akita train error
guandailu opened this issue · 0 comments
I am running the Akita tutorial, but I have the following errors:
2024-07-30 09:37:31.023852: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Model: "model_1"
Layer (type) Output Shape Param # Connected to
sequence (InputLayer) [(None, 1048576, 4)] 0
stochastic_reverse_complement ( ((None, 1048576, 4), 0 sequence[0][0]
stochastic_shift (StochasticShi (None, 1048576, 4) 0 stochastic_reverse_complement[0][
re_lu (ReLU) (None, 1048576, 4) 0 stochastic_shift[0][0]
conv1d (Conv1D) (None, 1048576, 96) 4224 re_lu[0][0]
batch_normalization (BatchNorma (None, 1048576, 96) 384 conv1d[0][0]
max_pooling1d (MaxPooling1D) (None, 524288, 96) 0 batch_normalization[0][0]
re_lu_1 (ReLU) (None, 524288, 96) 0 max_pooling1d[0][0]
conv1d_1 (Conv1D) (None, 524288, 96) 46080 re_lu_1[0][0]
batch_normalization_1 (BatchNor (None, 524288, 96) 384 conv1d_1[0][0]
max_pooling1d_1 (MaxPooling1D) (None, 262144, 96) 0 batch_normalization_1[0][0]
re_lu_2 (ReLU) (None, 262144, 96) 0 max_pooling1d_1[0][0]
conv1d_2 (Conv1D) (None, 262144, 96) 46080 re_lu_2[0][0]
batch_normalization_2 (BatchNor (None, 262144, 96) 384 conv1d_2[0][0]
max_pooling1d_2 (MaxPooling1D) (None, 131072, 96) 0 batch_normalization_2[0][0]
re_lu_3 (ReLU) (None, 131072, 96) 0 max_pooling1d_2[0][0]
conv1d_3 (Conv1D) (None, 131072, 96) 46080 re_lu_3[0][0]
batch_normalization_3 (BatchNor (None, 131072, 96) 384 conv1d_3[0][0]
max_pooling1d_3 (MaxPooling1D) (None, 65536, 96) 0 batch_normalization_3[0][0]
re_lu_4 (ReLU) (None, 65536, 96) 0 max_pooling1d_3[0][0]
conv1d_4 (Conv1D) (None, 65536, 96) 46080 re_lu_4[0][0]
batch_normalization_4 (BatchNor (None, 65536, 96) 384 conv1d_4[0][0]
max_pooling1d_4 (MaxPooling1D) (None, 32768, 96) 0 batch_normalization_4[0][0]
re_lu_5 (ReLU) (None, 32768, 96) 0 max_pooling1d_4[0][0]
conv1d_5 (Conv1D) (None, 32768, 96) 46080 re_lu_5[0][0]
batch_normalization_5 (BatchNor (None, 32768, 96) 384 conv1d_5[0][0]
max_pooling1d_5 (MaxPooling1D) (None, 16384, 96) 0 batch_normalization_5[0][0]
re_lu_6 (ReLU) (None, 16384, 96) 0 max_pooling1d_5[0][0]
conv1d_6 (Conv1D) (None, 16384, 96) 46080 re_lu_6[0][0]
batch_normalization_6 (BatchNor (None, 16384, 96) 384 conv1d_6[0][0]
max_pooling1d_6 (MaxPooling1D) (None, 8192, 96) 0 batch_normalization_6[0][0]
re_lu_7 (ReLU) (None, 8192, 96) 0 max_pooling1d_6[0][0]
conv1d_7 (Conv1D) (None, 8192, 96) 46080 re_lu_7[0][0]
batch_normalization_7 (BatchNor (None, 8192, 96) 384 conv1d_7[0][0]
max_pooling1d_7 (MaxPooling1D) (None, 4096, 96) 0 batch_normalization_7[0][0]
re_lu_8 (ReLU) (None, 4096, 96) 0 max_pooling1d_7[0][0]
conv1d_8 (Conv1D) (None, 4096, 96) 46080 re_lu_8[0][0]
batch_normalization_8 (BatchNor (None, 4096, 96) 384 conv1d_8[0][0]
max_pooling1d_8 (MaxPooling1D) (None, 2048, 96) 0 batch_normalization_8[0][0]
re_lu_9 (ReLU) (None, 2048, 96) 0 max_pooling1d_8[0][0]
conv1d_9 (Conv1D) (None, 2048, 96) 46080 re_lu_9[0][0]
batch_normalization_9 (BatchNor (None, 2048, 96) 384 conv1d_9[0][0]
max_pooling1d_9 (MaxPooling1D) (None, 1024, 96) 0 batch_normalization_9[0][0]
re_lu_10 (ReLU) (None, 1024, 96) 0 max_pooling1d_9[0][0]
conv1d_10 (Conv1D) (None, 1024, 96) 46080 re_lu_10[0][0]
batch_normalization_10 (BatchNo (None, 1024, 96) 384 conv1d_10[0][0]
max_pooling1d_10 (MaxPooling1D) (None, 512, 96) 0 batch_normalization_10[0][0]
re_lu_11 (ReLU) (None, 512, 96) 0 max_pooling1d_10[0][0]
conv1d_11 (Conv1D) (None, 512, 48) 13824 re_lu_11[0][0]
batch_normalization_11 (BatchNo (None, 512, 48) 192 conv1d_11[0][0]
re_lu_12 (ReLU) (None, 512, 48) 0 batch_normalization_11[0][0]
conv1d_12 (Conv1D) (None, 512, 96) 4608 re_lu_12[0][0]
batch_normalization_12 (BatchNo (None, 512, 96) 384 conv1d_12[0][0]
dropout (Dropout) (None, 512, 96) 0 batch_normalization_12[0][0]
add (Add) (None, 512, 96) 0 max_pooling1d_10[0][0]
dropout[0][0]
re_lu_13 (ReLU) (None, 512, 96) 0 add[0][0]
conv1d_13 (Conv1D) (None, 512, 48) 13824 re_lu_13[0][0]
batch_normalization_13 (BatchNo (None, 512, 48) 192 conv1d_13[0][0]
re_lu_14 (ReLU) (None, 512, 48) 0 batch_normalization_13[0][0]
conv1d_14 (Conv1D) (None, 512, 96) 4608 re_lu_14[0][0]
batch_normalization_14 (BatchNo (None, 512, 96) 384 conv1d_14[0][0]
dropout_1 (Dropout) (None, 512, 96) 0 batch_normalization_14[0][0]
add_1 (Add) (None, 512, 96) 0 add[0][0]
dropout_1[0][0]
re_lu_15 (ReLU) (None, 512, 96) 0 add_1[0][0]
conv1d_15 (Conv1D) (None, 512, 48) 13824 re_lu_15[0][0]
batch_normalization_15 (BatchNo (None, 512, 48) 192 conv1d_15[0][0]
re_lu_16 (ReLU) (None, 512, 48) 0 batch_normalization_15[0][0]
conv1d_16 (Conv1D) (None, 512, 96) 4608 re_lu_16[0][0]
batch_normalization_16 (BatchNo (None, 512, 96) 384 conv1d_16[0][0]
dropout_2 (Dropout) (None, 512, 96) 0 batch_normalization_16[0][0]
add_2 (Add) (None, 512, 96) 0 add_1[0][0]
dropout_2[0][0]
re_lu_17 (ReLU) (None, 512, 96) 0 add_2[0][0]
conv1d_17 (Conv1D) (None, 512, 48) 13824 re_lu_17[0][0]
batch_normalization_17 (BatchNo (None, 512, 48) 192 conv1d_17[0][0]
re_lu_18 (ReLU) (None, 512, 48) 0 batch_normalization_17[0][0]
conv1d_18 (Conv1D) (None, 512, 96) 4608 re_lu_18[0][0]
batch_normalization_18 (BatchNo (None, 512, 96) 384 conv1d_18[0][0]
dropout_3 (Dropout) (None, 512, 96) 0 batch_normalization_18[0][0]
add_3 (Add) (None, 512, 96) 0 add_2[0][0]
dropout_3[0][0]
re_lu_19 (ReLU) (None, 512, 96) 0 add_3[0][0]
conv1d_19 (Conv1D) (None, 512, 48) 13824 re_lu_19[0][0]
batch_normalization_19 (BatchNo (None, 512, 48) 192 conv1d_19[0][0]
re_lu_20 (ReLU) (None, 512, 48) 0 batch_normalization_19[0][0]
conv1d_20 (Conv1D) (None, 512, 96) 4608 re_lu_20[0][0]
batch_normalization_20 (BatchNo (None, 512, 96) 384 conv1d_20[0][0]
dropout_4 (Dropout) (None, 512, 96) 0 batch_normalization_20[0][0]
add_4 (Add) (None, 512, 96) 0 add_3[0][0]
dropout_4[0][0]
re_lu_21 (ReLU) (None, 512, 96) 0 add_4[0][0]
conv1d_21 (Conv1D) (None, 512, 48) 13824 re_lu_21[0][0]
batch_normalization_21 (BatchNo (None, 512, 48) 192 conv1d_21[0][0]
re_lu_22 (ReLU) (None, 512, 48) 0 batch_normalization_21[0][0]
conv1d_22 (Conv1D) (None, 512, 96) 4608 re_lu_22[0][0]
batch_normalization_22 (BatchNo (None, 512, 96) 384 conv1d_22[0][0]
dropout_5 (Dropout) (None, 512, 96) 0 batch_normalization_22[0][0]
add_5 (Add) (None, 512, 96) 0 add_4[0][0]
dropout_5[0][0]
re_lu_23 (ReLU) (None, 512, 96) 0 add_5[0][0]
conv1d_23 (Conv1D) (None, 512, 48) 13824 re_lu_23[0][0]
batch_normalization_23 (BatchNo (None, 512, 48) 192 conv1d_23[0][0]
re_lu_24 (ReLU) (None, 512, 48) 0 batch_normalization_23[0][0]
conv1d_24 (Conv1D) (None, 512, 96) 4608 re_lu_24[0][0]
batch_normalization_24 (BatchNo (None, 512, 96) 384 conv1d_24[0][0]
dropout_6 (Dropout) (None, 512, 96) 0 batch_normalization_24[0][0]
add_6 (Add) (None, 512, 96) 0 add_5[0][0]
dropout_6[0][0]
re_lu_25 (ReLU) (None, 512, 96) 0 add_6[0][0]
conv1d_25 (Conv1D) (None, 512, 48) 13824 re_lu_25[0][0]
batch_normalization_25 (BatchNo (None, 512, 48) 192 conv1d_25[0][0]
re_lu_26 (ReLU) (None, 512, 48) 0 batch_normalization_25[0][0]
conv1d_26 (Conv1D) (None, 512, 96) 4608 re_lu_26[0][0]
batch_normalization_26 (BatchNo (None, 512, 96) 384 conv1d_26[0][0]
dropout_7 (Dropout) (None, 512, 96) 0 batch_normalization_26[0][0]
add_7 (Add) (None, 512, 96) 0 add_6[0][0]
dropout_7[0][0]
re_lu_27 (ReLU) (None, 512, 96) 0 add_7[0][0]
conv1d_27 (Conv1D) (None, 512, 64) 30720 re_lu_27[0][0]
batch_normalization_27 (BatchNo (None, 512, 64) 256 conv1d_27[0][0]
re_lu_28 (ReLU) (None, 512, 64) 0 batch_normalization_27[0][0]
one_to_two (OneToTwo) (None, 512, 512, 64) 0 re_lu_28[0][0]
concat_dist2d (ConcatDist2D) (None, 512, 512, 65) 0 one_to_two[0][0]
re_lu_29 (ReLU) (None, 512, 512, 65) 0 concat_dist2d[0][0]
conv2d (Conv2D) (None, 512, 512, 48) 28080 re_lu_29[0][0]
batch_normalization_28 (BatchNo (None, 512, 512, 48) 192 conv2d[0][0]
symmetrize2d (Symmetrize2D) (None, 512, 512, 48) 0 batch_normalization_28[0][0]
re_lu_30 (ReLU) (None, 512, 512, 48) 0 symmetrize2d[0][0]
conv2d_1 (Conv2D) (None, 512, 512, 24) 10368 re_lu_30[0][0]
batch_normalization_29 (BatchNo (None, 512, 512, 24) 96 conv2d_1[0][0]
re_lu_31 (ReLU) (None, 512, 512, 24) 0 batch_normalization_29[0][0]
conv2d_2 (Conv2D) (None, 512, 512, 48) 1152 re_lu_31[0][0]
batch_normalization_30 (BatchNo (None, 512, 512, 48) 192 conv2d_2[0][0]
dropout_8 (Dropout) (None, 512, 512, 48) 0 batch_normalization_30[0][0]
add_8 (Add) (None, 512, 512, 48) 0 symmetrize2d[0][0]
dropout_8[0][0]
symmetrize2d_1 (Symmetrize2D) (None, 512, 512, 48) 0 add_8[0][0]
re_lu_32 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_1[0][0]
conv2d_3 (Conv2D) (None, 512, 512, 24) 10368 re_lu_32[0][0]
batch_normalization_31 (BatchNo (None, 512, 512, 24) 96 conv2d_3[0][0]
re_lu_33 (ReLU) (None, 512, 512, 24) 0 batch_normalization_31[0][0]
conv2d_4 (Conv2D) (None, 512, 512, 48) 1152 re_lu_33[0][0]
batch_normalization_32 (BatchNo (None, 512, 512, 48) 192 conv2d_4[0][0]
dropout_9 (Dropout) (None, 512, 512, 48) 0 batch_normalization_32[0][0]
add_9 (Add) (None, 512, 512, 48) 0 symmetrize2d_1[0][0]
dropout_9[0][0]
symmetrize2d_2 (Symmetrize2D) (None, 512, 512, 48) 0 add_9[0][0]
re_lu_34 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_2[0][0]
conv2d_5 (Conv2D) (None, 512, 512, 24) 10368 re_lu_34[0][0]
batch_normalization_33 (BatchNo (None, 512, 512, 24) 96 conv2d_5[0][0]
re_lu_35 (ReLU) (None, 512, 512, 24) 0 batch_normalization_33[0][0]
conv2d_6 (Conv2D) (None, 512, 512, 48) 1152 re_lu_35[0][0]
batch_normalization_34 (BatchNo (None, 512, 512, 48) 192 conv2d_6[0][0]
dropout_10 (Dropout) (None, 512, 512, 48) 0 batch_normalization_34[0][0]
add_10 (Add) (None, 512, 512, 48) 0 symmetrize2d_2[0][0]
dropout_10[0][0]
symmetrize2d_3 (Symmetrize2D) (None, 512, 512, 48) 0 add_10[0][0]
re_lu_36 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_3[0][0]
conv2d_7 (Conv2D) (None, 512, 512, 24) 10368 re_lu_36[0][0]
batch_normalization_35 (BatchNo (None, 512, 512, 24) 96 conv2d_7[0][0]
re_lu_37 (ReLU) (None, 512, 512, 24) 0 batch_normalization_35[0][0]
conv2d_8 (Conv2D) (None, 512, 512, 48) 1152 re_lu_37[0][0]
batch_normalization_36 (BatchNo (None, 512, 512, 48) 192 conv2d_8[0][0]
dropout_11 (Dropout) (None, 512, 512, 48) 0 batch_normalization_36[0][0]
add_11 (Add) (None, 512, 512, 48) 0 symmetrize2d_3[0][0]
dropout_11[0][0]
symmetrize2d_4 (Symmetrize2D) (None, 512, 512, 48) 0 add_11[0][0]
re_lu_38 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_4[0][0]
conv2d_9 (Conv2D) (None, 512, 512, 24) 10368 re_lu_38[0][0]
batch_normalization_37 (BatchNo (None, 512, 512, 24) 96 conv2d_9[0][0]
re_lu_39 (ReLU) (None, 512, 512, 24) 0 batch_normalization_37[0][0]
conv2d_10 (Conv2D) (None, 512, 512, 48) 1152 re_lu_39[0][0]
batch_normalization_38 (BatchNo (None, 512, 512, 48) 192 conv2d_10[0][0]
dropout_12 (Dropout) (None, 512, 512, 48) 0 batch_normalization_38[0][0]
add_12 (Add) (None, 512, 512, 48) 0 symmetrize2d_4[0][0]
dropout_12[0][0]
symmetrize2d_5 (Symmetrize2D) (None, 512, 512, 48) 0 add_12[0][0]
re_lu_40 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_5[0][0]
conv2d_11 (Conv2D) (None, 512, 512, 24) 10368 re_lu_40[0][0]
batch_normalization_39 (BatchNo (None, 512, 512, 24) 96 conv2d_11[0][0]
re_lu_41 (ReLU) (None, 512, 512, 24) 0 batch_normalization_39[0][0]
conv2d_12 (Conv2D) (None, 512, 512, 48) 1152 re_lu_41[0][0]
batch_normalization_40 (BatchNo (None, 512, 512, 48) 192 conv2d_12[0][0]
dropout_13 (Dropout) (None, 512, 512, 48) 0 batch_normalization_40[0][0]
add_13 (Add) (None, 512, 512, 48) 0 symmetrize2d_5[0][0]
dropout_13[0][0]
symmetrize2d_6 (Symmetrize2D) (None, 512, 512, 48) 0 add_13[0][0]
cropping2d (Cropping2D) (None, 448, 448, 48) 0 symmetrize2d_6[0][0]
upper_tri (UpperTri) (None, 99681, 48) 0 cropping2d[0][0]
dense (Dense) (None, 99681, 2) 98 upper_tri[0][0]
switch_reverse_triu (SwitchReve (None, 99681, 2) 0 dense[0][0]
stochastic_reverse_complement[0][
Total params: 751,506
Trainable params: 746,002
Non-trainable params: 5,504
None
model_strides [2048]
target_lengths [99681]
target_crops [-49585]
2024-07-30 09:37:32.487935: I tensorflow/core/profiler/lib/profiler_session.cc:136] Profiler session initializing.
2024-07-30 09:37:32.487981: I tensorflow/core/profiler/lib/profiler_session.cc:155] Profiler session started.
2024-07-30 09:37:32.488016: I tensorflow/core/profiler/lib/profiler_session.cc:172] Profiler session tear down.
2024-07-30 09:37:32.527294: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2024-07-30 09:37:32.527885: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2794625000 Hz
Epoch 1/10000
1/3504 [..............................] - ETA: 29:15:00 - loss: 1.7668 - pearsonr: 0.0199 - r2: -7.18172024-07-30 09:38:02.775668: I tensorflow/core/profiler/lib/profiler_session.cc:136] Profiler session initializing.
2024-07-30 09:38:02.775727: I tensorflow/core/profiler/lib/profiler_session.cc:155] Profiler session started.
2/3504 [..............................] - ETA: 20:41:28 - loss: 1.4813 - pearsonr: 0.0338 - r2: -5.04252024-07-30 09:38:23.867246: I tensorflow/core/profiler/lib/profiler_session.cc:71] Profiler session collecting data.
2024-07-30 09:38:23.896953: I tensorflow/core/profiler/lib/profiler_session.cc:172] Profiler session tear down.
2024-07-30 09:38:23.918338: I tensorflow/core/profiler/rpc/client/save_profile.cc:137] Creating directory: ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23
2024-07-30 09:38:23.933617: I tensorflow/core/profiler/rpc/client/save_profile.cc:143] Dumped gzipped tool data for trace.json.gz to ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23/bm15.trace.json.gz
2024-07-30 09:38:23.954239: I tensorflow/core/profiler/rpc/client/save_profile.cc:137] Creating directory: ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23
2024-07-30 09:38:23.956466: I tensorflow/core/profiler/rpc/client/save_profile.cc:143] Dumped gzipped tool data for memory_profile.json.gz to ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23/bm15.memory_profile.json.gz
2024-07-30 09:38:23.967561: I tensorflow/core/profiler/rpc/client/capture_profile.cc:251] Creating directory: ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23Dumped tool data for xplane.pb to ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23/bm15.xplane.pb
Dumped tool data for overview_page.pb to ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23/bm15.overview_page.pb
Dumped tool data for input_pipeline.pb to ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23/bm15.input_pipeline.pb
Dumped tool data for tensorflow_stats.pb to ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23/bm15.tensorflow_stats.pb
Dumped tool data for kernel_stats.pb to ./data/1m/train_out/train/plugins/profile/2024_07_30_09_38_23/bm15.kernel_stats.pb
3504/3504 [==============================] - ETA: 0s - loss: 0.3867 - pearsonr: 0.0205 - r2: -0.1191 Traceback (most recent call last):
File "/group/zhougrp4/dguan/microc/12_modelling/Akita/basenji/bin/akita_train.py", line 182, in
main()
File "/group/zhougrp4/dguan/microc/12_modelling/Akita/basenji/bin/akita_train.py", line 171, in main
seqnn_trainer.fit_keras(seqnn_model)
File "/group/zhougrp4/dguan/microc/12_modelling/Akita/basenji/basenji/trainer.py", line 141, in fit_keras
seqnn_model.model.fit(
File "/group/zhougrp4/dguan/bin/.conda/akita/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1131, in fit
val_logs = self.evaluate(
File "/group/zhougrp4/dguan/bin/.conda/akita/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1384, in evaluate
self.reset_metrics()
File "/group/zhougrp4/dguan/bin/.conda/akita/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1669, in reset_metrics
m.reset_states()
File "/group/zhougrp4/dguan/bin/.conda/akita/lib/python3.8/site-packages/tensorflow/python/keras/metrics.py", line 253, in reset_states
K.batch_set_value([(v, 0) for v in self.variables])
File "/group/zhougrp4/dguan/bin/.conda/akita/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "/group/zhougrp4/dguan/bin/.conda/akita/lib/python3.8/site-packages/tensorflow/python/keras/backend.py", line 3706, in batch_set_value
x.assign(np.asarray(value, dtype=dtype(x)))
File "/group/zhougrp4/dguan/bin/.conda/akita/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py", line 888, in assign
raise ValueError(
ValueError: Cannot assign to variable count:0 due to variable shape (2,) and value shape () are incompatible
My conda environment:
name: akita
channels:
- nvidia
- conda-forge
- bioconda
- defaults
dependencies: - _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_gnu
- abseil-cpp=20200923.3=h9c3ff4c_0
- absl-py=2.1.0=pyhd8ed1ab_0
- aiohttp=3.9.5=py38h01eb140_0
- aiosignal=1.3.1=pyhd8ed1ab_0
- alsa-lib=1.2.12=h4ab18f5_0
- anyio=4.4.0=pyhd8ed1ab_0
- argon2-cffi=23.1.0=pyhd8ed1ab_0
- argon2-cffi-bindings=21.2.0=py38h01eb140_4
- arrow=1.3.0=pyhd8ed1ab_0
- astor=0.8.1=pyh9f0ad1d_0
- astropy=5.1=py38h7deecbd_0
- asttokens=2.4.1=pyhd8ed1ab_0
- astunparse=1.6.3=pyhd8ed1ab_0
- async-lru=2.0.4=pyhd8ed1ab_0
- async-timeout=4.0.3=pyhd8ed1ab_0
- attr=2.5.1=h166bdaf_1
- attrs=23.2.0=pyh71513ae_0
- babel=2.14.0=pyhd8ed1ab_0
- backcall=0.2.0=pyh9f0ad1d_0
- beautifulsoup4=4.12.3=pyha770c72_0
- bedtools=2.31.1=hf5e1c6e_2
- blas=1.0=openblas
- bleach=6.1.0=pyhd8ed1ab_0
- blinker=1.8.2=pyhd8ed1ab_0
- bottleneck=1.3.7=py38ha9d4c09_0
- brotli=1.0.9=h5eee18b_8
- brotli-bin=1.0.9=h5eee18b_8
- brotli-python=1.0.9=py38h6a678d5_8
- bzip2=1.0.8=h5eee18b_6
- c-ares=1.32.3=h4bc722e_0
- ca-certificates=2024.7.4=hbcca054_0
- cached-property=1.5.2=hd8ed1ab_1
- cached_property=1.5.2=pyha770c72_1
- cairo=1.18.0=h3faef2a_0
- certifi=2024.7.4=pyhd8ed1ab_0
- cffi=1.16.0=py38h6d47a40_0
- charset-normalizer=3.3.2=pyhd3eb1b0_0
- click=8.1.7=unix_pyh707e725_0
- comm=0.2.2=pyhd8ed1ab_0
- contourpy=1.0.5=py38hdb19cb5_0
- cryptography=39.0.0=py38h1724139_0
- cudatoolkit=11.0.221=h6bb024c_0
- cudnn=8.0.0=cuda11.0_0
- cycler=0.11.0=pyhd3eb1b0_0
- cython=3.0.10=py38h5eee18b_0
- dbus=1.13.18=hb2f20db_0
- debugpy=1.8.2=py38h854fd01_0
- decorator=5.1.1=pyhd8ed1ab_0
- defusedxml=0.7.1=pyhd8ed1ab_0
- entrypoints=0.4=pyhd8ed1ab_0
- exceptiongroup=1.2.2=pyhd8ed1ab_0
- executing=2.0.1=pyhd8ed1ab_0
- expat=2.6.2=h6a678d5_0
- font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
- font-ttf-inconsolata=2.001=hcb22688_0
- font-ttf-source-code-pro=2.030=hd3eb1b0_0
- font-ttf-ubuntu=0.83=h8b1ccd4_0
- fontconfig=2.14.2=h14ed4e7_0
- fonts-anaconda=1=h8fa9717_0
- fonts-conda-ecosystem=1=hd3eb1b0_0
- fonttools=4.51.0=py38h5eee18b_0
- fqdn=1.5.1=pyhd8ed1ab_0
- freetype=2.12.1=h4a9f257_0
- frozenlist=1.4.1=py38h01eb140_0
- gettext=0.22.5=h59595ed_2
- gettext-tools=0.22.5=h59595ed_2
- giflib=5.2.2=hd590300_0
- glib=2.80.2=hf974151_0
- glib-tools=2.80.2=hb6ce0ca_0
- google-pasta=0.2.0=pyh8c360ce_0
- graphite2=1.3.14=h295c915_1
- grpc-cpp=1.36.4=hf89561c_1
- gst-plugins-base=1.14.1=h6a678d5_1
- gstreamer=1.14.1=h5eee18b_1
- h11=0.14.0=pyhd8ed1ab_0
- h2=4.1.0=pyhd8ed1ab_0
- h5py=2.10.0=nompi_py38h9915d05_106
- harfbuzz=8.5.0=hfac3d4d_0
- hdf5=1.10.6=nompi_h6a2412b_1114
- hpack=4.0.0=pyh9f0ad1d_0
- httpcore=1.0.5=pyhd8ed1ab_0
- httpx=0.27.0=pyhd8ed1ab_0
- hyperframe=6.0.1=pyhd8ed1ab_0
- icu=73.2=h59595ed_0
- idna=3.7=py38h06a4308_0
- importlib-metadata=8.2.0=pyha770c72_0
- importlib_metadata=8.2.0=hd8ed1ab_0
- importlib_resources=6.4.0=py38h06a4308_0
- intervaltree=3.1.0=pyhd3eb1b0_0
- ipykernel=6.29.5=pyh3099207_0
- ipython=8.12.2=pyh41d4057_0
- isoduration=20.11.0=pyhd8ed1ab_0
- jedi=0.19.1=pyhd8ed1ab_0
- jinja2=3.1.4=pyhd8ed1ab_0
- joblib=1.4.2=py38h06a4308_0
- jpeg=9e=h166bdaf_2
- json5=0.9.25=pyhd8ed1ab_0
- jsonpointer=3.0.0=py38h578d9bd_0
- jsonschema=4.23.0=pyhd8ed1ab_0
- jsonschema-specifications=2023.12.1=pyhd8ed1ab_0
- jsonschema-with-format-nongpl=4.23.0=hd8ed1ab_0
- jupyter-lsp=2.2.5=pyhd8ed1ab_0
- jupyter_client=8.6.2=pyhd8ed1ab_0
- jupyter_core=5.7.2=py38h578d9bd_0
- jupyter_events=0.10.0=pyhd8ed1ab_0
- jupyter_server=2.14.2=pyhd8ed1ab_0
- jupyter_server_terminals=0.5.3=pyhd8ed1ab_0
- jupyterlab=4.2.4=pyhd8ed1ab_0
- jupyterlab_pygments=0.3.0=pyhd8ed1ab_1
- jupyterlab_server=2.27.3=pyhd8ed1ab_0
- keras-preprocessing=1.1.2=pyhd8ed1ab_0
- keyutils=1.6.1=h166bdaf_0
- kiwisolver=1.4.4=py38h6a678d5_0
- krb5=1.20.1=hf9c8cef_0
- lame=3.100=h7b6447c_0
- lcms2=2.12=hddcbb42_0
- ld_impl_linux-64=2.38=h1181459_1
- lerc=4.0.0=h27087fc_0
- libasprintf=0.22.5=h661eb56_2
- libasprintf-devel=0.22.5=h661eb56_2
- libbrotlicommon=1.0.9=h5eee18b_8
- libbrotlidec=1.0.9=h5eee18b_8
- libbrotlienc=1.0.9=h5eee18b_8
- libcap=2.69=h0f662aa_0
- libclang-cpp15=15.0.7=default_h127d8a8_5
- libclang13=14.0.6=default_h9986a30_1
- libcups=2.3.3=h36d4200_3
- libcurl=7.88.1=h91b91d3_2
- libdeflate=1.20=hd590300_0
- libedit=3.1.20230828=h5eee18b_0
- libev=4.33=h7f8727e_1
- libevent=2.1.10=h9b69904_4
- libexpat=2.6.2=h59595ed_0
- libffi=3.4.4=h6a678d5_1
- libflac=1.4.3=h59595ed_0
- libgcc-ng=14.1.0=h77fa898_0
- libgcrypt=1.11.0=h4ab18f5_1
- libgettextpo=0.22.5=h59595ed_2
- libgettextpo-devel=0.22.5=h59595ed_2
- libgfortran-ng=11.2.0=h00389a5_1
- libgfortran5=11.2.0=h1234567_1
- libglib=2.80.2=hf974151_0
- libgomp=14.1.0=h77fa898_0
- libgpg-error=1.50=h4f305b6_0
- libiconv=1.17=hd590300_2
- libjpeg-turbo=2.1.4=h166bdaf_0
- libllvm14=14.0.6=hcd5def8_4
- libllvm15=15.0.7=hb3ce162_4
- libllvm18=18.1.7=hb77312f_0
- libnghttp2=1.52.0=ha637b67_1
- libnsl=2.0.1=hd590300_0
- libogg=1.3.5=h27cfd23_1
- libopenblas=0.3.21=h043d6bf_0
- libopus=1.3.1=h7b6447c_0
- libpng=1.6.43=h2797004_0
- libpq=12.15=h37d81fd_1
- libprotobuf=3.15.8=h780b84a_1
- libsndfile=1.2.2=hc60ed4a_1
- libsodium=1.0.18=h36c2ea0_1
- libsqlite=3.46.0=hde9e2c9_0
- libssh2=1.10.0=haa6b8db_3
- libstdcxx-ng=14.1.0=hc0a3c3a_0
- libsystemd0=255=h3516f8a_1
- libtiff=4.2.0=hf544144_3
- libuuid=2.38.1=h0b41bf4_0
- libvorbis=1.3.7=h7b6447c_0
- libwebp-base=1.3.2=h5eee18b_0
- libxcb=1.15=h7f8727e_0
- libxkbcommon=1.7.0=h662e7e4_0
- libxml2=2.12.7=hc051c1a_1
- libzlib=1.2.13=h4ab18f5_6
- lz4-c=1.9.4=h6a678d5_1
- markdown=3.6=pyhd8ed1ab_0
- markupsafe=2.1.5=py38h01eb140_0
- matplotlib=3.7.2=py38h06a4308_0
- matplotlib-base=3.7.2=py38h1128e8f_0
- matplotlib-inline=0.1.7=pyhd8ed1ab_0
- mistune=3.0.2=pyhd8ed1ab_0
- mpg123=1.32.6=h59595ed_0
- multidict=6.0.5=py38h01eb140_0
- mysql=5.7.20=hf484d3e_1001
- mysql-common=8.0.32=h14678bc_0
- mysql-libs=8.0.32=h54cf53e_0
- natsort=7.1.1=pyhd3eb1b0_0
- nbclient=0.10.0=pyhd8ed1ab_0
- nbconvert-core=7.16.4=pyhd8ed1ab_1
- nbformat=5.10.4=pyhd8ed1ab_0
- ncurses=6.4=h6a678d5_0
- nest-asyncio=1.6.0=pyhd8ed1ab_0
- networkx=3.1=py38h06a4308_0
- notebook-shim=0.2.4=pyhd8ed1ab_0
- nspr=4.35=h6a678d5_0
- nss=3.100=hca3bf56_0
- numexpr=2.8.4=py38hd2a5715_1
- oauthlib=3.2.2=pyhd8ed1ab_0
- olefile=0.47=pyhd8ed1ab_0
- openjpeg=2.4.0=hb52868f_1
- openssl=1.1.1w=hd590300_0
- opt_einsum=3.3.0=pyhc1e730c_2
- overrides=7.7.0=pyhd8ed1ab_0
- packaging=24.1=py38h06a4308_0
- pandas=2.0.3=py38h1128e8f_0
- pandocfilters=1.5.0=pyhd8ed1ab_0
- parso=0.8.4=pyhd8ed1ab_0
- patsy=0.5.6=py38h06a4308_0
- pcre2=10.43=hcad00b1_0
- pexpect=4.9.0=pyhd8ed1ab_0
- pickleshare=0.7.5=py_1003
- pillow=8.2.0=py38ha0e1e83_1
- pip=24.0=py38h06a4308_0
- pixman=0.43.2=h59595ed_0
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
- platformdirs=3.10.0=py38h06a4308_0
- ply=3.11=py38_0
- pooch=1.7.0=py38h06a4308_0
- prometheus_client=0.20.0=pyhd8ed1ab_0
- prompt-toolkit=3.0.47=pyha770c72_0
- prompt_toolkit=3.0.47=hd8ed1ab_0
- psutil=6.0.0=py38hfb59056_0
- ptyprocess=0.7.0=pyhd3deb0d_0
- pulseaudio-client=17.0=hb77b528_0
- pure_eval=0.2.3=pyhd8ed1ab_0
- pyasn1=0.6.0=pyhd8ed1ab_0
- pyasn1-modules=0.4.0=pyhd8ed1ab_0
- pybedtools=0.10.0=py38hd638cd3_2
- pybigwig=0.3.22=py38hf8a4f86_0
- pycparser=2.22=pyhd8ed1ab_0
- pyerfa=2.0.0=py38h27cfd23_0
- pygments=2.18.0=pyhd8ed1ab_0
- pyjwt=2.8.0=pyhd8ed1ab_1
- pyopenssl=23.2.0=pyhd8ed1ab_1
- pyparsing=3.0.9=py38h06a4308_0
- pyqt=5.15.10=py38h6a678d5_0
- pyqt5-sip=12.13.0=py38h5eee18b_0
- pysam=0.21.0=py38h1c8baaf_0
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.15=h257c98d_0_cpython
- python-dateutil=2.9.0post0=py38h06a4308_2
- python-fastjsonschema=2.20.0=pyhd8ed1ab_0
- python-flatbuffers=1.12=pyhd8ed1ab_1
- python-json-logger=2.0.7=pyhd8ed1ab_0
- python-tzdata=2023.3=pyhd3eb1b0_0
- python_abi=3.8=2_cp38
- pytz=2024.1=py38h06a4308_0
- pyu2f=0.1.5=pyhd8ed1ab_0
- pyyaml=6.0.1=py38h5eee18b_0
- pyzmq=26.0.3=py38ha44f8e3_0
- qt-main=5.15.2=h110a718_10
- re2=2021.04.01=h9c3ff4c_0
- readline=8.2=h5eee18b_0
- referencing=0.35.1=pyhd8ed1ab_0
- requests=2.32.3=py38h06a4308_0
- requests-oauthlib=2.0.0=pyhd8ed1ab_0
- rfc3339-validator=0.1.4=pyhd8ed1ab_0
- rfc3986-validator=0.1.1=pyh9f0ad1d_0
- rpds-py=0.19.1=py38h4005ec7_0
- rsa=4.9=pyhd8ed1ab_0
- scikit-learn=1.3.0=py38h1128e8f_1
- scipy=1.10.1=py38h32ae08f_1
- seaborn=0.13.2=py38h06a4308_0
- send2trash=1.8.3=pyh0d859eb_0
- setuptools=69.5.1=py38h06a4308_0
- sip=6.7.12=py38h6a678d5_0
- six=1.16.0=pyhd3eb1b0_1
- snappy=1.1.10=hdb0a2a9_1
- sniffio=1.3.1=pyhd8ed1ab_0
- sortedcontainers=2.4.0=pyhd3eb1b0_0
- soupsieve=2.5=pyhd8ed1ab_1
- sqlite=3.45.3=h5eee18b_0
- stack_data=0.6.2=pyhd8ed1ab_0
- statsmodels=0.14.0=py38ha9d4c09_0
- tabulate=0.9.0=py38h06a4308_0
- tensorboard-plugin-wit=1.8.1=pyhd8ed1ab_0
- tensorflow-base=2.4.0=py38h01d9eeb_0
- termcolor=2.4.0=pyhd8ed1ab_0
- terminado=0.18.1=pyh0d859eb_0
- threadpoolctl=3.5.0=py38h2f386ee_0
- tinycss2=1.3.0=pyhd8ed1ab_0
- tk=8.6.14=h39e8969_0
- tomli=2.0.1=py38h06a4308_0
- tornado=6.4.1=py38h5eee18b_0
- traitlets=5.14.3=pyhd8ed1ab_0
- types-python-dateutil=2.9.0.20240316=pyhd8ed1ab_0
- typing_extensions=4.12.2=pyha770c72_0
- typing_utils=0.1.0=pyhd8ed1ab_0
- unicodedata2=15.1.0=py38h5eee18b_0
- uri-template=1.3.0=pyhd8ed1ab_0
- urllib3=2.2.2=py38h06a4308_0
- wcwidth=0.2.13=pyhd8ed1ab_0
- webcolors=24.6.0=pyhd8ed1ab_0
- webencodings=0.5.1=pyhd8ed1ab_2
- websocket-client=1.8.0=pyhd8ed1ab_0
- werkzeug=3.0.3=pyhd8ed1ab_0
- wheel=0.43.0=py38h06a4308_0
- wrapt=1.16.0=py38h01eb140_0
- xcb-util=0.4.0=hd590300_1
- xcb-util-image=0.4.0=h8ee46fc_1
- xcb-util-keysyms=0.4.0=h8ee46fc_1
- xcb-util-renderutil=0.3.9=hd590300_1
- xcb-util-wm=0.4.1=h8ee46fc_1
- xkeyboard-config=2.42=h4ab18f5_0
- xorg-kbproto=1.0.7=h7f98852_1002
- xorg-libice=1.1.1=hd590300_0
- xorg-libsm=1.2.4=h7391055_0
- xorg-libx11=1.8.9=h8ee46fc_0
- xorg-libxau=1.0.11=hd590300_0
- xorg-libxext=1.3.4=h0b41bf4_2
- xorg-libxrender=0.9.11=hd590300_0
- xorg-renderproto=0.11.1=h7f98852_1002
- xorg-xextproto=7.3.0=h0b41bf4_1003
- xorg-xf86vidmodeproto=2.3.1=h7f98852_1002
- xorg-xproto=7.0.31=h27cfd23_1007
- xz=5.2.6=h166bdaf_0
- yaml=0.2.5=h7b6447c_0
- yarl=1.9.4=py38h01eb140_0
- zeromq=4.3.5=h59595ed_1
- zipp=3.17.0=py38h06a4308_0
- zlib=1.2.13=h4ab18f5_6
- zstd=1.5.6=ha6fb4c9_0
- pip:
- asciitree==0.3.3
- bioframe==0.7.2
- cachetools==5.4.0
- cooler==0.10.2
- cooltools==0.7.1
- cytoolz==0.12.3
- dill==0.3.8
- flatbuffers==24.3.25
- gast==0.4.0
- google-auth==2.32.0
- google-auth-oauthlib==1.0.0
- grpcio==1.65.1
- imageio==2.34.2
- keras==2.13.1
- lazy-loader==0.4
- libclang==18.1.1
- llvmlite==0.41.1
- multiprocess==0.70.16
- numba==0.58.1
- numpy==1.21.5
- protobuf==4.25.4
- pyfaidx==0.8.1.1
- pywavelets==1.4.1
- scikit-image==0.21.0
- simplejson==3.19.2
- tensorboard==2.13.0
- tensorboard-data-server==0.7.2
- tensorflow==2.13.1
- tensorflow-estimator==2.13.0
- tensorflow-io-gcs-filesystem==0.34.0
- tifffile==2023.7.10
- toolz==0.12.1
- typing-extensions==4.5.0
prefix: user/bin/.conda/akita