Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66shttps://github.com/tensorflow/addons/issues/2807https://github.com/tensorflow/addons 

Help would be appreciated because im slowly losing my mind :(,
Jonas

Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66shttps://github.com/tensorflow/addons/issues/2807https://github.com/tensorflow/addons 

Help would be appreciated because im slowly losing my mind :(,
Jonas

Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66shttps://github.com/tensorflow/addons/issues/2807https://github.com/tensorflow/addons 

Help would be appreciated because im slowly losing my mind :(,
Jonas

Introduction
In the world of online learning and selling, you need a platform that helps you sell both digital and physical products easily. A good Learning Management System can make this process simple, save you money, and improve customer experience. In this article I wrote what features to look for in an LMS and introduced a great option, EzyCourse.
Key Features of a Good LMS for Selling Products
Easy to Use
An LMS should be simple to use. This helps you create and manage courses and products without any trouble. A clear layout helps users find what they need quickly.
E-commerce Features
To sell both digital and physical products, an LMS should have:

• Product Listings: Easily create and manage product listings with details, pictures, and prices.
• Payment Options: Support different payment methods for easy buying.
• Inventory Management: Track stock levels and manage orders for physical products.

Simple Checkout
A simple and secure checkout process helps reduce the number of abandoned carts. Customers should be able to buy products easily.
Marketing Tools
Marketing tools can help boost sales. Features like discount codes, promotions, and email marketing keep customers interested.
Reports and Insights
Good reporting and insights help you understand sales and customer behavior. This includes details about product sales, revenue, and customer demographics.
Selling Digital Products with an LMS
Digital products like eBooks, software, and online courses are popular because they are cheap to make and can be delivered instantly. A good LMS for digital products should have:

• Instant Delivery: Automatically deliver digital products after purchase.
• Download Management: Securely manage product downloads to prevent unauthorized access.
• Content Protection: Protect your content with features like watermarking and access controls.

Selling Physical Products with an LMS
For selling physical products, an LMS should provide:

• Shipping Integration: Real-time shipping rates and tracking with shipping carriers.
• Order Management: Tools to manage orders, print shipping labels, and handle returns.
• Customer Notifications: Automated notifications for order updates and delivery.

EzyCourse: A Great LMS Solution
EzyCourse is great for selling both digital and physical products.
Easy to Use
EzyCourse is designed to be simple. Its dashboard is easy to navigate, making it easy to create and manage courses and products.
E-commerce Features
EzyCourse has strong e-commerce features. It allows you to list products easily and manage them well. It supports different payment gateways for secure transactions.
Simple Checkout
EzyCourse offers a smooth and secure checkout process, which helps reduce abandoned carts.
Marketing Tools
EzyCourse provides marketing tools like discount codes and email marketing, helping you promote products and engage customers.
Reports and Insights
EzyCourse offers detailed reports and insights, helping you understand sales and customer behavior.
Selling Digital Products
EzyCourse is great for managing digital products. It ensures instant delivery, secure downloads, and content protection.
Selling Physical Products
EzyCourse integrates with shipping carriers for real-time shipping rates and tracking. It also offers tools to manage orders, print shipping labels, and handle returns. Automated customer notifications keep buyers informed.
Conclusion
In the world of online learning and selling, an LMS that supports both digital and physical products is important. Platforms like EzyCourse offer great features and are easy to use, making them ideal for businesses that want to streamline their operations and improve customer experience. If you can choose the right LMS, you can manage your products efficiently, save money, and achieve success.

Hello Reddit Community,
For my Bachelor Thesis im currently trying to train my first ever model with tensorflow, but I'm encountering a strange issue where my model only predicts 2 classes out of the 475 possible classes. The model was trained on a HPC with 304 Nvidia A100 and 352 Nvidia A40 GPGPUs in 82 nodes.
Thats my training script:

 import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.applications import EfficientNetB7 from tensorflow.keras import layers, models from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import tensorflow_addons as tfa import logging import json # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Check if GPUs are available gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) tf.config.set_visible_devices(gpus, 'GPU') logging.info(f"Using {len(gpus)} GPUs.") except RuntimeError as e: logging.error(e) else: logging.error("No GPUs found. Check your device configuration.") # Data directory data_dir = "/app/FOOD475/" # Image dimensions and batch size img_height, img_width = 600, 600 batch_size = 64 # Data preprocessing and augmentation train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.25 ) # Load and preprocess images train_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='training' ) validation_generator = train_datagen.flow_from_directory( data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='categorical', subset='validation' ) # Model creation function def create_model(input_shape, num_classes): base_model = EfficientNetB7(include_top=False, input_shape=input_shape, weights='imagenet') base_model.trainable = True inputs = layers.Input(shape=input_shape) x = base_model(inputs, training=True) x = layers.GlobalAveragePooling2D()(x) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs, outputs) return model def find_latest_saved_model(checkpoint_dir): logging.info(f"Looking in checkpoint directory: {checkpoint_dir}") if not os.path.exists(checkpoint_dir): logging.error(f"Checkpoint directory does not exist: {checkpoint_dir}") return None, 0 subdirs = [os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, d))] if not subdirs: logging.info("No subdirectories found for checkpoints.") return None, 0 latest_subdir = max(subdirs, key=lambda x: int(os.path.basename(x))) latest_epoch = int(os.path.basename(latest_subdir)) logging.info(f"Latest model directory: {latest_subdir}, Epoch: {latest_epoch}") if os.path.exists(os.path.join(latest_subdir, 'saved_model.pb')): return latest_subdir, latest_epoch else: logging.info("No saved_model.pb found in the latest directory.") return None, 0 # Mirrored strategy for multi-GPU training strategy = tf.distribute.MirroredStrategy() with strategy.scope(): saved_model_dir = 'model_training' checkpoint_dir = os.path.join(saved_model_dir, 'checkpoints') latest_saved_model, latest_epoch = find_latest_saved_model(checkpoint_dir) if latest_saved_model: logging.info(f"Loading model from {latest_saved_model}") model = tf.keras.models.load_model(latest_saved_model) else: logging.info("No saved model found. Creating a new model.") model = create_model((img_height, img_width, 3), len(train_generator.class_indices)) if not os.path.exists(saved_model_dir): os.makedirs(saved_model_dir) summary_path = os.path.join(saved_model_dir, 'model_summary.txt') with open(summary_path, 'w') as f: model.summary(print_fn=lambda x: f.write(x + '\n')) logging.info(f"Model summary saved to {summary_path}") optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002) model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.TopKCategoricalAccuracy(k=5), tfa.metrics.F1Score(num_classes=len(train_generator.class_indices), average='macro')]) # Custom Callback for Saving the Best Model in SavedModel format class SaveBestModelTF(tf.keras.callbacks.Callback): def __init__(self, monitor='val_accuracy', saved_model_dir='model_training'): super(SaveBestModelTF, self).__init__() self.monitor = monitor self.saved_model_dir = saved_model_dir def on_epoch_end(self, epoch, logs=None): current = logs.get(self.monitor) if current is None: logging.warning(f"Monitor '{self.monitor}' for saving the model is not available in logs.") return logging.info(f"Epoch {epoch + 1}: saving model to {self.saved_model_dir}/checkpoints/{epoch + 1}") epoch_path = os.path.join(self.saved_model_dir, 'checkpoints', str(epoch + 1)) if not os.path.exists(epoch_path): os.makedirs(epoch_path) self.model.save(epoch_path, save_format='tf') # Callbacks for monitoring progress tensorboard_cb = TensorBoard(log_dir='./logs') # Save class indices to a JSON file class_indices_path = 'model_training/class_indices.json' if not os.path.exists(os.path.dirname(class_indices_path)): os.makedirs(os.path.dirname(class_indices_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(class_indices_path)} created.") with open(class_indices_path, 'w') as file: json.dump(train_generator.class_indices, file) logging.info(f"Class indices saved to {class_indices_path}") # Model training total_epochs = 7 model.fit( train_generator, initial_epoch=latest_epoch, # Start from the next epoch epochs=total_epochs, validation_data=validation_generator, callbacks=[SaveBestModelTF(saved_model_dir=saved_model_dir), tensorboard_cb] ) # Evaluate the model eval_result = model.evaluate(validation_generator) logging.info(f'Validation Loss: {eval_result[0]}, Validation Accuracy: {eval_result[1]}') # Save the final model as a SavedModel format (including .pb files) model.save('model_training/finished_model') logging.info("Finished model saved in SavedModel format at 'model_training/finished_model'") # Convert to TensorFlow Lite converter = tf.lite.TFLiteConverter.from_saved_model('model_training/finished_model') tflite_model = converter.convert() tflite_path = 'model_training/lite_model/trained_model_lite.tflite' if not os.path.exists(os.path.dirname(tflite_path)): os.makedirs(os.path.dirname(tflite_path), exist_ok=True) logging.info(f"Directory {os.path.dirname(tflite_path)} created.") with open(tflite_path, 'wb') as f: f.write(tflite_model) logging.info(f"Model converted and saved as {tflite_path}") 

During training i got following output:

Found 182235 images belonging to 475 classes. Found 60544 images belonging to 475 classes. Epoch 1/7 2848/2848 [==============================] - 11914s 4s/step - loss: 1.7624 - accuracy: 0.5931 - top_k_categorical_accuracy: 0.8152 - f1_score: 0.4739 - val_loss: 1.1666 - val_accuracy: 0.7043 - val_top_k_categorical_accuracy: 0.9013 - val_f1_score: 0.6053 Epoch 2/7 2848/2848 [==============================] - 11096s 4s/step - loss: 0.8293 - accuracy: 0.7788 - top_k_categorical_accuracy: 0.9435 - f1_score: 0.7094 - val_loss: 0.9409 - val_accuracy: 0.7533 - val_top_k_categorical_accuracy: 0.9277 - val_f1_score: 0.6818 Epoch 3/7 2848/2848 [==============================] - 11123s 4s/step - loss: 0.6247 - accuracy: 0.8274 - top_k_categorical_accuracy: 0.9632 - f1_score: 0.7760 - val_loss: 0.8422 - val_accuracy: 0.7761 - val_top_k_categorical_accuracy: 0.9386 - val_f1_score: 0.7080 Epoch 4/7 2848/2848 [==============================] - 11101s 4s/step - loss: 0.5070 - accuracy: 0.8562 - top_k_categorical_accuracy: 0.9743 - f1_score: 0.8165 - val_loss: 0.8002 - val_accuracy: 0.7885 - val_top_k_categorical_accuracy: 0.9428 - val_f1_score: 0.7249 Epoch 5/7 2848/2848 [==============================] - 11079s 4s/step - loss: 0.4261 - accuracy: 0.8766 - top_k_categorical_accuracy: 0.9814 - f1_score: 0.8445 - val_loss: 0.7757 - val_accuracy: 0.7940 - val_top_k_categorical_accuracy: 0.9458 - val_f1_score: 0.7404 Epoch 6/7 2848/2848 [==============================] - 11100s 4s/step - loss: 0.3641 - accuracy: 0.8932 - top_k_categorical_accuracy: 0.9856 - f1_score: 0.8657 - val_loss: 0.7639 - val_accuracy: 0.8003 - val_top_k_categorical_accuracy: 0.9472 - val_f1_score: 0.7432 Epoch 7/7 2848/2848 [==============================] - 11129s 4s/step - loss: 0.3142 - accuracy: 0.9068 - top_k_categorical_accuracy: 0.9889 - f1_score: 0.8838 - val_loss: 0.7701 - val_accuracy: 0.8014 - val_top_k_categorical_accuracy: 0.9470 - val_f1_score: 0.7474 946/946 [==============================] - 2671s 3s/step - loss: 0.7682 - accuracy: 0.8008 - top_k_categorical_accuracy: 0.9470 - f1_score: 0.7456 

And when I try to load the model and make a prediction with this code:

class own: def __init__(self): if not os.path.exists("models/own"): raise FileNotFoundError(f"Model path models/own does not exist") try: self.model = tf.keras.models.load_model("models/own", custom_objects={'F1Score': F1Score}) except Exception as e: print(f"Error loading model: {e}") raise if not os.path.exists("models/own/class_indices.json"): raise FileNotFoundError(f"Class indices path models/own/class_indices.json does not exist") with open("models/own/class_indices.json", 'r') as file: self.class_indices = json.load(file) self.index_to_class = {v: k for k, v in self.class_indices.items()} def classify(self, img_path): if not os.path.exists(img_path): raise FileNotFoundError(f"Image path {img_path} does not exist") # Load and preprocess the image img = tf.keras.preprocessing.image.load_img(img_path, target_size=(600, 600)) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array /= 255.0 # Make prediction predictions = self.model.predict(img_array) print("Raw predictions:", predictions) top_index = np.argmax(predictions[0]) top_class = self.index_to_class[top_index] print(f"Top class: {top_class}, Probability: {predictions[0][top_index]}") top_n = 5 top_indices = np.argsort(predictions[0])[-top_n:][::-1] for idx in top_indices: print(f"Class: {self.index_to_class[idx]}, Probability: {predictions[0][idx]}") return top_class 

it always either predicts Steak or Omelette:

2024-06-01 14:17:27.571776: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\tfa_eol_msg.py:23: UserWarning: TensorFlow Addons (TFA) has ended development and introduction of new features. TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024. Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). For more information see: https://github.com/tensorflow/addons/issues/2807 warnings.warn( C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.12.0 and strictly below 2.15.0 (nightly versions are not supported). The versions of TensorFlow you are currently using is 2.15.0 and is not supported. Some things might work, some things might not. If you were to encounter a bug, do not file an issue. If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. You can find the compatibility matrix in TensorFlow Addon's readme: https://github.com/tensorflow/addons warnings.warn( WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\legacy\saved_model\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. 2024-06-01 14:17:31.363666: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\functional.py:156: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. WARNING:tensorflow:From C:\Users\[Name]\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. 1/1 [==============================] - 4s 4s/step Raw predictions: [[4.23421043e-05 1.45377373e-06 1.09034730e-02 1.19525917e-04 4.45407240e-05 5.72818244e-05 5.68609731e-03 5.15926695e-05 1.89958355e-05 1.39491487e-04 3.20717366e-03 9.63417915e-06 1.22947793e-03 4.01171012e-04 3.64649204e-05 1.75396308e-05 3.09416023e-03 7.56465085e-03 2.89075997e-05 3.90331191e-03 2.16231216e-03 4.18351328e-06 5.89632022e-04 9.40740295e-03 6.80321036e-03 2.32697069e-03 4.23964392e-03 1.56047070e-04 2.14435873e-04 6.95710623e-05 1.38103365e-04 1.78470847e-03 3.75193194e-03 5.94434096e-03 5.69255608e-05 7.57165905e-03 1.52613886e-03 9.48755944e-04 8.21925176e-04 3.18029453e-03 3.89393512e-03 8.41296278e-05 8.34997976e-04 3.14124190e-04 6.81638776e-04 1.10320523e-02 1.10815199e-04 6.18589204e-03 2.17406079e-02 3.72037102e-05 1.65579877e-05 1.30886221e-02 1.01435784e-04 2.13157946e-05 1.25499619e-05 8.94762017e-03 4.36880719e-03 4.78018774e-03 8.53170827e-03 1.45823974e-02 1.05571962e-05 1.12631078e-05 5.09415939e-03 8.12840741e-03 1.48212257e-05 1.52864438e-02 9.66716034e-05 2.25000476e-04 3.60531732e-04 9.28066402e-06 8.15156789e-04 1.09069003e-02 3.43796797e-04 2.53324561e-05 7.89516326e-03 1.44943051e-05 4.06841224e-04 1.67445414e-05 3.78527766e-05 1.80476491e-04 3.33699776e-04 4.13847056e-06 3.32273915e-03 6.51864940e-03 7.48403618e-05 2.68448726e-04 1.54245936e-03 2.95383972e-03 2.26996126e-05 3.64100002e-03 2.81597768e-05 3.11967051e-05 1.48438021e-05 8.46863433e-04 4.05767525e-04 1.75380992e-04 4.76581818e-06 5.42160356e-04 2.19287374e-03 1.18714366e-02 1.41884899e-04 8.76697595e-06 3.85931274e-03 4.37544841e-05 4.01919424e-05 3.87528981e-03 3.88057524e-05 2.69062322e-04 4.46968805e-03 1.17368818e-05 3.70194939e-05 1.55831876e-04 1.63894765e-05 2.38729117e-04 1.19046052e-03 2.12675819e-04 1.08185853e-03 3.01667496e-05 6.18575094e-03 3.91955400e-05 1.40065713e-05 3.02084809e-04 6.46927813e-03 3.37069832e-05 5.15250103e-05 2.31142567e-05 2.20274273e-03 3.17445702e-05 1.04452763e-02 6.80019803e-05 7.81101780e-03 1.23853814e-02 1.04819983e-02 3.20679283e-05 6.71340758e-03 6.94293885e-06 1.98310101e-03 5.29599565e-05 9.02036484e-03 4.57535089e-06 1.93145883e-03 4.06190008e-03 8.42716638e-03 1.50314684e-03 8.58115556e-04 1.22383237e-03 8.49474862e-04 5.48258470e-03 6.09953167e-05 1.57669128e-03 5.43692382e-03 4.88058169e-04 6.75312986e-05 3.43937165e-04 1.93276245e-03 4.06867871e-03 5.20323374e-05 7.78318281e-05 1.93508764e-04 1.14409677e-05 2.21324177e-03 1.90052821e-03 8.52691382e-03 2.43102224e-03 2.88419239e-03 2.53974522e-05 9.51182563e-04 2.32981285e-03 9.86064842e-05 4.14316915e-03 1.66544644e-03 1.02754391e-04 3.95776224e-05 3.02393187e-06 1.32082617e-02 4.14707232e-04 3.40229672e-05 4.81802830e-03 1.90598912e-05 4.08358377e-04 5.95443300e-04 1.22634810e-04 5.74091624e-04 8.57623760e-03 2.60962266e-03 2.95263715e-03 1.58088005e-05 1.64122172e-02 2.09987498e-04 2.36775051e-03 3.00696083e-05 3.46693669e-05 1.16249910e-04 6.94001559e-03 1.58400853e-05 1.95188422e-05 2.19169408e-04 3.09433235e-04 5.44128183e-04 6.35302160e-04 7.07127433e-03 1.19772732e-04 5.37439200e-06 1.91133395e-02 1.27979312e-02 3.89739592e-03 1.97048103e-05 2.29625002e-05 2.21050854e-04 1.92064399e-04 1.20139657e-05 3.20516920e-05 4.26828819e-06 3.64828011e-05 7.55213068e-06 2.67963973e-03 3.17923805e-05 6.19895945e-05 3.99544797e-06 2.68664648e-04 1.83274597e-02 8.71072552e-05 1.38439747e-04 4.96710254e-06 3.56023484e-05 1.34899991e-03 2.05766381e-04 3.96062108e-03 5.61600551e-03 5.31910664e-05 6.77773132e-05 1.36139952e-02 7.41477634e-05 1.63904135e-03 4.74587978e-06 1.45082246e-04 2.09337009e-06 8.13181920e-04 3.63194500e-04 6.46722084e-03 5.02364383e-05 6.90550078e-05 6.36972545e-05 2.09673337e-04 1.79036579e-05 2.36021675e-04 6.37291942e-06 5.70875318e-06 2.56235455e-03 2.72009202e-04 3.77103061e-05 5.63449021e-06 2.25979857e-05 2.61697169e-05 3.42375762e-03 1.04161156e-02 2.22223607e-05 6.27681802e-05 1.88465419e-04 2.82149922e-05 4.01149562e-04 1.31122259e-04 5.97863036e-05 2.41098423e-05 7.71318519e-05 3.57087993e-04 3.41462255e-05 1.01930054e-04 5.23206063e-06 2.95026781e-04 7.02897159e-05 3.99115682e-02 1.89455808e-03 1.74146010e-06 1.14775894e-05 7.84916210e-06 1.93041191e-03 2.37918808e-03 3.49449110e-03 6.98623667e-03 7.64393993e-03 4.12582303e-05 1.24030013e-03 1.72785169e-03 7.18316660e-05 5.17749111e-04 7.84919783e-03 1.04525541e-04 9.83856899e-06 8.77521088e-05 1.68125369e-02 4.09213862e-05 1.09552668e-04 2.54421811e-05 4.65482954e-05 6.95294410e-04 6.72869501e-05 2.40904570e-04 2.15112406e-04 3.85226776e-05 2.51369456e-05 4.68338234e-03 1.26862462e-04 9.00995801e-04 4.16984549e-05 7.36891707e-06 1.51534463e-04 1.48332631e-03 4.95935837e-03 1.91499032e-02 3.01804044e-04 6.28613270e-05 4.78365598e-03 8.38827982e-05 1.70516931e-02 1.52653758e-03 5.85798814e-04 3.11521399e-05 2.11968741e-04 7.41351105e-05 1.40834545e-05 8.93215940e-04 1.45371505e-05 4.96711982e-05 4.11317131e-04 8.89070239e-03 5.06997202e-03 3.08362325e-03 2.77415646e-04 3.75299685e-04 1.19906381e-05 1.50029315e-03 1.14443043e-04 2.52026439e-05 9.22407198e-04 3.51146841e-03 1.11564566e-06 1.36691102e-04 3.53032886e-03 2.15746608e-04 8.79282816e-05 4.36248304e-03 1.77966576e-04 1.47887832e-03 6.94399816e-04 8.03673174e-04 5.23004041e-04 3.90421192e-04 1.06344873e-03 3.55399796e-04 6.01265463e-04 1.55850008e-04 1.33491016e-03 1.09734829e-04 4.38019342e-04 2.42487862e-04 6.84730615e-03 1.02040754e-03 1.07652310e-03 3.51822848e-04 9.20735547e-05 7.50967592e-04 1.44127226e-02 3.58455327e-05 5.16555374e-05 1.31370616e-03 9.02966480e-04 1.24254671e-03 5.20300702e-04 8.57163919e-04 3.66344648e-05 2.01024144e-04 6.52487564e-04 5.93215809e-04 5.76604251e-03 6.19325438e-04 1.16480421e-03 2.37531040e-05 2.50119111e-03 7.08868974e-05 5.99786472e-05 2.55976247e-05 4.62695534e-05 4.24469297e-04 6.20667648e-04 4.15926515e-05 7.03983005e-06 8.77018738e-06 5.21141301e-05 2.11411956e-04 7.74205779e-04 5.31276630e-04 6.44316664e-04 4.07212786e-03 2.68336060e-03 1.74210854e-05 3.76385942e-05 6.74255705e-03 4.46323538e-05 2.76757801e-05 2.56290223e-04 1.22213329e-04 1.22734054e-03 7.73016480e-04 1.11903930e-02 3.16570923e-02 2.75775470e-04 5.73344238e-04 2.86890985e-03 1.10085262e-03 1.35615155e-05 2.66479654e-03 1.99418981e-03 4.31017601e-04 9.68350447e-04 3.51598108e-04 8.54862970e-04 3.52715979e-05 1.46333405e-04 5.10955288e-05 1.48639630e-03 1.80458324e-03 7.51840998e-05 1.13529910e-04 3.89828119e-06 8.74532212e-04 1.12358983e-04 3.93593837e-05 6.01037289e-04 2.06997487e-04 3.94766452e-03 1.09549124e-04 2.11403880e-04 6.95336203e-04 5.99777419e-03 5.45272342e-05 2.56420486e-03 2.20299728e-04 4.23851707e-05 6.69996080e-04 2.66609713e-04 1.55276459e-04 2.75739990e-02 3.43240798e-03 2.68303775e-05 1.52821158e-04 9.82575657e-05 4.00313947e-05 6.07266993e-05 5.28094570e-05 1.02948405e-04 6.20577412e-05 2.12161940e-05 2.99842539e-03 1.17558768e-04 1.58015324e-03 3.30074807e-04 1.19093776e-04 2.52985101e-05 1.59350988e-02 4.89539379e-05 1.05491054e-05 1.09012712e-04 2.97089737e-05 7.28885690e-03 1.87386977e-05 1.85028894e-05 5.79945299e-05 1.54079917e-05 9.85169099e-05 1.05076749e-03 7.55816349e-04 2.62255053e-05 1.18091421e-05 2.95209320e-05]] Top class: omelette, Probability: 0.03991156816482544 Class: omelette, Probability: 0.03991156816482544 Class: steak, Probability: 0.03165709227323532 Class: tacos, Probability: 0.027573999017477036 Class: breakfast_burrito, Probability: 0.021740607917308807 Class: pulled_pork_sandwich, Probability: 0.01914990320801735 (own): omelette - 3.66s 

Help would be appreciated because im slowly losing my mind :(,
Jonas

[INDEX]
I banked the fire and stared into the golden eyes of Beatale before I crept into my makeshift tent.
I still had my auric vision running and couldn’t help but notice the thin silver cord that ran from me to Horse. Firming up my aura, I reached out with my hand and grabbed it. I could feel the nearly imperceptible vibration between my fingers as I used my mind to probe at the thread. I could feel a bright spark of intellect, a light at the end of a tunnel. Pushing with my mind, I slid down the thread until the spark grew larger and eventually filled my inner vision with a hazy white light. Horsey thoughts nudged at me curiously.
I slid into the haze and immediately lost all sense of direction. If it wasn’t for the silver thread, I’d have no idea how to exit this shifting white fog. Horsey thoughts got stronger as I followed the thread while the haze thinned and cleared to reveal an endless prairie of green grass. I found myself standing before a naked man wearing a horse mask and I stared in shock. It was obviously me wearing a cheap costume horse mask — there was no mistaking my tattoos.
“What did you expect?” Horse neighed at me. “I am you and you are me and we are all together. Goo goo ga joob.”
Horse made a shooing motion with his hands and I accelerated backwards through the white haze and slammed into my own body with a gasp. I stared at the tarp overhead for a long minute, processing this new revelation. Horse was a part of me, a piece of my spirit. Whatever psychic stuff I did with that silver cord lead me into a house of mirrors where I got to look at myself pretending to be a horse. I can’t even deal with that right now.
Rolling into my blankets, I dropped off to sleep.
*Ding*
-=- - Welcome to the Dreamworld - Included in the Psychic Skills pack, the Inner Sanctum is your psychic domain. It is the mental fortress that you must secure and maintain to defend against psychic and spiritual assaults. All of your neurosis and fears are symbolised in this realm and must be defeated or subjugated before you can become master of the domain. Good luck. -=-
I banished the pop-up and looked around. I knew I was asleep, but everything was just as real as when I was awake. I was breathing, I could feel the floor under my feet, and if it weren’t for the pop-up, I would have sworn I had been teleported. The room I was in resembled an oversized luxury prison cell, maybe a thirty foot cube. No windows. Rough stone walls with thick mortar. Large brass wall sconces were set directly into the stone and suffused the room with a warm, golden light provided by glowing rocks. The stone floor had colourful Persian rugs tastefully placed. A high plaster ceiling was painted with a rendition of Michelangelo’s ‘Creation of Adam’, depicting me as both Adam and God.
There was a comfy sofa in front of a large screen television that hung from one wall and an ornate grandfather clock ticked loudly in the corner. It was currently 10:08 PM. Another wall was a floor to ceiling bookshelf, stuffed with books of varying sizes. The third wall was covered with pictures and I could see at a glance that they were images from my life. The fourth wall had a thick riveted steel door on the right side, a full sized mirror on the left, and a computer workstation in the middle.
The picture wall was my first target. A few were quite large, nearly life sized, while others were tiny prints no larger than the palm of my hand. Scenes of my life were displayed in each one. The largest was me riding Horse with a shit-scared expression, shooting at a pack of wolves. Others were smaller, each with different frames. Some ornate gold or silver, others plain wood, a few wrapped in briars or barbed wire. Nanny Ramsey holding me as a young child. My dog Jean with a red ball in his mouth. My parents, screaming at me. I turned my attention to the books. Books are safe. Books don’t judge you.
The sweet, musty scent of a used book store filled my nostrils as I drew close to the honey coloured shelves. Hundreds of volumes filled the wall from floor to ceiling, with a ladder that could be rolled along a rail to access the top. I smiled at the sight. I had always wanted a library like this. I pulled a book at random and read the title, “Confused Fantasies about Joseph Harris, part XXIV of the Middle School Years”.
I slid the book back onto the shelf. Let’s see what’s on TV.
The remote was a slim, futuristic looking affair with a minimum of buttons. I pointed it at the television and moments later the huge screen came to life and presented me with a simple menu for movies, divided into six categories: Happy, Surprised, Afraid, Disgusted, Angry, and Sad. I scrolled through the offerings for a minute, reading the titles and reviews about the movies of my life. It really bothered me that there were so few selections in the Happy section.
The number of Sad movies increased by one.
I walked over to the mirror and noticed there was a small sticky note pasted to it. “Astral Realm. Experienced users only.” I shoved the note in my pocket and stared at my image. Sturdy black boots, black denim jeans and shirt with mother-of-pearl buttons, deep brown gun belt slung at my hip, red bandanna and black felt hat. All I needed was a pencil moustache and I would look like the stereotypical villain in any spaghetti western. At that very moment I decided to grow out a goatee. I’d rather be mistaken for a bad guy than a victim.
So how does this astral realm thing work?
The mirror appeared to be nothing more than a mirror. It was cold, smooth glass surrounded by a wrought iron frame, and reflected my image. I didn’t necessarily want to go walking into danger, but I wanted to know how it worked. I pushed and prodded the glass in frustration until I noticed my image grinning at me. I jumped back in surprise and it doubled over in silent laughter.
“Hilarious, dude. You got me,” I huffed. “So how do I get in?”
My mirror-self tipped his hat and stepped to side.
I reached up to the mirror again and my hand passed through, vanishing as if cut off. Okay, just a quick peek and we’ll explore the rest of the room. I stepped through and the world shifted around me. I was standing back at the campsite. My body was insubstantial as a ghost and the tarp was a wisp of substance running straight through me. Non living things don’t seem to have much presence in this realm. Glancing down, I saw my sleeping body rolled up in the blankets, a thin silver thread running from it to me, and another thread running to Horse.
Looking around, I surveyed the campsite. My astral vision seemed to be on and had an unlimited range. I could see the life all around me, the distant forest was a sea of greenish-gold, grasses and brush nearby glowed with spectral light. Tiny ghost insects scurried while ghost mice nibbled at whatever ghost mice nibble on. Ghost seeds and ghost insects, I suppose. I turned my attention overhead and gaped at the sight of a monstrous serpentine spirit flying through the inky void. I dropped back through the tent and rolled inside my body. That was plenty enough for now.
I rolled through the mirror and landed flat on my back, staring at the fresco on the ceiling. Vinnie-God winked at me and Vinnie-Adam grinned. Climbing to my knees, I brushed non-existent dust from my trousers and watched mirror-me doubled over in soundless laughter.
“Hey, laughing-boy!” I yelled at him. “You’re like the guardian or something, right? You got it covered?”
Mirror-me stood and saluted with a smile, then gave me two thumbs up. A moment later, his face took on a serious expression and he wriggled his right hand in the ‘maybe’ motion. Then he pointed at me, tapped his wrist, and then a finger to his head.
It all depends on how fast I learn stuff, I guess.
Two thumbs up and a winning smile reflected back to me.
A large cork board was mounted to the wall over the computer and a small note was pinned to it. “Note to self: Don’t fuck with the Elvish womens.”
The computer screen featured a screensaver of me as Vitruvian Man doing callisthenics over the words ‘HumanOS’. I tapped the spacebar and was rewarded with the sound of powerful fans kicking to life as the computer emerged from sleep mode and prompted me for a password. Should I assume it’s the same as the password on the computer I pawned in my previous life?
Password: *******esi
I was rewarded with a sweet R&M desktop and a couple of icons. System, NeuralNet, My-Tunes, My-Movies, My-Office.
System was just what I expected, lots of .dna files and other confusing scariness that allowed me to tweak my physical body and mental state. My-Tunes was a collection of every song I’d ever heard and My-Movies was a collection of every movie I’d ever seen. Not that I’m complaining, but it would have been nice to have “My-Games” so I could play RDR. My-Office was a clone of the popular software by a similar name. I have no idea what I’ll ever need a spreadsheet for in this world.
NuralNet opened up a search engine called Me-Seeks, featuring a familiar blue guy.
I typed in “beer” and several thousand results were displayed, anything I’d ever read, heard, or watched about beer, including how to make it. This right here made the price of admission totally worth it, access to an exact copy of everything I’d ever read, and I was a voracious reader. Sadly, most of the stuff I read was futurology — solar panels, electronics, biotech advancements, quantum computing. The material for steam engines, blacksmithing, farming and the like, were slim pickings. That’s okay though, I could still reproduce the Gutenberg press, the cotton gin, simple internal combustion engines, and basic batteries along with some sketchy knowledge of metal alloys, acids, bases, and other things I had read over the years. All that wasted time watching “How Things Work” was finally going to pay off. I copied a few likely money makers to My-Office, saved the file, and exported to my Notes, just in case they didn’t exist on Aerth.
A popup covered the screen.
📱 [New Upgrade Available!] 📱
🎉 Enhance Your Experience with the Latest HumanOS Features! 🎉
🌟 Features Include:

• 🧠 Memory Booster: Increase your Memory for smoother multitasking and improved performance.
• 🔋 Energy Optimizer: Extend your battery life with advanced power-saving techniques.
• 🛠️ Utility Upgrades: Access new apps and utilities to enhance your daily life and adventures.
• 🗺️ Advanced Mapping: Never get lost again with enhanced GPS and location tracking.

🔥 Special Offer: Only 2000 credits for version 2.0 or 5000 credits for version 3.0! 🔥
[Upgrade Now ✅] [Remind Me Later ❌]
Apparently I could upgrade myself, which reduced the cost of using my Utilities while providing other minor benefits. My Utilities would level up as I used them, which would increase their battery cost, so if I didn’t keep pace with an update to the OS they could become prohibitively expensive to operate.
Stupid pay-to-win world.
So, do I pay 2000 credits for version 2.0 or 5000 credits for version 3.0?
I selected version 3.0 and klicked [Install]. After watching it download the update, it popped up another screen that asked if I wanted to update now, or wait until Midnight for the mandatory update.
I selected [No] just as the grandfather clock chimed 10:30 PM. I wondered if time ran slower in here, because it seemed like I had spent a lot more time on the computer than 15 minutes. Walking over to the imposing steel door, I noticed a bronze key with a thin chain in the lock. There was another sticky note on the door. “Subconscious. Please keep the key with you at all times.”
That’s not scary at all, is it?
I unlocked the door with a loud clunk and pulled it open to reveal a bedroom straight out of some royal castle. I could tell immediately that it had seen better days. The tapestries on the wall were frayed and fading. The canopy over the bed had a few holes in it. A thin layer of dust covered the mantle of a small fireplace set into the wall. There was a window letting in bright sunlight and I moved over to look outside.
I was on the third floor of a keep surrounded by the walls and turrets of a modest castle. A castle that had fallen into serious disrepair. Did this represent the state of my inner mind? One tower was shattered and the curtain wall under it damaged. The lower bailey was full of litter. I could see a few soldiers walking around the allure, keeping watch.
I have people in my subconscious?
Someone behind me cleared their throat.
Whirling, I discovered a familiar old man standing in the door of the bedroom. What was left of his hair formed a white halo around his head, his face was unshaven and covered with several days of growth. He was dressed like a poor and tattered manservant, but carried himself with a dignified air.
“Woodhouse?”
“It’s nice to see the master at home,” He said with a proper English accent. “There are many matters that require the master’s attention.”
“Uh, sure,” I said, hanging the key around my neck and tucking it in my shirt. “And who are you again?”
“Your personal manservant, of course” he said with a slight bow. Walking over to the steel door, he pulled it closed and it locked with a solid thunk. “Master should always keep his inner sanctum closed. One never knows if something nasty will creep in.”
“Thank you, uh, Woodhouse. I’ll remember that,” I said, rubbing the back of my neck. “So what needs tending and how do things work around here?”
He smiled and beckoned me with a white gloved hand. “If master would be so kind as to follow me, I’ll introduce him to the staff and explain the duties and obligations of his domain.”
I’m 99.9% certain that everyone here is just me wearing a mask, so I shrugged and followed Woodhouse out of the bedroom and into the rest of my subconscious.
Five minutes later I was on the ground floor and seated on a shabby throne with the cast of a popular —and probably very copyright protected— animation in front of me. Woodhouse was the head butler and my personal manservant. Pam was the cook and demanded that I start importing sugar and alcohol before she was shushed by Woodhouse. Carol was a maid. Krieger was chancellor and Cyril was the steward. Archer and Lana were in charge of security. Ray was the marshal in charge of everything from the stables to the blacksmith.
I stared in disbelief at the motley crew kneeling in front of me. No wonder my inner mind was in such shambles. I was overcome with an irrational sense of anger at myself.
“Arright, listen up,” I barked, my voice echoing around the room. “I swear to God that I will fire every single one of you and hire circus clowns to replace you if you keep fucking things up. No joke. Circus clowns, got it?”
I ran a hand over my face as Ray pissed himself. “The only reason I’m not putting a boot in your asses right now is because I realise that you’re aspects of me, and the people you represent are pretty damn good at their jobs when they give enough of a shit to actually do them. As a team, you’re dysfunctionally fantastic and always seem to come out ahead no matter the odds.”
Heaving a sigh, I continued. “Things have changed and I need to get my shit together. I’m going to need every one of you to pull your weight and help me help you. Get back to your duties, I’ll meet you one on one later.”
My subconscious caretakers scurried out of the room.
“I’ll have one of the maids tend to the piss,” Woodhouse assured me.
“Never mind that,” I snapped. “I honestly had no idea my mind was such a shit show. I’m very disappointed in myself.” I pictured the Angry, Sad, and Disgusted counters on my personal movies clicking up. “Show me what needs to be done and let’s get started.”
During Woodhouse’s walking tour, everything clicked into place. This was some altered version of Bodiam castle, a location that was on my bucket list of places to visit. The royal council room, located behind the throne room, contained a “living” tapestry on the wall that showed the castle and surrounding land in real time. The castle was located in the middle of a small lake, and a single wood bridge led to the mainland. A small town surrounded the lake and a wall encircled the town. Outside the wall, the land was an irregular patchwork of forest and field, with a stinking swamp to the south. The entire “kingdom” was maybe ten miles across, surrounded by impassable mountains with innumerable creeks that fed the lake which drained into the southern swamp.
“Zombies are the problem, sir.” Woodhouse said, as I surveyed the living tapestry of my mental domain.
“Zombies?” I prompted.
“Yes sir, Zombies” Woodhouse continued. “Nasty bitey things that come in from the mountains and harass the peasants. They’ve gotten especially worse over the last few months. The soldiers do what they can, but they seem to have lost all motivation. Probably because they haven’t been paid.”
“And who pays them?”
“Typically chancellor Krieger is in charge of financial matters, although Steward Figgis has taken over the duty, sir.”
“Then let’s make Figgis our first stop.”
“Very good, sir.”
The office of the steward was run by Cyril Figgis, who managed the kingdom in my absence. It was overflowing with paperwork and charts, books and scrolls piled high on every flat surface. Cyril was desperately attempting to tidy things when Woodhouse and I walked in.
“Yo..you..your majesty,” Cyril stuttered, bowing low. Scrolls fell from his overloaded arms, spilling across the floor. He dropped to his knees and scrambled to gather them up. “I didn’t expect you to visit so soon. Please forgive the mess, housekeeping has been slacking…”
This was the guy who ran things while I was conscious.
“Shut up, Cyril” I said. “You’re responsible for everything in this office. That includes keeping it organised and tidy.”
“Y..yes milord.”
“It’s my understanding that you’re in charge of making sure everyone gets paid. So why aren’t we paying people?” I asked.
“We’re nearly out of Fuks, your majesty. I’ve been saving them for emergencies.”
“Fucks?”
“Fuks,” Cyril explained, pushing a pile of books off a large chest and opening it. Reaching inside he pulled out two small bags and emptied them on top of his cluttered desk. “Gold and Silver Fuks, the currency of the kingdom. I can’t maintain the kingdom when I have no Fuks to give.”
Behold the subconscious kingdom of Vincent J. Carter, it runs on Fuks.
“So how do I get more fuks?” I asked, examining one of the coins. It had an image of me on one side and symbol on the other that could be interpreted as “peace among worlds”.
“You kill the zombies, your majesty.”
Of course I do.
Woodhouse and I left Cyril’s office and headed towards the office of the chancellor where Krieger worked. It seemed that Cyril took over financial matters when Krieger became erratic and proposed luring all the zombies into the city and setting it on fire. Not sure how that corresponds to my own self-destructive behaviour, but I’ve had some dark thoughts over the last couple of months and I’m sure they’re reflected here.
Krieger’s office was much neater in comparison to Cyril’s, but it wasn’t by much. Shelves lined the walls and were filled with an array of questionable items, including a still snapping zombie head in a jar. While the office of the chancellor was supposed to be in charge of financial matters, it looked more like a dodgy rummage sale.
Krieger was launching sword blades at a pig carcass when we walked in.
“What exactly are you doing?” I asked, standing in the doorway.
“Hm? Oh, your majesty!” he said, turning around and bowing deeply. “I’m testing a new invention. It’s a spring loaded hilt that shoots sword blades. Very useful for our soldiers.”
“Stupidest idea ever,” I snapped. “I hate everything about it.”
“Okay,” Krieger said, tossing the hilt into a nearby pile of junk. “But don’t blame me when you need to shoot a sword at a zombie and don’t have one.”
“So why aren’t you managing the financial affairs? Collecting taxes, paying people, stuff like that?”
“Because the population has declined so much none of that matters?”
“What do you mean?”
“Wellll, the population represents things you care about,” Krieger said, going into lecture mode. “And the zombies and other monsters are real or imagined problems in your way. Since you don’t care about too many things the population has shrunk to just what’s needed to keep everything running on the bare minimum of fuks. And since you don’t seem to have any long or short term goals, there’s no need to kill off the zombies and get more fuks. Everything is fine just the way it is.”
“No, it’s not Krieger” I said, grinding my teeth. “My mind is in a shambles. It’s a joke. I want it fixed. No, I want it better than fixed. I want it improved.”
“Oh! I’ve got just the thing for that!” He said, digging around in his pockets, “It’s a spring-loaded hilt that shoots swords!”
Pam and Cheryl were hanging out a gallery window jeering at Archer and Lana sparring in the inner courtyard.
“What the hell are you doing!” I snapped
They whirled in surprise and then dropped into deep curtseys.
“Your majesty!”
I took a deep breath, trying to regain my centre. “Get to work cleaning this place up. Find a room, clean it, and move on to the next. Start with my bedroom, then the throne room and the council chamber, then everything else.”
Cheryl spoke up. “Can’t do it. We got no fuks to clean with.”
“You need fuks to clean?”
“Gotta buy stuff,” Pam said. “Cleaning supplies, food. You wanna eat, you’re gonna have to spend some fuks.”
“Talk to Cyril,” I ordered. “Tell him I said to get you supplied.”
They ran off in the direction of the stewards office.
I watched Archer and Lana bashing each other enthusiastically through the window.
Several minutes later the sparring couple stopped and bowed when Woodhouse and I stepped into the inner courtyard.
“Your majesty”
“My liege”
“Enough,” I said. “If you have enough energy to smash each other, you have enough energy to smash zombies. Tell me what I need to know so I can start gathering fuks.”
Archer shrugged and spoke first. “You just kill the zombies and other monsters. They drop fuks.”
“Anything special about the zombies?” I asked. “Are they fast? Do people get turned into zombies when bitten?”
“Nope,” Lana said, resting her wooden sword on her shoulder. “Most of them are slow shamblers and just need a good wack to the head to kill them.”
“Some are special,” Archer interjected. “Occasionally you’ll have some fast ones, or those that need holy water to kill. They’re just bad memories, figments of your personality that need to be eliminated. Some are worse than others.”
“The zombies are bad memories?” I asked, imagining all the bad memories that I had.
“Memories, thoughts, insecurities, metaphysical mumbo-jumbo,” Woodhouse supplied. “They are endless, but constant vigilance can keep them under control.”
“So let’s get started,” I said. “Lead the way.”
Lana and Archer lead me up to the parapet over the front gate where I looked over at the dozens of zombies milling about aimlessly in front of the entrance to my mind. Pulling out my gun, I began to pick them off, easy as shooting fish in a barrel. The crack of my spell pistol attracted more zombies and I dispatched them with ease until no more were left around the gate. As I fired each shot I could feel some sort of existential energy flowing from me, draining some hidden reserve.
“Gather up the Fuks,” I commanded. “And Lana?”
“Mi’lord?”
“There’s no excuse for this. From now on, I expect the walls to be clear of all zombies.”
“Yes mi’lord,” she said, giving me a small bow.
Turning to Archer, I shook my head. “You’re obviously my personal narcissism, so just try to stay out of Lana’s way, or better yet - try to kill more zombies than her. If you think you can.”
Archer scoffed. “No contest. I took top marks in sharpshooting.”
“That means I should expect to see results by tomorrow. I look forward to it.”
Archer looked panicked for a moment then smiled. “Sure, I can give you results.”
Turning back to Woodhouse I said “Show me what else need attending.”
Woodhouse led me through the town that represented my mind, pointing out each business that had fallen into disrepair, suggested others that needed improvements, and additions that would benefit me. In the distance, I could hear Lana and Archer shooting at the crowd of zombies and with each echoing shot I felt a tiny bit better about everything.
[INDEX]

Every single day there is a question from someone here saying "where do I start for AWS Solutions Architect Associate" when there are a few hundred articles from those who passed already.
So here is a master list of resources to help those who have this question.
Cloud Practitioner version of this is here
If you find this post useful - upvote. I am happy to take feedback / suggestions / changes etc - please comment!

tl;dr

1. Get 1 video course and watch it end to end - the subreddit favourites are below / scroll down further for links
   • I cannot afford any courses / need a free option - get Andrew Brown's YouTube course
   • I want to just learn bare minimum to pass exam - Stephane Maarek on Udemy
   • I really want to learn this AWS and cloud stuff well and be good at it - Adrian Cantrill
2. Read whitepapers / review new announcements from re:Invent 2023
3. Do one decent set of practice exams from one provider- subreddit favourites below / scroll down further for links
   • Tutorialsdojo (personal favourite - I passed ALL my exams using "TD")
   • Udemy (Stephane Maarek)

Take and Pass exam!

Subreddit Search

Following my own usual guidance, you can always use the subreddit search feature and read articles from everyone in the last month who posted about this exam / passed it. There is a wealth of detail / experience here to learn from :
Link : https://www.reddit.com/AWSCertifications/search/?q=saa+solutions+architect+associate+pass&type=link&t=month

Exam Details

If you have absolutely no clue about the exam - start here.
The exam code is SAA-C03
AWS page with all the details : https://aws.amazon.com/certification/certified-solutions-architect-associate/
Always read the Exam Guide (tells you whats in / out of scope) : https://d1.awsstatic.com/training-and-certification/docs-sa-assoc/AWS-Certified-Solutions-Architect-Associate_Exam-Guide.pdf

Minimum Viable Path to Certification

Most people usually need 3 things to pass the exam

1. A single video based course introducing AWS and all the key exam topics

Typically these are courses where someone reads from some slides, shows you the AWS console and how to use it and then gives you tips on what to remember - there are free and paid versions of these.

1. Additional material on key topics.

For SAA-C03 - there are some recommended whitepapers on WAF and also since 6 months have passed since the last re:Invent 2023 - any of the major announcements from then now are in scope for the exam. You wont see too many new things but there is a chance there are some random questions that were not covered in any practice exam / course.

1. One good quality practice exam

Note : do not fall for some random "dump" found on internet or a file your mate gave you to study.
Also note - you do NOT need more than 1 of each category. You can buy more than one practice exam for sure but doing one is enough IMHO.

1. Video Courses

Free Video based Courses

Free from AWS's own training service (Skillbuilder) :
There is an "Exam Prep" course from Skillbuilder but note that this just covers the high level domains but is not a comprehensive deep dive.
https://explore.skillbuilder.aws/learn/course/external/view/elearning/14760/exam-prep-aws-certified-solutions-architect-associate-saa-c03
Optional : There is a slightly extended version of this in the paid tier with additional exam-style questions, flashcards and more importantly FREE hands on labs and the official practice exam.
https://explore.skillbuilder.aws/learn/course/external/view/elearning/14776/exam-prep-aws-certified-solutions-architect-associate-saa-c03-with-practice-material
There is a 7 day (extended to 10 days sometimes) free trial for the paid tier which can help you cram this. You can subscribe, immediately cancel but still enjoy 7 days free.
Please note that this course is not enough on its own to pass and you may want to try additional material below.
YouTube based video course
This course below is a better alternative to the SkillBuilder course above but is about 50 hours.
Andrew Brown is an AWS community hero who runs his own training site called exampro.co but offers most of the material for free on FreeCodeCamp's YouTube channel.
The 2024 refresh of the SAA course is here : https://youtu.be/c3Cn4xYfxJY
Andrew also has additional (free / paid) content on his site to check out.

PAID Video based courses

Adrian Cantrill's courses :
Adrian Cantrill is an independent content creator and has his own site from where you can obtain courses.
His courses go above and beyond what the exam needs and this is exactly why the community loves these courses as you get more practical knowledge than just cramming for the exam. The additional coverage means these courses are longer and not as cheap as other courses that cover just the exam material but in the general opinion of everyone who has taken the course it is absolutely worth it.
Link : https://learn.cantrill.io/
Udemy Courses :
Udemy is a marketplace for courses created by independent authors.
Two of the well known authors are mentioned below but please note that Udemy's pricing model can be a bit weird. One day it may show 150 USD for a course and another day 15 USD. This price it high and discount it heavily model catches out most people - so NEVER pay more than USD 20 for anything on Udemy.
Just wait for a day or so and prices may change. Opening Udemy in another incognito browser etc usually yields a different price or follow the authors on social media for codes that shrink the cost.
Stephane Maarek :
Go via his site : https://courses.datacumulus.com/ for links to his Solutions Architect Associate with the best available coupon.
Neil Davis :
https://www.udemy.com/course/aws-certified-solutions-architect-associate-hands-on/
Either one of these Udemy courses is sufficient. You still need to combine it with practice exams but you do not need more than 1 video course.
Other sites :
Exampro.co
As mentioned above Andrew Brown has his own site with additional material over his YouTube course.
Cloud Academy
https://cloudacademy.com/learning-paths/aws-solutions-architect-associate-saa-c03-certification-preparation-for-aws-1-7446/ has both a learning plan and a practice exam at the end.

2. Additional Material

I will update this section soon with some additional guidance soon as I am not happy yet (please let me know in comments if there are key additional coverage I should include) - I am scouring recent exam pass posts to see whats current and also want to add links to re:Invent 2023 announcements. I also am thinking of adding in links to "cheat sheets" / docs - let me know if this would be useful.
WAF - Well Architected Framework
https://aws.amazon.com/architecture/well-architected/
You need to know at some decent depth on what the pillars are and what they do.
Read the whitpapers from https://aws.amazon.com/whitepapers/
Specifically I found the Reliability and Cost Optimization white papers very useful.

3. Practice Exams

Please do NOT fall for "dumps" - if anyone offers you the EXACT list of AWS questions or guarantees the question bank matches the exam - these are dumps. The links below are either official or well regarded sources.
Free :
AWS skillbuilder has one free official exam with just 20 free questions.
To be honest its not really worth it - you can search for "Official practic exam skillbuilder SAA-C03" using your favourite search engine to find it.
exampro.co
Has 1 free practice exam you can sign up to.
Paid :
Official Practice exam
https://explore.skillbuilder.aws/learn/course/external/view/elearning/13593/exam-prep-official-practice-exam-aws-certified-solutions-architect-associate-saa-c03-english - there is a free 7 day trial available for you to use as this exam may not be worth a month's subscription fee
Tutorialsdojo.com
Highly recommended independent resource for practice exam questions with a very useful "review mode" and every question comes with detailed explanations on answers
Udemy
Stephane Maarek : again go via his site : https://courses.datacumulus.com/
Neal Davis : https://www.udemy.com/course/aws-certified-solutions-architect-associate-hands-on/
Other popular sites :
Exampro.co
Andrew Brown has I believe 3 practice exams as well on his site. One is free - the other two you pay for.
Whizlabs
I havent used them personally but https://www.whizlabs.com/aws-solutions-architect-associate/
Cloud Academy
https://cloudacademy.com/learning-paths/aws-solutions-architect-associate-saa-c03-certification-preparation-for-aws-1-7446/ has both a learning plan and a practice exam at the end.

Not Recommended sites :

Sites that are sadly NOT recommended anymore - Avoid A Cloud Guru / Pluralsight as their courses are not considered the best anymore. They used to be leaders but somehow have fallen behind and their subscription model doesnt work in a world with cheap one time purchase courses.
If you want a sandbox to experiment - then ACG offers one but so do Whizlabs and Tutorialsdojo.

Optional / Complementary material

I have an article where you can find complementary / alternatives to the Solutions Architect Exam - most are free and includes the "AWS Knowledge : Architecting Free Digital Badge"
https://www.reddit.com/AWSCertifications/comments/1d1o522/no_payment_options_to_learn_aws_with_digital/
This material isnt exam focused but if you want some free alternatives / cannot afford to pay for the exam - then check out the link.

FAQ

1. Do I need ALL this material?

No. Just one of each is fine. Example : just Adrian's Course + tutorialsdojo

1. Do I really need to do hands on work?

Yes - it is recommended that you get some hands on work at the Associate level. You can use one of the sandboxes but be careful using your own free tier account that you dont end up with leaving resources running too long and getting a big bill. Always secure your account and set billing alarms and dont create an account till you know how to do these!

1. Where can I find vouchers for the exam?

Check this thread : https://www.reddit.com/AWSCertifications/comments/18woit6/2024_aws_vouchers_exam_discounts_othe?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

1. Can I cheat my way using Dumps that I found online / my mate gave me / found on GitHub / YouTube?

Using dumps there is a high chance you fail and/or get caught / banned - the risk isnt worth it. Stick with genuine resources.

1. Can I pass with just free resources as I cannot afford the resources?

Its possible but please it is recommended to atleast spend on decent practice exams. If you cannot afford the exam / resources - just get the free digital badges (Architecting) for the interim

1. I skipped CCP / CLF - is that okay?

Yes - its okay to have skipped the foundational level - almost all the courses above teach you from scratch.

1. Can someone who is new to IT do this exam?

Yes - Many people start from scratch and get to the Associate level. Just make sure you are investing the time required.

1. Is it worth it?

Plenty of threads on this subreddit covering this. You have to make up your own mind if its worth it to you or not.

1. Do I need to do coding?

While there is no coding involved in the course - knowing how to use the AWS CLI / being able to do some basic scripting would be very helpful anyway. You can also use free tools like CoPilot / Code Whisperer to help you with pieces you struggle with.

1. Can I use ChatGPT / Amazon Q etc to learn?

Many of these Generative AI tools can still give you incorrect answers. So do not rely on them fully. If it helps you to quickly get the concept, use them but make sure to double check the results against official docs.

1. Are there books to learn from instead of videos?

Books get out of date too quickly and I do not recommend learning from them. However there is an official Sybex Guide to the exam. Tutorialsdojo and Neal Davis (Digital Cloud) also have an ebook. You can google for links to these.
Good Luck folks!

Over a year ago, I was going through a particularly stressful time and went to my neurologist concerned that I was possibly having Medication Adaption Headaches (MAH aka Medication Overuse Headaches aka Rebound Headaches) or would develop them.
I was having a migraine or headache almost daily. I had been cycling through pain meds to avoid using too much of the same thing and too many triptans mistakenly thinking that this would keep me safe.
My neurologist didn't take any time to discuss why I thought I might be having MAH or what should be done if I was already having them. He did give me the prescription for Aimovig that I asked for, but also a recommendation and prescription for Panadol migraine (same as Excedrin migraine) which I had never taken before. The prescription wasn’t needed to get the Panadol migraine, but it was needed to have it reimbursed by my insurance. I thought because it is OTC in the US (which is where I'm from) that it would be better (again mistakenly) than taking so many triptans.
The aimovig was like putting a bandaid on a gash that needed stitches. I made it another year before I had an absolutely horrible flare-up about 60 days ago that led me to do my own research because my neurologist had failed me horribly and I decided it was most definitely MAH and I needed to detox.
The Detox
I quit taking all pain OTC pain meds and triptans for 60 days (as is recommended in most treatment guides). It took me roughly 9 days to have a noticeable drop in my migraines back to episodic. My migraines have lessened in severity and length over the last 60 days. Though the first week or so was the most challenging to get through, I also had hormonal migraines that were tough. The few other non-hormonal migraines I had later in the 60 days, I was able to clearly identify triggers for. This hadn’t been the case for me in the past. I've also now been able to abort a hormonal migraine with other methods listed here.
Other Options for Pain Relief
(for any meds or supplements always consult your doctor)
Ginger is a great natural painkiller. There is some BS study that says it is as effective as sumatriptan, it most definitely isn't and I'm not going to try to sell it as that, but I would say it is probably as effective as an NSAID. Unfortunately, I’m unable to get GCRP inhibitors where I am so I didn’t have other migraine abortive options, just this.
Benadryl (note: this is the brand name in US & CA, it’s different in Europe) helps me with migraine pain during an attack (sometimes even helps avoid an attack).
A TENS unit was very helpful with migraine pain, but also with cramps during my 60 day detox and I’ll definitely continue using it going forward.
Migraine Cap was especially helpful after the migraine to help with the residual soreness.
Migraine Relief Nasal Inhaler, hot showers, decongestant meds, and decongestant nasal spray* help me because nasal congestion is a major symptom for me. When the congestion is worse, the pain is worse. If I can relieve some congestion, I can also relieve some pain. So I use these as needed depending on the severity of the congestion.
*It is important to note that decongestant nasal sprays can cause rebound congestion if used frequently, follow dosage and warnings on the label.
Myofascial Release & dry needling - this isn’t so much for migraine pain, but it helps me manage back and neck pain that contributes to my migraines and helps me with pain management overall.
The Pain Relief Options That I Wish I Could Have Used or Tried
Balms and patches that you put on your forehead- personally my skin is too sensitive for it, I have tried in the past and it just makes my skin burn (but so does most sunscreen when applied to my face). I’m mentioning these because I think they are a great option for some people and as I was looking through this sub for more ideas of what I could use, they are something that I saw repeatedly that I wish my skin would allow me to use.
Celafy, Nerivio, and Relivion all looked like interesting devices, but sadly aren’t available where I am.
Heated eye massager also looked very appealing and should have been available, but the wrong item was delivered when I tried ordering it and I didn’t feel like trying my luck again. I will definitely get one when I go to the US.
GCRP-inhibitors - these aren’t available where I am so I didn’t have the option to use these as abortives while detoxing from pain meds. I definitely would like the option to be able to use these as abortives for migraines. One study did note they could cause MAH (this is listed below and linked) but there's no good research regarding this as they are so new. I just feel obligated to mention this.
About MAH
Please educate yourself. I have included links to sources. Consult your doctor if you think you might have MAH and advocate for treatment.
1 You have to add up your pain med use!!!
2 OTC Pain Meds+ Triptans + Rx Pain Meds* = 10 Days Maximum Per Month
*Opioids and butalbital may lead to MAH in about 5 days
3 Approximately 50% of patients with chronic migraine have MAH that may revert to episodic headache after drug withdrawal.
Chronic migraine is classified as 15 or more headache days w/ 8 migraine days a month.
Episodic Migraine is classified as 14 or fewer headache and migraine days a month.
4 The name for MAH changed a few times and the one I chose to use is focused on the mechanism that causes the condition rather than the name that sounds like it is blaming the patient (Medication Overuse Headaches). Here’s an article regarding the name dispute.
5 One article even listed GCRP inhibitors as possibly contributing to MAH. But as these medications are new, the research isn't there yet to say if they really do contribute. I just had found it surprising to see and felt obligated to note it.
6 Risk Factors

• Having a primary headache disorder such as migraines.
• Having other chronic pain or gastrointestinal issues.
• Being less physical activity (defined as less than 3 hours of hard physical activity per week)
• Smoking
• Mood disorders
• OCD
• Depression
• Anxiety
• Substance-related disorders
• Cutaneous allodynia 7 (Cutaneous allodynia (CA) is defined as pain resulting from application of a non-noxious stimulus to normal skin)

8 Withdrawal treatment does not only reduce the headache attacks, but also improves responsiveness to acute or prophylactic drugs. Withdrawal symptoms normally last between 2 to 10 days, and do not persist longer than 4 weeks.
Going Forward
I have a number of MAH risk factors including migraines, other chronic pain, anxiety, family history of substance-related disorders, being less physical activity (especially during the time that the stressful situation was happening), and cutaneous allodynia. Had I known about all of these risk factors and that alternating meds would not protect me from MAH, I would have done things very differently. I’ll have to be very careful to not develop MAH again, and actually am thinking of extending my detox because of my risk factors and some concerning statistics regarding allodynia in particular. For now I'm going to try to continue managing my pain with other methods while I can comfortably. Actually just last night I had a hormonal migraine that I managed to abort with a combination of things I listed here that just 2 months ago it would have been at least a level 4 with triptans.
When I do start using pain meds again, I’ll definitely be tracking meds more carefully and adhering to a strict 10 day max per month for OTCs plus triptans. I’ve made an annual tracker that you can print with the maximum days noted for reference.
To the Mods - I’ve noticed many posts with discussion related to MAH being removed. I’ve instructed others to consult their doctor thereby trying to adhere to the sub rules, please let me know if there is something else that might need to be adjusted in order to adhere to the rules.

If you’ve been following me on here, you know I am a drop shipping veteran. I have been doing it for the past 11 years and I think I am qualified to say I know the ins and outs of the business model.
It still surprises me that Most people on YouTube (the so-called Dropshipping gurus) still promise you that you will become rich in two weeks without hardly putting in any effort. Let me give it to you straight: nothing could be further from the truth. I’ve made well over seven figures with drop shipping and I also failed a lot. So let me share with you the information that is missing from 99% of what you see on YouTube.

1. Do NOT copy what’s already working

that’s one of the phrases they tell you most often. Just copy what’s already working and you’re done. Usually the method shown is researching your competitors, look at their best selling products and sell the same stuff. But think about it. If we are all doing that, in the long run, we’re all competing, only based on price, driving our profit margin down. Customers aren’t stupid and know they have seen that product somewhere else for a cheaper price. So do not go out and sell products everyone else is selling. Instead, make sure to do proper niche research. Select a niche first (you can download a guide containing 300+ niches for free in the links section of PassionsToProfits). Then spend some solid time, researching it deeply. Join private Facebook groups around your niche, go through all the posts to get into peoples minds and how they think, how they talk about products, what they value in terms of features and benefits, etc. Do the same thing on Reddit. Then look at best selling products in your niche on Amazon and look at the NEGATIVE reviews. That will tell you what can be improved. Those are good ideas for when you go out and research products. Only when you have done your homework, go onto websites like AliExpress and try to find unique products that hardly anyone is selling already. Yes, that takes time and work. And lots of patience. I have found a few of my bestsellers hidden on page 53 in the search results. Sometimes it took me two weeks before finding a really good product I could attempt to sell.

1. Advertise your products properly

By that I mean do not rip off someone else’s product video or image and run with it. Order a sample or two, analyze the product, use it yourself and shoot your own product video. Again, that takes work, but it will pay off. Make sure to show the main product benefits in the first few seconds of your video, followed by the characteristics/features and additionalinformation (instructions, assembly instructions, etc.).

1. You should NOT use drop shipping from China forever

It’s great for testing the validity of a product, but you should not use this business model for a long time. The main reason has to do with supply chain issues. It takes forever for products to arrive, and you can get away with it if your product is truly unique and people cannot get it elsewhere. But even then, two weeks is a long time for any product to arrive. So once you find a winning product, make sure to look for bulk order options and import it into your country to a local fulfillment warehouse. That way you can get products quickly to your customers doors, and also avoid the typical downtime during Chinese new year.

1. Not any product is suitable for drop shipping

I won’t go into too much detail about this here, because I wrote a detailed post which you can check out here: https://www.reddit.com/PassionsToProfits/s/SAg2p9JCGe
I could go on and on, but I’ll stop here for now. Feel free to share your experiences in the comments or ask questions. I’ll do my best to answer them.
Note: Nowadays, I am more focused on print on demand, because it eliminates all the supply chain headaches. The majority of suppliers and fulfillment companies are based in the US and I hardly receive any customer emails asking where their stuff is. Plus, product quality is great and I have happy customers :)

Hi there! First post on reddit so please forgive me if i'm not using any fancy text.
I've been DM'ing now on and off for around near 5 years, originally I ran for 2 separate friend groups with Vastly different likes and dislikes. I had to stop one groups campaign due to work schedules and as the such (gotta love adult life ;-;)
So recently my current group consists of close friends so it is quite easy to get things across but it can also be very easy to lose control of the table if we start having jokes and a good laugh.
the above is not the issues I'm having as the DM however the stuff above i'm fine with as long as we are hanging out and having a good time. My issues span from multiple small issues which I will list below Beginning with myself as I know I make plenty of mistakes as I DM but I'm looking for advice to make sure i'm not going crazy and being a C**T with some of my table rules i've implemented
My Issues I need to work on: Note Taking - I lose track of my notes (this has been getting better moved to an app called obsidian which I have stuck with and this seems to be working wonders for me) but still needs work.
World lore - Kinda a addition to the note taking issue I have, I like to chop change some things but I still want it to make canonical sense to the worlds timeline which can be a little messy at times.
Pacing - Oh god I'm awful for pacing sometimes I'm too fast or i'm far too slow my players spend an entire 7 hour session on 1 street corner being my worst example.
Sometimes Retconning Small things - I Don't know if I think its okay to do this, I've only had to do it twice in total I Hate retconning stuff as I feel like if maybe a player that doesn't shine to often finally gets out their shell i'm worried if I retcon something It can really take away from that players motivation.
So above are my issues for sakes of time I'm going to copy paste a recent paragraph I sent to my players parts like player names will be edited of course. -------------------------------------------------------------------------------------------------------
So shappening dudes and dudets, DnD is going to be on the back burner for me for a Little bit cause personally feeling burnt out for a couple reasons that I'll get to in a little bit. So first things first the main thing I'm going to an induction day so I'm hoping I get this job (I got the Job whoop whoop) and DnD will be moved to every 2nd Saturday if thats gonna be a issue we can discuss that down the line. Now couple of mini Issues I want to get off my chest. So the Number 3 joke died ages ago for me and its just annoying so stopping that would be nice thank you, anyone that says it going forward you'll just take 3 damage. Unfortunately the interrupting and pacing is horrendous I'm gonna do my best to keep my focus on the game and I ask so do you. For the time being kinda a add on to the previous my max player count I can do is 6 no more, "Player Name" please let "His Partner" know I do not mind if she watches but I can't have her play not until I feel like the pacing is better and no ones shouting over each other. The Cards, They are fun but i'm gonna rejig them to be less annoying and alot less OP, (I'm removing that bag of holding card) personal thing - If I ask something to do with in the game could you please just acknowledge you have seen it, cause I've typed in plane text plenty of times before and it just doesn't get listened to it starts with one person then everyone else follows if you get me and it can make some little instructions that make things easier for me alot harder. As the DM I want a bit more control So one of the major ones that I ask is NO ONE calls "Roll a persuasion to persuade me" or anything along that lines, it is unnecessary and tbh I find it a bit rude. NO Rape or Rolling Cock size. (its just weird and its disgusting you'd think of it tbh)
There are a few more other things that personally bother me but those are the main ones, the minor ones just consist of 1. Don't Make up your own lore of my own creation and then treat it as cannon, I didn't ask and I don't care.

1. Don't ask for any more custom items, if I give you something be happy and let it be a surprise. 3."did we level up?" i know its a joke but tone it down before it gets worse.
2. If you create a backstory please give me the footnotes of the main points that I can work with, don't give me a lore book about how garfunkle the black white man slain a beast. I won't read it. (Edit - I don't mind if they give me a book to read I do like lore I just mean here that I'd prefer notes of the main points in their characters story)
3. I would prefer Game things are sent on discord concerning characters thats a personal thing, I created the discord for me to be organised and have it all in one place so if you please could just send me stuff on that even if its a dnd beyond link I would like it all in one place. I get this is gonna be alot to read but I hope I get my point across. I am the dungeon master and I want to create a game for you guys to enjoy but I have neglected my own joy for the game and have been more lazy with it because of this. I have alot of stuff to fix from last session especially and I'm gonna try to be more focused on the game and create a better experience and all I ask is please listen, pay attention and any question on the world so you can clarify things you may ask me, if there is any issues during the session call for a break if its that major so we may discuss at the table and if its a small issue we discuss it after the session. I may rule things wrong but i'll attempt to rule it in a way that makes sense and then I shall research and let you know afterwards, I'd rather not search rules in the middle of the game and taking half a hour per turn. ---------------------------------------------------------------------------------------------------------------------

So context for some things the number 3 joke is from shrek "NUMBER 3 MY LORD" and I use magic the gathering cards for little 1 session boons they are a hell of a lot of fun and I'll post under this if anyone else play's DnD and MTG (I'd recommend using the Dungeons and dragons sets for Magic if you wish to try this out)
Revitalize x2 - Add a Medium Potion to Inventory
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Treasure x3 - Add 100 Gold
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Choose Your Weapon - Choose one for the Remainder of the Session
Archery - +2 to Range attacks
or
Two Weapon Fighting - +2 to Melee Attacks
​
Curse of Surveillance - You Grow an eyeball onto the palm of your hand, something or someone is watching you (The DM determines who) This curse is passed on
when someone else draws the card or removed with the "Remove curse Spell"
​
Improvised weaponry - You feel compelled to use the first random object as your weapon for the session.
​
Triumphant Adventurer - Roll a D20,
Even Numbers add 3 Platinum to your Inventory.
Odd Numbers Summons a Gold Plated Cocky knight to gloat how rich he is for 1 minute.
​
Hunters Mark - You Gain the Ability Hunters Mark for the Session it does not Require Concentration.
​
Contact Other Plane - You ask anything from the DM roll a d100 to discover the outcome.
​
Hoard Robber - The best robber in all the land has found you and took every bit of coin you have leaving a single fake gold piece.
​
Dawnbringer Cleric - You can Choose One to use throughout the session
Cure Wounds
Dispel Magic
Gentle Repose
​
Priest of Ancient lore - A old Dwarf appears in front of you radiating a holy light asking "Would you like to know the lore of this land?"
​
Boots of Speed - You Gain Extra 15ft Speed for the Session.
​
Silver Raven - A Unknown Vampires Raven Follows you.
​
Check for Traps - You becoming Increasingly paranoid of traps for the session the DM at Random will ask you to check for traps at your feet.
​
Blessed Defiance - You Summon A white Spirit, it says nothing, it is friendly to only you, it will follow and defend you for this session.
​
Chaos Channeler - A wild magic sorcerer Explodes from the multiverse, he looks excited as he transports himself again creating a Minor wild magic effect.

So this is a pretty large post But I am looking for advice on maybe rules I should consider going forward, how to keep proper control of a party of 6 and just anything I should improve as a DM and anything I can further communicate to my players at the table.

Abu Dhabi Tour Best Deal
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Community Fibre offers the best value fibre-to-the-home (FTTP) internet service in the UK. You can get a gigabit plan for only £26/month, which is about half of what you would pay for gigabit from other providers. If you don't need speeds quite that fast, they also offer a 150 mbps plan for £21/month.
There is also a 3 gigabit plan for £56/month, which is among the fastest in the UK, but it is really only for people who have a very specific need for this much bandwidth. The £26 gigabit plan offers the best value. All plans are symmetric (same upload as download).
And since it's full fibre to your home (XGS-PON), the connection is rock solid. In my case, I get 2ms latency and virtually no jitter and zero packet loss. This is a screenshot of a Speedtest to my router:
https://i.imgur.com/q3GzPhG.png
The catch is that it is only available in London and surrounding areas - although they do plan to expand nationally. Check via the link below if they are available at your address.
If the service is available, it's almost certainly worth switching from your existing provider, because no one can match Community Fibre's value. When I called my old ISP to cancel, they tried to convince me to stay, then I told them I was switching to Community Fibre and they immediately gave up and said they couldn't match their prices.
But it gets better because if you use a referral link, you will get a £50 Amazon voucher. And of course you will then get your own referral code to share - each time someone joins with your link, you will both get £50 Amazon vouchers. So you can actually make money with this ISP.
Plans are 12 or 24 months - naturally the latter is the better value (all aforementioned prices are for the 24-month plans). When your contract ends, the price will only go up by £4/month - at that point you can sign a new contract to get a lower price again. Other providers charge much higher premiums for being out of contract, sometimes double.
For me, booking a slot was very easy, and the installers that came to my property were extremely friendly, answered all of my questions, and did the install and the routing of the fibre exactly how I wanted it. In general, Community Fibre ranks very high in customer service and customer satisfaction as they are a smaller altnet and not part of a large corporation. See here:
https://www.ispreview.co.uk/index.php/2023/12/opensignal-rate-uk-broadband-isps-by-quality-and-speed.html
So if this all sounds good to you and Community Fibre is available at your address, you can use my referral link to get a £50 Amazon voucher when you order:
Referral link: https://communityfibre.co.uk/friends?referral=s0-47EPyCh
Non-referral link: https://communityfibre.co.uk
You will receive the voucher within 90 days of activation. Thank you for reading and let me know if you have questions.

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Looking to enhance your 1911 revolver's performance and accuracy? Look no further than our roundup of the top compensators available on the market. In this article, we'll introduce you to the best-rated compensators for the classic 1911, providing you with the information you need to make an informed decision.

The Top 13 Best 1911 Compensator

1. Comfortable 1911 Inside-the-Waistband Holster for Optimal Concealed Carry - Experience unparalleled comfort and superior concealment with the C&G Holsters Covert IWB 1911 holster, expertly designed by Veterans and Law Enforcement in America.
2. Precision Competitive Holster for 1911 4.25'' - Kydex, Aluminum, Adjustable - The Pro Ball Joint Competition Holster transforms your 1911 4.25'' into a precision and performance-driven shooting tool, with adjustable ride height, aluminum ball joint, Kydex shell, and optic compatibility for an unmatched competitive edge.
3. Speed Master 2.0 Leather Chest Holster for 1911 Pistols - The Speed Master 2.0 is a versatile leather chest holster for 1911 pistols and DA revolvers, featuring an open top design, adjustable tension unit, and convenient paddle or belt slot attachments for a custom carry experience.
4. Premium Western 1911 Inside-the-Waistband Holster - Experience ultimate comfort, versatility, and durability with the Galco Royal Guard 1911 Inside-the-Waistband Holster, designed for real-world concealment and perfect for semiautomatic guns and double-action revolvers.
5. Durable Tactical 1911 Compensator for Enhanced Performance - The BCMGunfighter Compensator Mod 7 - 5.56 is engineered for tactical purposes, providing effective muzzle rise reduction, flash suppression, noise dampening, and side pressure control, making it a top choice for serious shooters.
6. Hybrid Titanium Compensator for 1911 Pistol - Enhance your 1911 handgun's performance with the EGW Hybrid Titanium Compensator, perfect for Steel Challenge and Bianchi Cup competitions, featuring reduced loads and seamless integration into international orders.
7. Premium 1911 Inside Waistband Holster - The Vedder Holsters 1911 w/out Rail 4.25" RapidTuck holster, designed for Colt Commander frames, offers a comfortable IWB carry with premium leather and molded Kydex, perfect for various carry positions and proudly made in the USA.
8. Speed Master 2.0 5in 1911 Concealed Holster with Paddle and Belt Options - The Galco SM2212RB Speed Master 2.0 holster offers versatility and customization with its adjustable belt slot and paddle attachment methods, providing a perfect carry solution for 1911 Compensator users.
9. Universal Single Mag Holder for 1911 Compensators - Experience unbeatable durability and custom-fit retention with the C&G Universal Single Mag Holder, expertly crafted by veteran & law enforcement professionals for optimal 1911 compatibility.
10. Stainless Steel 1911 Gun Compensator - Experience minimal side blast and noise with Bravo's BCMGunfighter Compensator Mod 0, a stainless steel 7.62mm AR10 accessory that ensures maximum recoil mitigation, muzzle-rise compensation, and flash reduction.
11. Premium Independence Leather 1911 Holster with Reinforced Belt Loop - The Independence leather holster is a premium 1911 5" Government with Rail Only OWB holster, offering maximum protection, comfort, and ease of use for your firearm.
12. Galco TacSlide Belt Holster for 1911 Compensator: Fast, Concealable, Economical - Experience seamless concealment and swift draws with the Galco Tac Slide Belt Holster for your Colt Kimber Para 1911 5" - the perfect blend of steerhide and Kydex for unbeatable durability and comfort.
13. CNC 7-Port 5-Chamber Comp Cone for 1911 Handguns - The EGW 11830 CNC 7-Port 5-Chamber Comp Cone is a high-quality compensator made from heat-treated stress-proof Carbon Steel, perfect for use with standard-length barrels and custom hybrid or bull barrels with the proper threading.

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Reviews

🔗Comfortable 1911 Inside-the-Waistband Holster for Optimal Concealed Carry

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Over the past few months, I've had the opportunity to try out C&G's Inside the Waistband Covert holster for my 1911. The first thing that struck me was how well-designed and comfortable it was. As someone who carries most of the day, the last thing I want is my holster to be uncomfortable or distracting.
One of the standout features was the solid locking retention. It gave me peace of mind, knowing my firearm was secure when I was on the move. The holster was also designed to fit most RMRed Dots on the market, which was a nice touch.
However, there were a couple of cons I noticed. First, the open bottom allowed for threaded barrels and compensators, but it could be a bit challenging to get a perfect fit for my specific firearm. Secondly, the 1.5-inch belt clip could be a bit snug for my thicker belt.
Overall, my experience with C&G's Covert holster has been mostly positive. The holster offers a comfortable and secure fit, and the craftsmanship is top-notch. While there were a few minor drawbacks, the pros definitely outweigh the cons.

🔗Precision Competitive Holster for 1911 4.25'' - Kydex, Aluminum, Adjustable

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https://preview.redd.it/uaviuox6fx3d1.jpg?width=720&format=pjpg&auto=webp&s=c878830e3a9082f69923c9671e16b1278804c13d
As a shooting enthusiast, I recently came across the 1911 4.25'' No Rail Pro Ball Joint Competition Holster, and I must say, it has greatly enhanced my experience. Its adjustable ride height, which allows me to fine-tune my grip, is a game-changer.
The aluminum ball joint provides a smooth, sturdy connection that ensures a reliable hold, while the Kydex shell offers a perfect fit. Plus, the optic compatibility adds a touch of convenience. However, I also noticed that the holster can be quite heavy, which might affect my mobility during competitions.
Overall, this holster has proven to be a valuable investment for any serious shooter.

🔗Speed Master 2.0 Leather Chest Holster for 1911 Pistols

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https://preview.redd.it/nqmfbx77fx3d1.jpg?width=720&format=pjpg&auto=webp&s=68ad3f2a292e235226e808078c046ffa2c489c2d
I had the pleasure of trying out the Galco SM2212R Speed Master 2.0 1911 5in Tan, and I must say, it's a fantastic choice for gun enthusiasts. This versatile holster offers a sleek design and comes in both paddle and belt slot configurations, ensuring you can choose the carry method that suits your needs best.
One of the standout features of this holster is the high-quality premium steerhide construction. The build feels sturdy and reliable, giving me confidence in its performance. The adjustable tension unit is a convenient feature that allowed me to customize the holster's grip, ensuring my firearm stayed securely in place.
However, there were a couple of downsides I encountered during my experience. The slight butt-forward or neutral cant can vary depending on the firearm, which might not be ideal for everyone. Additionally, the holster might not fit as snugly as desired on larger pistol barrels.
Despite these minor drawbacks, the Galco SM2212R Speed Master 2.0 is an impressive and versatile holster that provides excellent concealment and security. If you're in the market for a high-quality and customizable 1911 holster, this might just be the right fit for you.

🔗Premium Western 1911 Inside-the-Waistband Holster

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I recently had the chance to try the Galco Royal Guard 1911 Inside-the-Waistband Holster Black - a sleek and versatile option for gun owners. This holster boasts an innovative design that keeps your weapon secure while still ensuring that you have easy access to it when you need it most. I particularly appreciated the rough side of the leather facing out, providing great stability and protection for your gun.
Wearing the Galco Royal Guard was a comfortable experience, allowing for real-world concealment even of larger defensive guns. The smooth leather pocket was reinforced for sturdiness, and it granted unrestricted movement and easy reholstering. In my opinion, the combat grip accessibility rounded out this holster's utility to make it an invaluable choice for those seeking speed and agility.
A few aspects of the Galco Royal Guard could use some improvement, but overall, I was impressed with the product. Its premium natural color horsehide constructed to fit 1-3/4" belts was a definite plus. And for those with 1-1/4" belts, there's an optional belt channel to consider. While there is still room for improvement, the Royal Guard holds promise as a reliable choice for any gun enthusiast.

🔗Durable Tactical 1911 Compensator for Enhanced Performance

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I recently added the BCMGUNFIGHTER Compensator Mod 7 - 5.56 to my trusty 1911 setup, and let me tell you, it's been a game-changer for my tactical operations. This ain't no lightweight accessory, it's a solid piece of machinery that reduces muzzle rise and flash to a minimum, making my targets all the more accurate.
It's also helped keep things nice and quiet during reconnaissance missions, and the lateral force it counteracts has been a lifesaver when I gotta take quick shots on the go. Sure, it might be a bit of a heavy-hitter compared to some other comps out there, but the benefits I've experienced have made it a more than worthy addition to my arsenal. So, if you're looking for a versatile compensator for your tactical ops, this bad boy deserves a serious look.

🔗Hybrid Titanium Compensator for 1911 Pistol

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The EGW Hybrid Titanium Compensator 575 x 40 is a sleek and sturdy addition to any 1911 firearm. I've been using it in my daily training sessions, and it's performed exceptionally well. The titanium construction not only adds durability but also keeps the weight down, making it a perfect choice for those who value portability.
One highlight of this compensator is its ability to reduce recoil significantly, which is especially useful for those newer to the sport or for those looking for a smoother shooting experience. This feature, combined with its robust design, has proven to improve the overall performance of my handgun.
However, there were a couple of drawbacks to this product. Firstly, the international shipping restrictions can be a hassle for those living outside the US. And secondly, the lack of an extensive review history makes it challenging to gauge the overall user satisfaction with this product.
Overall, the EGW Hybrid Titanium Compensator 575 x 40 is a high-quality accessory for 1911 firearms with a unique titanium construction that reduces recoil. While there are a few drawbacks to consider, the pros outweigh the cons, making it a worthy addition to many shooters' kits.

🔗Premium 1911 Inside Waistband Holster

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I recently had the chance to try out the Vedder Holsters 1911 w/out rail 4.25" (Colt Commander frameNOT SIG) RapidTuck, an inside the waistband (IWB) hybrid holster that's handcrafted with premium leather and molded Kydex. The moment I started using it, I was struck by the attention to detail and the perfect fit for my 1911 w/without rail 4.25" Colt Commander frame.
One of the key features that stood out to me was the comfort of the holster. The leather is soft and well-finished, making it comfortable even for extended periods of wear. Whether you carry in the 3 o'clock position, appendix carry, or cross draw, the RapidTuck offers various carry options that are both comfortable and secure.
Another great aspect of the holster is the custom Kydex shell, which is molded to fit your firearm perfectly. This ensures proper clearance with all front sights and loaded chamber indicators, providing peace of mind even in the harshest conditions. The included Rock Solid Spring Steel Clip is a nice touch, offering two belt accommodations and a tuckable design for deep concealment.
While I enjoyed using this holster, there were a couple of minor drawbacks that I noticed. Firstly, the adjustable ride height feature is a bit more challenging to use than some other holsters on the market. Secondly, the limited color options might not appeal to everyone looking for a personalized touch. Despite these small drawbacks, the overall quality and performance of the 1911 w/out rail 4.25" (Colt Commander frameNOT SIG) RapidTuck make it a solid choice for anyone in the market for an IWB hybrid holster.

🔗Speed Master 2.0 5in 1911 Concealed Holster with Paddle and Belt Options

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I recently gave the Galco SM2212RB Speed Master 2.0 a try for my 1911 pistol. It impressed me with its ability to quickly switch between a paddle and belt holster, which is perfect for my lifestyle. The open-top design allows me to draw my pistol faster, while the covered trigger ensures added safety.
The steerhide material feels premium and holds a good grip on the pistol. However, I noticed that the holster might be a bit snug for larger belts, but overall, the Speed Master 2.0 is a versatile and dependable choice for concealed carry.

🔗Universal Single Mag Holder for 1911 Compensators

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Imagine this: after a long day at the range or simply enjoying your time at home, you're done with holstering and retrieving your magazines. You reach for your trusty mag holder - a solid, reliable, and convenient accessory that's been your reliable companion for months. But suddenly, it's gone. You need a new one. Fast. Enter C&G's Universal single mag holder.
This is the kind of mag holder that makes you say, "I never knew I needed this until now! " It's a game-changer, designed to hold your magazines in place, making your life easier and hassle-free. The C&G's Universal single mag holder is made with high-quality materials, ensuring it lasts long, stays sturdy, and remains a reliable tool in your arsenal.
The Universal aspect of this product is a testament to its versatility, as it fits almost all pistols or mags. And let's talk about the fit - it's made for a purpose and designed to ensure that your magazines stay where they should. No more fumbling or worrying about your magazines falling out when you need them most.
But what about the looks? Well, let's just say it's not an eyesore. It manages to blend form and function, seamlessly becoming a part of your daily arsenal. The fact that it's made in America by the best professionals in the field is a cherry on top.
However, like any other product, it does have a small downside. The clip might take some getting used to, as it's a little bigger and wider than what most users would prefer. But it's a small cost to pay for a product that performs so magnificently.
In conclusion, the C&G's Universal single mag holder is a top-notch product that delivers on its promises. It's a reliable and efficient accessory that makes holstering and retrieving your magazines a breeze. It might have a minor issue with the clip size, but overall, it's a must-have for anyone who wants to ensure their magazines stay secure and easily accessible.

🔗Stainless Steel 1911 Gun Compensator

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I recently had the chance to try out the BCM Gunfighter Comp Mod1 762 5/8x24 in a few shooting sessions, and let me tell you, it was quite an experience. One of the most impressive features of this compensator is its stainless steel construction. Don't get me wrong, I'm no gunsmith, but I could tell that the quality was top-notch. It just felt solid and well-built in my hands.
During my time with the Gunfighter, I noticed that it really reduced the muzzle rise and the noise associated with typical comps. It was like shooting a silenced revolver, which made it a joy to use, especially during team CQB drills. The inclusion of a crush washer was another plus, as it made the installation process a breeze.
However, there were a couple of things that I wasn't completely thrilled about. First, it was a bit heavier than some other compensators I've used, which made it a bit unwieldy in certain situations. But overall, the benefits outweighed the drawbacks, and I found myself reaching for it on my next few range trips.
So, in conclusion, if you're looking for a well-built, noisy, and muzzle rise-reducing compensator, the BCM Gunfighter Comp Mod1 762 5/8x24 is definitely worth checking out.

🔗Premium Independence Leather 1911 Holster with Reinforced Belt Loop

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The Independence holster is a beautifully crafted leather masterpiece, with a proprietary design and attention to detail that sets it apart in the crowded gun holster market. The 8 oz premium leather feels soft and durable against your firearm, providing a sense of comfort and assurance as you carry your 1911 5" Government.
What I love most about this OWB holster is the reinforced belt loop design, which allows for effortless on and off placement. No more fumbling with clips or loops; this holster's custom tailoring ensures perfect fit, maximized retention, and minimal printing for a comfortable carry experience.
One area where it could use improvement is the compatibility with different firearm sights, particularly those with sharp edges that can catch on the leather when drawing. But overall, the Independence holster is a top-notch leather choice for your favorite 1911.

🔗Galco TacSlide Belt Holster for 1911 Compensator: Fast, Concealable, Economical

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As a gun enthusiast, I recently had the chance to try out the Galco Tac Slide Belt Holster for my Colt Kimber Para 1911 5" Black RH TS212B. First and foremost, the design of this holster is quite impressive, with its combination of steerhide and Kydex materials, which provides both durability and comfort in one package. The adjustable belt slots make it easy to wear and also allow for excellent concealment, even with larger frames.
One of the standout features of this holster is the open top and neutral cant, which make the draw incredibly smooth and efficient, even when wearing heavier pants or jackets. This is a crucial aspect for those who need to access their weapon quickly and discreetly.
However, the Tac Slide does have its drawbacks. For one, the holster itself is quite bulky, which can be a disadvantage for those who prefer a more minimalistic design. Additionally, while the steerhide back plate is comfortable, it can be a bit fussy when trying to reholster the firearm, particularly if the holster is not set up properly.
Overall, despite these minor drawbacks, I found the Galco Tac Slide Belt Holster to be a reliable and functional option for concealing and carrying my Colt Kimber Para 1911. It's a versatile option that works well with both semi-autos and double-action revolvers, making it a solid choice for gun owners looking for a convenient and secure way to carry their weapon.

🔗CNC 7-Port 5-Chamber Comp Cone for 1911 Handguns

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I've had the chance to use the EGW 11830 CNC 7-Port 5-Chamber Comp Cone in my 1911 pistol, and I must say it's made quite a difference in its performance. It's built with heat-treated, stress-proof Carbon Steel, which adds to its durability and reliability. One of the most noticeable things about this cone is its unique 10-degree angle, designed to control muzzle flip and keep the gun steady even during intense shooting sessions.
However, one downside I noticed was that installing it required a bit of extra effort, as it required a reverse plug due to its lack of a bushing to hold the recoil spring. Nonetheless, once in place, it lined up perfectly with the barrel bore and provided a smooth, precise shooting experience.

Buyer's Guide

The 1911 Compensator is a vital accessory for 1911-series firearms. It aims to reduce recoil and muzzle rise while enhancing accuracy. This buyer's guide will provide you with essential information, recommendations, and considerations to help you choose the right 1911 compensator for your needs.

Materials

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When selecting a 1911 compensator, consider the materials used in its construction. High-quality compensators are usually made from durable materials like stainless steel, titanium, or brass. These materials provide long-lasting performance and better heat dissipation, which can improve accuracy.

Size and Weight

The size and weight of a 1911 compensator are crucial factors in determining its compatibility with your firearm. Ensure the compensator's dimensions match those of your barrel and that its weight balance is appropriate for your shooting style and preferences. A well-balanced compensator can improve recoil management and control.

Design

The design of a 1911 compensator influences its effectiveness in recoil reduction and muzzle rise compensation. Popular designs include compensators with multiple ports or a single, large port for better performance at various distances. Consider the design that best suits your shooting requirements and preferred shooting distances.
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Ease of Installation

Installing a 1911 compensator should be a straightforward process. Choose a compensator with user-friendly installation methods, such as simple screw-in designs or those requiring minimal gunsmith work. This will save you time and money in the long run, allowing you to focus on maximizing the benefits of your new compensator.

Budget

1911 compensators come in different price ranges. Consider your budget when selecting a compensator, but remember that investing in a quality product will provide better performance and durability in the long run. Avoid compromising on key features and materials in an effort to save money.

Maintenance

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To ensure the longevity of your 1911 compensator, choose one with easy-to-clean and durable materials that can withstand frequent use without corrosion or damage. A compensator with user-friendly cleaning methods will make maintenance a breeze.

Customer Reviews and Ratings

Prior to making a purchase, research customer reviews and ratings for various 1911 compensators. This can provide valuable insights into the product's performance, durability, and overall user satisfaction.
Selecting the right 1911 compensator for your firearm is crucial for optimizing its performance. By carefully considering factors such as materials, size, design, ease of installation, budget, maintenance, and customer reviews, you can make an informed decision that enhances your shooting experience.

FAQ

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What is a 1911 compensator?

A 1911 compensator is an accessory designed to be attached to the barrel of a 1911 handgun. This device helps to reduce recoil and improve accuracy by redirecting the gas generated during firing in a forward direction.

Which 1911 compensators are available in the market?

Several 1911 compensators are available in the market from different manufacturers, including ATI, Troy, and Dead Air. Each compensator may have its own unique design and features.

What are the benefits of using a 1911 compensator?

• Reduced recoil, making shooting more comfortable and controllable
• Improved accuracy by reducing muzzle rise and moving the point of impact closer to the point of aim
• Enhanced performance in rapid-fire situations

How do I install a 1911 compensator?

Installing a 1911 compensator typically requires some gunsmithing skills. You'll need to remove the barrel from the 1911 handgun and thread the compensator onto the barrel, followed by re-installing the barrel into the handgun.

Are there any drawbacks to using a 1911 compensator?

Yes, there are some potential drawbacks. One is the increased weight of the handgun, which may affect the overall balance and feel of the gun. Additionally, some users may experience a reduction in the overall effective range of the handgun due to the compensator's design.

Do I need a specific 1911 handgun model to use a compensator?

Most 1911 compensators are compatible with the standard 1911 handgun models, but it's always a good idea to check the specifications of the compensator you're interested in to ensure it fits your handgun model.

How much do 1911 compensators typically cost?

The cost of 1911 compensators can vary widely depending on the brand, materials used, and features offered. Generally, you can expect to pay between $100 to $300 for a quality 1911 compensator.
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If you’ve been following me on here, you know I am a dropashipping veteran. I have been doing it for the past 11 years and I think I am qualified to say I know the ins and outs of the business model.
It still surprises me that Most people on YouTube (the so-called Dropshipping gurus) still promise you that you will become rich in two weeks without hardly putting in any effort. Let me give it to you straight: nothing could be further from the truth. I’ve made well over seven figures with drop shipping and I also failed a lot. So let me share with you the information that is missing from 99% of what you see on YouTube.

1. Do NOT copy what’s already working

That’s one of the phrases they tell you most often. Just copy what’s already working and you’re done. Usually the method shown is researching your competitors, look at their best selling products and sell the same stuff. But think about it. If we are all doing that, in the long run, we’re all competing, only based on price, driving our profit margin down. Customers aren’t stupid and know they have seen that product somewhere else for a cheaper price. So do not go out and sell products everyone else is selling. Instead, make sure to do proper niche research. Select a niche first (you can download a guide containing 300+ niches for free in the link section of this subreddit). Then spend some solid time, researching it deeply. Join private Facebook groups around your niche, go through all the posts to get into peoples minds and how they think, how they talk about products, what they value in terms of features and benefits, etc. Do the same thing on Reddit. Then look at best selling products in your niche on Amazon and look at the NEGATIVE reviews. That will tell you what can be improved. Those are good ideas for when you go out and research products. Only when you have done your homework, go onto websites like AliExpress and try to find unique products that hardly anyone is selling already. Yes, that takes time and work. And lots of patience. I have found a few of my bestsellers hidden on page 53 in the search results. Sometimes it took me two weeks before finding a really good product I could attempt to sell.

1. Advertise your products properly

By that I mean do not rip off someone else’s product video or image and run with it. Order a sample or two, analyze the product, use it yourself and shoot your own product video. Again, that takes work, but it will pay off. Make sure to show the main product benefits in the first few seconds of your video, followed by the characteristics/features and additionalinformation (instructions, assembly instructions, etc.).

1. You should NOT use drop shipping from China forever

It’s great for testing the validity of a product, but you should not use this business model for a long time. The main reason has to do with supply chain issues. It takes forever for products to arrive, and you can get away with it if your product is truly unique and people cannot get it elsewhere. But even then, two weeks is a long time for any product to arrive. Customers will complain. I would too. So once you find a winning product, make sure to look for bulk order options and import it into your country to a local fulfillment warehouse. That way you can get products quickly to your customers doors, and also avoid the typical downtime during Chinese new year.

1. Not any product is suitable for drop shipping

I won’t go into too much detail about this here, because I wrote a detailed post which you can check out here: https://www.reddit.com/PassionsToProfits/s/SAg2p9JCGe
I could go on and on, but I’ll stop here for now. Feel free to share your experiences in the comments or ask questions. I’ll do my best to answer them.
Note: Nowadays, I am more focused on print on demand, because it eliminates all the supply chain headaches. The majority of suppliers and fulfillment companies are based in the US and I hardly receive any customer emails asking where their stuff is. Plus, product quality is great and I have happy customers :)

In the 19, nearly 20, years I have lived my life, I was raised a Christian by American conservatives in a middle class environment and am fortunate to experience countless memories of joy, laughter, and growth with (mostly) everybody I have met. Each memory with the people in my life holds a special place in my heart, and I will forever cherish the bond we built.
The thing is that until the end of eighth grade, I had a strange obsession with East Asia. Looking back, it was very embarrassing and I condemn my parents for enabling me to become a weeaboo (by “weeaboo”, I mean “a person who is overly obsessed with East Asian culture, especially Japanese culture, to the point that they fetishize the culture in an unhealthy way”). I was the textbook example of a weeaboo who had a terrible case of “yellow fever” (sexual preference towards East Asians). Loving the image of East Asian culture without having any real idea what it stood for and seeing the East as a utopia, my fetishization of East Asia, especially Japan, was born out of the shame I have with the Western culture I was raised in. I never felt like I could fit in with my Western peers who I often looked down upon and did not want to be associated with. It got to the point that I became unsatisfied with my home town, my physical appearance, and even my closest friends. This combined with the surge in anime, K-pop, and other media on the internet really got me hooked and believing really fetish-y things about the Sinosphere. I hate being reminded of it and have tried to move on.
However, I am thankful for my exposure to Eastern culture, though it was through a very bastardized, Westernized lens. I am grateful for my exposure, even though it started out with something as intellectually undemanding as Vocaloid music (songs sung by a Japanese voice synthesizer). The best part about the exposure was that it helped me leave Christianity and join Buddhism at age 11, which greatly helped with my mental health considering I was experiencing suicidal thoughts since the age of 8. Though I had awful misunderstandings of Buddhism in the beginning and still do not really have a Buddhist teacher, I am glad that I have the resources to connect myself with other Buddhists and take refuge in the Triple Gem.
As I left my gross misconceptions of the Sinosphere behind back when I was 14 while still having a healthy interest in it, my eyes were eventually opened to perils which threaten not only the homelands of Buddhism (East, South, and Southeast Asia), but also the sustainability of modern humans. These two perils are Western culture and capitalist fascism.
The West exoticizes and misrepresents Buddhism and the culture of its home, the East, as a whole. I am ashamed to be born in a culture where this was encouraged, which I am worried could lead me to fetishizing Eastern culture again.
But what I know for sure is that the West’s hyperindividualism is harming people, both those whose lands are invaded and its own people. This combined with the West’s growing rejection of education, including that of the knowledge the West itself has given to the world for humanity’s benefit, makes it clear that it is lacking some of the Sinosphere’s cultural strengths. Everybody should hold collectivism and education to the same degree that the Sinosphere does, otherwise we would be left with an unsustainable society that would destroy itself.
There is nothing wrong with speaking Western languages, eating Western foods, watching (most types of) Western media, wearing Western clothes, and especially nothing wrong with using Western inventions, but we are now seeing that the West’s hyperindividualism and rejection of education is destructive and spreading like a cancer.
It is only Buddhists who make an effort to assimilate to the East (had they not been born into it) who can see the West with its hyperindividualism and uneducatedness, promoted by its creation of its spreading ideologies such as capitalist fascism and social Darwinism, for what it really is: a cancer. I can now see the direction the United States, the most populated and powerful country in the West, is going due to the rise of ultra-capitalism and/or fascism supporters.
Rarely the phrase “Western”, as in “formed by the combined foundations of Greco-Roman civilization and Western Christianity” (Gabbe), raised positive thoughts in my mind since I learned about it shortly after discovering Buddhism. “Western” when used in the context of medicine is an exception to this, but we are now seeing more and more Westerners dishonor the progress their ancestors made towards modern, mainstream, dare I say, Western medicine as they fall for anti-vaccine and anti-mask pseudoscience.
Nowadays, some who use the word in a derogatory context are uneducated reactionaries that bash anything and everything Western, yet hypocritically promote the Western political ideology of fascism. A strength that a majority (though now a decreasing number) of Western countries have is their progressivism, supporting scientific advancements, women’s rights, racial equality, and the LGBT+. However, this is not just becoming less common; being a progressive Westerner is not enough, not enough to end Western imperialism, to save the sacred truths taught to us by the Shakyamuni Buddha, or to empower the working class.
Although I never fully approved of Western culture after my weeaboo phase ended, my early teen self still ended up falling into the anti-social justice warrior side of YouTube that I now recognized hindered my understanding of what actually ruined my country, the United States of America. I still did not feel comfortable calling myself a Westerner but mainly because the West did not widely accept Buddhism and has several times in its history persecuted Buddhists. At the same time, I was deceived by a bastardized form of Buddhism common among Westerners (known as “secular Buddhism”, which picks and chooses aspects of the Buddha’s teachings instead of accepting them as a whole), so I was a bit more of the classic, stereotypical atheist neckbeard who fetishized the East up until 2020. Since then, my views became more progressive similar to those of American liberals and I denounced traditionalist Western beliefs, but like the average American liberal, I did not see Western culture, both traditional and progressive, as the peril I now see it. It was not until around the end of 2023 when I discovered the Western problem.
It was a slow burn that started with my discovery of Buddhists on the internet talking about how the West misrepresented Buddhism to appeal to “self-help” consumerists, Christians, and New Age followers. In the Westerner, I originally saw only a person who followed harmless customs, traditions, and other norms that came from a part of the world where Buddhism was not the dominant religion (if you could even call the non-theistic dharma as taught by the Gautama Buddha a religion). And so, I did not believe that Western civilization needed to fall for the safety of the dharma, let alone for its own people. After all, I thought to myself, the West has contributed so much to science and the modern world as we know it. I still believe to this day that there are no superior cultures and that each one simply has its own unique strengths and weaknesses, some of which are only subjective. However, while looking through Buddhist forums, I was shocked to hear about the West’s pollution of Buddhism and my knowledge on Buddhism skyrocketed as I learned that I fell victim to the Dunning-Kruger effect. I started reading sutras and immersing myself with Buddhism by listening to those who have much more experience than I do. There are hardly any Buddhists in my community and the only Buddhist center within reach is a New Kadampa Tradition meditation center (FYI: the New Kadampa Tradition must be avoided since it has a reputation for financially exploiting members and its monastics have allegations of drug trafficking and sexual abuse), so books and the internet are all I have left.
Practicing Buddhism in the West is nearly impossible without a community, without a Buddhist teacher, without any resources written by Eastern Buddhists. Reddit user u/Tendai-Student, a “lay Tendai Student [sic] with aspirations to become a Priest [sic]” states the following:
It is exceedingly challenging for a Westerner who is interested in Buddhism to find reliable information. Bookstores' Buddhist sections are rife with myths about the religion (we will come to some of these misconceptions below). Buddhism-related disinformation abounds in university classes. Misinformation about Buddhism abounds in publications with a Buddhist theme. Even Buddhism-related english-speaking [sic] Reddit boards are prone to carry false information.
Buddhism is constantly distorted in the same way: to make it more agreeable to Abrahamic faiths(especially Christianity in the west) [sic]. To imply that it is subject to Western standards, Western religion, and Western consumerism and materialism.
…Asian teachers are frequently excluded from English-speaking Buddhist places (meditation centers, university forums, periodicals). Asians make up the majority of Buddhists in the United States, despite the fact that popular images of Buddhism in the West make it appear otherwise. In the minds of Westerners, Buddhism is a religion of white converts. They don't even pay attention to the odd lack of Asians in some Buddhist areas. (u/Tendai-Student)
It is no wonder that I went through a phase when I was a weeaboo with “yellow fever”. The Westerner commodifies and commercializes these Buddhist practices and East Asian customs like they do with several other cultures. Its misuse and stealing of Buddhism is the worst because its teachings are for us to end suffering by ridding ourselves of the three poisons: greed, ignorance, and hatred (which the Westerner promotes).
My realization of this drew me away from the West, similar to when my obsession with the East began. The difference is that my interest in the East now is not because of a fantasy born out of misguidance, especially not a sexual one. I now know that there is more to the East than its pop culture. But I cannot help thinking that none of this would have happened and I would better understand Buddhism had I been born to and raised by Buddhists in East Asia, or even a majority Buddhist country in South or Southeast Asia.
However, the possibility of a cycle starting with a yo-yoing fetishization of the East makes me anxious. When I realized what I was doing at first was fetishization, I did further research and found out that the West is to blame for its portrayal of the East in its media. This in turn makes me denounce the West and brings me back towards my obsession with the Sinosphere, which could lead to more fetishization.
Despite this, I am glad that at the very least, my interest is more than just wanting to live a kawaii lifestyle, hoping to have a “submissive housewife who will look young forever”, or all that neckbeard squick. I do have to say that there is something else that is drawing me towards the Sinosphere, not to mention that it is the region where Buddhism is dominant (the same is true to a lesser extent with the Indosphere). Even though I am not a huge fan of tradition since I am very progressive, when a region’s culture gets something right, they get it right. In addition to Buddhist values, the Sinosphere holds education and collectivism to a high degree. It is no wonder I find their people so much more intelligent and caring than people from my culture.
It is common knowledge that countries such as Japan, China, South Korea, and Singapore have the highest average IQs. To add to this (unbeknownst to many), even less developed countries, e.g. Mongolia, with high Buddhist populations around the same region, have average IQs higher than developing and undeveloped nations outside the region. The most agreed upon reason for this is cultural factors rather than genetic or economic factors. To conclude, Buddhism combined with values in the East Asian cultural sphere creates the best “brains” to represent humanity, thus the West should make way for them, especially considering the East’s superior collectivism.
Of course cultures do not stay the same forever because they change over time. One big thing that is different now in the Sinosphere and Indosphere (the latter I am mentioning because it is where Buddhism came from, though it is not as dominant in the cultural region as it was) is that they are generally much more patriarchal and anti-LGBT+ than they were up until the last several centuries. However, Buddhism treats same-sex relations and being transgender the same as heterosexuality and being cisgender (preferring celibacy among monastics, though depending on the school of Buddhism, those in the monastic order may be treated as their birth gender, even if they are transgender), and in addition, the Buddha taught that women are just as capable of attaining enlightenment as men. Even outside of Buddhism, there are records of same-sex relations as early as the Shang dynasty in China and the temple walls in Khajuraho, India depict homosexual activity. As for feminism, China was matrilineal until the Han dynasty era, when Confucianism and filial piety became mainstream in the area, while India, home to over 100 different ethnic communities, has had a few matriarchal and egalitarian societies pre-European colonization. In the modern era, numerous people in the two cultural spheres are becoming more supportive of gender equality and the LGBT+, which in some cases may be due to Westernization (not that it redeems it) or simply the individuals’ progressive political views not influenced by Western culture.
What has stayed the same for the most part, besides Buddhism, is the Sinosphere’s and Indosphere’s value of collectivism in honor-shame societies and the former cultural sphere’s emphasis on education; this is what Westerners, as well as people all over the world, need for themselves. If the West is going to fall due to hyperconsumerism, late stage capitalism, and uneducated leaders, those living in the West would be better off joining Buddhism and assimilating to the East. Arguably, the best way to do this is to move to a majority Buddhist country, preferably one in the Sinosphere (its core countries being China, Japan, the Koreas, Taiwan, and Vietnam). Leave everybody you know from your home behind, especially non-Buddhists. Just to make things clear, Westerners are not necessarily evil and it is not their fault they were raised in a Western culture, but having these people in your life will hold you back from collectivism, quality education free of anti-intellectual quackery, and above all, understanding the dharma.
After you have left everybody in your life and started anew, you can immerse yourself in the culture. Again, abandoning Western food, media, clothing, and especially inventions and scientific breakthroughs is very unnecessary. Your main focus is reprogramming your mind to think like a person (specifically a Buddhist person) in the Sinosphere/Indosphere, utilizing the high educational standards, putting the collective over the individual, and taking refuge in the Triple Gem. Before moving, though, it is best to make yourself familiar with the customs and learn the language of the place you are moving to. To aid your assimilation, it would not hurt to start dating one of the locals who strongly identifies with the culture, regardless of their race. Someone living there who is not ethnically East, South, or Southeast Asian who is still very involved in the culture would be very helpful to your assimilation as one who is ethnically East, South, or Southeast Asian (I am clarifying this to discourage racial fetishization). This may be difficult as you would have to win over approval from their parents, let alone convince them to see you as another Easterner, but if you manage to do so, that would be fantastic. To make things easier, you could plan to move to a country where people treat women as equals and are relatively accepting of the LGBT+ so you would not have to worry about gender roles or whatever. Think of places in the Sinosphere such as Singapore, Taiwan, Japan, Hong Kong, or if you are planning on going to the Indosphere (which is not too big of a step down) since they did give us Buddhism after all, Nepal and Thailand. Your most important goal, however, is to rewire your brain to think in a more Sinic or Indic way and be more in touch with Buddhism.
You can hardly consider yourself a Westerner if you manage to do so, being Western only in your country of origin (and possibly race as well). I am definitely not like those other “people” from the West who strongly cling to Western culture because they just do not understand. Western cultural merit is almost solely from the proxy of our ancestors’ inventions, scientific discoveries, and political revolutions. Considering that the West is being brought towards the wrong direction in the modern era, we should get out of there culturally, if not physically, until it all hits the fan.
If the West continues its defilement of the rest of the world, when it falls, it will bring it all down with it. We must not lose or else everybody loses.
This pressure has a good side; because the bigger the great threat becomes, the more we will push ourselves to assimilate and raise children to fight for us. Considering the infectability of Western anti-intellectualism and “main character syndrome”, how could our Western peers know better? Buddhism is not a proselytizing “religion”, so our best bet is eliminating the promoter of the three poisons, the Westerner (especially the Christian Westerner), from our own lives. How it will run to us as its society collapses under itself and we welcome it to assimilate but say “we told you so”! The older I get, the better I know the Westerner. The better I know the Westerner, the easier it gets to excuse hostility against them, especially from the Sinosphere.
From my perspective, the ones to blame are not the angry, low-middle class white males in the rural United States nor the boba conservative bananas and right-wing coconuts who suck up to the West’s biggest scum, but rather the ones who have brainwashed them to fall for chauvinism, reactionarism, and laissez faire capitalism.
Realizing this, I am now closely investigating the sources of these beliefs which make up the foundation of social Darwinism and, when combined with totalitarian thinking, capitalist fascism. This is after I noticed that these systems are unsustainable and would destroy themselves from the inside out. The slow, painful destruction of communities who fall victim to them are well known to me. If one looks carefully, they can see the consequences that have been unfolding since the 2020 Coronavirus Pandemic. You may wonder: were the founders aware of this? My guess would be that they were not but were evil nonetheless because they were too selfish to think about the future, their descendants.
If this is the case, then it is the duty of us, the opponents of these ideologies, to spread the word faster than the ideologies are currently spreading in the West. It is hard for me to believe it is not the case considering that both Western political ideologies are fundamentally reactionary. Besides, I doubt they would want civilization as we know it to collapse.
I have a social Darwinist as a maternal uncle who sometimes meets with my parents, maternal grandfather, and younger brother and with his political conversations, often sourced from flawed studies, Russian news, and 4chan, I can easily study the principles of its theories. Both of my parents are also conservatives who support Trump and other immoral American politicians. Being raised by the two of them, I bet I could disguise myself as a Western right-wing traditionalist, maybe even a social Darwinist, since I know the way they speak, to whom they flock to, and how to make them give one their full attention. It would probably be easy to do this as some right-wing grifters can fool American right-wing audiences into thinking that they share the same beliefs (e.g. Thomas MacDonald).
Their kind are gullible because they do not listen to fact checkers and often do not do research to see if who they are listening to really practices what they preach.
Even though there are Westerners who are not like this, the West cannot coexist with Buddhism, let alone the cultures where it is dominant, as the West ruled by colonizing tirthikas and it will likely always be for as long as it lasts. And just because their culture is not as viable as the one founded on Buddhist, Sinic, or even Indic values does not give them the right to imperialize the rest of the world and bring it down with them. We can welcome the Westerner willing to change its ways, turn it into one of an Easterner, and have its culture go through a quick and painless demise, or the Westerner can continue its power trip, destroy everything it touches along with itself, and society will suffer a slow and painful death. This is what the conclusion that I have come to so far as I examine capitalist fascism and Westerners’ connection to it.
The Western doctrine of capitalist fascism rejects an aspect of maitrī, fulfilling beings’ basic needs, and substitutes it for a privilege towards the bourgeoisie and the exploitation of the workers’ labor (also known as Vergegenständlichung or “objectification”). Thus it denies the worth of the collective, only concerns itself with greedy individuals, and thus is immoral. Unlike what the non-Buddhist capitalist wants people to believe, all beings have an altruistic Buddha nature, but it is corrupted, being difficult to notice as it has only conditions without a beginning (listed in the Avijjā Sutta). Abandoning capitalism, both fascist and non-fascist, gives power to the people as it ensures a more guaranteed right to life instead of having not even one thousand billionaires own more than half of Earth’s population combined, more than each one of those billionaires could ever spend in their lifetimes.
Should the Westerner, especially one who pushes capitalist fascism, strengthen its grip on humanity, it can be said that it would make its own naraka.
And so I stand by my plan and encourage others to do the same because it is in the name of the Unsurpassable Enlightened One. By protecting our kind against the Westerner, we are defending the Triple Gem.
If it is not already clear, the disapproval I feel towards the societal values and prevailing norms of the West has led me to question my place in this environment. I believe that meaningful change can only be fostered if the West is put into its place and the Sino-Buddhist East motivates our minds.
In Vietnam, where the culture is predominantly Sinic with some Indic aspects and little European influence, we can see the promotion of quality education, collectivism, and Buddhism (practiced by a forgivable 15% of the population), very unlike the nearby country of the Philippines. In the Philippines, its citizens cling to the Anglo-Saxon and Hispanic culture brought to the country by American and Spaniard imperialists. The effects of this are very clear in their average IQs (Vietnam: 89.53 vs. Philippines: 81.64) and PISA scores (Vietnam: 1403 vs. Philippines: 1058). They are both developing countries in Southeast Asia that were colonized by the West, but because Vietnam kept its culture more pure and stuck to Buddhism (or at least Sinic philosophies), its people are better educated compared to the nearby Westernized countries in a similar economic situation.
In short, Westernization leads to the following:

1. The native culture becomes diluted
2. If Western thinking intrudes, mental degeneration takes hold of the native population and its society slowly degrades along with the West itself as it eats itself from the inside out

Those who cause this to happen must be stopped, especially those who endanger Buddhism. We must not wait for the fruition of their karma for their sacrilege of the Tathagata’s teachings because by then it would be too late, and even if it is instant karma, every bodhisattva’s job is to end suffering.
Those who spread the harmful ideologies bring themselves and others away from the Buddha’s word are polluting humanity by having them join their rat race that will only end in their own demise. They are leading to the ruin of many and thus, I do not consider them to be human but instead parasites.
There is a disgraceful Western belief that for a short amount of time was not held by the majority but is now very pervasive in the West and also is the foundation of reactionarism, chauvinism, and capitalism in all cultures. It says: “My individual rights matter the most and freedom means my right to violate the rights of others.”
This Western babble is followed by numerous all around the world and sows disharmony in societies where it becomes the norm. This idea provides basis for several types of Westerners, including but not limited to:

• the “Karen”; a middle-aged, entitled, often white and anti-mask/anti-vaccine female who makes trivial complaints
• the “Chud”; a racist, misogynistic, right-wing male (usually young) who browses 4chan everyday and has a high likelihood of becoming a mass shooter
• the dayangma (Hanzi: 大洋马); a sex-obsessed young adult Western female, majority white but sometimes POC, is conventionally attractive (to Westerners), sleeps around, and in some cases creates spawn that do not know their father(s)
• the angry white male; self-explanatory, right-wing and traditionalist (sometimes a social Darwnist or capitalist fascist), commonly rural, evangelical Christian and have never seen a POC before
• the “Trad Wife”; a prudish, sometimes uneducated, female who lets Western males walk all over it and raises spawn who are likely to become angry whites
• the New Agehippy; generally a teenager or young adult, a well-meaning but poorly educated white liberal or social democrat who misappropriates Buddhism and appeases Muslims
• the AmeriKKKan fathegrandfather; a middle-aged to elderly, chauvinist male with children, sometimes also grandchildren, and is unconcerned about social justice and similarly to the “Karen”, falls for pseudoscientific conspiracy theories
• the neckbearded otaku (Hiragana: おたく); a hairy white male with “yellow fever” and mediocre hygiene, for the most part subreddit/Discord server moderators who stalk and kidnap East Asian ladies (if not teenage girls)

The growth of these groups is evidence of the degradation of Western culture, showing that it must retire as the dominant culture and make way for the much more sustainable East. Once the manuṣya realm on Earth is completely tainted by the West, Buddhas can no longer arise in the world because the dharma would be known by nobody and the Vinaya are forgotten or destroyed.
The future generation will not remember the dharma unless we halt the growth of the parasitic culture that promotes overconsumption, hyperindividualism, and anti-intellectualism.
The Westerner has a remarkable contrast to the Sinic or Indic. The Westerner has a grasp on this world so strong with its weaponry since the 16th century, using force to disrupt the traditional lives of whatever native people it saw, safe for those in a few countries (even though some of those countries are still being Westernized). The Dutch, English, French, Portuguese, and Spanish built colonies from the Americas to Southeast Asia. The kingdoms were blessed with powerful militaries, strong economies, stable governments, and advanced technology that allowed their cultures to spread. But after half a millennium and looking back, was any of this really earned? And is the Westerner’s conquest over yet?
Since the Great Schism of Christianity, the Westerner trained itself for roughly one thousand years. It trained itself in several aspects, but it forgot an important piece, the dharma. The cunning Westerner, blessed with advancements, used them to tyrannize other peoples on a scale never before seen. This was the beginning of the Latter Day of the Dharma. The dharma is declining because of the savage Westerner. And so, it leeched off of any people it got a hold of, including predominantly Buddhist peoples. Even during the decolonization of the 20th century, fundamentalist Christianity spread and threatened the dharma. To make matters worse, previously Buddhist peoples clung to Christianity as taught by their colonizers; the French in Vietnam and the Spanish and Americans in the Philippines. To this day, the Philippines is a lost cause along with its majority Muslim neighbors in Maritime Southeast Asia. The cunning Westerner turned the Filipino against us and now Buddhists make up only 2% of the Philippines’ population. Now, the Westerner sees Buddhism as nothing more than an aesthetic, a self-help lifestyle, or a decoration that they can commercialize and cherry pick aspects to integrate into their religion or lack thereof.
It is excellent for someone from the West to learn the dharma as this will turn them into a more compassionate and wise person, but they must not enforce the Western gaze onto it and discard parts of the Shakyamuni Buddha’s words they do not like. To be fair, some aspects of Buddhism would be nearly impossible for a Westerner to understand unless they assimilate.
Buddhism is not materialist or blind belief without evidence and it belongs to the East, so stop pretending to be something you are not while pushing stereotypes of Asian Buddhists.
However, even though Buddhism is not materialist or very in line with the Western worldview, it is uniquely human. Walpola Rahula, a Sri Lankan Buddhist monk and writer explains it this way:
Among the founders of religions the Buddha (if we are permitted to call him the founder of a religion in the popular sense of the term) was the only teacher who did not claim to be other than a human being, pure and simple. Other teachers were either God, or his incarnations in different forms, or inspired by him. The Buddha was not only a human being; he claimed no inspiration from any god or external power either. He attributed all his realization, attainments and achievements to human endeavour and human intelligence. A man and only a man can become Buddha. Every man has within himself the potentiality of becoming a Buddha, if he so wills it and endeavours. We can call the Buddha a man par excellence. He was so perfect in his 'human-ness' that he came to be regarded later in popular religion almost as 'super-human'. Man's position, according to Buddhism, is supreme. Man is his own master, and there is no higher being or power that sits in judgment over his destiny. (Rahula 3)
How could one even consider the Westerners who diluted Buddhism human themselves at this point? If it were not for them, Westerners may have a better understanding of the teachings of the “man par excellence”. We are lucky that the only Westerners who necessarily see us as inferior are white nationalists and fundamentalist Christians, otherwise the Westerner could have committed a genocide that would have left millions of us dead. Westerners are competitive beings, so they rarely act in concord towards each other. It is only when there is something that draws them together or away from a common danger.
If everybody on Earth becomes a Westerner, they would wallow in their shamelessness and would have nobody left to exploit except for each other until they destroy themselves.
Until they are the only ones left, they will vilify and exploit anything non-Western until they only have each other, then leading to a chaotic world of undignified militaries, economic inequality, corrupt governments, and little or no innovations.
Unless the Westerner considers even the slightest of inspiration from the East, it will continue to follow hyperindividualism and have apathy towards its education. That is why the West is falling. Those from the West who are smart enough to realize that the West’s flaws that it spreads are deciding that the West is not worth maintaining and its resignation is overdue. If those from the West abandon it to assimilate to the East, it would make the West’s death quicker but more dignified.
This is more than a fad but rather the realization that Western society would be best being a passing fad itself. The West gave us great inventions, food, clothes, scientific discoveries, etc. and once it is gone, the East can pick up where it left off just fine.
We will never abandon the Triple Gem because we recognize it to be more than a spiritual, exotic aesthetic or trend. To do so would make us just like those others in the West who Asian Buddhists look down upon. When the time is right, each and every one of us will surround ourselves with the people who know the dharma better than anyone you have met in the West and we can finally be at their level. We shall be Western only in our country of origin and/or race, but in every other way, we will be Easterners; Buddhist Easterners who will take back what rightfully belongs to us.
When we (and hopefully Buddhists outside of both the Eastern and the Western world) do this, consumerism will lose some of its biggest prey. Even though it may not seem like it at first considering we are abandoning everyone we have ever known, we are doing our ancestors a favor by joining the culture that strives towards the end of suffering. We will be leaving our cultures’ ways of thinking behind, but doing this will save face for our lineage, especially the Western lineage as we would be preventing the creation of more “Karens”, “Chuds”, dayangmas, “neckbeards”, and other degenerates. We will not be annoying dorky nerds and certainly not “neckbeards” who are overly obsessed with and fetishize the culture but people making an effort to get closer to the dharma and surrender to the East.
Although we are collectivists, we must seek personal liberation first for the good of other beings. Once the West collapses and its former supporters come running to us, we shall welcome them. If some do not recognize this before it is too late, well boo hoo! They will have a better birth with the world we will create. Some of them, especially their unlucky spawn, would probably be better off dead and reborn into a better life, maybe even the Pure Land.
The way it is looking now, the West is falling and becoming the world’s laughingstock, which is a good thing. The quicker it falls, the less painful it will be for the Westerner and everybody else. Western culture will not be missed, but we can keep the best of it and continue the innovations that the creators would wish to see. We will remember the legacy of them and be thankful while never forgiving or forgetting the ones who ruined the West.
Works Cited
“Ignorance Avijjā Sutta (AN 10:61).” Translated by Ṭhānissaro Bhikkhu. Dhammatalks. 2017, https://www.dhammatalks.org/suttas/AN/AN10_61.html. Accessed 31 May 2024.
Gabbe. “Western Culture.” Wikipedia. 25 May 2024. https://en.wikipedia.org/wiki/Western_culture#:~:text=The%20core%20of%20Western%20civilization,Roman%20civilization%20and%20Western%20Christianity. Accessed 31 May 2024.
Rahula Thero, Walpola. What the Buddha Taught. Oneworld Publications, 1959. Accessed 31 May 2024.
u/Tendai-Student. “栄真Eishin (u/Tendai-Student).” Reddit, 31 May 2024, https://www.reddit.com/useTendai-Student/. Accessed 31 May 2024.
u/Tendai-Student. “Buddhism is being MISREPRESENTED in the West Marginalisation, cultural appropriation, misconceptions and what you can do.” Reddit, 2023, https://www.reddit.com/WrongBuddhism/comments/14zc6xg/buddhism_is_being_misrepresented_in_the_west/. Accessed 31 May 2024.