GithubAction: Error while running

ivatar39 commented 3 years ago

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 ../herculescharts && cd .. && rm -r hercules_charts" git log...

0%| | 0/19 [00:00<?, ?it/s] 11%|█ | 2/19 [00:00<00:01, 15.76it/s] 21%|██ | 4/19 [00:00<00:00, 16.22it/s] 37%|███▋ | 7/19 [00:00<00:00, 17.34it/s] 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 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'`

kown7 commented 3 years ago

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 commented 3 years ago

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 commented 3 years ago

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

nepp95 commented 2 years ago

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?

Screenshot 2022-01-05 001925


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 commented 2 years ago

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

EgoPingvina commented 1 month ago

https://github.com/src-d/hercules/blob/master/.github/workflows/main.yml still not work.

src-d / hercules

GithubAction: Error while running #372