What's New in TensorFlow 2.5

After version 2.4, the Google Brain team has now released the upgraded version of TensorFlow, version 2.5.0. The latest version comes with several new and improved features. TensorFlow 2.5 now supports Python 3.9, and TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0. In this article, we discuss the major updates and features of TensorFlow 2.5.0.

Major Features and Improvements

  • Support for Python3.9 has been added.
  • tf.data:
    • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
    • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
    • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
    • Options returned by tf.data.Dataset.options() are no longer mutable.
    • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
  • tf.lite
    • Enabled the new MLIR-based quantization backend by default
    • The new backend is used for 8 bits full integer post-training quantization
    • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)
    • Set experimental_new_quantizer in tf.lite.TFLiteConverter to False to disable this change
  • tf.keras
    • tf.keras.metrics.AUC now support logit predictions.
    • Enabled a new supported input type in Model.fittf.keras.utils.experimental.DatasetCreator, which takes a callable, dataset_fnDatasetCreator is intended to work across all tf.distribute strategies, and is the only input type supported for Parameter Server strategy.
  • tf.distribute
    • tf.distribute.experimental.ParameterServerStrategy now supports training with Keras Model.fit when used with DatasetCreator.
    • Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.
  • TPU embedding support
    • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
  • PluggableDevice
  • oneAPI Deep Neural Network Library (oneDNN) CPU performance optimizations from Intel-optimized TensorFlow are now available in the official x86-64 Linux and Windows builds.
    • They are off by default. Enable them by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.
    • We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.
  • TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0

Breaking Changes

  • The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

Bug Fixes and Other Changes

  • tf.keras:

    • Preprocessing layers API consistency changes:
      • StringLookup added output_modesparse, and pad_to_max_tokens arguments with same semantics as TextVectorization.
      • IntegerLookup added output_modesparse, and pad_to_max_tokens arguments with same semantics as TextVectorization. Renamed max_valuesoov_value and mask_value to max_tokensoov_token and mask_token to align with StringLookup and TextVectorization.
      • TextVectorization default for pad_to_max_tokens switched to False.
      • CategoryEncoding no longer supports adaptIntegerLookup now supports equivalent functionality. max_tokens argument renamed to num_tokens.
      • Discretization added num_bins argument for learning bins boundaries through calling adapt on a dataset. Renamed bins argument to bin_boundaries for specifying bins without adapt.
    • Improvements to model saving/loading:
      • model.load_weights now accepts paths to saved models.
    • Keras inputs can now be created directly from arbitrary tf.TypeSpecs.
    • Two new learning rate schedules added: tf.keras.optimizers.schedules.CosineDecay andtf.keras.optimizers.schedules.CosineDecayRestarts.
  • tf.data:

    • Exposing tf.data.experimental.ExternalStatePolicy, which can be used to control how external state should be handled during dataset serialization or iterator checkpointing.
    • Changing tf.data.experimental.save to store the type specification of the dataset elements. This avoids the need for explicitly specifying the element_spec argument of tf.data.experimental.load when loading the previously saved dataset.
    • Add .element_spec property to tf.data.DatasetSpec to access the inner spec. This can be used to extract the structure of nested
    • datasets.
    • Add tf.data.experimental.AutoShardingPolicy.HINT which can be used to provide hints to tf.distribute-based auto-sharding as to where in the input pipeline to insert sharding transformations.
    • Make tf.data.Options persistent across tf.function and GraphDef boundaries.
  • XLA compilation:

    • tf.function(experimental_compile=True) has become a stable API, renamed tf.function(jit_compile=True).
    • XLA can now compile MirroredStrategy: the step function passed tostrategy.run can now be annoted with jit_compile=True.
  • tf.distribute:

    • Rename experimental_prefetch_to_device in tf.distribute.InputOptions to experimental_fetch_to_device to better reflect the purpose.
  • tf.lite:

    • class tflite::Subgraph:
      • Removed the tensors() method and the non-const overload of the nodes_and_registration() method, both of which were previously documented as temporary and to be removed.
        • Uses of tensors() can be replaced by calling the existing methods tensors_size() and tensor(int).
        • Uses of the non-const overload of nodes_and_registration can be replaced by calling the existing methods nodes_size() and context(), and then calling the GetNodeAndRegistration method in the TfLiteContext returned by context().
    • NNAPI
      • Removed deprecated Interpreter::UseNNAPI(bool) C++ API.
        • Use NnApiDelegate() and related delegate configuration methods directly.
      • Replaced the model cache key for models computation algorithm with one guaranteed to be stable across runs.
  • 16 bits quantization

  • Added int16x8 support for ABS, REDUCE_MAX and REDUCE_MIN operators.

  • Additional tests and fixes for ADD and SUB operators.

  • Added support for saved model’s session initializer through TFLiteConverter.from_saved_model.

  • Added DEPTH_TO_SPACE support in Post training quantization.

  • Added dynamic range quantization support for the BatchMatMul op.

    • Both symmetric and asymmetric quantized input tensor are supported.
  • Add RFFT2D as builtin op. (RFFT2D also supports RFFTD.) Currently only supports float32 input.

  • Add 5D support to SLICE op.

  • TFLite Supports SingatureDef:

    • TFLiteConverter exports models with SignatureDef
    • Interpreter supports getting a list of signatures and getting callable function for a given signaturedef.
  • Add int8 support for ReshapeV2.

  • Add experimental support for optimization with sparsity.

  • Add nominal support for unsigned 32-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged.

  • Add support for static hash tables through TFLiteConverter.from_saved_model.

  • The Python TF Lite Interpreter bindings now has an option experimental_preserve_all_tensors to aid in debugging conversion.

  • Quantized x86 execution defaults to Ruy GEMM library for platforms with AVX support.

  • Deprecate tf.compat.v1.lite.experimental.get_potentially_supported_ops. Use tf.lite.TFLiteConverter directly to check whether a model is convertible.

  • Add support to select one of three different built-in op resolvers

  • Enabled post training with calibrations for models that require user provided TensorFlow Lite custom op libraries via converter.target_spec._experimental_custom_op_registerers. used in Python Interpreter API.

  • TF Core:

    • Corrected higher-order gradients of control flow constructs (tf.condtf.while_loop, and compositions like tf.foldl) computed with tf.GradientTape inside a tf.function.
    • Changed the default step size in gradient_checker_v2.compute_gradients to be exactly representable as a binary floating point numbers. This avoids poluting gradient approximations needlessly, which is some cases leads to false negatives in op gradient tests.
    • Added tf.config.experimental.get_memory_info, returning a dict with the current and peak memory usage. Deprecated tf.config.experimental.get_memory_usage in favor of this new function.
    • Extended tf.config.experimental.enable_tensor_float_32_execution to control Tensor-Float-32 evaluation in RNNs.
    • Added a ‘experimental_payloads’ field to tf.errors.OpError and its subclasses to support more detailed error reporting. This is inspired from Abseil Status payloads: https://github.com/abseil/abseil-cpp/blob/master/absl/status/status.h
  • tf.summary:

    • New tf.summary.graph allows manual write of TensorFlow graph (tf.Graph or tf.compat.v1.GraphDef) as a summary. This is not a replacement for the trace-based API.
  • Set /d2ReducedOptimizeHugeFunctions by default for Windows builds. This provides a big compile-time speedup, and effectively raises the minimum supported MSVC version to 16.4 (current: 16.8).

  • TensorRT

    • Removed the deprecated session_config parameter for the TF1-TRT converter TrtGraphConverter. Previously, we issued a warning when the value of the parameter is not None.
    • The TF2-TRT converter TrtGraphConverterV2 takes an object of class TrtConversionParams as a parameter. Removed three deprecated fields from this class: rewriter_config_templateis_dynamic_op, and max_batch_size. Previously, we issued a warning when the value of rewriter_config_template is not None. We issued an error when the value of is_dynamic_op is not True. We didn’t use the value for max_batch_size for building TensorRT engines. Add parameters use_dynamic_shape to enable dynamic shape support. The default is to disable dynamic shape support. Add dynamic_shape_profile_strategy for selecting a dynamic shape profile strategy. The default is profile strategy is Range.
    • Issue a warning when function get_tensorrt_rewriter_config is used.
  • TF XLA

    • Add new enum value MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED to tf.config.experimental.mlir_bridge_rollout to enable a “safe” mode. This runs the MLIR bridge only when an analysis of the graph only when an analysis of the graph determines that it is safe to run.
    • Add new enum value MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED' to tf.config.experimental.mlir_bridge_rollout` to enable a fallback for the MLIR bridge in a “safe” mode. This runs the MLIR bridge in a FallbackEnabled mode when an analysis of the graph determines that the graph does not have unsupported features.
  • Deterministic Op Functionality:

    • Add determinism-unimplemented exception-throwing to the segment-sum ops. When the environment variable TF_DETERMINISTIC_OPS is set to "true" or "1" (when op-determinism is expected), an attempt to run the following ops on a GPU will throw tf.errors.UnimplementedError (with an understandable message) when data is a floating-point type, including complex types (if supported): tf.math.segment_prodtf.math.segment_sumtf.math.unsorted_segment_meantf.math.unsorted_segment_sqrt_ntf.math.unsorted_segment_prodtf.math.unsorted_segment_sum, and therefore also tf.convert_to_tensor when value is of type tf.IndexedSlices (such as in the back prop though tf.gather into a dense embedding). See issue 39751 which this change addresses, but does not solve. This exception-throwing behavior can be disabled by setting the environment variable TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS to "true" or "1". For more information about these changes, see the description in pull request 47772.
    • In previous versions of TensorFlow, when a GPU was available, tf.sparse.sparse_dense_matmul introduced truly random noise in the forward path for data of type tf.float32 but not for data of type tf.float64 (for which there was no GPU implementation). In this current release, GPU support for other floating-point types (tf.float16tf.float64tf.complex64, and tf.complex128) has been added for this op. If you were relying on the determinism of the tf.float64 CPU implementation being automatically selected because of the absence of the tf.float64 GPU implementation, you with either need to force the op to run on the CPU or use a different data type.
  • Security

    • Fixes a heap buffer overflow in RaggedBinCount (CVE-2021-29512)
    • Fixes a heap out of bounds write in RaggedBinCount (CVE-2021-29514)
    • Fixes a type confusion during tensor casts which leads to dereferencing null pointers (CVE-2021-29513)
    • Fixes a reference binding to null pointer in MatrixDiag* ops (CVE-2021-29515)
    • Fixes a null pointer dereference via invalid Ragged Tensors (CVE-2021-29516)
    • Fixes a division by zero in Conv3D (CVE-2021-29517)
    • Fixes vulnerabilities where session operations in eager mode lead to null pointer dereferences (CVE-2021-29518)
    • Fixes a CHECK-fail in SparseCross caused by type confusion (CVE-2021-29519)
    • Fixes a segfault in SparseCountSparseOutput (CVE-2021-29521)
    • Fixes a heap buffer overflow in Conv3DBackprop* (CVE-2021-29520)
    • Fixes a division by 0 in Conv3DBackprop* (CVE-2021-29522)
    • Fixes a CHECK-fail in AddManySparseToTensorsMap (CVE-2021-29523)
    • Fixes a division by 0 in Conv2DBackpropFilter (CVE-2021-29524)
    • Fixes a division by 0 in Conv2DBackpropInput (CVE-2021-29525)
    • Fixes a division by 0 in Conv2D (CVE-2021-29526)
    • Fixes a division by 0 in QuantizedConv2D (CVE-2021-29527)
    • Fixes a division by 0 in QuantizedMul (CVE-2021-29528)
    • Fixes vulnerabilities caused by invalid validation in SparseMatrixSparseCholesky (CVE-2021-29530)
    • Fixes a heap buffer overflow caused by rounding (CVE-2021-29529)
    • Fixes a CHECK-fail in tf.raw_ops.EncodePng (CVE-2021-29531)
    • Fixes a heap out of bounds read in RaggedCross (CVE-2021-29532)
    • Fixes a CHECK-fail in DrawBoundingBoxes (CVE-2021-29533)
    • Fixes a heap buffer overflow in QuantizedMul (CVE-2021-29535)
    • Fixes a CHECK-fail in SparseConcat (CVE-2021-29534)
    • Fixes a heap buffer overflow in QuantizedResizeBilinear (CVE-2021-29537)
    • Fixes a heap buffer overflow in QuantizedReshape (CVE-2021-29536)
    • Fixes a division by zero in Conv2DBackpropFilter (CVE-2021-29538)
    • Fixes a heap buffer overflow in Conv2DBackpropFilter (CVE-2021-29540)
    • Fixes a heap buffer overflow in StringNGrams (CVE-2021-29542)
    • Fixes a null pointer dereference in StringNGrams (CVE-2021-29541)
    • Fixes a CHECK-fail in QuantizeAndDequantizeV4Grad (CVE-2021-29544)
    • Fixes a CHECK-fail in CTCGreedyDecoder (CVE-2021-29543)
    • Fixes a heap buffer overflow in SparseTensorToCSRSparseMatrix (CVE-2021-29545)
    • Fixes a division by 0 in QuantizedBiasAdd (CVE-2021-29546)
    • Fixes a heap out of bounds in QuantizedBatchNormWithGlobalNormalization (CVE-2021-29547)
    • Fixes a division by 0 in QuantizedBatchNormWithGlobalNormalization (CVE-2021-29548)
    • Fixes a division by 0 in QuantizedAdd (CVE-2021-29549)
    • Fixes a division by 0 in FractionalAvgPool (CVE-2021-29550)
    • Fixes an OOB read in MatrixTriangularSolve (CVE-2021-29551)
    • Fixes a heap OOB in QuantizeAndDequantizeV3 (CVE-2021-29553)
    • Fixes a CHECK-failure in UnsortedSegmentJoin (CVE-2021-29552)
    • Fixes a division by 0 in DenseCountSparseOutput (CVE-2021-29554)
    • Fixes a division by 0 in FusedBatchNorm (CVE-2021-29555)
    • Fixes a division by 0 in SparseMatMul (CVE-2021-29557)
    • Fixes a division by 0 in Reverse (CVE-2021-29556)
    • Fixes a heap buffer overflow in SparseSplit (CVE-2021-29558)
    • Fixes a heap OOB access in unicode ops (CVE-2021-29559)
    • Fixes a heap buffer overflow in RaggedTensorToTensor (CVE-2021-29560)
    • Fixes a CHECK-fail in LoadAndRemapMatrix (CVE-2021-29561)
    • Fixes a CHECK-fail in tf.raw_ops.IRFFT (CVE-2021-29562)
    • Fixes a CHECK-fail in tf.raw_ops.RFFT (CVE-2021-29563)
    • Fixes a null pointer dereference in EditDistance (CVE-2021-29564)
    • Fixes a null pointer dereference in SparseFillEmptyRows (CVE-2021-29565)
    • Fixes a heap OOB access in Dilation2DBackpropInput (CVE-2021-29566)
    • Fixes a reference binding to null in ParameterizedTruncatedNormal (CVE-2021-29568)
    • Fixes a set of vulnerabilities caused by lack of validation in SparseDenseCwiseMul (CVE-2021-29567)
    • Fixes a heap out of bounds read in MaxPoolGradWithArgmax (CVE-2021-29570)
    • Fixes a heap out of bounds read in RequantizationRange (CVE-2021-29569)
    • Fixes a memory corruption in DrawBoundingBoxesV2 (CVE-2021-29571)
    • Fixes a reference binding to nullptr in SdcaOptimizer (CVE-2021-29572)
    • Fixes an overflow and a denial of service in tf.raw_ops.ReverseSequence (CVE-2021-29575)
    • Fixes a division by 0 in MaxPoolGradWithArgmax (CVE-2021-29573)
    • Fixes an undefined behavior in MaxPool3DGradGrad (CVE-2021-29574)
    • Fixes a heap buffer overflow in MaxPool3DGradGrad (CVE-2021-29576)
    • Fixes a heap buffer overflow in AvgPool3DGrad (CVE-2021-29577)
    • Fixes an undefined behavior and a CHECK-fail in FractionalMaxPoolGrad (CVE-2021-29580)
    • Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-29578)
    • Fixes a heap buffer overflow in MaxPoolGrad (CVE-2021-29579)
    • Fixes a segfault in CTCBeamSearchDecoder (CVE-2021-29581)
    • Fixes a heap OOB read in tf.raw_ops.Dequantize (CVE-2021-29582)
    • Fixes a CHECK-fail due to integer overflow (CVE-2021-29584)
    • Fixes a heap buffer overflow and undefined behavior in FusedBatchNorm (CVE-2021-29583)
    • Fixes a division by zero in padding computation in TFLite (CVE-2021-29585)
    • Fixes a division by zero in optimized pooling implementations in TFLite (CVE-2021-29586)
    • Fixes a division by zero in TFLite’s implementation of SpaceToDepth (CVE-2021-29587)
    • Fixes a division by zero in TFLite’s implementation of GatherNd (CVE-2021-29589)
    • Fixes a division by zero in TFLite’s implementation of TransposeConv (CVE-2021-29588)
    • Fixes a heap OOB read in TFLite’s implementation of Minimum or Maximum (CVE-2021-29590)
    • Fixes a null pointer dereference in TFLite’s Reshape operator (CVE-2021-29592)
    • Fixes a stack overflow due to looping TFLite subgraph (CVE-2021-29591)
    • Fixes a division by zero in TFLite’s implementation of DepthToSpace (CVE-2021-29595)
    • Fixes a division by zero in TFLite’s convolution code (CVE-2021-29594)
    • Fixes a division by zero in TFLite’s implementation of EmbeddingLookup (CVE-2021-29596)
    • Fixes a division by zero in TFLite’s implementation of BatchToSpaceNd (CVE-2021-29593)
    • Fixes a division by zero in TFLite’s implementation of SpaceToBatchNd (CVE-2021-29597)
    • Fixes a division by zero in TFLite’s implementation of SVDF (CVE-2021-29598)
    • Fixes a division by zero in TFLite’s implementation of Split (CVE-2021-29599)
    • Fixes a division by zero in TFLite’s implementation of OneHot (CVE-2021-29600)
    • Fixes a division by zero in TFLite’s implementation of DepthwiseConv (CVE-2021-29602)
    • Fixes a division by zero in TFLite’s implementation of hashtable lookup (CVE-2021-29604)
    • Fixes a integer overflow in TFLite concatentation (CVE-2021-29601)
    • Fixes a integer overflow in TFLite memory allocation (CVE-2021-29605)
    • Fixes a heap OOB write in TFLite (CVE-2021-29603)
    • Fixes a heap OOB read in TFLite (CVE-2021-29606)
    • Fixes a heap OOB and null pointer dereference in RaggedTensorToTensor (CVE-2021-29608)
    • Fixes vulnerabilities caused by incomplete validation in SparseAdd (CVE-2021-29609)
    • Fixes vulnerabilities caused by incomplete validation in SparseSparseMinimum (CVE-2021-29607)
    • Fixes vulnerabilities caused by incomplete validation in SparseReshape (CVE-2021-29611)
    • Fixes vulnerabilities caused by invalid validation in QuantizeAndDequantizeV2 (CVE-2021-29610)
    • Fixes a heap buffer overflow in BandedTriangularSolve (CVE-2021-29612)
    • Fixes vulnerabilities caused by incomplete validation in tf.raw_ops.CTCLoss (CVE-2021-29613)
    • Fixes an interpreter crash from vulnerabilities in tf.io.decode_raw (CVE-2021-29614)
    • Fixes a stack overflow in ParseAttrValue with nested tensors (CVE-2021-29615)
    • Fixes a null dereference in Grappler’s TrySimplify (CVE-2021-29616)
    • Fixes a crash in tf.transpose with complex inputs (CVE-2021-29618)
    • Fixes a crash in tf.strings.substr due to CHECK-fail (CVE-2021-29617)
    • Fixes a segfault in tf.raw_ops.SparseCountSparseOutput (CVE-2021-29619)
    • Fixes a segfault in tf.raw_ops.ImmutableConst (CVE-2021-29539)
    • Updates curl to 7.76.0 to handle CVE-2020-8169CVE-2020-8177CVE-2020-8231CVE-2020-8284CVE-2020-8285 and CVE-2020-8286.
  • Other

    • Added show_debug_info to mlir.convert_graph_def and mlir.convert_function.
    • Added Arm Compute Library (ACL) support to --config=mkl_aarch64 build.

#tensorflow #deep-learning #data-science #python

What is GEEK

Buddha Community

What's New in TensorFlow 2.5

Let Developers Just Need to Grasp only One Button Component

 From then on, developers only need to master one Button component, which is enough.

Support corners, borders, icons, special effects, loading mode, high-quality Neumorphism style.


✨ Features

Rich corner effect

Exquisite border decoration

Gradient effect

Flexible icon support

Intimate Loading mode

Cool interaction Special effects

More sense of space Shadow

High-quality Neumorphism style

🛠 Guide

⚙️ Parameters

🔩 Basic parameters

onPressedVoidCallbacktruenullClick callback. If null, FButton will enter an unavailable state
onPressedDownVoidCallbackfalsenullCallback when pressed
onPressedUpVoidCallbackfalsenullCallback when lifted
onPressedCancelVoidCallbackfalsenullCallback when cancel is pressed
styleTextStylefalsenulltext style
disableStyleTextStylefalsenullUnavailable text style
textStringfalsenullbutton text
colorColorfalsenullButton color
disabledColorColorfalsenullColor when FButton is unavailable
paddingEdgeInsetsGeometryfalsenullFButton internal spacing
cornerFCornerfalsenullConfigure corners of Widget
cornerStyleFCornerStylefalseFCornerStyle.roundConfigure the corner style of Widget. round-rounded corners, bevel-beveled
strokeColorColorfalseColors.blackBorder color
strokeWidthdoublefalse0Border width. The border will appear when strokeWidth > 0
gradientGradientfalsenullConfigure gradient colors. Will override the color
activeMaskColorColorColors.transparentThe color of the mask when pressed
surfaceStyleFSurfacefalseFSurface.FlatSurface style. Default [FSurface.Flat]. See [FSurface] for details

💫 Effect parameters

clickEffectboolfalsefalseWhether to enable click effects
hoverColorColorfalsenullFButton color when hovering
onHoverValueChangedfalsenullCallback when the mouse enters/exits the component range
highlightColorColorfalsenullThe color of the FButton when touched. effect:true required

🔳 Shadow parameters

shadowColorColorfalseColors.greyShadow color
shadowOffsetOffsetfalseOffset.zeroShadow offset
shadowBlurdoublefalse1.0Shadow blur degree, the larger the value, the larger the shadow range

🖼 Icon & Loading parameters

imageWidgetfalsenullAn icon can be configured for FButton
imageMargindoublefalse6.0Spacing between icon and text
imageAlignmentImageAlignmentfalseImageAlignment.leftRelative position of icon and text
loadingboolfalsefalseWhether to enter the Loading state
loadingWidgetWidgetfalsenullLoading widget in loading state. Will override the default Loading effect
clickLoadingboolfalsefalseWhether to enter Loading state after clicking FButton
loadingColorColorfalsenullLoading colors
loadingStrokeWidthdoublefalse4.0Loading width
hideTextOnLoadingboolfalsefalseWhether to hide text in the loading state
loadingTextStringfalsenullLoading text
loadingSizedoublefalse12Loading size

🍭 Neumorphism Style

isSupportNeumorphismboolfalsefalseWhether to support the Neumorphism style. Open this item [highlightColor] will be invalid
lightOrientationFLightOrientationfalseFLightOrientation.LeftTopValid when [isSupportNeumorphism] is true. The direction of the light source is divided into four directions: upper left, lower left, upper right, and lower right. Used to control the illumination direction of the light source, which will affect the highlight direction and shadow direction
highlightShadowColorColorfalsenullAfter the Neumorphism style is turned on, the bright shadow color

📺 Demo

🔩 Basic Demo

// FButton #1
  height: 40,
  alignment: Alignment.center,
  text: "FButton #1",
  style: TextStyle(color: Colors.white),
  color: Color(0xffffab91),
  onPressed: () {},

// FButton #2
  padding: const EdgeInsets.fromLTRB(12, 8, 12, 8),
  text: "FButton #2",
  style: TextStyle(color: Colors.white),
  color: Color(0xffffab91),
  corner: FCorner.all(6.0),

// FButton #3
  padding: const EdgeInsets.fromLTRB(12, 8, 12, 8),
  text: "FButton #3",
  style: TextStyle(color: Colors.white),
  disableStyle: TextStyle(color: Colors.black38),
  color: Color(0xffF8AD36),

  /// set disable Color
  disabledColor: Colors.grey[300],
  corner: FCorner.all(6.0),

By simply configuring text andonPressed, you can construct an available FButton.

If onPressed is not set, FButton will be automatically recognized as not unavailable. At this time, ** FButton ** will have a default unavailable status style.

You can also freely configure the style of FButton when it is not available via the disabledXXX attribute.

🎈 Corner & Stroke

// #1
  width: 130,
  text: "FButton #1",
  style: TextStyle(color: Colors.white),
  color: Color(0xffFF7043),
  onPressed: () {},
  clickEffect: true,
  /// 配置边角大小
  /// set corner size
  corner: FCorner.all(25),

// #2
  width: 130,
  text: "FButton #2",
  style: TextStyle(color: Colors.white),
  color: Color(0xffFFA726),
  onPressed: () {},
  clickEffect: true,
  corner: FCorner(
    leftBottomCorner: 40,
    leftTopCorner: 6,
    rightTopCorner: 40,
    rightBottomCorner: 6,

// #3
  width: 130,
  text: "FButton #3",
  style: TextStyle(color: Colors.white),
  color: Color(0xffFFc900),
  onPressed: () {},
  clickEffect: true,
  corner: FCorner(leftTopCorner: 10),
  /// 设置边角风格
  /// set corner style
  cornerStyle: FCornerStyle.bevel,
  strokeWidth: 0.5,
  strokeColor: Color(0xffF9A825),

// #4
  width: 130,
  padding: EdgeInsets.fromLTRB(6, 16, 30, 16),
  text: "FButton #4",
  style: TextStyle(color: Colors.white),
  color: Color(0xff00B0FF),
  onPressed: () {},
  clickEffect: true,
  corner: FCorner(
      rightTopCorner: 25,
      rightBottomCorner: 25),
  cornerStyle: FCornerStyle.bevel,
  strokeWidth: 0.5,
  strokeColor: Color(0xff000000),

You can add rounded corners to FButton via the corner property. You can even control each fillet individually。

By default, the corners of FButton are rounded. By setting cornerStyle: FCornerStyle.bevel, you can get a bevel effect.

FButton supports control borders, provided that strokeWidth> 0 can get the effect 🥳.

🌈 Gradient

  width: 100,
  height: 60,
  text: "#1",
  style: TextStyle(color: Colors.white),
  color: Color(0xffFFc900),
  /// 配置渐变色
  /// set gradient
  gradient: LinearGradient(colors: [
  onPressed: () {},
  clickEffect: true,
  corner: FCorner.all(8),

Through the gradient attribute, you can build FButton with gradient colors. You can freely build many types of gradient colors.

🍭 Icon

  width: 88,
  height: 38,
  padding: EdgeInsets.all(0),
  text: "Back",
  style: TextStyle(color: Colors.white),
  color: Color(0xffffc900),
  onPressed: () {
    toast(context, "Back!");
  clickEffect: true,
  corner: FCorner(
    leftTopCorner: 25,
    leftBottomCorner: 25,),
  /// 配置图标
  /// set icon
  image: Icon(
    color: Colors.white,
    size: 12,

  /// 配置图标与文字的间距
  /// Configure the spacing between icon and text
  imageMargin: 8,

  onPressed: () {},
  image: Icon(
    color: Colors.grey,
  imageMargin: 8,

  /// 配置图标与文字相对位置
  /// Configure the relative position of icons and text
  imageAlignment: ImageAlignment.top,
  text: "Print",
  style: TextStyle(color: textColor),
  color: Colors.transparent,

The image property can set an image for FButton and you can adjust the position of the image relative to the text, throughimageAlignment.

If the button does not need a background, just set color: Colors.transparent.

🔥 Effect

  width: 200,
  text: "Try Me!",
  style: TextStyle(color: textColor),
  color: Color(0xffffc900),
  onPressed: () {},
  clickEffect: true,
  corner: FCorner.all(9),
  /// 配置按下时颜色
  /// set pressed color
  highlightColor: Color(0xffE65100).withOpacity(0.20),
  /// 配置 hover 状态时颜色
  /// set hover color
  hoverColor: Colors.redAccent.withOpacity(0.16),

The highlight color of FButton can be configured through the highlightColor property。

hoverColor can configure the color when the mouse moves to the range of FButton, which will be used during Web development.

🔆 Loading

  text: "Click top loading",
  style: TextStyle(color: textColor),
  color: Color(0xffffc900),

  /// 配置 loading 大小
  /// set loading size
  loadingSize: 15,

  /// 配置 loading 与文本的间距
  // Configure the spacing between loading and text
  imageMargin: 6,
  /// 配置 loading 的宽
  /// set loading width
  loadingStrokeWidth: 2,

  /// 是否支持点击自动开始 loading
  /// Whether to support automatic loading by clicking
  clickLoading: true,

  /// 配置 loading 的颜色
  /// set loading color
  loadingColor: Colors.white,

  /// 配置 loading 状态时的文本
  /// set loading text
  loadingText: "Loading...",

  /// 配置 loading 与文本的相对位置
  /// Configure the relative position of loading and text
  imageAlignment: ImageAlignment.top,

// #2
  width: 170,
  height: 70,
  text: "Click to loading",
  style: TextStyle(color: textColor),
  color: Color(0xffffc900),
  onPressed: () { },
  imageMargin: 8,
  loadingSize: 15,
  loadingStrokeWidth: 2,
  clickLoading: true,
  loadingColor: Colors.white,
  loadingText: "Loading...",

  /// loading 时隐藏文本
  /// Hide text when loading
  hideTextOnLoading: true,

  width: 170,
  height: 70,
  alignment: Alignment.center,
  text: "Click to loading",
  style: TextStyle(color: Colors.white),
  color: Color(0xff90caf9),
  imageMargin: 8,
  clickLoading: true,
  hideTextOnLoading: true,

  /// 配置自定义 loading 样式
  /// Configure custom loading style
  loadingWidget: CupertinoActivityIndicator(),

Through the loading attribute, you can configure Loading effects for ** FButton **.

When FButton is in Loading state, FButton will enter an unavailable state, onPress will no longer be triggered, and unavailable styles will also be applied.

At the same time loadingText will overwritetext if it is not null.

The click start Loading effect can be achieved through the clickLoading attribute.

The position of loading will be affected by theimageAlignment attribute.

When hideTextOnLoading: true, if FButton is inloading state, its text will be hidden.

Through loadingWidget, developers can set completely customized loading styles.


  width: 200,
  text: "Shadow",
  textColor: Colors.white,
  color: Color(0xffffc900),
  onPressed: () {},
  clickEffect: true,
  corner: FCorner.all(28),
  /// 配置阴影颜色
  /// set shadow color
  shadowColor: Colors.black87,

  /// 设置组件高斯与阴影形状卷积的标准偏差。
  /// Sets the standard deviation of the component's Gaussian convolution with the shadow shape.
  shadowBlur: _shadowBlur,

FButton allows you to configure the color, size, and position of the shadow.

🍭 Neumorphism Style


  /// 开启 Neumorphism 支持
  /// Turn on Neumorphism support
  isSupportNeumorphism: true,

  /// 配置光源方向
  /// Configure light source direction
  lightOrientation: lightOrientation,

  /// 配置亮部阴影
  /// Configure highlight shadow
  highlightShadowColor: Colors.white,

  /// 配置暗部阴影
  /// Configure dark shadows
  shadowColor: mainShadowColor,
  strokeColor: mainBackgroundColor,
  strokeWidth: 3.0,
  width: 190,
  height: 60,
  text: "FWidget",
  style: TextStyle(
      color: mainTextTitleColor, fontSize: neumorphismSize_2_2),
  alignment: Alignment.center,
  color: mainBackgroundColor,

FButton brings an incredible, ultra-high texture Neumorphism style to developers.

Developers only need to configure the isSupportNeumorphism parameter to enable and disable the Neumorphism style.

If you want to adjust the style of Neumorphism, you can make subtle adjustments through several attributes related to Shadow, among which:

shadowColor: configure the shadow of the shadow

highlightShadowColor: configure highlight shadow

FButton also provides lightOrientation parameters, and even allows developers to adjust the care angle, and has obtained different Neumorphism effects.

😃 How to use?

Add dependencies in the project pubspec.yaml file:

🌐 pub dependency

  fbutton: ^<version number>

⚠️ Attention,please go to [pub] (https://pub.dev/packages/fbutton) to get the latest version number of FButton

🖥 git dependencies

      url: 'git@github.com:Fliggy-Mobile/fbutton.git'
      ref: '<Branch number or tag number>'

Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add fbutton_nullsafety

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

  fbutton_nullsafety: ^5.0.0

Alternatively, your editor might support or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:fbutton_nullsafety/fbutton_nullsafety.dart';

Download Details:

Author: Fliggy-Mobile

Source Code: https://github.com/Fliggy-Mobile/fbutton

#button  #flutter 

Dylan  Iqbal

Dylan Iqbal


Big Data Visualization: What, Why, Tips and Tools

Wondering what is big data visualization and how you can apply it for your business? Here's a guide to help you get started.

Because we live in a data-driven society, it’s likely that you’re constantly bombarded with complex sets of data that you need to transmit to your coworkers in an easy-to-grasp way.

The challenge is that almost no one wants to look at large lists of numbers and data, and important information can be easily lost within the midst of chaotic spreadsheets. But there is a solution, and that is big data visualization.

Today, we’ll be covering what big data visualization is and why it’s important, different big data visualization techniques you can use, tips and tricks for creating easily intelligible large data sets and the best big data visualization tools you can use.

By the end of this article, you’ll feel like a real data scientist and be competent in creating pie charts, bar charts, heat maps, histograms, interactive charts and more for big data visualization.

So let’s get into it, shall we?

Table of Contents

What is Big Data Visualization?

Why is Data Visualization Important in Big Data?

What Are the Types of Big Data Visualization?

5 Big Data Visualization Tips for Beginners

4 Tools for Big Data Visualization


What is Big Data Visualization?

Big data visualization is the representation of large sets of data through visual aids, whether that be through pie charts, heat maps, bar charts or any other kind of chart types or visual representation.

Analyzing and understanding large data sets and data analytics is no easy task and it can be especially difficult trying to relay that same information to colleagues who are not data-driven or data scientists.

That’s where big data visualization comes in. By transforming your large data sets into visually appealing infographics or interactive charts, you can easily convey your data points to fellow decision-makers.

When your data is plotted out on graphs in a visual way and metrics are made easily readable, no data gets lost in the mix, no matter how large or small, and it makes decision-making for the future a breeze.

Because you can’t make adequate decisions or advance significantly without analyzing your raw data, it’s important that companies use great data visualization methods to keep everyone in the loop.

Let’s take BMW for example.

Image Source

In 2020, BMW was able to track the number of sales for electric cars that they had and then compare it to other car companies’ sales, but not only.

They also were able to track the countries that bought the highest amount of their electric cars.

Image Source

This is a prime example of big data visualization in action. When you track your analytics and data, you can see where your wins are and when to celebrate or where your losses are and how you can make adjustments for the future.

Now, imagine for a moment that all this information was just written out plainly on a spreadsheet and had unstructured data all over it.

It would be hard to understand and assess how the company is doing and would take a long time to communicate to employees how their work has affected the sales of the cars.

This is why visualizing big data is so important. With just a glance and within seconds, you can easily see what cars are selling best and in what countries.

No time is wasted going through spreadsheets and trying to make sense of unstructured data — just visual analytics laid out for all to see and understand.


Why is Data Visualization Important in Big Data?

We live in a time where the internet and social media have exploded at an extraordinary rate, and information can be gathered within seconds and at the tips of anyone’s fingers.

With the rise of this technological era, it’s important that data can be visualized and consumed quickly and efficiently — especially since the human brain now has an attention span of about 8 seconds, according to this study by the Technical University of Denmark.

Because companies, businesses and organizations can gather data more quickly than ever, this means that they need to be able to visualize that data in an equally quick and easily consumable way.

The best way to efficiently communicate your ever-coming, new data is through visualizing big data. This will bring your complex data to life and anyone who looks at it will be able to understand and grasp it with just a glance.

Customize this template and make it your own!

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Take the image above as an example. With just a quick look at the statistics that are clearly visualized, you can make a data-based assumption.

Now, imagine if this data was just written out plainly on a spreadsheet. It would take much longer to understand and make an assumption based on the numbers.

By using big data visualization techniques, you’ll be able to get the most value from your data and analytics and make sure that everyone who says your data analysis will be able to interpret, understand and use your data. This, in turn, will help your company excel.

When you use data visualization techniques, it will optimize your use of data, help decision making and planning go smoothly, you’ll be able to identify and mitigate risk, extract loads of useful data and insights and improve your overall strategy and direction of your company.

There are no losses to using a visual representation of data, only wins. But there are lots of different types of data visualization that you can use.

Let’s discuss the different types of big data visualization and assess which one will work best for you.


What Are the Types of Big Data Visualization?

There are lots of different types of data visualization that data analysts like to use and depending on the amount of data. A data analyst may choose to use a pie chart to express their numerical data or a bar chart.

When looking at big data analytics regarding locations, one might choose to use an interactive heat map or maybe a pivot table.

We’re going to look at 8 common types of big data visualization and some data visualization examples for each to help you decipher which one will work best for you.


Type #1: Line Charts

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A line chart, also known as a line graph, is a graphic representation of data that plots a fixed value on one side and a variable on the other.

A line chart is a fantastic way to represent the relationship of data. You can use a line chart to represent changes and fluctuations of things within a certain period of time.


Type #2: Bar Charts

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A bar chart, also known as a bar graph, uses bars to compare different data points or data sets.

Many data scientists will use bar charts to visually represent their data analysis. You can use a bar chart to compare large amounts of data, fluctuations of quantities or different categories.

The taller the bar, the larger the numerical value and vice versa.


Type #3: Pie Charts

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Pie charts, donut charts, circle graphs or whatever you choose to call them, are representations of data that are split into smaller segments and sizes to represent their numerical value.

When you use a pie chart, it becomes easy to see and compare how the different segments relate and differ from each other.

When using a pie chart, try not to overload it with too many different values. When you split the pie chart into more than 7 segments, it can become difficult to understand the data.


Type #4: Heat Maps

Image Source

A heat map is a visual representation of data that is laid out on a map or table and uses different nuances and intensities of colors to represent its data.

Using a heat map can be especially helpful when you need to analyze data that seems to be never-ending. When you have an extremely wide value range, using a heat map makes it much more simple to quickly visualize and analyze large amounts of complex data at a glance.


Type #5: Histograms

histogram - weights of newborns

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A histogram is a graphical and visual representation of complex data sets and the frequency of said numerical data displayed through bars.

Histograms are very similar to bar graphs but vary in the fact that they mostly focus on the repeated frequency of numerical data.

Type #6: Scatter Plots

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A scatter plot, scatter chart or scatter graph, is a diagram that uses dots to represent and emphasize the different values of two or more numeric variables on an X and Y-axis.

Scatter plots are extremely useful to use when you have multiple large data sets and you want to know how they relate to each other and compare the importance of each value.


Type #7: Treemaps

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Treemaps are the visual representation of hierarchical data by using color-coded rectangles.

Users can use treemaps as a method to compare multiple sets of data and reflect the weight of each value in a project.


Type #8: Funnel Charts

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Funnel charts are typically used in sales and represent the different stages that your users or customers go through during the sales process and demonstrate decreasing values as they move through your funnel.

By using a funnel chart, you can accurately see where you are losing or gaining your customers during the sales process.


5 Big Data Visualization Tips for Beginners

Now that we’ve covered what big data visualization is, its importance and 9 different types of data visualization, you may feel like you’re a professional in data science.

Now that you’re familiar with the basics of data visualization, it’s time that we equip you with some of our best data visualization techniques.

Here are our top 5 best data visualization techniques for you to use when creating a visual representation of your data.


Tip #1: Use a Powerful Data Visualization Tool

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You can’t create powerful graphs without a powerful data visualization tool.

Sure, you could use something like Google charts, but to create unique, engaging charts, you’ll want to use a data visualization tool like Visme that's packed with amazing functionality.

Visme is a powerful data visualization tool with many integration functionalities. As you can see in the image above, you can create everything from funnel charts and tables to interactive data maps and graphs in this editor.

When you need to visualize big data, Visme is the way to go. When you create a graph in our big data visualization tool, your data can be updated in real-time with our integration tools.

You can import all your data from Google Sheets, Microsoft Excel, Google Analytics and other data sources, then see it come to life automatically on your project while you sit back and relax.

Visme also has many open-source elements and graphics for you to use to keep your infographic intriguing. To have the perfect interactive data visualization, you can use word clouds, tables, treemaps, animated characters and graphic design elements and more to implement into your design.

They’re also a powerhouse filled with lots of useful and educational tutorials on how to create the perfect chart for your raw data. Visme also has lots of tutorials for all things graphic design.

So why not use a tool that has everything you need for creating visuals for your data analysis and tons of tutorials to go with it? You can start your free account with Visme today and start living out your data analyst dreams.

It’s important to use a strong data visualization software for your data analysis and presentations. Stick around and soon we’ll get into our list of best tools for big data visualization.


Tip #2: Pick the Correct Form of Big Data Visualization

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When it comes to visualizing your data, you need to make sure that you choose the correct chart type.

Because there are so many different ways to display your data, you need to weigh out the cons and pros of each and find out which one will work best for your infographic or presentation.

Take for example pie charts and bar graphs.

When you analyze data that is very different, you might want to use a pie chart. But if you want to represent data entries that are close together, you could use a bar chart for that.

If you’re trying to create data visualization for sales, you could use a funnel chart, pyramid chart or cone chart for that.

Each different visualization method has its time and place, and you need to analyze your data and think about what method will work best for your respective data.

Refer above to the “Big Data Visualization Types” section above to see which one will suit you best.


Tip #3: Make Sure Your Data is Easily Comprehensible

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The whole point of big data visualization is to make it easy to understand at a glance.

It won’t be easily intelligible if you just start piling in large amounts of unstructured data and simply hope for the best. Or imagine you have tens of tiny little numbers on a bar graph that no one can see or read.

You need to make sure that anyone on your team, whether a data scientist or not, can understand what you’re trying to convey at a glance.

You can do this by using clear and bold text, contrasting font colors and background colors, not adding too many values to one chart and using compelling images to highlight your point, just like in the example above.

By adding too much text or too many values to a single graph, you risk confusing your audience even more. So keep it as simple and concise as possible.


Tip #4: Always Use Legends to Further Explain Your Data

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Using legends is absolutely vital for making your data easy to understand, so whether you’re creating a pie chart or bar graph, make sure you’re using a legend.

A legend is an area of your design that further explains each segment of your chart.

Many times people will assign a color to a segment in their chart, just like in the example above, and on the side add a little graphic element that explains what each color represents.

The legend is responsible for keeping the audience engaged and understanding everything you’re trying to convey.


Tip #5: Use Multiple Charts for Big Data

Visualize big data easily with this drag-and-drop tool!

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If you have a large amount of data that needs to be conveyed to your team, try using multiple graphs to do so.

Incorporating tons of data into a single chart will only make it hard for the human brain to stay on track and focus to try and understand what you want to share.

The best rule of thumb to follow here is KISS — keep it simple, stupid.

So instead of simply adding all your data to one pie chart and making it have 30 pie slices, why not create multiple graphs and break it down into bite-sized pieces? Pun intended.

By creating multiple matching charts, you can keep your data easily intelligible, cohesive and right on brand.

Just like in the example above, you can clearly understand all the data that’s being displayed because it is written out on two different donut charts.

You want to make sure your information is understandable by anyone at a glance, and you can do so by breaking down your data.


4 Tools for Big Data Visualization

Now that you know essentially all there is to know about big data visualization, it’s time you choose a tool that will help you create those visuals.

We’ll be covering 4 data visualization software you can use to get the job done.

Let’s jump right into it.


Tool #1: Visme

Start visualizing data with beautiful charts and graphs!

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If you want to create compelling and professional data visualization, then you need a tool like Visme.

Visme makes it easy for both designers and non-designers alike to visualize their data in interactive and engaging ways.

For example, you can create incredible animated charts, add your own audio files to them that you can record right within the editor, add tons of professionally design data widgets and import all your data from third-party websites such as Microsoft Excel, Google sheets etc.

The best part? You can save endless amounts of time and effort by using one of our hundreds of customizable templates for displaying your data.

Simply scroll through tons of professionally design templates for charts and data choose one that suits your style.

Everything can be customized on each and every template and you can even add your own brand colors, logo and font to keep everything right on-brand with your other designs.

Not only can you create loads of beautiful charts, graphs and infographics with Visme, but you can also create anything else design-related. You can create presentations, infographics, multi-page reports and proposals, branded social graphics and more.

If you’re looking for a powerful data visualization tool with high functionality for many other types of designs, Visme is the one for you. Plus, you can create a free account and use for as long as you like — no trial period or hidden costs!


Tip #2: Tableau

Tableau is an interactive data visualization software with a focus on business intelligence. Their goal is to help people make data that can be easily understood by anyone.

Tableau is a tool that is used in the business intelligence industry and it can help you simplify raw data into a simple format. With drag and drop functionalities, you can create data visualization fairly quickly and then share it with others.

In Tableau you can create lots of different data visualizations, from a correlation matrix to a simple bar graph.

Another plus for the software is that you can infuse the Tableau dashboard with artificial intelligence and machine learning from Aible.

You can start a free trial with Tableau, but it is a bit pricey after your trial is up. At $70/month billed annually, you’ll have to make sure you absolutely love the product before buying it.


Tool #3: Microsoft’s Power BI

Power BI by Microsoft is a business analytics service that helps you create interactive data visualizations.

Whether your data is on an Excel spreadsheet an on-premises hybrid data warehouses, Power BI will help you bring that data together to create reports and graphs to share with your team.

There are three versions of Power BI that you can use: the desktop app, the mobile app or their website.

You can use Power BI to help you visualize big data with your team by using some of their other popular apps like Microsoft Excel and work together in real-time to create compelling data.

Power BI has some basic templates that you can use to get a jump start on creating your data.

Power BI is quite affordable, coming in at $9.99/month.

If you’re not completely sold on using Power BI, let’s move on to our next tool.


Tool #4: Datawrapper

Datawrapper is an online tool that you can use to create data visualizations that are interactive and responsive, with no code or programming languages like python or javascript required.

With big users like the New York Times and the UN, they do have quite a few things to boast about.

Data wrapper is an open-source and easy-to-use data visualization software where you can create basic charts and graphs, maps and line charts that can be embedded into your website.

As for the price, you can use their free plan and create lots of charts, maps and tables, but they will be watermarked and there are a few other inconveniences that come with the free plan.

The next plan comes in at $599/month, which is definitely on the pricey side.

And that concludes our list of 4 tools for data visualization.


Now Over to You

If you want a data visualization software that will help you convey your data in a fun and engaging way, then you most likely will love using Visme.

Not only is Visme a powerful data visualization tool, but it’s so much more. You can use Visme to create all of your graphic design needs, from sales presentations to pitch decks, social media posts, infographics, videos, eBooks and more.

What are you waiting for? Create your free account today and free your inner data scientist.

Originally published at https://visme.co

#datavisualization #bigdata

Were  Joyce

Were Joyce


Spring Integration AWS 2.5.1 and Spring Cloud Stream Kinesis Binder 2.2.0 Available

Dear Spring Community,

Today it’s my pleasure to announce releases of Spring Integration for Amazon Web Services extension versions 2.5.1 and Spring Cloud Stream Binder for AWS Kinesis 2.2.0.

These releases can be downloaded from Maven Central:

compile 'org.springframework.integration:spring-integration-aws:2.5.1'

If you don’t use Kinesis Binder. Or via Binder dependency:

compile 'org.springframework.cloud:spring-cloud-stream-binder-kinesis:2.2.0'

Spring Integration for AWS extension of 2.5.1 has some minor bug fixes after the previous 2.5.0 announcement.

The Spring Cloud Stream Binder for AWS Kinesis 2.2.0 is the first release in the project generation which is based on a new independent Spring Cloud for AWS artifact. It also was upgraded to the latest Spring Cloud 2020.0.3 and has some other bug fixes and improvements.

#[object object] #aws 2.5.1 #spring cloud #spring cloud stream kinesis binder 2.2.0 available #spring integration aws 2.5.1 and spring cloud stream kinesis binder 2.2.0 available

5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

  1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

Jignesh Parekh


Top 10 Web Design Companies in Miami-2021


There are many web design agencies around the world that can help you build a great business website. But we help you to find the best out of all. Know how you can approach the best Miami web design firms, so that you know what people expect out of your business website.

It’s time for you to evaluate different web design agencies for your business. To assist you in your search for a partner, we’ve compiled this list of the top web design companies in Miami.

We have best searched for the firms that stand out of the crowd and deliver the best for their client’s not only in terms of work, but keeping in mind the time, efficiency and quality of work.


#1 Webforest Agency

#2 Fuzeinc

#3 Gohooper

#4 On The Map Marketing

#5 Webdesign Miami

#6 Final Webdesign

#7 Web Designer Express

#8 Pop creative

#9 CV Digital marketing

#10 Lucid Digital LLC





#1 Webforest Agency

Location: Miami, India, Paris

Google Star Rating: 4.8


Webforest is a web design and development agency, creating websites with strategy and technology that covers a wide range of platforms. Our capabilities are focused on making your brand prominent and dynamic. Best web design agency Miami, We Live, Breathe and Work in INDIA.

7 Years. 500+ Projects Completed. Working with 15+ Overseas Web Agencies. After years of experience in the digital space, we've produced cutting-edge & versatile digital solutions for different agencies with their tools. Of Course with their time zone!













#2 Fuzeinc

Location: Miami

Google Star Rating: 4.6


We are a diverse and passionate team of designers, developers, branding experts, copywriters, marketers & more. It is hard to put us in a box, but one thing we all have in common is a drive to produce amazing work that takes our clients to new heights. Best in web design company in Miami.













#3 Gohooper

Location: Miami, Nashville

Google Star Rating: 4.5


Go Hooper Web Design was founded in 2006 by John Hooper and his best friend and soul mate Jessica Hooper. John was introduced to programming and development when he was 12 yrs. old (that's when 3.5 inch floppy disks were a big deal). 

Years later when he envisioned having a web agency, it was only a natural progression. At first the business was formed more out of necessity because of the typical run-around that webmaster's give their clients. 

He couldn't stand all the technical jargon and complexity that the average web design company in Miami was creating, leaving clients frustrated and in the dark. So in order to solve this problem he started GoHooper Web Design where the objective was to make everything transparent and understandable from the get go. website design in Miami







#4 On The Map Marketing

Location: Miami Office, Raleigh Office, Los Angeles Office, Tampa Office, New York Office, Riga Office

Google Star Rating: 4.3


On The Map Marketing is located in beautiful downtown Miami. The team is constantly growing to meet the needs of our clients, with over 50 people filling the conference rooms, offices and sales floors. Best in Miami web design firms

Each person is important to us because we wouldn't be here without our clients. Each business is important because it's the passion of business owners that gives us our motivation and direction when building their online presence. website design in Miami Finally, each dollar is important because we know that John Wanamaker nailed it when he said: "Half the money I spend on advertising is wasted; trouble is I don't know which half."










#5 Webdesign Miami

Location: Miami

Google Star Rating: 4.9


website design in Miami, a subsidiary of Silva Heeren, a graphic design and Miami web design firms with over 20 years of professional experience, provides customized website design and development to the Miami area businesses and professionals.

We have been successfully developing websites and eCommerce  web design company in Miami for small businesses and start-up companies since the year 1998. If you are starting up your new business or if you have had your company for many years, we can help you create your business website and improve your online presence.











#6 Final Webdesign

Location: Miami, Fort Lauderdale

Google Star Rating: 4.9


Final Web Design was founded by a leading marketing and sales director, along with an experienced e-commerce CEO and engineer. Combined, they have over 25 years of experience in the constantly changing and evolving industry of web development and inbound marketing. Since its incorporation, the company has grown to include expert programmers, coders, content writers, and an accredited Google Partners online marketing team. Top website design in Miami.












#7 Webdesigner Express

Location: Miami, 

Google Star Rating: 4.9


When Web Designer Express and GCT Productions.com were founded, they were created with the ideal that every business should be able to have their own website. Now more than ever, the internet is an indispensable tool for all kinds of business. 

Providing you tangible benefits such as increased bottom line, accelerated marketing campaigns and allowing you to give your customers instantaneous feedback, while also providing more intangible services - presence, style and professional standing- to you and your clientele. web design agency Miami











#8 Popcreative

Location: Miami

Google Star Rating: 4.9


pop creative is a Miami, FL based web design agency Miami that partners with small businesses, home service providers, medical organizations and national franchises to create innovative digital experiences, smart websites and aggressive digital marketing campaigns.

As a Google partner agency we pride ourselves in implementing cutting edge technology into all our projects complimented with world class service, communication and results.

POP Creative is led by an experienced and proven team of internet marketing gurus, brand designers, multimedia developers and wordsmiths. Together our tight knit and bold team dives into your business to establish a killer digital brand and unleash your growth potential. web design company in Miami.










#9 CV Digitalmarketing

Location: Miami, Medellín

Google Star Rating: 4.8


We want you to fall in love with the digital world and see its unlimited benefits. TOGETHER, we'll find the strategy your business needs to reach the right audience and create an effective bond between your brand and the customer. Best website design in Miami

To achieve success, we analyze your company’s current situation in order to position ourselves at the key moment, so not to fail. We design the right strategy that best suits your needs and start to connect with the people who require your products or services. 

At CV Digital web design agency Miamiy we not only sell services, but also offer real online solutions so that together help your business grows.










#10 Lucid Digital LLC

Location: Miami

Google Star Rating: 4.5


Since 2011 we have been helping companies all over the world succeed online. We started building simple websites and have expanded our team to have the ability to build complex, database driven websites and mobile applications along with powerful digital marketing to companies of all sizes. Bets Miami web design firms.