DyNet (Python 3.8+ Fork)

2.2 · active · verified Fri Apr 17

DyNet38 is a specialized fork of the DyNet neural network library, providing Python 3.8+ compatible wheels. It allows users to leverage DyNet's imperative neural network framework with modern Python versions, focusing on dynamic computation graphs, making it suitable for NLP and other sequence-based tasks. The current version is 2.2, and its release cadence largely follows the upstream DyNet project, primarily providing compatibility fixes.

Common errors

```
ModuleNotFoundError: No module named 'dynet38'
```
cause The installed package `dynet38` provides the `dynet` module, not `dynet38`.

fix Change your import statement from `import dynet38` to `import dynet` or `import dynet as dy`.
```
RuntimeError: DyNet is not initialized. Call dy.init() first.
```
cause The mandatory `dy.init()` function was not called before attempting any DyNet operations or creating expressions.

fix Add `import dynet as dy; dy.init()` at the very beginning of your main script or entry point.
```
Segmentation fault (core dumped) OR Out of memory error during repeated graph computations.
```
cause The computation graph is not being reset for each new input, leading to continuous memory allocation and potential overflow.

fix Ensure `dy.renew_cg()` is called at the beginning of each iteration where a new computation graph is constructed (e.g., inside your training loop or prediction function).

Warnings

gotcha Despite installing `dynet38` via pip, the Python module you need to import is `dynet`. Attempting to `import dynet38` will result in a `ModuleNotFoundError`.
Fix: Always use `import dynet` or `import dynet as dy` after `pip install dynet38`.
breaking DyNet requires explicit initialization via `dy.init()` before any computation graph operations can occur. Failure to do so will result in a `RuntimeError`.
Fix: Ensure `dy.init()` is called once at the very beginning of your script, typically without arguments unless specific configurations (e.g., GPU, random seed, memory) are needed.
gotcha When training or performing inference with multiple examples, it's crucial to clear the computation graph for each new input using `dy.renew_cg()`. Failing to do so can lead to memory accumulation, performance degradation, and incorrect gradient computations.
Fix: Call `dy.renew_cg()` at the beginning of any function or loop iteration that constructs a new computation graph for a specific input.
gotcha DyNet's performance is highly dependent on optimized BLAS libraries (e.g., MKL, OpenBLAS). While `dynet38` provides pre-built wheels, the performance might vary. For optimal speed, especially on CPU, consider linking against a highly optimized BLAS library.
Fix: For basic usage, `pip install dynet38` is sufficient. For critical performance applications, investigate building DyNet from source, explicitly linking to MKL or OpenBLAS. Also, consider setting the `DYNET_NUM_THREADS` environment variable.

Install

pip install dynet38 Install for Python 3.8+

Imports

dynet
wrong:
```
import dynet38
```
correct:
```
import dynet
```
The installed package is 'dynet38', but it provides the 'dynet' module internally.
dy
```
import dynet as dy
```
Common and recommended alias for the dynet module.
ParameterCollection
```
from dynet import ParameterCollection
```
Trainer
```
from dynet import Trainer
```

Quickstart

This quickstart demonstrates the core workflow of DyNet: initializing the library, creating a `ParameterCollection` to manage model weights, and defining a function to build a dynamic computation graph for each input. It highlights the crucial `dy.init()` call and `dy.renew_cg()` for managing graph state.

import dynet as dy
import random

# Initialize DyNet (mandatory) - can specify options like --dynet-gpus, --dynet-mem
dy.init()

# Create a model (parameter collection) to hold network weights
m = dy.ParameterCollection()

# Add parameters for a simple feedforward layer
# W: weight matrix (32 output units, 10 input units)
# b: bias vector (32 output units)
# V: output weight matrix (1 output unit, 32 input units)
W = m.add_parameters((32, 10))
b = m.add_parameters(32)
V = m.add_parameters((1, 32))

# Define a function to build the computation graph for a given input
def build_graph(x_val):
    # Renew the computation graph for each new example
    dy.renew_cg()

    # Convert Python list (input vector) to DyNet Expression
    x = dy.inputVector(x_val)

    # Get parameters as DyNet Expressions for the current graph
    W_expr = dy.parameter(W)
    b_expr = dy.parameter(b)
    V_expr = dy.parameter(V)

    # Perform operations to build the graph: tanh activation, logistic output
    h = dy.tanh(W_expr * x + b_expr)
    o = dy.logistic(V_expr * h)
    return o

# Example usage with random input data
input_data = [random.random() for _ in range(10)]
output_expression = build_graph(input_data)

# Get the scalar value from the output expression
output_value = output_expression.value()

print(f"Input (first 5 values): {[f'{v:.2f}' for v in input_data[:5]]}...")
print(f"Predicted Output: {output_value[0]:.4f}")

# For training, you would then get loss, call backward(), and update with a Trainer.
# Example (not run here): 
# trainer = dy.SimpleSGDTrainer(m)
# loss = dy.sum_batches(dy.square(output_expression - dy.scalarInput(target_value)))
# loss.backward()
# trainer.update()

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