Tensorflow 2.14 on P1Gen6 ADA AD104 NVIDIA GPU - OK using tensorflow/tensorflow:2.14.0-gpu on CUDA 12.6

obriensystems commented 3 hours ago

(venv-cuda)
micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ nvidia-smi
Sat Nov 30 18:54:26 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.94                 Driver Version: 560.94         CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                  Driver-Model | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA RTX 3500 Ada Gene...  WDDM  |   00000000:01:00.0 Off |                  Off |
| N/A   58C    P3             21W /   98W |       0MiB /  12282MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

code
import tensorflow as tf
#import keras
#from keras.utils import multi_gpu_model
#import keras.backend as k
#https://github.com/microsoft/tensorflow-directml/issues/352

# https://www.tensorflow.org/guide/distributed_training
#
# https://www.tensorflow.org/tutorials/distribute/keras
# https://keras.io/guides/distributed_training/
#strategy = tf.distribute.MirroredStrategy()
#print('Number of devices: {}'.format(strategy.num_replicas_in_sync))

# windows git-bash
#set PYTHONIOENCODING=utf-8

#NUM_GPUS = 2
#strategy = tf.distribute.MirroredStrategy()#num_gpus=NUM_GPUS)

# working on dual RTX-4090
#strategy = tf.distribute.MirroredStrategy(devices=["/gpu:0", "/gpu:1"])
#WARNING:tensorflow:Some requested devices in `tf.distribute.Strategy` are not visible to TensorFlow: /replica:0/task:0/device:GPU:1,/replica:0/task:0/device:GPU:0
#Number of devices: 2

# Working on Lenovo P1 Gen 6 - 1 3500 GPU but with gpu0 the iris embedded 13900 intel gpu
#strategy = tf.contrib.distribute.MirroredStrategy(devices=["/gpu:0"])
strategy = tf.distribute.MirroredStrategy()

# cpu only
#strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
#central_storage_strategy = tf.distribute.experimental.CentralStorageStrategy()
#strategy = tf.distribute.MultiWorkerMirroredStrategy() # not in tf 1.5
#print("mirrored_strategy: ",mirrored_strategy)
#strategy = tf.distribute.OneDeviceStrategy(device="/gpu:1")
#mirrored_strategy = tf.distribute.MirroredStrategy(devices=["/gpu:0","/gpu:1"],cross_device_ops=tf.contrib.distribute.AllReduceCrossDeviceOps(all_reduce_alg="hierarchical_copy"))
#mirrored_strategy = tf.distribute.MirroredStrategy(devices= ["/gpu:0","/gpu:1"],cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())

#print('Number of devices: {}'.format(strategy.num_replicas_in_sync))

# https://learn.microsoft.com/en-us/windows/ai/directml/gpu-faq
#a = tf.constant([1.])
#b = tf.constant([2.])
#c = tf.add(a, b)

#gpu_config = tf.GPUOptions()
#gpu_config.visible_device_list = "1"#"0,1"
#gpu_config.visible_device_list = "0,1"
#gpu_config.allow_growth=True

#session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_config))
#print(session.run(c))
#tensorflow.python.framework.errors_impl.AlreadyExistsError: TensorFlow device (DML:0) is being mapped to multiple DML devices (0 now, and 1 previously), which is not supported. This may be the result of providing different GPU configurations (ConfigProto.gpu_options, for example different visible_device_list) when creating multiple Sessions in the same process. This is not  currently supported, see https://github.com/tensorflow/tensorflow/issues/19083
#from keras import backend as K
#K.set_session(session)

cifar = tf.keras.datasets.cifar100
(x_train, y_train), (x_test, y_test) = cifar.load_data()

with strategy.scope():
# https://www.tensorflow.org/api_docs/python/tf/keras/applications/resnet50/ResNet50
# https://keras.io/api/models/model/
  parallel_model = tf.keras.applications.ResNet50(
#model = tf.keras.applications.ResNet50(
    include_top=True,
    weights=None,
    input_shape=(32, 32, 3),
    classes=100,)
# https://saturncloud.io/blog/how-to-do-multigpu-training-with-keras/  
  #parallel_model = multi_gpu_model(model, gpus=2)
  loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
# https://keras.io/api/models/model_training_apis/
  parallel_model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
  # 4096 5s 399ms 104c
parallel_model.fit(x_train, y_train, epochs=25, batch_size=512 )#2048)#5120)#7168)#7168)

dockerfile

# https://hub.docker.com/r/nvidia/cuda/tags
#FROM nvidia/cuda:12.2.0-runtime-ubi9
#FROM tensorflow/tensorflow:latest-gpu
# https://hub.docker.com/layers/tensorflow/tensorflow/2.14.0-gpu/images/sha256-64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206?context=explore
FROM tensorflow/tensorflow:2.14.0-gpu
     WORKDIR /src
COPY /src/tflow.py .
CMD ["python", "tflow.py"]

#RUN pip install -U jupyterlab pandas matplotlib
#EXPOSE 8888
#ENTRYPOINT ["jupyter", "lab","--ip=0.0.0.0","--allow-root","--no-browser"]

micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ ./build.sh
#0 building with "desktop-linux" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 414B 0.0s done
#1 DONE 0.0s

#2 [auth] tensorflow/tensorflow:pull token for registry-1.docker.io
#2 DONE 0.0s

#3 [internal] load metadata for docker.io/tensorflow/tensorflow:2.14.0-gpu
#3 DONE 0.9s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 57B done
#5 DONE 0.0s

#6 [1/3] FROM docker.io/tensorflow/tensorflow:2.14.0-gpu@sha256:64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206
#6 resolve docker.io/tensorflow/tensorflow:2.14.0-gpu@sha256:64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206 done
#6 sha256:68d96d40670e06f0c7b926edf49528a881cf2fca37714de065eacfc75e831192 11.70kB / 11.70kB done
...
#6 extracting sha256:91c6784a9bf6ec3f4bd6f1562e09ae227761f1d4a3b44f8a2d9d27a436cebb4e done
#6 extracting sha256:2ef3e831f8658e728821077ea54594b5bc43898fd70b3b55851d43c3ebaba8f6 done
#6 DONE 165.9s

#7 [2/3] WORKDIR /src
#7 DONE 0.9s

#8 [3/3] COPY /src/tflow.py .
#8 DONE 0.0s

#9 exporting to image
#9 exporting layers 0.0s done
#9 writing image sha256:1d6810df1690137c7ea77998e44557269fbcdfbfcf20ba14748865748bdbeb1a done
#9 naming to docker.io/library/ml-tensorflow-win done
#9 DONE 0.0s
2024-11-30 23:48:43.537015: I tensorflow/core/util/port.cc:111] 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`.
2024-11-30 23:48:43.558784: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-11-30 23:48:43.558819: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-11-30 23:48:43.558832: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-11-30 23:48:43.562966: 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: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-30 23:48:45.152510: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
...
Your kernel may have been built without NUMA support.
2024-11-30 23:48:45.352833: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1977] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.
2024-11-30 23:48:45.352849: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:48:45.352865: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9581 MB memory:  -> device: 0, name: NVIDIA RTX 3500 Ada Generation Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
169001437/169001437 [==============================] - 15s 0us/step
Epoch 1/25
2024-11-30 23:49:12.649878: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8600
2024-11-30 23:49:14.510726: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f322800cb40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-11-30 23:49:14.510749: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA RTX 3500 Ada Generation Laptop GPU, Compute Capability 8.9
2024-11-30 23:49:14.513676: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-11-30 23:49:14.570970: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
98/98 [==============================] - 29s 84ms/step - loss: 4.3393 - accuracy: 0.0846
Epoch 2/25
98/98 [==============================] - 5s 54ms/step - loss: 3.5924 - accuracy: 0.1715
Epoch 3/25
98/98 [==============================] - 5s 54ms/step - loss: 3.1520 - accuracy: 0.2434
Epoch 4/25
98/98 [==============================] - 5s 54ms/step - loss: 2.9126 - accuracy: 0.2939
Epoch 5/25
98/98 [==============================] - 5s 54ms/step - loss: 3.7606 - accuracy: 0.2035
Epoch 6/25
98/98 [==============================] - 5s 55ms/step - loss: 3.3880 - accuracy: 0.2272
Epoch 7/25
98/98 [==============================] - 5s 55ms/step - loss: 2.8392 - accuracy: 0.3091
Epoch 8/25
98/98 [==============================] - 5s 55ms/step - loss: 2.7753 - accuracy: 0.3222
Epoch 9/25
98/98 [==============================] - 5s 55ms/step - loss: 2.5441 - accuracy: 0.3679
Epoch 10/25
98/98 [==============================] - 5s 55ms/step - loss: 2.2937 - accuracy: 0.4159
Epoch 11/25
98/98 [==============================] - 5s 55ms/step - loss: 2.0584 - accuracy: 0.4679
Epoch 12/25
98/98 [==============================] - 5s 55ms/step - loss: 1.8501 - accuracy: 0.5134
Epoch 13/25
98/98 [==============================] - 5s 55ms/step - loss: 1.6035 - accuracy: 0.5678
Epoch 14/25
98/98 [==============================] - 5s 56ms/step - loss: 1.3341 - accuracy: 0.6309
Epoch 15/25
98/98 [==============================] - 6s 57ms/step - loss: 1.1129 - accuracy: 0.6875
Epoch 16/25
98/98 [==============================] - 6s 58ms/step - loss: 0.9613 - accuracy: 0.7251
Epoch 17/25
98/98 [==============================] - 6s 57ms/step - loss: 0.7963 - accuracy: 0.7765
Epoch 18/25
98/98 [==============================] - 5s 56ms/step - loss: 0.7567 - accuracy: 0.7839
Epoch 19/25
98/98 [==============================] - 6s 57ms/step - loss: 0.5892 - accuracy: 0.8342
Epoch 20/25
98/98 [==============================] - 6s 57ms/step - loss: 0.5843 - accuracy: 0.8339
Epoch 21/25
98/98 [==============================] - 6s 57ms/step - loss: 1.3317 - accuracy: 0.6774
Epoch 22/25
98/98 [==============================] - 6s 58ms/step - loss: 0.6691 - accuracy: 0.8199
Epoch 23/25
98/98 [==============================] - 6s 58ms/step - loss: 0.3243 - accuracy: 0.9192
Epoch 24/25
98/98 [==============================] - 6s 59ms/step - loss: 1.0674 - accuracy: 0.7385
Epoch 25/25
98/98 [==============================] - 6s 59ms/step - loss: 1.0427 - accuracy: 0.7503

obriensystems commented 3 hours ago

256, 512, 1024, 2048, 4096 batch size

micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ ./build.sh
#0 building with "desktop-linux" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 575B done
#1 DONE 0.0s

#2 [internal] load metadata for docker.io/tensorflow/tensorflow:2.14.0-gpu
#2 DONE 0.5s

#3 [internal] load .dockerignore
#3 transferring context: 2B done
#3 DONE 0.0s

#4 [1/3] FROM docker.io/tensorflow/tensorflow:2.14.0-gpu@sha256:64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 3.67kB 0.0s done
#5 DONE 0.0s

#6 [2/3] WORKDIR /src
#6 CACHED

#7 [3/3] COPY /src/tflow.py .
#7 DONE 0.0s

#8 exporting to image
#8 exporting layers 0.0s done
#8 writing image sha256:d820728b9b315c1efdca05a5df834ebee7a4367b7752f51e93e3fb581bccc798 done
#8 naming to docker.io/library/ml-tensorflow-win done
#8 DONE 0.0s
2024-11-30 23:57:26.685484: I tensorflow/core/util/port.cc:111] 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`.
2024-11-30 23:57:26.706273: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-11-30 23:57:26.706309: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-11-30 23:57:26.706321: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-11-30 23:57:26.710656: 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: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-30 23:57:27.906699: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:27.909251: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:27.909301: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:27.910960: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:27.910997: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:27.911009: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:28.039226: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:28.039275: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:28.039281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1977] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.
2024-11-30 23:57:28.039301: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-11-30 23:57:28.039338: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9581 MB memory:  -> device: 0, name: NVIDIA RTX 3500 Ada Generation Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
169001437/169001437 [==============================] - 9s 0us/step
Epoch 1/25
2024-11-30 23:57:48.472889: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8600
2024-11-30 23:57:51.465069: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f54603fc950 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-11-30 23:57:51.465098: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA RTX 3500 Ada Generation Laptop GPU, Compute Capability 8.9
2024-11-30 23:57:51.468427: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-11-30 23:57:51.525029: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
196/196 [==============================] - 34s 57ms/step - loss: 4.3636 - accuracy: 0.0868
Epoch 2/25
196/196 [==============================] - 8s 40ms/step - loss: 3.9713 - accuracy: 0.1480
Epoch 3/25
196/196 [==============================] - 8s 40ms/step - loss: 3.7218 - accuracy: 0.1683
Epoch 4/25
196/196 [==============================] - 8s 41ms/step - loss: 3.2891 - accuracy: 0.2331
Epoch 5/25
196/196 [==============================] - 8s 41ms/step - loss: 3.6898 - accuracy: 0.1783
Epoch 6/25
196/196 [==============================] - 8s 42ms/step - loss: 3.3448 - accuracy: 0.2245
Epoch 7/25
196/196 [==============================] - 8s 42ms/step - loss: 3.1134 - accuracy: 0.2653
Epoch 8/25
196/196 [==============================] - 8s 43ms/step - loss: 3.0021 - accuracy: 0.2784
Epoch 9/25
196/196 [==============================] - 8s 43ms/step - loss: 2.7545 - accuracy: 0.3221
Epoch 10/25
196/196 [==============================] - 9s 44ms/step - loss: 2.7476 - accuracy: 0.3218
Epoch 11/25
196/196 [==============================] - 8s 43ms/step - loss: 2.6039 - accuracy: 0.3515
Epoch 12/25
196/196 [==============================] - 9s 44ms/step - loss: 2.3861 - accuracy: 0.3956
Epoch 13/25
196/196 [==============================] - 9s 44ms/step - loss: 2.3027 - accuracy: 0.4242
Epoch 14/25
196/196 [==============================] - 9s 44ms/step - loss: 2.1197 - accuracy: 0.4542
Epoch 15/25
196/196 [==============================] - 9s 44ms/step - loss: 1.8711 - accuracy: 0.5049
Epoch 16/25
196/196 [==============================] - 9s 45ms/step - loss: 1.6557 - accuracy: 0.5570
Epoch 17/25
196/196 [==============================] - 9s 44ms/step - loss: 1.4631 - accuracy: 0.6041
Epoch 18/25
196/196 [==============================] - 9s 45ms/step - loss: 1.3836 - accuracy: 0.6176
Epoch 19/25
196/196 [==============================] - 9s 45ms/step - loss: 1.9901 - accuracy: 0.5074
Epoch 20/25
196/196 [==============================] - 9s 44ms/step - loss: 1.7544 - accuracy: 0.5436
Epoch 21/25
196/196 [==============================] - 9s 45ms/step - loss: 1.8138 - accuracy: 0.5295
Epoch 22/25
196/196 [==============================] - 9s 44ms/step - loss: 1.1861 - accuracy: 0.6827
Epoch 23/25
196/196 [==============================] - 9s 45ms/step - loss: 0.8640 - accuracy: 0.7623
Epoch 24/25
196/196 [==============================] - 9s 43ms/step - loss: 0.6471 - accuracy: 0.8293
Epoch 25/25
196/196 [==============================] - 8s 43ms/step - loss: 0.5260 - accuracy: 0.8597
(venv-cuda)
micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ ./build.sh
#0 building with "desktop-linux" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 575B done
#1 DONE 0.0s

#2 [auth] tensorflow/tensorflow:pull token for registry-1.docker.io
#2 DONE 0.0s

#3 [internal] load metadata for docker.io/tensorflow/tensorflow:2.14.0-gpu
#3 DONE 0.6s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [1/3] FROM docker.io/tensorflow/tensorflow:2.14.0-gpu@sha256:64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206
#5 DONE 0.0s

#6 [internal] load build context
#6 transferring context: 3.67kB done
#6 DONE 0.0s

#7 [2/3] WORKDIR /src
#7 CACHED

#8 [3/3] COPY /src/tflow.py .
#8 CACHED

#9 exporting to image
#9 exporting layers done
#9 writing image sha256:1d6810df1690137c7ea77998e44557269fbcdfbfcf20ba14748865748bdbeb1a done
#9 naming to docker.io/library/ml-tensorflow-win done
#9 DONE 0.0s
2024-12-01 00:04:25.038485: I tensorflow/core/util/port.cc:111] 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`.
2024-12-01 00:04:25.058796: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-12-01 00:04:25.058830: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-12-01 00:04:25.058841: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-12-01 00:04:25.063577: 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: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-12-01 00:04:26.074299: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.076994: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.077051: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.079172: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.079201: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.079211: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.226348: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.226451: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.226492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1977] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.
2024-12-01 00:04:26.226564: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:04:26.226595: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9581 MB memory:  -> device: 0, name: NVIDIA RTX 3500 Ada Generation Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
169001437/169001437 [==============================] - 6s 0us/step
Epoch 1/25
2024-12-01 00:04:43.941642: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8600
2024-12-01 00:04:46.425421: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f61bc946980 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-12-01 00:04:46.425451: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA RTX 3500 Ada Generation Laptop GPU, Compute Capability 8.9
2024-12-01 00:04:46.428552: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-12-01 00:04:46.484245: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
98/98 [==============================] - 30s 83ms/step - loss: 4.4019 - accuracy: 0.0753
Epoch 2/25
98/98 [==============================] - 5s 54ms/step - loss: 3.5497 - accuracy: 0.1767
Epoch 3/25
98/98 [==============================] - 5s 54ms/step - loss: 3.1803 - accuracy: 0.2434
Epoch 4/25
98/98 [==============================] - 5s 54ms/step - loss: 3.3044 - accuracy: 0.2308
Epoch 5/25
98/98 [==============================] - 5s 54ms/step - loss: 3.0593 - accuracy: 0.2603
Epoch 6/25
98/98 [==============================] - 5s 54ms/step - loss: 2.6995 - accuracy: 0.3353
Epoch 7/25
98/98 [==============================] - 5s 54ms/step - loss: 2.3841 - accuracy: 0.3945
Epoch 8/25
98/98 [==============================] - 5s 55ms/step - loss: 2.3127 - accuracy: 0.4143
Epoch 9/25
98/98 [==============================] - 5s 54ms/step - loss: 2.0369 - accuracy: 0.4691
Epoch 10/25
98/98 [==============================] - 5s 54ms/step - loss: 1.7624 - accuracy: 0.5369
Epoch 11/25
98/98 [==============================] - 5s 55ms/step - loss: 1.6354 - accuracy: 0.5610
Epoch 12/25
98/98 [==============================] - 5s 55ms/step - loss: 1.3999 - accuracy: 0.6258
Epoch 13/25
98/98 [==============================] - 5s 56ms/step - loss: 3.3840 - accuracy: 0.2818
Epoch 14/25
98/98 [==============================] - 5s 55ms/step - loss: 3.4657 - accuracy: 0.2331
Epoch 15/25
98/98 [==============================] - 5s 56ms/step - loss: 3.0036 - accuracy: 0.3019
Epoch 16/25
98/98 [==============================] - 5s 56ms/step - loss: 2.4746 - accuracy: 0.3717
Epoch 17/25
98/98 [==============================] - 5s 56ms/step - loss: 2.1085 - accuracy: 0.4415
Epoch 18/25
98/98 [==============================] - 5s 56ms/step - loss: 1.8274 - accuracy: 0.5083
Epoch 19/25
98/98 [==============================] - 6s 56ms/step - loss: 1.5464 - accuracy: 0.5782
Epoch 20/25
98/98 [==============================] - 6s 57ms/step - loss: 1.2816 - accuracy: 0.6471
Epoch 21/25
98/98 [==============================] - 6s 57ms/step - loss: 1.0437 - accuracy: 0.7191
Epoch 22/25
98/98 [==============================] - 6s 57ms/step - loss: 0.7747 - accuracy: 0.7823
Epoch 23/25
98/98 [==============================] - 6s 58ms/step - loss: 0.8367 - accuracy: 0.7624
Epoch 24/25
98/98 [==============================] - 6s 58ms/step - loss: 0.6280 - accuracy: 0.8256
Epoch 25/25
98/98 [==============================] - 6s 58ms/step - loss: 0.4482 - accuracy: 0.8822
(venv-cuda)
micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ java --version
java 21.0.5 2024-10-15 LTS
Java(TM) SE Runtime Environment (build 21.0.5+9-LTS-239)
Java HotSpot(TM) 64-Bit Server VM (build 21.0.5+9-LTS-239, mixed mode, sharing)
(venv-cuda)
micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ ./build.sh
ERROR: listing workers for Build: failed to list workers: Canceled: context canceled
(venv-cuda)
micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ ./build.sh
#0 building with "desktop-linux" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 575B done
#1 DONE 0.0s

#2 [internal] load metadata for docker.io/tensorflow/tensorflow:2.14.0-gpu
#2 ...

#3 [auth] tensorflow/tensorflow:pull token for registry-1.docker.io
#3 DONE 0.0s

#2 [internal] load metadata for docker.io/tensorflow/tensorflow:2.14.0-gpu
#2 DONE 0.8s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [1/3] FROM docker.io/tensorflow/tensorflow:2.14.0-gpu@sha256:64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206
#5 DONE 0.0s

#6 [internal] load build context
#6 transferring context: 3.68kB 0.0s done
#6 DONE 0.0s

#7 [2/3] WORKDIR /src
#7 CACHED

#8 [3/3] COPY /src/tflow.py .
#8 DONE 0.0s

#9 exporting to image
#9 exporting layers 0.0s done
#9 writing image sha256:bbcea2a57cf72678d126f28fe2eb940774d4bd9acf9b5ecba9f66bbb589e6153 done
#9 naming to docker.io/library/ml-tensorflow-win done
#9 DONE 0.0s
2024-12-01 00:25:31.999107: I tensorflow/core/util/port.cc:111] 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`.
2024-12-01 00:25:32.229234: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-12-01 00:25:32.229266: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-12-01 00:25:32.230800: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-12-01 00:25:32.340545: 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: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-12-01 00:25:34.385224: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.426195: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.426241: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.429134: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.429191: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.429204: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.595716: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.595763: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.595767: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1977] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.
2024-12-01 00:25:34.595799: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:25:34.595810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9581 MB memory:  -> device: 0, name: NVIDIA RTX 3500 Ada Generation Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
169001437/169001437 [==============================] - 18s 0us/step
Epoch 1/25
2024-12-01 00:26:04.454519: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8600
2024-12-01 00:26:06.652346: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f9a1359f940 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-12-01 00:26:06.652378: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA RTX 3500 Ada Generation Laptop GPU, Compute Capability 8.9
2024-12-01 00:26:06.659005: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-12-01 00:26:06.733169: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
49/49 [==============================] - 32s 201ms/step - loss: 4.6684 - accuracy: 0.0623
Epoch 2/25
49/49 [==============================] - 5s 103ms/step - loss: 3.6396 - accuracy: 0.1590
Epoch 3/25
49/49 [==============================] - 5s 105ms/step - loss: 3.1207 - accuracy: 0.2439
Epoch 4/25
49/49 [==============================] - 5s 105ms/step - loss: 2.8306 - accuracy: 0.3066
Epoch 5/25
49/49 [==============================] - 5s 106ms/step - loss: 2.7187 - accuracy: 0.3275
Epoch 6/25
49/49 [==============================] - 5s 106ms/step - loss: 2.6684 - accuracy: 0.3385
Epoch 7/25
49/49 [==============================] - 5s 106ms/step - loss: 2.4572 - accuracy: 0.3773
Epoch 8/25
49/49 [==============================] - 5s 106ms/step - loss: 2.9407 - accuracy: 0.2756
Epoch 9/25
49/49 [==============================] - 5s 105ms/step - loss: 2.4240 - accuracy: 0.3785
Epoch 10/25
49/49 [==============================] - 5s 106ms/step - loss: 2.0339 - accuracy: 0.4637
Epoch 11/25
49/49 [==============================] - 5s 106ms/step - loss: 1.6721 - accuracy: 0.5452
Epoch 12/25
49/49 [==============================] - 5s 105ms/step - loss: 1.3453 - accuracy: 0.6221
Epoch 13/25
49/49 [==============================] - 5s 106ms/step - loss: 1.1025 - accuracy: 0.6861
Epoch 14/25
49/49 [==============================] - 5s 106ms/step - loss: 0.8672 - accuracy: 0.7436
Epoch 15/25
49/49 [==============================] - 5s 106ms/step - loss: 0.6552 - accuracy: 0.8053
Epoch 16/25
49/49 [==============================] - 5s 105ms/step - loss: 0.5098 - accuracy: 0.8456
Epoch 17/25
49/49 [==============================] - 5s 105ms/step - loss: 0.8505 - accuracy: 0.7490
Epoch 18/25
49/49 [==============================] - 5s 106ms/step - loss: 1.0267 - accuracy: 0.7125
Epoch 19/25
49/49 [==============================] - 5s 105ms/step - loss: 0.9959 - accuracy: 0.7142
Epoch 20/25
49/49 [==============================] - 5s 106ms/step - loss: 0.4886 - accuracy: 0.8532
Epoch 21/25
49/49 [==============================] - 5s 105ms/step - loss: 0.2797 - accuracy: 0.9231
Epoch 22/25
49/49 [==============================] - 5s 105ms/step - loss: 0.1975 - accuracy: 0.9440
Epoch 23/25
49/49 [==============================] - 5s 105ms/step - loss: 0.1371 - accuracy: 0.9600
Epoch 24/25
49/49 [==============================] - 5s 107ms/step - loss: 0.0981 - accuracy: 0.9735
Epoch 25/25
49/49 [==============================] - 5s 106ms/step - loss: 0.0758 - accuracy: 0.9776
(venv-cuda)
micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ ./build.sh
#0 building with "desktop-linux" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 575B done
#1 DONE 0.0s

#2 [auth] tensorflow/tensorflow:pull token for registry-1.docker.io
#2 DONE 0.0s

#3 [internal] load metadata for docker.io/tensorflow/tensorflow:2.14.0-gpu
#3 DONE 1.0s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [1/3] FROM docker.io/tensorflow/tensorflow:2.14.0-gpu@sha256:64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206
#5 DONE 0.0s

#6 [internal] load build context
#6 transferring context: 3.68kB 0.0s done
#6 DONE 0.0s

#7 [2/3] WORKDIR /src
#7 CACHED

#8 [3/3] COPY /src/tflow.py .
#8 DONE 0.0s

#9 exporting to image
#9 exporting layers 0.0s done
#9 writing image sha256:91662b98c48f03fb3acd6fbc253841ce7aac8e445d0cdb8380442f5e29a648f1 done
#9 naming to docker.io/library/ml-tensorflow-win done
#9 DONE 0.0s
2024-12-01 00:30:21.013693: I tensorflow/core/util/port.cc:111] 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`.
2024-12-01 00:30:21.034515: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-12-01 00:30:21.034574: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-12-01 00:30:21.034588: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-12-01 00:30:21.039309: 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: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-12-01 00:30:22.066704: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.069331: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.069380: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.071225: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.071251: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.071259: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.238991: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.239037: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.239044: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1977] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.
2024-12-01 00:30:22.239062: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:30:22.239076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9581 MB memory:  -> device: 0, name: NVIDIA RTX 3500 Ada Generation Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
169001437/169001437 [==============================] - 6s 0us/step
Epoch 1/25
2024-12-01 00:30:39.622591: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8600
2024-12-01 00:30:41.973276: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0dc02065d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-12-01 00:30:41.973300: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA RTX 3500 Ada Generation Laptop GPU, Compute Capability 8.9
2024-12-01 00:30:41.976530: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-12-01 00:30:42.035687: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

25/25 [==============================] - 34s 400ms/step - loss: 4.9395 - accuracy: 0.0354
Epoch 2/25
25/25 [==============================] - 5s 207ms/step - loss: 3.9236 - accuracy: 0.1054
Epoch 3/25
25/25 [==============================] - 5s 207ms/step - loss: 3.4189 - accuracy: 0.1878
Epoch 4/25
25/25 [==============================] - 5s 209ms/step - loss: 3.0047 - accuracy: 0.2666
Epoch 5/25
25/25 [==============================] - 5s 208ms/step - loss: 2.6428 - accuracy: 0.3382
Epoch 6/25
25/25 [==============================] - 5s 208ms/step - loss: 2.3087 - accuracy: 0.4076
Epoch 7/25
25/25 [==============================] - 5s 208ms/step - loss: 2.1035 - accuracy: 0.4594
Epoch 8/25
25/25 [==============================] - 5s 209ms/step - loss: 1.8614 - accuracy: 0.5035
Epoch 9/25
25/25 [==============================] - 5s 208ms/step - loss: 1.5357 - accuracy: 0.5820
Epoch 10/25
25/25 [==============================] - 5s 209ms/step - loss: 1.4527 - accuracy: 0.6061
Epoch 11/25
25/25 [==============================] - 5s 209ms/step - loss: 1.0684 - accuracy: 0.6985
Epoch 12/25
25/25 [==============================] - 5s 209ms/step - loss: 0.7071 - accuracy: 0.7972
Epoch 13/25
25/25 [==============================] - 5s 209ms/step - loss: 0.5945 - accuracy: 0.8280
Epoch 14/25
25/25 [==============================] - 5s 208ms/step - loss: 0.5797 - accuracy: 0.8359
Epoch 15/25
25/25 [==============================] - 5s 208ms/step - loss: 0.5358 - accuracy: 0.8441
Epoch 16/25
25/25 [==============================] - 5s 208ms/step - loss: 0.4175 - accuracy: 0.8808
Epoch 17/25
25/25 [==============================] - 5s 209ms/step - loss: 0.3493 - accuracy: 0.8983
Epoch 18/25
25/25 [==============================] - 5s 209ms/step - loss: 0.4485 - accuracy: 0.8755
Epoch 19/25
25/25 [==============================] - 5s 209ms/step - loss: 0.7039 - accuracy: 0.8063
Epoch 20/25
25/25 [==============================] - 5s 209ms/step - loss: 1.0323 - accuracy: 0.7179
Epoch 21/25
25/25 [==============================] - 5s 209ms/step - loss: 0.5772 - accuracy: 0.8379
Epoch 22/25
25/25 [==============================] - 5s 209ms/step - loss: 0.2776 - accuracy: 0.9209
Epoch 23/25
25/25 [==============================] - 5s 208ms/step - loss: 0.2011 - accuracy: 0.9501
Epoch 24/25
25/25 [==============================] - 5s 209ms/step - loss: 0.2049 - accuracy: 0.9618
Epoch 25/25
25/25 [==============================] - 5s 209ms/step - loss: 0.1222 - accuracy: 0.9830
(venv-cuda)
micha@p1gen6 MINGW64 /c/wse_github/ObrienlabsDev/machine-learning (main)
$ ./build.sh
#0 building with "desktop-linux" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 575B done
#1 DONE 0.0s

#2 [auth] tensorflow/tensorflow:pull token for registry-1.docker.io
#2 DONE 0.0s

#3 [internal] load metadata for docker.io/tensorflow/tensorflow:2.14.0-gpu
#3 DONE 0.8s

#4 [internal] load .dockerignore
#4 transferring context: 2B done
#4 DONE 0.0s

#5 [1/3] FROM docker.io/tensorflow/tensorflow:2.14.0-gpu@sha256:64602abcd8cc4f4bdd6268ca0abc39e6d37113d700886afd15f6dd151210b206
#5 DONE 0.0s

#6 [internal] load build context
#6 transferring context: 3.67kB 0.0s done
#6 DONE 0.0s

#7 [2/3] WORKDIR /src
#7 CACHED

#8 [3/3] COPY /src/tflow.py .
#8 DONE 0.0s

#9 exporting to image
#9 exporting layers 0.0s done
#9 writing image sha256:a5ee876f43f024c7a064c48b3d61ae7d854230bbec86ef4729f987e0e96aba63 done
#9 naming to docker.io/library/ml-tensorflow-win done
#9 DONE 0.0s
2024-12-01 00:35:19.583397: I tensorflow/core/util/port.cc:111] 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`.
2024-12-01 00:35:19.606388: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-12-01 00:35:19.606418: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-12-01 00:35:19.606429: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-12-01 00:35:19.610639: 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: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-12-01 00:35:21.166299: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.168924: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.168970: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.170887: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.170914: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.170923: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.315538: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.315611: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.315620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1977] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.
2024-12-01 00:35:21.315645: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:880] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-12-01 00:35:21.315679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9581 MB memory:  -> device: 0, name: NVIDIA RTX 3500 Ada Generation Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
169001437/169001437 [==============================] - 8s 0us/step
Epoch 1/25
2024-12-01 00:35:40.830076: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8600
2024-12-01 00:35:44.185017: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 5.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
2024-12-01 00:35:44.422820: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f40b45739e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-12-01 00:35:44.422842: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA RTX 3500 Ada Generation Laptop GPU, Compute Capability 8.9
2024-12-01 00:35:44.425879: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-12-01 00:35:44.487522: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
2024-12-01 00:35:51.967788: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.02GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
2024-12-01 00:35:51.967857: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.02GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
2024-12-01 00:35:51.967866: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.14GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
2024-12-01 00:35:52.727086: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.41GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
2024-12-01 00:35:57.297448: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.33GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
2024-12-01 00:35:57.297528: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.33GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
12/13 [==========================>...] - ETA: 0s - loss: 5.3903 - accuracy: 0.02432024-12-01 00:36:09.574078: W tensorflow/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 4.60GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.
13/13 [==============================] - 39s 763ms/step - loss: 5.3775 - accuracy: 0.0247
Epoch 2/25
13/13 [==============================] - 5s 385ms/step - loss: 4.2209 - accuracy: 0.0707
Epoch 3/25
13/13 [==============================] - 5s 370ms/step - loss: 3.8432 - accuracy: 0.1215
Epoch 4/25
13/13 [==============================] - 5s 368ms/step - loss: 3.5030 - accuracy: 0.1739
Epoch 5/25
13/13 [==============================] - 5s 376ms/step - loss: 3.3365 - accuracy: 0.2107
Epoch 6/25
13/13 [==============================] - 5s 371ms/step - loss: 3.0246 - accuracy: 0.2688
Epoch 7/25
13/13 [==============================] - 5s 373ms/step - loss: 2.6487 - accuracy: 0.3396
Epoch 8/25
13/13 [==============================] - 5s 374ms/step - loss: 2.2471 - accuracy: 0.4246
Epoch 9/25
13/13 [==============================] - 5s 375ms/step - loss: 1.9108 - accuracy: 0.4988
Epoch 10/25
13/13 [==============================] - 5s 374ms/step - loss: 1.6222 - accuracy: 0.5708
Epoch 11/25
13/13 [==============================] - 5s 376ms/step - loss: 1.4085 - accuracy: 0.6119
Epoch 12/25
13/13 [==============================] - 5s 385ms/step - loss: 1.1184 - accuracy: 0.6859
Epoch 13/25
13/13 [==============================] - 5s 396ms/step - loss: 1.0108 - accuracy: 0.7160
Epoch 14/25
13/13 [==============================] - 5s 397ms/step - loss: 0.8975 - accuracy: 0.7460
Epoch 15/25
13/13 [==============================] - 5s 417ms/step - loss: 0.6930 - accuracy: 0.8035
Epoch 16/25
13/13 [==============================] - 5s 415ms/step - loss: 0.5735 - accuracy: 0.8375
Epoch 17/25
13/13 [==============================] - 5s 414ms/step - loss: 0.4665 - accuracy: 0.8621
Epoch 18/25
13/13 [==============================] - 5s 389ms/step - loss: 0.3995 - accuracy: 0.8819
Epoch 19/25
13/13 [==============================] - 5s 375ms/step - loss: 0.3647 - accuracy: 0.8939
Epoch 20/25
13/13 [==============================] - 5s 384ms/step - loss: 0.3451 - accuracy: 0.8980
Epoch 21/25
13/13 [==============================] - 5s 381ms/step - loss: 0.3194 - accuracy: 0.9040
Epoch 22/25
13/13 [==============================] - 5s 411ms/step - loss: 0.2781 - accuracy: 0.9201
Epoch 23/25
13/13 [==============================] - 5s 401ms/step - loss: 0.4304 - accuracy: 0.8811
Epoch 24/25
13/13 [==============================] - 5s 382ms/step - loss: 0.2810 - accuracy: 0.9201
Epoch 25/25
13/13 [==============================] - 5s 382ms/step - loss: 0.1870 - accuracy: 0.9457

ObrienlabsDev / machine-learning

Tensorflow 2.14 on P1Gen6 ADA AD104 NVIDIA GPU - OK using tensorflow/tensorflow:2.14.0-gpu on CUDA 12.6 #33