GithubAction: Error while running

[ivatar39]

Hi, so I did setup as in the example and while running Hercules, got errors.
Workflow file:
`on: [push]

jobs:
hercules-charts:
runs-on: ubuntu-latest
name: Charts generated by src-d/hercules
steps:
- uses: actions/checkout@master
with:
fetch-depth: 0
- name: Hercules
uses: src-d/hercules@master
- uses: actions/upload-artifact@master
with:
name: hercules_charts
path: hercules_charts.tar
`
Output:

`Run src-d/hercules@master
with:
args: --burndown --burndown-people --devs --couples
/usr/bin/docker run --name srcdherculeslatest_801fcc --label 5588e4 --workdir /github/workspace --rm -e INPUT_ARGS -e HOME -e GITHUB_JOB -e GITHUB_REF -e GITHUB_SHA -e GITHUB_REPOSITORY -e GITHUB_REPOSITORY_OWNER -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RETENTION_DAYS -e GITHUB_ACTOR -e GITHUB_WORKFLOW -e GITHUB_HEAD_REF -e GITHUB_BASE_REF -e GITHUB_EVENT_NAME -e GITHUB_SERVER_URL -e GITHUB_API_URL -e GITHUB_GRAPHQL_URL -e GITHUB_WORKSPACE -e GITHUB_ACTION -e GITHUB_EVENT_PATH -e GITHUB_ACTION_REPOSITORY -e GITHUB_ACTION_REF -e GITHUB_PATH -e GITHUB_ENV -e RUNNER_OS -e RUNNER_TOOL_CACHE -e RUNNER_TEMP -e RUNNER_WORKSPACE -e ACTIONS_RUNTIME_URL -e ACTIONS_RUNTIME_TOKEN -e ACTIONS_CACHE_URL -e GITHUB_ACTIONS=true -e CI=true -v "/var/run/docker.sock":"/var/run/docker.sock" -v "/home/runner/work/_temp/_github_home":"/github/home" -v "/home/runner/work/_temp/_github_workflow":"/github/workflow" -v "/home/runner/work/_temp/runner_file_commands":"/github/file_commands" -v "/home/runner/work/carboneum_flutter/carboneum_flutter":"/github/workspace" srcd/hercules:latest "/bin/bash" "-c" "hercules --burndown --burndown-people --devs --couples --pb . | labours -m all -f pb --disable-projector -o hercules_charts && cd hercules_charts && tar -cf ../hercules_charts.tar * ../hercules_charts* && cd .. && rm -r hercules_charts"
git log...

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53%|█████▎ | 10/19 [00:00<00:00, 19.33it/s]
84%|████████▍ | 16/19 [00:00<00:00, 24.08it/s]
100%|██████████| 19/19 [00:00<00:00, 30.91it/s]
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:388: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:388: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:106: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.

INFO:tensorflow:creating the model...
INFO:tensorflow:Reading model from: /tmp/hercules_labours_efwv3h10
INFO:tensorflow:Matrix dim: (5,5) SubMatrix dim: (5,5)
INFO:tensorflow:n_submatrices: 1
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:124: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:278: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:190: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensors(tensor).repeat(num_epochs).
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:199: QueueRunner.init (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the tf.data module.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:199: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the tf.data module.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:125: WholeFileReader.init (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.map(tf.read_file).
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:127: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:130: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:132: The name tf.VarLenFeature is deprecated. Please use tf.io.VarLenFeature instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:147: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a tf.sparse.SparseTensor and use tf.sparse.to_dense instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:153: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.batch(batch_size) (or padded_batch(...) if dynamic_pad=True).
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:116: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:249: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:268: The name tf.train.AdagradOptimizer is deprecated. Please use tf.compat.v1.train.AdagradOptimizer instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:270: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

2021-04-11 09:19:44.374535: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2021-04-11 09:19:44.378933: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2095205000 Hz
2021-04-11 09:19:44.379204: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4bf4f60 executing computations on platform Host. Devices:
2021-04-11 09:19:44.379289: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): ,
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:303: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:306: The name tf.log is deprecated. Please use tf.math.log instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/adagrad.py:76: calling Constant.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:354: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

INFO:tensorflow:initializing the variables...
2021-04-11 09:19:44.548469: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
INFO:tensorflow:starting the input threads...
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/labours/_vendor/swivel.py:419: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the tf.data module.
INFO:tensorflow: 100/10000 submatrices trained (1.0%), 574.4 submatrices/sec | loss 57.447186
INFO:tensorflow: 201/10000 submatrices trained (2.0%), 3058.7 submatrices/sec | loss 51.929779
INFO:tensorflow: 300/10000 submatrices trained (3.0%), 3227.9 submatrices/sec | loss 47.034813
INFO:tensorflow: 400/10000 submatrices trained (4.0%), 3096.2 submatrices/sec | loss 42.558212
INFO:tensorflow: 500/10000 submatrices trained (5.0%), 3427.5 submatrices/sec | loss 38.507442
INFO:tensorflow: 601/10000 submatrices trained (6.0%), 3328.2 submatrices/sec | loss 34.807297
INFO:tensorflow: 700/10000 submatrices trained (7.0%), 3052.5 submatrices/sec | loss 31.525620
INFO:tensorflow: 800/10000 submatrices trained (8.0%), 3400.2 submatrices/sec | loss 28.524775
INFO:tensorflow: 900/10000 submatrices trained (9.0%), 3795.0 submatrices/sec | loss 25.809549
INFO:tensorflow: 1000/10000 submatrices trained (10.0%), 3619.9 submatrices/sec | loss 23.352734
INFO:tensorflow: 1101/10000 submatrices trained (11.0%), 3685.5 submatrices/sec | loss 21.108660
INFO:tensorflow: 1200/10000 submatrices trained (12.0%), 3542.9 submatrices/sec | loss 19.118465
INFO:tensorflow: 1300/10000 submatrices trained (13.0%), 3577.0 submatrices/sec | loss 17.298607
INFO:tensorflow: 1400/10000 submatrices trained (14.0%), 3526.9 submatrices/sec | loss 15.651988
INFO:tensorflow: 1500/10000 submatrices trained (15.0%), 3496.5 submatrices/sec | loss 14.162112
INFO:tensorflow: 1601/10000 submatrices trained (16.0%), 2261.5 submatrices/sec | loss 12.801235
INFO:tensorflow: 1700/10000 submatrices trained (17.0%), 2874.8 submatrices/sec | loss 11.594312
INFO:tensorflow: 1800/10000 submatrices trained (18.0%), 2866.9 submatrices/sec | loss 10.490691
INFO:tensorflow: 1900/10000 submatrices trained (19.0%), 3006.3 submatrices/sec | loss 9.492129
INFO:tensorflow: 2000/10000 submatrices trained (20.0%), 2989.4 submatrices/sec | loss 8.588629
INFO:tensorflow: 2100/10000 submatrices trained (21.0%), 2881.3 submatrices/sec | loss 7.771141
INFO:tensorflow: 2200/10000 submatrices trained (22.0%), 3292.8 submatrices/sec | loss 7.031469
INFO:tensorflow: 2300/10000 submatrices trained (23.0%), 3139.8 submatrices/sec | loss 6.362209
INFO:tensorflow: 2400/10000 submatrices trained (24.0%), 2412.3 submatrices/sec | loss 5.750894
INFO:tensorflow: 2500/10000 submatrices trained (25.0%), 2751.5 submatrices/sec | loss 5.208725
INFO:tensorflow: 2600/10000 submatrices trained (26.0%), 2565.5 submatrices/sec | loss 4.783202
INFO:tensorflow: 2700/10000 submatrices trained (27.0%), 2701.8 submatrices/sec | loss 8.845915
INFO:tensorflow: 2800/10000 submatrices trained (28.0%), 2993.8 submatrices/sec | loss 8.004674
INFO:tensorflow: 2900/10000 submatrices trained (29.0%), 2833.1 submatrices/sec | loss 7.243300
INFO:tensorflow: 3000/10000 submatrices trained (30.0%), 3009.3 submatrices/sec | loss 6.554252
INFO:tensorflow: 3100/10000 submatrices trained (31.0%), 2446.7 submatrices/sec | loss 5.930758
INFO:tensorflow: 3200/10000 submatrices trained (32.0%), 3380.8 submatrices/sec | loss 5.366554
INFO:tensorflow: 3300/10000 submatrices trained (33.0%), 3410.3 submatrices/sec | loss 4.856030
INFO:tensorflow: 3400/10000 submatrices trained (34.0%), 3367.3 submatrices/sec | loss 4.394065
INFO:tensorflow: 3500/10000 submatrices trained (35.0%), 3363.6 submatrices/sec | loss 3.976058
INFO:tensorflow: 3600/10000 submatrices trained (36.0%), 2856.0 submatrices/sec | loss 3.597828
INFO:tensorflow: 3700/10000 submatrices trained (37.0%), 2601.2 submatrices/sec | loss 3.255578
INFO:tensorflow: 3800/10000 submatrices trained (38.0%), 1758.7 submatrices/sec | loss 2.945902
INFO:tensorflow: 3900/10000 submatrices trained (39.0%), 1912.9 submatrices/sec | loss 2.665694
INFO:tensorflow: 4000/10000 submatrices trained (40.0%), 2067.2 submatrices/sec | loss 2.412149
INFO:tensorflow: 4100/10000 submatrices trained (41.0%), 2977.7 submatrices/sec | loss 2.182731
INFO:tensorflow: 4200/10000 submatrices trained (42.0%), 3205.3 submatrices/sec | loss 1.975141
INFO:tensorflow: 4300/10000 submatrices trained (43.0%), 3347.9 submatrices/sec | loss 1.787304
INFO:tensorflow: 4400/10000 submatrices trained (44.0%), 3501.5 submatrices/sec | loss 1.617345
INFO:tensorflow: 4501/10000 submatrices trained (45.0%), 3122.1 submatrices/sec | loss 1.462096
INFO:tensorflow: 4601/10000 submatrices trained (46.0%), 3535.6 submatrices/sec | loss 1.323083
INFO:tensorflow: 4700/10000 submatrices trained (47.0%), 3619.0 submatrices/sec | loss 1.198492
INFO:tensorflow: 4800/10000 submatrices trained (48.0%), 3265.9 submatrices/sec | loss 1.084561
INFO:tensorflow: 4900/10000 submatrices trained (49.0%), 3458.3 submatrices/sec | loss 0.981473
INFO:tensorflow: 5000/10000 submatrices trained (50.0%), 3362.1 submatrices/sec | loss 0.888193
INFO:tensorflow: 5101/10000 submatrices trained (51.0%), 3427.1 submatrices/sec | loss 0.802987
INFO:tensorflow: 5201/10000 submatrices trained (52.0%), 3121.5 submatrices/sec | loss 0.726689
INFO:tensorflow: 5300/10000 submatrices trained (53.0%), 2738.2 submatrices/sec | loss 0.658309
INFO:tensorflow: 5400/10000 submatrices trained (54.0%), 2771.4 submatrices/sec | loss 0.595775
INFO:tensorflow: 5500/10000 submatrices trained (55.0%), 3959.2 submatrices/sec | loss 0.539193
INFO:tensorflow: 5601/10000 submatrices trained (56.0%), 2735.6 submatrices/sec | loss 0.487507
INFO:tensorflow: 5700/10000 submatrices trained (57.0%), 2775.6 submatrices/sec | loss 0.441664
INFO:tensorflow: 5800/10000 submatrices trained (58.0%), 2674.7 submatrices/sec | loss 0.399742
INFO:tensorflow: 5900/10000 submatrices trained (59.0%), 2822.4 submatrices/sec | loss 0.361809
INFO:tensorflow: 6000/10000 submatrices trained (60.0%), 2982.6 submatrices/sec | loss 0.327484
INFO:tensorflow: 6100/10000 submatrices trained (61.0%), 2668.4 submatrices/sec | loss 0.296425
INFO:tensorflow: 6200/10000 submatrices trained (62.0%), 2802.1 submatrices/sec | loss 0.268321
INFO:tensorflow: 6300/10000 submatrices trained (63.0%), 2685.0 submatrices/sec | loss 0.242889
INFO:tensorflow: 6400/10000 submatrices trained (64.0%), 3212.2 submatrices/sec | loss 0.219875
INFO:tensorflow: 6500/10000 submatrices trained (65.0%), 2975.4 submatrices/sec | loss 0.199051
INFO:tensorflow: 6600/10000 submatrices trained (66.0%), 3457.4 submatrices/sec | loss 0.180206
INFO:tensorflow: 6700/10000 submatrices trained (67.0%), 3584.9 submatrices/sec | loss 0.163154
INFO:tensorflow: 6800/10000 submatrices trained (68.0%), 3580.7 submatrices/sec | loss 0.147723
INFO:tensorflow: 6900/10000 submatrices trained (69.0%), 2853.6 submatrices/sec | loss 0.133760
INFO:tensorflow: 7000/10000 submatrices trained (70.0%), 2529.3 submatrices/sec | loss 0.121124
INFO:tensorflow: 7100/10000 submatrices trained (71.0%), 2943.7 submatrices/sec | loss 0.109689
INFO:tensorflow: 7200/10000 submatrices trained (72.0%), 2973.1 submatrices/sec | loss 0.099340
INFO:tensorflow: 7300/10000 submatrices trained (73.0%), 3401.9 submatrices/sec | loss 0.089975
INFO:tensorflow: 7400/10000 submatrices trained (74.0%), 2723.7 submatrices/sec | loss 0.081501
INFO:tensorflow: 7500/10000 submatrices trained (75.0%), 3155.3 submatrices/sec | loss 0.073831
INFO:tensorflow: 7600/10000 submatrices trained (76.0%), 2797.9 submatrices/sec | loss 0.066890
INFO:tensorflow: 7700/10000 submatrices trained (77.0%), 2600.4 submatrices/sec | loss 0.060609
INFO:tensorflow: 7800/10000 submatrices trained (78.0%), 2596.2 submatrices/sec | loss 0.054924
INFO:tensorflow: 7900/10000 submatrices trained (79.0%), 2657.4 submatrices/sec | loss 0.049779
INFO:tensorflow: 8000/10000 submatrices trained (80.0%), 2655.9 submatrices/sec | loss 0.045122
INFO:tensorflow: 8100/10000 submatrices trained (81.0%), 2805.9 submatrices/sec | loss 0.040907
INFO:tensorflow: 8200/10000 submatrices trained (82.0%), 2599.2 submatrices/sec | loss 0.037093
INFO:tensorflow: 8300/10000 submatrices trained (83.0%), 3173.9 submatrices/sec | loss 0.033640
INFO:tensorflow: 8400/10000 submatrices trained (84.0%), 2670.2 submatrices/sec | loss 0.030515
INFO:tensorflow: 8500/10000 submatrices trained (85.0%), 2922.1 submatrices/sec | loss 0.027686
INFO:tensorflow: 8600/10000 submatrices trained (86.0%), 3344.0 submatrices/sec | loss 0.025125
INFO:tensorflow: 8700/10000 submatrices trained (87.0%), 2922.6 submatrices/sec | loss 0.022807
INFO:tensorflow: 8800/10000 submatrices trained (88.0%), 3990.0 submatrices/sec | loss 0.020709
INFO:tensorflow: 8900/10000 submatrices trained (89.0%), 3834.2 submatrices/sec | loss 0.018810
INFO:tensorflow: 9000/10000 submatrices trained (90.0%), 3643.2 submatrices/sec | loss 0.017090
INFO:tensorflow: 9100/10000 submatrices trained (91.0%), 3699.4 submatrices/sec | loss 0.015533
INFO:tensorflow: 9200/10000 submatrices trained (92.0%), 3546.1 submatrices/sec | loss 0.014123
INFO:tensorflow: 9300/10000 submatrices trained (93.0%), 2551.0 submatrices/sec | loss 0.012847
INFO:tensorflow: 9400/10000 submatrices trained (94.0%), 2652.2 submatrices/sec | loss 0.011691
INFO:tensorflow: 9500/10000 submatrices trained (95.0%), 2955.2 submatrices/sec | loss 0.010645
INFO:tensorflow: 9600/10000 submatrices trained (96.0%), 2848.0 submatrices/sec | loss 0.009697
INFO:tensorflow: 9700/10000 submatrices trained (97.0%), 3115.3 submatrices/sec | loss 0.008839
INFO:tensorflow: 9800/10000 submatrices trained (98.0%), 2870.9 submatrices/sec | loss 0.008061
INFO:tensorflow: 9900/10000 submatrices trained (99.0%), 1997.2 submatrices/sec | loss 0.007357
INFO:tensorflow: 10000/10000 submatrices trained (100.0%), 2171.3 submatrices/sec | loss 0.006719
INFO:tensorflow:Writing row embeddings to: /tmp/hercules_labours_efwv3h10/row_embedding.tsv
INFO:tensorflow:Writing column embeddings to: /tmp/hercules_labours_efwv3h10/col_embedding.tsv
INFO:tensorflow:Elapsed: 3.8362302780151367
INFO:tensorflow:creating the model...
INFO:tensorflow:Reading model from: /tmp/hercules_labours_1zymdeb7
INFO:tensorflow:Matrix dim: (370,370) SubMatrix dim: (370,370)
INFO:tensorflow:n_submatrices: 1
INFO:tensorflow:initializing the variables...
INFO:tensorflow:starting the input threads...
INFO:tensorflow: 100/5000 submatrices trained (2.0%), 176.1 submatrices/sec | loss 117.419067
INFO:tensorflow: 200/5000 submatrices trained (4.0%), 251.7 submatrices/sec | loss 107.003517
INFO:tensorflow: 300/5000 submatrices trained (6.0%), 237.8 submatrices/sec | loss 97.616951
INFO:tensorflow: 400/5000 submatrices trained (8.0%), 231.2 submatrices/sec | loss 88.731133
INFO:tensorflow: 500/5000 submatrices trained (10.0%), 222.8 submatrices/sec | loss 80.620552
INFO:tensorflow: 600/5000 submatrices trained (12.0%), 254.0 submatrices/sec | loss 73.237427
INFO:tensorflow: 700/5000 submatrices trained (14.0%), 253.4 submatrices/sec | loss 66.524803
INFO:tensorflow: 800/5000 submatrices trained (16.0%), 251.8 submatrices/sec | loss 60.426285
INFO:tensorflow: 900/5000 submatrices trained (18.0%), 259.0 submatrices/sec | loss 54.888363
INFO:tensorflow: 1000/5000 submatrices trained (20.0%), 253.5 submatrices/sec | loss 49.861134
INFO:tensorflow: 1101/5000 submatrices trained (22.0%), 256.7 submatrices/sec | loss 45.255192
INFO:tensorflow: 1200/5000 submatrices trained (24.0%), 227.5 submatrices/sec | loss 41.158661
INFO:tensorflow: 1300/5000 submatrices trained (26.0%), 267.1 submatrices/sec | loss 37.369839
INFO:tensorflow: 1400/5000 submatrices trained (28.0%), 247.2 submatrices/sec | loss 34.272724
INFO:tensorflow: 1500/5000 submatrices trained (30.0%), 243.3 submatrices/sec | loss 31.161715
INFO:tensorflow: 1600/5000 submatrices trained (32.0%), 256.7 submatrices/sec | loss 28.311520
INFO:tensorflow: 1700/5000 submatrices trained (34.0%), 254.5 submatrices/sec | loss 25.776636
INFO:tensorflow: 1800/5000 submatrices trained (36.0%), 254.4 submatrices/sec | loss 23.452452
INFO:tensorflow: 1900/5000 submatrices trained (38.0%), 251.5 submatrices/sec | loss 21.343958
INFO:tensorflow: 2000/5000 submatrices trained (40.0%), 256.8 submatrices/sec | loss 19.412983
INFO:tensorflow: 2100/5000 submatrices trained (42.0%), 246.3 submatrices/sec | loss 17.679497
INFO:tensorflow: 2200/5000 submatrices trained (44.0%), 254.3 submatrices/sec | loss 16.165144
INFO:tensorflow: 2300/5000 submatrices trained (46.0%), 247.5 submatrices/sec | loss 14.733594
INFO:tensorflow: 2400/5000 submatrices trained (48.0%), 253.5 submatrices/sec | loss 13.434818
INFO:tensorflow: 2501/5000 submatrices trained (50.0%), 248.0 submatrices/sec | loss 12.245244
INFO:tensorflow: 2600/5000 submatrices trained (52.0%), 254.9 submatrices/sec | loss 11.187303
INFO:tensorflow: 2700/5000 submatrices trained (54.0%), 246.8 submatrices/sec | loss 10.207927
INFO:tensorflow: 2800/5000 submatrices trained (56.0%), 262.1 submatrices/sec | loss 9.336803
INFO:tensorflow: 2900/5000 submatrices trained (58.0%), 245.9 submatrices/sec | loss 8.537844
INFO:tensorflow: 3000/5000 submatrices trained (60.0%), 250.6 submatrices/sec | loss 7.812690
INFO:tensorflow: 3100/5000 submatrices trained (62.0%), 247.0 submatrices/sec | loss 7.154451
INFO:tensorflow: 3200/5000 submatrices trained (64.0%), 250.0 submatrices/sec | loss 6.556880
INFO:tensorflow: 3300/5000 submatrices trained (66.0%), 251.3 submatrices/sec | loss 6.014314
INFO:tensorflow: 3400/5000 submatrices trained (68.0%), 250.2 submatrices/sec | loss 5.521621
INFO:tensorflow: 3500/5000 submatrices trained (70.0%), 254.8 submatrices/sec | loss 5.074150
INFO:tensorflow: 3600/5000 submatrices trained (72.0%), 255.5 submatrices/sec | loss 4.667675
INFO:tensorflow: 3700/5000 submatrices trained (74.0%), 249.7 submatrices/sec | loss 4.298379
INFO:tensorflow: 3800/5000 submatrices trained (76.0%), 253.6 submatrices/sec | loss 3.962794
INFO:tensorflow: 3900/5000 submatrices trained (78.0%), 256.5 submatrices/sec | loss 3.654874
INFO:tensorflow: 4000/5000 submatrices trained (80.0%), 244.2 submatrices/sec | loss 3.409462
INFO:tensorflow: 4100/5000 submatrices trained (82.0%), 246.0 submatrices/sec | loss 3.200579
INFO:tensorflow: 4200/5000 submatrices trained (84.0%), 239.2 submatrices/sec | loss 2.964880
INFO:tensorflow: 4300/5000 submatrices trained (86.0%), 248.4 submatrices/sec | loss 2.750436
INFO:tensorflow: 4400/5000 submatrices trained (88.0%), 250.3 submatrices/sec | loss 2.555278
INFO:tensorflow: 4500/5000 submatrices trained (90.0%), 255.4 submatrices/sec | loss 2.377620
INFO:tensorflow: 4600/5000 submatrices trained (92.0%), 253.1 submatrices/sec | loss 2.215850
INFO:tensorflow: 4700/5000 submatrices trained (94.0%), 244.6 submatrices/sec | loss 2.353528
INFO:tensorflow: 4801/5000 submatrices trained (96.0%), 249.1 submatrices/sec | loss 2.193604
INFO:tensorflow: 4900/5000 submatrices trained (98.0%), 246.0 submatrices/sec | loss 2.050436
INFO:tensorflow: 5000/5000 submatrices trained (100.0%), 242.2 submatrices/sec | loss 1.918356
INFO:tensorflow:Writing row embeddings to: /tmp/hercules_labours_1zymdeb7/row_embedding.tsv
INFO:tensorflow:Writing column embeddings to: /tmp/hercules_labours_1zymdeb7/col_embedding.tsv
INFO:tensorflow:Elapsed: 20.537511587142944
INFO:tensorflow:creating the model...
INFO:tensorflow:Reading model from: /tmp/hercules_labours_nx1nsxsk
INFO:tensorflow:Matrix dim: (5,5) SubMatrix dim: (5,5)
INFO:tensorflow:n_submatrices: 1
INFO:tensorflow:initializing the variables...
INFO:tensorflow:starting the input threads...
INFO:tensorflow: 100/10000 submatrices trained (1.0%), 918.7 submatrices/sec | loss 15.912416
INFO:tensorflow: 200/10000 submatrices trained (2.0%), 2782.4 submatrices/sec | loss 14.397369
INFO:tensorflow: 300/10000 submatrices trained (3.0%), 3194.4 submatrices/sec | loss 13.026570
INFO:tensorflow: 400/10000 submatrices trained (4.0%), 2802.6 submatrices/sec | loss 11.786304
INFO:tensorflow: 500/10000 submatrices trained (5.0%), 3149.1 submatrices/sec | loss 10.664139
INFO:tensorflow: 600/10000 submatrices trained (6.0%), 2996.5 submatrices/sec | loss 9.648821
INFO:tensorflow: 700/10000 submatrices trained (7.0%), 2706.6 submatrices/sec | loss 8.730189
INFO:tensorflow: 800/10000 submatrices trained (8.0%), 2903.6 submatrices/sec | loss 7.898962
INFO:tensorflow: 900/10000 submatrices trained (9.0%), 3051.2 submatrices/sec | loss 7.146900
INFO:tensorflow: 1000/10000 submatrices trained (10.0%), 3060.6 submatrices/sec | loss 6.466442
INFO:tensorflow: 1100/10000 submatrices trained (11.0%), 3069.5 submatrices/sec | loss 5.850783
INFO:tensorflow: 1200/10000 submatrices trained (12.0%), 2759.8 submatrices/sec | loss 5.293733
INFO:tensorflow: 1300/10000 submatrices trained (13.0%), 2626.8 submatrices/sec | loss 4.789716
INFO:tensorflow: 1400/10000 submatrices trained (14.0%), 2817.6 submatrices/sec | loss 4.333685
INFO:tensorflow: 1500/10000 submatrices trained (15.0%), 2983.6 submatrices/sec | loss 3.921072
INFO:tensorflow: 1600/10000 submatrices trained (16.0%), 2915.4 submatrices/sec | loss 3.547759
INFO:tensorflow: 1700/10000 submatrices trained (17.0%), 2931.2 submatrices/sec | loss 3.209989
INFO:tensorflow: 1800/10000 submatrices trained (18.0%), 2915.5 submatrices/sec | loss 2.904378
INFO:tensorflow: 1900/10000 submatrices trained (19.0%), 2934.7 submatrices/sec | loss 2.627863
INFO:tensorflow: 2000/10000 submatrices trained (20.0%), 2869.9 submatrices/sec | loss 2.377672
INFO:tensorflow: 2100/10000 submatrices trained (21.0%), 2949.9 submatrices/sec | loss 2.151299
INFO:tensorflow: 2200/10000 submatrices trained (22.0%), 2927.6 submatrices/sec | loss 1.946482
INFO:tensorflow: 2300/10000 submatrices trained (23.0%), 2809.8 submatrices/sec | loss 1.761163
INFO:tensorflow: 2400/10000 submatrices trained (24.0%), 2680.5 submatrices/sec | loss 1.593489
INFO:tensorflow: 2500/10000 submatrices trained (25.0%), 2858.9 submatrices/sec | loss 1.441778
INFO:tensorflow: 2601/10000 submatrices trained (26.0%), 2867.2 submatrices/sec | loss 1.303206
INFO:tensorflow: 2700/10000 submatrices trained (27.0%), 2806.4 submatrices/sec | loss 1.180312
INFO:tensorflow: 2800/10000 submatrices trained (28.0%), 2833.9 submatrices/sec | loss 1.067939
INFO:tensorflow: 2900/10000 submatrices trained (29.0%), 2852.4 submatrices/sec | loss 0.966264
INFO:tensorflow: 3000/10000 submatrices trained (30.0%), 2845.0 submatrices/sec | loss 0.874269
INFO:tensorflow: 3100/10000 submatrices trained (31.0%), 3088.3 submatrices/sec | loss 0.791033
INFO:tensorflow: 3200/10000 submatrices trained (32.0%), 3049.7 submatrices/sec | loss 0.715721
INFO:tensorflow: 3300/10000 submatrices trained (33.0%), 3015.8 submatrices/sec | loss 0.647579
INFO:tensorflow: 3400/10000 submatrices trained (34.0%), 3074.3 submatrices/sec | loss 0.585925
INFO:tensorflow: 3500/10000 submatrices trained (35.0%), 3170.9 submatrices/sec | loss 0.530141
INFO:tensorflow: 3600/10000 submatrices trained (36.0%), 3084.7 submatrices/sec | loss 0.479668
INFO:tensorflow: 3700/10000 submatrices trained (37.0%), 3212.7 submatrices/sec | loss 0.434001
INFO:tensorflow: 3800/10000 submatrices trained (38.0%), 3212.5 submatrices/sec | loss 0.392681
INFO:tensorflow: 3900/10000 submatrices trained (39.0%), 2721.9 submatrices/sec | loss 0.355295
INFO:tensorflow: 4000/10000 submatrices trained (40.0%), 3167.8 submatrices/sec | loss 0.321468
INFO:tensorflow: 4100/10000 submatrices trained (41.0%), 3016.2 submatrices/sec | loss 0.290862
INFO:tensorflow: 4200/10000 submatrices trained (42.0%), 3043.9 submatrices/sec | loss 0.263170
INFO:tensorflow: 4300/10000 submatrices trained (43.0%), 3102.3 submatrices/sec | loss 0.238115
INFO:tensorflow: 4400/10000 submatrices trained (44.0%), 3122.7 submatrices/sec | loss 0.215445
INFO:tensorflow: 4500/10000 submatrices trained (45.0%), 3160.4 submatrices/sec | loss 0.194932
INFO:tensorflow: 4600/10000 submatrices trained (46.0%), 3106.6 submatrices/sec | loss 0.176373
INFO:tensorflow: 4700/10000 submatrices trained (47.0%), 2924.8 submatrices/sec | loss 0.159581
INFO:tensorflow: 4800/10000 submatrices trained (48.0%), 2780.9 submatrices/sec | loss 0.144388
INFO:tensorflow: 4900/10000 submatrices trained (49.0%), 2773.6 submatrices/sec | loss 0.130641
INFO:tensorflow: 5000/10000 submatrices trained (50.0%), 2896.9 submatrices/sec | loss 0.118204
INFO:tensorflow: 5100/10000 submatrices trained (51.0%), 2743.2 submatrices/sec | loss 0.106950
INFO:tensorflow: 5200/10000 submatrices trained (52.0%), 2729.7 submatrices/sec | loss 0.096767
INFO:tensorflow: 5300/10000 submatrices trained (53.0%), 2648.5 submatrices/sec | loss 0.087553
INFO:tensorflow: 5400/10000 submatrices trained (54.0%), 2724.1 submatrices/sec | loss 0.079217
INFO:tensorflow: 5500/10000 submatrices trained (55.0%), 2794.1 submatrices/sec | loss 0.071675
INFO:tensorflow: 5600/10000 submatrices trained (56.0%), 2736.9 submatrices/sec | loss 0.064850
INFO:tensorflow: 5701/10000 submatrices trained (57.0%), 2786.5 submatrices/sec | loss 0.058617
INFO:tensorflow: 5800/10000 submatrices trained (58.0%), 2899.7 submatrices/sec | loss 0.053089
INFO:tensorflow: 5900/10000 submatrices trained (59.0%), 3233.3 submatrices/sec | loss 0.048035
INFO:tensorflow: 6000/10000 submatrices trained (60.0%), 3045.1 submatrices/sec | loss 0.043461
INFO:tensorflow: 6100/10000 submatrices trained (61.0%), 3342.6 submatrices/sec | loss 0.039323
INFO:tensorflow: 6200/10000 submatrices trained (62.0%), 3230.6 submatrices/sec | loss 0.035544
INFO:tensorflow: 6300/10000 submatrices trained (63.0%), 3255.4 submatrices/sec | loss 0.032192
INFO:tensorflow: 6400/10000 submatrices trained (64.0%), 3090.8 submatrices/sec | loss 0.029127
INFO:tensorflow: 6500/10000 submatrices trained (65.0%), 2758.0 submatrices/sec | loss 0.026354
INFO:tensorflow: 6600/10000 submatrices trained (66.0%), 2835.0 submatrices/sec | loss 0.023845
INFO:tensorflow: 6700/10000 submatrices trained (67.0%), 2792.1 submatrices/sec | loss 0.021574
INFO:tensorflow: 6800/10000 submatrices trained (68.0%), 2825.8 submatrices/sec | loss 0.019520
INFO:tensorflow: 6900/10000 submatrices trained (69.0%), 2710.4 submatrices/sec | loss 0.017662
INFO:tensorflow: 7001/10000 submatrices trained (70.0%), 2852.7 submatrices/sec | loss 0.015964
INFO:tensorflow: 7100/10000 submatrices trained (71.0%), 2850.8 submatrices/sec | loss 0.014459
INFO:tensorflow: 7200/10000 submatrices trained (72.0%), 2617.2 submatrices/sec | loss 0.013082
INFO:tensorflow: 7300/10000 submatrices trained (73.0%), 2602.0 submatrices/sec | loss 0.011837
INFO:tensorflow: 7400/10000 submatrices trained (74.0%), 2790.6 submatrices/sec | loss 0.010710
INFO:tensorflow: 7500/10000 submatrices trained (75.0%), 2715.4 submatrices/sec | loss 0.009690
INFO:tensorflow: 7600/10000 submatrices trained (76.0%), 2831.0 submatrices/sec | loss 0.008768
INFO:tensorflow: 7700/10000 submatrices trained (77.0%), 2665.7 submatrices/sec | loss 0.007933
INFO:tensorflow: 7800/10000 submatrices trained (78.0%), 2754.2 submatrices/sec | loss 0.007178
INFO:tensorflow: 7900/10000 submatrices trained (79.0%), 2643.5 submatrices/sec | loss 0.006494
INFO:tensorflow: 8000/10000 submatrices trained (80.0%), 2788.0 submatrices/sec | loss 0.005876
INFO:tensorflow: 8100/10000 submatrices trained (81.0%), 2032.0 submatrices/sec | loss 0.005316
INFO:tensorflow: 8200/10000 submatrices trained (82.0%), 2649.0 submatrices/sec | loss 0.004810
INFO:tensorflow: 8300/10000 submatrices trained (83.0%), 2597.4 submatrices/sec | loss 0.004352
INFO:tensorflow: 8400/10000 submatrices trained (84.0%), 2799.8 submatrices/sec | loss 0.003938
INFO:tensorflow: 8500/10000 submatrices trained (85.0%), 3012.0 submatrices/sec | loss 0.003563
INFO:tensorflow: 8601/10000 submatrices trained (86.0%), 2726.8 submatrices/sec | loss 0.003221
INFO:tensorflow: 8700/10000 submatrices trained (87.0%), 2816.8 submatrices/sec | loss 0.002917
INFO:tensorflow: 8800/10000 submatrices trained (88.0%), 2223.5 submatrices/sec | loss 0.002639
INFO:tensorflow: 8900/10000 submatrices trained (89.0%), 2832.3 submatrices/sec | loss 0.002388
INFO:tensorflow: 9000/10000 submatrices trained (90.0%), 2868.7 submatrices/sec | loss 0.002161
INFO:tensorflow: 9101/10000 submatrices trained (91.0%), 2844.3 submatrices/sec | loss 0.001953
INFO:tensorflow: 9200/10000 submatrices trained (92.0%), 2805.0 submatrices/sec | loss 0.001767
INFO:tensorflow: 9300/10000 submatrices trained (93.0%), 2910.3 submatrices/sec | loss 0.001600
INFO:tensorflow: 9400/10000 submatrices trained (94.0%), 2827.2 submatrices/sec | loss 0.001448
INFO:tensorflow: 9501/10000 submatrices trained (95.0%), 2876.2 submatrices/sec | loss 0.001309
INFO:tensorflow: 9600/10000 submatrices trained (96.0%), 2726.1 submatrices/sec | loss 0.001185
INFO:tensorflow: 9700/10000 submatrices trained (97.0%), 2822.4 submatrices/sec | loss 0.001073
INFO:tensorflow: 9800/10000 submatrices trained (98.0%), 2874.8 submatrices/sec | loss 0.000970
INFO:tensorflow: 9900/10000 submatrices trained (99.0%), 2918.5 submatrices/sec | loss 0.000878
INFO:tensorflow: 10000/10000 submatrices trained (100.0%), 2297.1 submatrices/sec | loss 0.000794
INFO:tensorflow:Writing row embeddings to: /tmp/hercules_labours_nx1nsxsk/row_embedding.tsv
INFO:tensorflow:Writing column embeddings to: /tmp/hercules_labours_nx1nsxsk/col_embedding.tsv
INFO:tensorflow:Elapsed: 3.8682050704956055

0it [00:00, ?it/s]
10it [00:00, 121.14it/s]
Reading the input... done
Running: burndown-project
Ratio of survived lines
90 days 0.460287
180 days 0.365047
270 days 0.360617
360 days 0.357076
450 days 0.350728
540 days 0.350728
570 days 0.350728
resampling to year, please wait...
matplotlib: backend is agg
Writing plot to hercules_charts/project.png
Running: overwrites-matrix
matplotlib: backend is agg
Writing plot to hercules_charts/matrix.png
Writing Swivel metadata...
Writing Swivel shards...
Training Swivel model...
Reading Swivel embeddings...
Writing Tensorflow Projector files...
Wrote hercules_charts_overwrites_meta.tsv
Wrote hercules_charts_overwrites_data.tsv
Wrote hercules_charts_overwrites.json
http://projector.tensorflow.org/?config=http://0.0.0.0:8000/hercules_charts_overwrites.json
Running: ownership
matplotlib: backend is agg
Writing plot to hercules_charts/people.png
Running: couples-files
Writing Swivel metadata...
Writing Swivel shards...
Training Swivel model...
Reading Swivel embeddings...
Writing Tensorflow Projector files...
Wrote hercules_charts_files_meta.tsv
Wrote hercules_charts_files_data.tsv
Wrote hercules_charts_files.json
http://projector.tensorflow.org/?config=http://0.0.0.0:8000/hercules_charts_files.json
Running: couples-people
Truncating the sparse matrix...
Writing Swivel metadata...
Writing Swivel shards...
Training Swivel model...
Reading Swivel embeddings...
Writing Tensorflow Projector files...
Wrote hercules_charts_people_meta.tsv
Wrote hercules_charts_people_data.tsv
Wrote hercules_charts_people.json
http://projector.tensorflow.org/?config=http://0.0.0.0:8000/hercules_charts_people.json
Running: couples-shotness
Structural hotness stats were not collected. Re-run hercules with --shotness. Also check --languages - the output may be empty.
Running: shotness
Structural hotness stats were not collected. Re-run hercules with --shotness. Also check --languages - the output may be empty.
Running: devs
Calculating the distance matrix
Ordering the series
Plotting
matplotlib: backend is agg
Writing plot to hercules_charts/time_series.png
Running: devs-efforts
matplotlib: backend is agg
Traceback (most recent call last):
File "/usr/local/bin/labours", line 33, in
sys.exit(load_entry_point('labours==10.7.2', 'console_scripts', 'labours')())
File "/usr/local/lib/python3.6/dist-packages/labours/cli.py", line 449, in main
modesmode
File "/usr/local/lib/python3.6/dist-packages/labours/cli.py", line 363, in devs_efforts
max_people=args.max_people,
File "/usr/local/lib/python3.6/dist-packages/labours/modes/devs.py", line 305, in show_devs_efforts
for tick in pyplot.gca().yaxis.iter_ticks():
AttributeError: 'YAxis' object has no attribute 'iter_ticks'`

[kown7]

I get the same traceback:

190it [19:03,  6.02s/it]  
Ordering the series
Plotting   
matplotlib: backend is agg
Running: devs-efforts 
Warning: truncated people to the most active 20
matplotlib: backend is agg
Traceback (most recent call last):
  File "/usr/local/bin/labours", line 11, in <module> 
sys.exit(main()) 
  File "/usr/local/lib/python3.6/dist-packages/labours/cli.py", line 449, in main     
    modes[mode]() 
  File "/usr/local/lib/python3.6/dist-packages/labours/cli.py", line 363, in devs_efforts
    max_people=args.max_people,
  File "/usr/local/lib/python3.6/dist-packages/labours/modes/devs.py", line 305, in show_devs_efforts
     for tick in pyplot.gca().yaxis.iter_ticks(): 
AttributeError: 'YAxis' object has no attribute 'iter_ticks'

Is there a similar fix as with the utf8 chars?

[andyjones]

It looks like the iter_ticks method was removed in matplotlib 3.3.0 which is the version that is installed in the docker image (I'm not sure why as the requirements.txt looks like it should pin it)

You could copy action.yml into your own repo and force install an older version of matplotlib as a temporary workaround:

name: "Hercules Insights"
description: "Run various Git history analyses with src-d/hercules"
author: "source{d}"
inputs:
  args:
    description: "hercules command line arguments"
    required: false
    default: "--burndown --burndown-people --devs --couples"
runs:
  using: "docker"
  image: "docker://srcd/hercules:latest"
  args:
    - "/bin/bash"
    - "-c"
    - "pip install 'matplotlib==3.2.0'
       && hercules ${{ inputs.args }} --pb . | labours -m all -f pb --disable-projector -o hercules_charts
       && cd hercules_charts
       && tar -cf ../hercules_charts.tar * ../hercules_charts_*
       && cd ..
       && rm -r hercules_charts"
branding:
  color: purple
  icon: bar-chart-2

[ngarbezza]

Got the same error and I fixed it with @andyjones ' suggestion, thanks!

[nepp95]

Copy and pasting this into my workflow gives me a ton of errors, so I think maybe there were some syntax changes within Github Actions.
I have now tried it like below, where the only thing changed is the Hercules part of course. Before it was like in the default Github Actions.

It now tells me that I am not using Pip the right way... Could you give me some feedback as to what I am doing wrong?

jobs:
  hercules-charts:
    runs-on: ubuntu-latest
    name: Charts generated by src-d/hercules
    steps:
      - uses: actions/checkout@v2
        with:
          fetch-depth: 0
      - uses: docker://srcd/hercules:latest
        with:
          args:
            "/bin/bash -c pip install 'matplotlib==3.2.0' && hercules --devs --pb . | labours -m all -f pb --disable-projector -o hercules_charts && cd hercules_charts && tar -cf ../hercules_charts.tar * ../hercules_charts_* && cd .. && rm -r hercules_charts"
      - uses: actions/upload-artifact@master
        with:
          name: hercules_charts```

[DaniruKun]

Ran into the same error, is there any permanent fix for this?