Python 3.11.11 : Colab simple test with CogVideoX-5B model and default example - part 050.

I tested this GitHub project from THUDM user on my colab google account and works well with CogVideoX-5B model.

You can find the default implementation on my colab GitHub repo.

The default example comes with thjs prompt:

prompt = ( "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " "atmosphere of this unique musical performance." )

The speed of rendering starts from :

38% ... 19/50 [30:54<50:57, 98.61s/it] using T4 GPU

... then run at :

100% ... 50/50 [1:26:09<00:00, 109.87s/it]

when the video render was 100% somehow google give this error, but the source code run well:

OutOfMemoryError: CUDA out of memory. Tried to allocate 1.32 GiB. GPU 0 has a total capacity of 14.74 GiB of which 654.12 MiB is free. Process 26970 has 14.10 GiB memory in use. Of the allocated memory 12.97 GiB is allocated by PyTorch, and 1.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

I think is need to set some extra memory on CUDA but this require to parse some documentation and is not a task for me now.

Python 3.11.11 : Colab simple test with VGG16 - part 049.

VGG16 is a deep convolutional neural network (CNN) trained on a massive image dataset called ImageNet. This architecture is known for its remarkable performance in image classification tasks and is widely used in various computer vision applications.

You can find one simple example on my GitHub project.

Python 3.10.12 : My colab test with Gated recurrent unit mechanism - part 037.

This is a simple example for Gated recurrent unit mechanism known as GRUs.

You can find this in my GitHub colab project.

import numpy as np
import tensorflow as tf
import keras
from keras import layers
units = 64
tf.keras.layers.GRU(
    units,
    activation="tanh",
    recurrent_activation="sigmoid",
    use_bias=True,
    kernel_initializer="glorot_uniform",
    recurrent_initializer="orthogonal",
    bias_initializer="zeros",
    kernel_regularizer=None,
    recurrent_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    recurrent_constraint=None,
    bias_constraint=None,
    dropout=0.0,
    recurrent_dropout=0.0,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    unroll=False,
    time_major=False,
    reset_after=True,
)
inputs = tf.random.normal([32, 10, 8])
gru = tf.keras.layers.GRU(4)
output = gru(inputs)
print(output.shape)

gru = tf.keras.layers.GRU(4, return_sequences=True, return_state=True)
whole_sequence_output, final_state = gru(inputs)
print(whole_sequence_output.shape)
print(final_state.shape)