, scheduler, scheduler_params) 246                 search_alg_params=search_alg_params, 247                 scheduler=scheduler, var(--ansi-green);">--> 248                 scheduler_params=scheduler_params 249             ) 250  margin-top: 0px; white-space: pre-wrap; display: inline;">/usr/local/lib/python3.7/dist-packages/zoo/chronos/autots/model/base_automodel.py in fit(self, data, epochs, batch_size, validation_data, metric_threshold, n_sampling, search_alg, search_alg_params, scheduler, scheduler_params) 77             search_alg_params=search_alg_params, 78             scheduler=scheduler, var(--ansi-green);">---> 79             scheduler_params=scheduler_params, 80         ) 81         self.best_model = self.auto_est._get_best_automl_model() margin-top: 0px; white-space: pre-wrap; display: inline;">/usr/local/lib/python3.7/dist-packages/zoo/orca/automl/auto_estimator.py in fit(self, data, epochs, validation_data, metric, metric_mode, metric_threshold, n_sampling, search_space, search_alg, search_alg_params, scheduler, scheduler_params) 193                               scheduler=scheduler, 194                               scheduler_params=scheduler_params) var(--ansi-green);">--> 195         self.searcher.run() 196         self._fitted = True 197  margin-top: 0px; white-space: pre-wrap; display: inline;">/usr/local/lib/python3.7/dist-packages/zoo/orca/automl/search/ray_tune/ray_tune_search_engine.py in run(self) 181             resources_per_trial=self.resources_per_trial, 182             verbose=1, var(--ansi-green);">--> 183             reuse_actors=True 184         ) 185         self.trials = analysis.trials pre-wrap; display: inline;">/usr/local/lib/python3.7/dist-packages/ray/tune/tune.py in run(run_or_experiment, name, metric, mode, stop, time_budget_s, config, resources_per_trial, num_samples, local_dir, search_alg, scheduler, keep_checkpoints_num, checkpoint_score_attr, checkpoint_freq, checkpoint_at_end, verbose, progress_reporter, log_to_file, trial_name_creator, trial_dirname_creator, sync_config, export_formats, max_failures, fail_fast, restore, server_port, resume, queue_trials, reuse_actors, trial_executor, raise_on_failed_trial, callbacks, loggers, ray_auto_init, run_errored_only, global_checkpoint_period, with_server, upload_dir, sync_to_cloud, sync_to_driver, sync_on_checkpoint) 442     if incomplete_trials: 443         if raise_on_failed_trial: var(--ansi-green);">--> 444             raise TuneError("Trials did not complete", incomplete_trials) 445         else: 446             logger.error("Trials did not complete: %s", incomplete_trials) pre-wrap; display: inline;">TuneError: ('Trials did not complete', [train_func_19925_00004])

user issue

opened by saiprasad2606 3

(raylet) socket.gaierror: [Errno -2] Name or service not known

When I run https://analytics-zoo.readthedocs.io/en/latest/doc/Orca/QuickStart/orca-tf2keras-quickstart.html tensorFlow 2 For example. ############ Error： (raylet) Traceback (most recent call last): (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/new_dashboard/agent.py", line 334, in (raylet) raise e (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/new_dashboard/agent.py", line 323, in (raylet) loop.run_until_complete(agent.run()) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/asyncio/base_events.py", line 568, in run_until_complete (raylet) return future.result() (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/new_dashboard/agent.py", line 138, in run (raylet) modules = self._load_modules() (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/new_dashboard/agent.py", line 92, in _load_modules (raylet) c = cls(self) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/new_dashboard/modules/reporter/reporter_agent.py", line 72, in init (raylet) self._metrics_agent = MetricsAgent(dashboard_agent.metrics_export_port) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/metrics_agent.py", line 76, in init (raylet) namespace="ray", port=metrics_export_port))) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/prometheus_exporter.py", line 334, in new_stats_exporter (raylet) options=option, gatherer=option.registry, collector=collector) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/prometheus_exporter.py", line 266, in init (raylet) self.serve_http() (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/ray/prometheus_exporter.py", line 321, in serve_http (raylet) port=self.options.port, addr=str(self.options.address)) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/prometheus_client/exposition.py", line 168, in start_wsgi_server (raylet) TmpServer.address_family, addr = _get_best_family(addr, port) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/site-packages/prometheus_client/exposition.py", line 157, in _get_best_family (raylet) infos = socket.getaddrinfo(address, port) (raylet) File "/usr/local/miniconda3/envs/zoo/lib/python3.7/socket.py", line 753, in getaddrinfo (raylet) for res in _socket.getaddrinfo(host, port, family, type, proto, flags): (raylet) socket.gaierror: [Errno -2] Name or service not known ##############

Hosts file

After running the example, session files are generated in /tmp/ray/ of the system

Runtime environment: Docker deployment uses Miniconda to install AZ and Ray

Conda create -n zoo python=3.7 conda activate zoo pip install --pre --upgrade analytics-zoo pip install analytics-zoo[ray] PIP install tensorflow = = 2.3.0

conda list

Name Version Build Channel _libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
absl-py 1.0.0 pypi_0 pypi aiohttp 3.7.0 pypi_0 pypi aiohttp-cors 0.7.0 pypi_0 pypi aioredis 1.1.0 pypi_0 pypi analytics-zoo 0.12.0b2022052501 pypi_0 pypi astunparse 1.6.3 pypi_0 pypi async-timeout 3.0.1 pypi_0 pypi attrs 21.4.0 pypi_0 pypi bigdl 0.13.1.dev1 pypi_0 pypi blessings 1.7 pypi_0 pypi ca-certificates 2022.4.26 h06a4308_0
cachetools 5.1.0 pypi_0 pypi certifi 2022.5.18.1 py37h06a4308_0
chardet 3.0.4 pypi_0 pypi charset-normalizer 2.0.12 pypi_0 pypi click 8.1.3 pypi_0 pypi colorama 0.4.4 pypi_0 pypi colorful 0.5.4 pypi_0 pypi conda-pack 0.3.1 pypi_0 pypi deprecated 1.2.13 pypi_0 pypi filelock 3.7.0 pypi_0 pypi gast 0.3.3 pypi_0 pypi google-api-core 2.8.0 pypi_0 pypi google-auth 2.6.6 pypi_0 pypi google-auth-oauthlib 0.4.6 pypi_0 pypi google-pasta 0.2.0 pypi_0 pypi googleapis-common-protos 1.56.1 pypi_0 pypi gpustat 0.6.0 pypi_0 pypi grpcio 1.46.3 pypi_0 pypi h5py 2.10.0 pypi_0 pypi hiredis 1.1.0 pypi_0 pypi idna 3.3 pypi_0 pypi importlib-metadata 4.11.4 pypi_0 pypi importlib-resources 5.7.1 pypi_0 pypi jsonschema 4.5.1 pypi_0 pypi keras-preprocessing 1.1.2 pypi_0 pypi libedit 3.1.20210910 h7f8727e_0
libffi 3.2.1 hf484d3e_1007
libgcc-ng 11.2.0 h1234567_0
libgomp 11.2.0 h1234567_0
libstdcxx-ng 11.2.0 h1234567_0
markdown 3.3.7 pypi_0 pypi msgpack 1.0.3 pypi_0 pypi multidict 6.0.2 pypi_0 pypi ncurses 6.3 h7f8727e_2
numpy 1.18.5 pypi_0 pypi nvidia-ml-py3 7.352.0 pypi_0 pypi oauthlib 3.2.0 pypi_0 pypi opencensus 0.9.0 pypi_0 pypi opencensus-context 0.1.2 pypi_0 pypi opencv-python 4.5.5.64 pypi_0 pypi openssl 1.0.2u h7b6447c_0
opt-einsum 3.3.0 pypi_0 pypi packaging 21.3 pypi_0 pypi pip 21.2.2 py37h06a4308_0
prometheus-client 0.14.1 pypi_0 pypi protobuf 3.20.1 pypi_0 pypi psutil 5.9.1 pypi_0 pypi py-spy 0.3.12 pypi_0 pypi py4j 0.10.7 pypi_0 pypi pyasn1 0.4.8 pypi_0 pypi pyasn1-modules 0.2.8 pypi_0 pypi pyparsing 3.0.9 pypi_0 pypi pyrsistent 0.18.1 pypi_0 pypi pyspark 2.4.6 pypi_0 pypi python 3.7.0 h6e4f718_3
pyyaml 6.0 pypi_0 pypi ray 1.2.0 pypi_0 pypi readline 7.0 h7b6447c_5
redis 4.1.4 pypi_0 pypi requests 2.27.1 pypi_0 pypi requests-oauthlib 1.3.1 pypi_0 pypi rsa 4.8 pypi_0 pypi scipy 1.4.1 pypi_0 pypi setproctitle 1.2.3 pypi_0 pypi setuptools 61.2.0 py37h06a4308_0
six 1.16.0 pypi_0 pypi sqlite 3.33.0 h62c20be_0
tensorboard 2.9.0 pypi_0 pypi tensorboard-data-server 0.6.1 pypi_0 pypi tensorboard-plugin-wit 1.8.1 pypi_0 pypi tensorflow 2.3.0 pypi_0 pypi tensorflow-estimator 2.3.0 pypi_0 pypi termcolor 1.1.0 pypi_0 pypi tk 8.6.11 h1ccaba5_1
typing-extensions 4.2.0 pypi_0 pypi urllib3 1.26.9 pypi_0 pypi werkzeug 2.1.2 pypi_0 pypi wheel 0.37.1 pyhd3eb1b0_0
wrapt 1.14.1 pypi_0 pypi xz 5.2.5 h7f8727e_1
yarl 1.7.2 pypi_0 pypi zipp 3.8.0 pypi_0 pypi zlib 1.2.12 h7f8727e_2

———————————————————— 1、Check python: from zoo.util.utils import detect_python_location detect_python_location()

2、Check ray installation /usr/local/miniconda3/envs/zoo/bin/python /usr/local/miniconda3/envs/zoo/bin/ray start --head --include-dashboard ture --dashboard-host 172.27.0.2 --port 35413 --redis-password 123456 --num-cpus 1

/usr/local/miniconda3/envs/zoo/bin/python /usr/local/miniconda3/envs/zoo/bin/ray start --address 172.27.0.2:35413 --redis-password 123456 --num-cpus 1

ray start --address=‘172.27.0.2:35413' --redis-password='0'

Cannot import name 'forecaster' from 'zoo.chronos'

System information

OS Platform and Distribution: macOS Big sur, also tested on WSL with windows 10
Zoo version (zoo.__version__): 0.11.2
pyspark version (pyspark.__version__): 2.4.6
Python version: Python 3.6.13 | Anaconda, Inc.| (default, Feb 23 2021, 12:58:59)
Code we can use to reproduce: [example notebook](https://github.com/intel-analytics/analytics-zoo/blob/master/pyzoo/zoo/chronos/use-case/fsi/stock_prediction_prophet.ipynb

Message

I'm exploring Chronos for time series. I've decided to use this example notebook to start.

When running from zoo.chronos import forecaster I got the following error message:

ImportError: cannot import name 'forecaster'

When list of the attributes with dir(zoo.chronos): I get data only. ['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'data']

user issue

opened by mbrhd 1

Fail at import: dependencies not installed

I'm exploring Chronos for time series. I've decided to use this example notebook to start.

When running from zoo.chronos.data import TSDataset I got the following error message:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/zoo/__init__.py", line 17, in <module>
    from zoo.common.nncontext import *
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/zoo/common/__init__.py", line 17, in <module>
    from .utils import *
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/zoo/common/utils.py", line 16, in <module>
    from bigdl.util.common import Sample as BSample, JTensor as BJTensor,\
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/bigdl/__init__.py", line 18, in <module>
    prepare_env()
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/bigdl/util/engine.py", line 155, in prepare_env
    __prepare_spark_env()
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/bigdl/util/engine.py", line 53, in __prepare_spark_env
    if exist_pyspark():
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/bigdl/util/engine.py", line 26, in exist_pyspark
    import pyspark
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/pyspark/__init__.py", line 51, in <module>
    from pyspark.context import SparkContext
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/pyspark/context.py", line 31, in <module>
    from pyspark import accumulators
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/pyspark/accumulators.py", line 97, in <module>
    from pyspark.serializers import read_int, PickleSerializer
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/pyspark/serializers.py", line 72, in <module>
    from pyspark import cloudpickle
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/pyspark/cloudpickle.py", line 145, in <module>
    _cell_set_template_code = _make_cell_set_template_code()
  File "/opt/anaconda3/envs/analytics-zoo-test/lib/python3.8/site-packages/pyspark/cloudpickle.py", line 126, in _make_cell_set_template_code
    return types.CodeType(
TypeError: an integer is required (got type bytes)

I fixed this by installing spark using: conda install pyspark

Then the same command from zoo.chronos.data import TSDataset fails because of pandas, packaging, and tsfresh not installed. I fixed this issue by installing pandas, packaging and tsfresh.

user issue

opened by mbrhd 1

Clarification on BigDL and Analytics-Zoo status

The Intel page for BigDL indicates that BigDL merging Analytics-Zoo into the BigDL project, and that Analytics-Zoo is now a legacy tool. This is backed up by BigDL seeming to provide all the functionality Analytics-Zoo offers. This statement only appears on the Intel website.

Could clarification be provided on this?

user issue

opened by bendavidsteel 2

[BigDL2.0 k8s] cluster mode remain issues

k8s cluster mode remain issues:

pytorch jep requires to specifyexport PYTHONHOME=/usr/local/envs/pytf1 on driver. On cluster mode, it can not be set in the driver pod. The ks8 image has two python envs. We can not hard code PYTHONHOME in image.
Tfpark on cluster mode throws ModuleNotFoundError: No module named 'nets'. The k8s image has already cloned slim models and set PYTHONPATH as opt/models/research/slim:$PYTHONPATH. In client mode, it can run successfully.
orca openvino example throws RuntimeError: The support of IR v4 has been removed from the product. Please, convert the original model using the Model Optimizer which comes with this version of the OpenVINO to generate supported IR version. It seems the code can not support openvino isntalled by conda install openvino-ie4py-ubuntu18 -c intel

opened by Le-Zheng 0

Releases(v0.11.2)

v0.11.2(Jan 24, 2022)

Highlights

Note: Analytics Zoo v0.11.2 has been updated to include functional and security updates. Users should update to the latest version.
Source code(tar.gz)
Source code(zip)
v0.11.1(Dec 22, 2021)

Highlights

Note: Analytics Zoo v0.11.1 has been updated to include functional and security updates. Users should update to the latest version.
Source code(tar.gz)
Source code(zip)
v0.11.0(Jul 19, 2021)
Highlights

Chronos: an overhaul of the previous Zouwu time-series analysis library, with:

Built-in support of ~100 algorithms for time series forecast (e.g., TCN, seq2seq, ARIMA, Prophet, etc.), anomaly detection (e.g., DBScan, AutoEncoder etc.), and feature transformations (using TSDataset).

Automatic tuning of built-in models (e.g., AutoProphet, AutoARIMA, AutoXGBoost, etc.) using AutoML

Simple APIs for tuning user-defined models (including PyTorch and Keras) with AutoML

Improved APIs, documentation, quick start examples, etc.

Reference implementation of large-scale feature transformation pipelines for recommendation systems (e.g., DLRM, DIEN, W&D, etc.)

Enhancements to Orca (scaling TF/PyTorch models to distributed Big Data) for end-to-end computer vision pipelines (distributed image preprocessing, training and inference); for more information, please see our CPVR 2021 tutorial.

Initial Python and PySpark application support for PPML (privacy preserving big data and machine learning)

Source code(tar.gz)
Source code(zip)
v0.10.0(May 25, 2021)
Highlights

Improved document website, including quickstarts for Orca, RayOnSpark, Zouwu and BigDL

Orca library: unified API for running distributed deep learning (TensorFlow, PyTorch, Keras, BigDL, OpenVINO, etc.) on distributed Big Data (using Apache Spark and Ray)

Experimental PPML support for privacy preserving big data analysis and machine learning (i.e., running unmodified Apache Spark, Apache Flink, BigDL and TF/PyTorch/OpenVINO inference in a secure fashion on cloud)

Improved AutoML and Time Series (Zouwu) support, including AutoXGBoost, TCN, ONNX inference, etc.

Improved Cluster Serving (including performance, stability, log information, etc.)

Source code(tar.gz)
Source code(zip)
v0.9.0(Dec 17, 2020)

Highlights
Source code(tar.gz)
Source code(zip)
v0.8.1(Apr 27, 2020)

Highlights
Source code(tar.gz)
Source code(zip)
v0.8.0(Apr 17, 2020)
Highlights

Improved support for running Analytics Zoo on K8s

Improvement to tfpark (including support of pre-made TensorFlow Estimator, and support of Spark Dataframe and tf.data.Dataset in zoo.tfpark.TFDataset)

Improvement to Cluster Serving (including support for performance mode, TensorFlow saved model, and better TensorBoard integration)

Improvement to time series analysis (including MTNet and project Zouwu)

Support for Distributed MXNet training on Ray

Upgrade OpenVINO support to 2020.R1

Source code(tar.gz)
Source code(zip)
v0.7.0(Jan 20, 2020)

Highlights
Source code(tar.gz)
Source code(zip)
v0.6.0(Oct 15, 2019)

Highlights
Source code(tar.gz)
Source code(zip)
v0.5.1(Jun 11, 2019)

Highlights
Source code(tar.gz)
Source code(zip)
v0.5.0(Jun 3, 2019)

Highlights
Source code(tar.gz)
Source code(zip)
v0.4.0(Jan 25, 2019)
Highlights

Support for BigDL 0.7.2 and Spark 2.4; see the download page for all the supported versions.

Initial OpenVINO support for the model serving API, which can use OpenVINO toolkit to accelerate the inference speed for the TensorFlow models on Analytics Zoo. please refer to the related document and example for more details.

Initial Persistent Memory support for distributed deep learning training, which can leverage Intel Optane DC Persistent Memory to cache large training data set; please refer to the related document for more details.

Various new features, including additional built-in models (such as sequence-to-sequence model and unsupervised time series anomaly detection model), learning rate schedule in Adam, Spark ML vector support in nnframes, TensorFlow Keras model support in TFOptimizer, etc.

Source code(tar.gz)
Source code(zip)
v0.3.0(Oct 30, 2018)
Highlights

Distributed TensorFlow (both training and inference) on Spark, which supports:

Data wrangling and analysis using PySpark

Deep learning model development using TensorFlow or Keras

Distributed training/inference on Spark and BigDL

All within a single unified pipeline and in a user-transparent fashion!

More support for text processing and models, including:

Common feature engineering operations for text data (such as tokenization, normalization, padding, etc.)

Word Embedding layers that directly load pretrained GloVe model

Text matching models (such as KNRM)

Various improvements and new features, such as:

Support for trainable variable (Parameter)

Support for Keras objectives (with zero-based label)

Improvements to model serving APIs

Improvements to example and use case documents

Source code(tar.gz)
Source code(zip)
v0.2.0(Jul 17, 2018)
Highlights

Support for both BigDL 0.5.0 and BigDL 0.6.0

New reference use case (image similarity based house recommendation)

Additional pre-trained models (Inception v3, MobileNet v2, quantized models)

Improved support for autograd and custom loss/layer

Improved support for model serving APIs

Source code(tar.gz)
Source code(zip)
v0.1.0(Jun 13, 2018)
Highlights

Support for building and productionizing end-to-end deep learning applications for big data

E2E analytics + deep learning pipelines (natively in Spark DataFrames and ML Pipelines) using nnframes

Flexible model definition using autograd, Keras & transfer learning APIs

Data preprocessing using built-in feature engineering operations

Out-of-the-box solutions for a variety of problem types using built-in deep learning models and reference use cases

Serving models using POJO model serving APIs for web services and other big data frameworks (e.g., Storm or Kafka)

Source code(tar.gz)
Source code(zip)

Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

Related tags

Overview

What is Analytics Zoo?

Why use Analytics Zoo?

How to use Analytics Zoo?

Comments

TensorFlow 2.4.0

Release 2.4.0

Major Features and Improvements

Breaking Changes

Release 2.4.0

Major Features and Improvements

Breaking Changes

System information

Message

Releases(v0.11.2)

v0.11.2(Jan 24, 2022)

Highlights

v0.11.1(Dec 22, 2021)

Highlights

v0.11.0(Jul 19, 2021)

Highlights

v0.10.0(May 25, 2021)

Highlights

v0.9.0(Dec 17, 2020)

Highlights

v0.8.1(Apr 27, 2020)

Highlights

v0.8.0(Apr 17, 2020)

Highlights

v0.7.0(Jan 20, 2020)

Highlights

v0.6.0(Oct 15, 2019)

Highlights

v0.5.1(Jun 11, 2019)

Highlights

v0.5.0(Jun 3, 2019)

Highlights

v0.4.0(Jan 25, 2019)

Highlights

v0.3.0(Oct 30, 2018)

Highlights

v0.2.0(Jul 17, 2018)

Highlights

v0.1.0(Jun 13, 2018)

Highlights

Owner

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

BigDL: Distributed Deep Learning Framework for Apache Spark

Distributed Deep learning with Keras & Spark

TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

[DEPRECATED] Tensorflow wrapper for DataFrames on Apache Spark

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

Microsoft Machine Learning for Apache Spark

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

Spark development environment for k8s

Code base of KU AIRS: SPARK Autonomous Vehicle Team

A basic Ray Tracer that exploits numpy arrays and functions to work fast.

Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

A high performance and generic framework for distributed DNN training

a distributed deep learning platform

Distributed Computing for AI Made Simple