- This version is a beta version. It is still in iteration and is not stable at present. Incompatible upgrade may be subsequently performed on APIs based on the feedback. For developers who want to experience the latest features of Paddle, welcome to this version. For industrial application scenarios requiring high stability, the stable Paddle Version 1.8 is recommended.
- This version mainly popularizes the dynamic graph development method and provides the encapsulation of high-level APIs. The dynamic graph mode has great flexibility and high-level APIs can greatly reduces duplicated codes. For beginners or basic task scenarios, the high-level API development method is recommended because it is simple and easy to use. For senior developers who want to implement complex functions, the dynamic graph API is commended because it is flexible and efficient.
- This version mainly popularizes the imperative programming development method and provides the encapsulation of high-level APIs. The imperative programming(dynamic graph) mode has great flexibility and high-level APIs can greatly reduces duplicated codes. For beginners or basic task scenarios, the high-level API development method is recommended because it is simple and easy to use. For senior developers who want to implement complex functions, the imperative programming API is commended because it is flexible and efficient.
- This version also optimizes the Paddle API directory system. The APIs in the original directory can create an alias and are still available, but it is recommended that new programs use the new directory structure.
## Basic Framework
### Basic APIs
- Networking APIs achieve dynamic and static unity and support operation in dynamic and static graph modes
- Networking APIs achieve dynamic and static unity and support operation in imperative programming and declarative programming modes(static graph)
- The API directory structure is adjusted. In the Paddle Version 1.x, the APIs are mainly located in the paddle.fluid directory. This version adjusts the API directory structure so that the classification is more reasonable. The specific adjustment rules are as follows:
- Moves the APIs related to the tensor operations in the original fluid.layers directory to the paddle.tensor directory
- Moves the networking-related operations in the original fluid.layers directory to the paddle.nn directory. Puts the types with parameters in the paddle.nn.layers directory and the functional APIs in the paddle.nn.functional directory
- Moves the special API for dynamic graphs in the original fluid.dygraph directory to the paddle.imperative directory
- Moves the special API for imperative programming in the original fluid.dygraph directory to the paddle.imperative directory
- Creates a paddle.framework directory that is used to store framework-related program, executor, and other APIs
- Creates a paddle.distributed directory that is used to store distributed related APIs
- Creates a paddle.optimizer directory that is used to store APIs related to optimization algorithms
- Adds device\_guard that is used to specify a device. Adds manual\_seed that is used to initialize a random number seed
- Some of the APIs in the original fluid directory have not been migrated to the paddle directory
- The following API under fluid.contrib directory are kept in the original location, not migrated：BasicGRUUnit, BasicLSTMUnit, BeamSearchDecoder, Compressor, HDFSClient, InitState, QuantizeTranspiler, StateCell, TrainingDecoder, basic_gru, basic_lstm, convert_dist_to_sparse_program, ctr_metric_bundle, extend_with_decoupled_weight_decay, fused_elemwise_activation, fused_embedding_seq_pool, load_persistables_for_increment, load_persistables_for_inference, match_matrix_tensor, memory_usage, mixed_precision.AutoMixedPrecisionLists, mixed_precision.decorate, multi_download, multi_upload, multiclass_nms2, op_freq_statistic, search_pyramid_hash, sequence_topk_avg_pooling, shuffle_batch, tree_conv, var_conv_2d
- The following APIs related to LodTensor are still under development and have not been migrated yet：LoDTensor, LoDTensorArray, create_lod_tensor, create_random_int_lodtensor, DynamicRNN, array_length, array_read, array_write, create_array, ctc_greedy_decoder, dynamic_gru, dynamic_lstm, dynamic_lstmp, im2sequence, linear_chain_crf, lod_append, lod_reset, sequence_concat, sequence_conv, sequence_enumerate, sequence_expand, sequence_expand_as, sequence_first_step, sequence_last_step, sequence_mask, sequence_pad, sequence_pool, sequence_reshape, sequence_reverse, sequence_scatter, sequence_slice, sequence_softmax, sequence_unpad, tensor_array_to_tensor
- The following APIs related to distributed training are still under development, not migrated yet
- The following APIs in fluid.nets directory will be implemented with high level API， not migrated：nets.glu, nets.img_conv_group, nets.scaled_dot_product_attention, nets.sequence_conv_pool, nets.simple_img_conv_pool
- The following APIs are to be improved, not migrated：dygraph.GRUUnit, layers.DecodeHelper, layers.GreedyEmbeddingHelper, layers.SampleEmbeddingHelper, layers.TrainingHelper, layers.autoincreased_step_counter, profiler.cuda_profiler, profiler.profiler, profiler.reset_profiler, profiler.start_profiler, profiler.stop_profiler
- The following APIs are no longer recommended and are not migrated：DataFeedDesc, DataFeeder, clip.ErrorClipByValue, clip.set_gradient_clip, dygraph_grad_clip.GradClipByGlobalNorm, dygraph_grad_clip.GradClipByNorm, dygraph_grad_clip.GradClipByValue, initializer.force_init_on_cpu, initializer.init_on_cpu, io.ComposeNotAligned.with_traceback, io.PyReader, io.load_params, io.load_persistables, io.load_vars, io.map_readers, io.multiprocess_reader, io.save_params, io.save_persistables, io.save_vars, io.xmap_readers, layers.BasicDecoder, layers.BeamSearchDecoder, layers.Decoder, layers.GRUCell, layers.IfElse, layers.LSTMCell, layers.RNNCell, layers.StaticRNN, layers.Switch, layers.While, layers.create_py_reader_by_data, layers.crop, layers.data, layers.double_buffer, layers.embedding, layers.fill_constant_batch_size_like, layers.gaussian_random_batch_size_like, layers.get_tensor_from_selected_rows, layers.load, layers.merge_selected_rows, layers.one_hot, layers.py_reader, layers.read_file, layers.reorder_lod_tensor_by_rank, layers.rnn, layers.uniform_random_batch_size_like, memory_optimize, release_memory, transpiler.memory_optimize, transpiler.release_memory
### High-level APIs
- Adds a paddle.incubate.hapi directory. Encapsulates common operations such as networking, training, evaluation, inference, and access during the model development process. Implements low-code development. Uses the dynamic graph implementation mode of MNIST task comparison. High-level APIs can reduce 80% of executable codes.
- Adds a paddle.incubate.hapi directory. Encapsulates common operations such as networking, training, evaluation, inference, and access during the model development process. Implements low-code development. Uses the imperative programming implementation mode of MNIST task comparison. High-level APIs can reduce 80% of executable codes.
- Adds model-type encapsulation. Inherits the layer type. Encapsulates common basic functions during the model development process, including:
- Provides a prepare API that is used to specify a loss function and an optimization algorithm
- Provides a fit API to implement training and evaluation. Implements the execution of model storage and other user-defined functions during the training process by means of callback
