Python 在 Macbook pro m1 max 上运行错误(在 Tensorflow 上运行)

标签 python tensorflow deep-learning lstm apple-m1

我正在尝试从 github binary-bot 运行此代码在我的新 MacBook Pro Max M1 芯片上:

金属设备设置为:

Apple M1 Max
systemMemory: 32.00 GB
maxCacheSize: 10.67 GB

我收到以下错误。有什么建议吗?

2021-12-19 17:26:25.248041: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2021-12-19 17:26:25.248181: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
1 Physical GPUs, 1 Logical GPUs
1 Physical GPUs, 1 Logical GPUs
Trying to connect to IqOption
Successfully Connected!
/Users/abdallahmohamed/Downloads/binary-bot-master/training.py:35: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  df = df.drop("future", 1)
/Users/abdallahmohamed/Downloads/binary-bot-master/training.py:35: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  df = df.drop("future", 1)
train data: 836 validation: 68
sells: 418, buys: 418
VALIDATION sells: 34, buys : 34
0.001-5-SEQ-2-40-16-PRED-1639927591
1 Physical GPUs, 1 Logical GPUs
2021-12-19 17:26:32.262259: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
Epoch 1/40
2021-12-19 17:26:33.223096: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:33.592036: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:33.646351: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:33.684523: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:33.786763: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:33.866171: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:33.932667: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
53/53 [==============================] - ETA: 0s - loss: 0.8687 - accuracy: 0.52272021-12-19 17:26:35.527128: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:35.652789: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:35.681668: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:26:35.710022: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 4s 34ms/step - loss: 0.8687 - accuracy: 0.5227 - val_loss: 0.6928 - val_accuracy: 0.5000
Epoch 2/40
53/53 [==============================] - ETA: 0s - loss: 0.7844 - accuracy: 0.5335WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.7844 - accuracy: 0.5335 - val_loss: 0.6890 - val_accuracy: 0.5000
Epoch 3/40
51/53 [===========================>..] - ETA: 0s - loss: 0.7257 - accuracy: 0.5588WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.7293 - accuracy: 0.5562 - val_loss: 0.6836 - val_accuracy: 0.5735
Epoch 4/40
52/53 [============================>.] - ETA: 0s - loss: 0.7421 - accuracy: 0.5649WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.7411 - accuracy: 0.5658 - val_loss: 0.7035 - val_accuracy: 0.4412
Epoch 5/40
52/53 [============================>.] - ETA: 0s - loss: 0.7205 - accuracy: 0.5565WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.7193 - accuracy: 0.5586 - val_loss: 0.7327 - val_accuracy: 0.4412
Epoch 6/40
52/53 [============================>.] - ETA: 0s - loss: 0.7233 - accuracy: 0.5637WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.7228 - accuracy: 0.5634 - val_loss: 0.7023 - val_accuracy: 0.5441
Epoch 7/40
51/53 [===========================>..] - ETA: 0s - loss: 0.7192 - accuracy: 0.5588WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.7187 - accuracy: 0.5586 - val_loss: 0.8523 - val_accuracy: 0.4559
Epoch 8/40
51/53 [===========================>..] - ETA: 0s - loss: 0.7111 - accuracy: 0.5613WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.7105 - accuracy: 0.5634 - val_loss: 0.7727 - val_accuracy: 0.4559
Epoch 9/40
53/53 [==============================] - ETA: 0s - loss: 0.7151 - accuracy: 0.5514WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 25ms/step - loss: 0.7151 - accuracy: 0.5514 - val_loss: 0.7105 - val_accuracy: 0.5147
Epoch 10/40
53/53 [==============================] - ETA: 0s - loss: 0.7046 - accuracy: 0.5371WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 25ms/step - loss: 0.7046 - accuracy: 0.5371 - val_loss: 0.6940 - val_accuracy: 0.5588
Epoch 11/40
53/53 [==============================] - ETA: 0s - loss: 0.7064 - accuracy: 0.5455WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 25ms/step - loss: 0.7064 - accuracy: 0.5455 - val_loss: 0.7433 - val_accuracy: 0.3971
Epoch 12/40
51/53 [===========================>..] - ETA: 0s - loss: 0.6991 - accuracy: 0.5784WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 25ms/step - loss: 0.6988 - accuracy: 0.5778 - val_loss: 0.6902 - val_accuracy: 0.5147
Epoch 13/40
52/53 [============================>.] - ETA: 0s - loss: 0.6812 - accuracy: 0.5757WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.6818 - accuracy: 0.5754 - val_loss: 0.8100 - val_accuracy: 0.4118
Epoch 14/40
52/53 [============================>.] - ETA: 0s - loss: 0.6876 - accuracy: 0.5673WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.6888 - accuracy: 0.5658 - val_loss: 0.7208 - val_accuracy: 0.5294
Epoch 15/40
52/53 [============================>.] - ETA: 0s - loss: 0.6815 - accuracy: 0.5505WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 23ms/step - loss: 0.6809 - accuracy: 0.5502 - val_loss: 0.6965 - val_accuracy: 0.5441
Epoch 16/40
51/53 [===========================>..] - ETA: 0s - loss: 0.6886 - accuracy: 0.5711WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.6900 - accuracy: 0.5670 - val_loss: 0.6529 - val_accuracy: 0.6029
Epoch 17/40
53/53 [==============================] - ETA: 0s - loss: 0.6959 - accuracy: 0.5598WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.6959 - accuracy: 0.5598 - val_loss: 0.7832 - val_accuracy: 0.4118
Epoch 18/40
52/53 [============================>.] - ETA: 0s - loss: 0.7002 - accuracy: 0.5325WARNING:tensorflow:Can save best model only with val_acc available, skipping.
53/53 [==============================] - 1s 24ms/step - loss: 0.6999 - accuracy: 0.5335 - val_loss: 0.7270 - val_accuracy: 0.3676
WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_4 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_5 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Trying to connect to IqOption
Successfully Connected!
2021-12-19 17:27:01.637577: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2021-12-19 17:27:01.792362: F tensorflow/core/framework/tensor.cc:681] Check failed: IsAligned() ptr = 0x2d0efcee0
zsh: abort      /Users/abdallahmohamed/miniforge3/envs/env_tensorflow/bin/python 

最佳答案

删除存储的模型并保存新模型后,它起作用了

关于Python 在 Macbook pro m1 max 上运行错误(在 Tensorflow 上运行),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/70412894/

相关文章:

python - 沿轴折叠掩码数组 - Python 中的 Numpy

python - 使用 pandas 从开始时间和持续时间(分钟)计算结束时间。标准方法错误

python - 使用 Keras fit_generator 时如何规范化数据

machine-learning - 当有多个分支时,caffe如何计算梯度?

machine-learning - 使用 sklearn 的 cross_validate 方法的复合估计器, "fit_time"参数包含什么?

python - 从另一个脚本控制Python脚本

python - 如何使用 pandas/matplotlib 绘制/管理 2 列分类数据?

python - 我想创建一个简单的电影推荐系统,其中 Y(i,j)=第 j 个用户评分的电影,如果电影已评分,则 R(i,j)=1,否则 r(i,j)=0

linux - 如果僵尸进程的父进程是init,如何清除僵尸进程占用的GPU内存?

nlp - LSTM 中使用 CNTK 的多对一设置