Impala example code does not appear to learn

Hi,
I have tried running both examples/impala/run_catch.py and examples/impala_lite.py and neither appears to learn. Here are the results:
run_catch.py (last 1000 frames)
I0609 18:44:53.119419<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19000, 'episode_return': -1.0}
I0609 18:44:53.141368<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19000, 'episode_return': 1.0}
I0609 18:44:53.204611<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19000, 'episode_return': -1.0}
I0609 18:44:53.225717<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19000, 'episode_return': -1.0}
I0609 18:44:53.274633<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(6.265114, dtype=float32), 'baseline_loss': array(14.034711, dtype=float32), 'entropy_loss': array(-6.5565724, dtype=float32), 'grad_norm_unclipped': array(5.881681, dtype=float32), 'total_loss': array(13.216903, dtype=float32), 'weight_norm': array(15.715801, dtype=float32), 'num_frames': 19080}
I0609 18:44:53.305066<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19040, 'episode_return': -1.0}
I0609 18:44:53.328914<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19040, 'episode_return': -1.0}
I0609 18:44:53.390049<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19040, 'episode_return': -1.0}
I0609 18:44:53.411116<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19040, 'episode_return': 1.0}
I0609 18:44:53.468948<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-1.2470411, dtype=float32), 'baseline_loss': array(12.461607, dtype=float32), 'entropy_loss': array(-7.525032, dtype=float32), 'grad_norm_unclipped': array(4.1086025, dtype=float32), 'total_loss': array(4.908512, dtype=float32), 'weight_norm': array(15.727972, dtype=float32), 'num_frames': 19120}
I0609 18:44:53.495550<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19080, 'episode_return': -1.0}
I0609 18:44:53.520038<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19080, 'episode_return': -1.0}
I0609 18:44:53.581381<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19080, 'episode_return': -1.0}
I0609 18:44:53.602796<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19080, 'episode_return': -1.0}
I0609 18:44:53.654468<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-0.4572831, dtype=float32), 'baseline_loss': array(1.7411115, dtype=float32), 'entropy_loss': array(-6.9297953, dtype=float32), 'grad_norm_unclipped': array(5.615561, dtype=float32), 'total_loss': array(0.3439747, dtype=float32), 'weight_norm': array(15.721443, dtype=float32), 'num_frames': 19160}
I0609 18:44:53.683824<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19120, 'episode_return': -1.0}
I0609 18:44:53.708656<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19120, 'episode_return': -1.0}
I0609 18:44:53.769641<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19120, 'episode_return': -1.0}
I0609 18:44:53.791554<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19120, 'episode_return': 1.0}
I0609 18:44:53.846095<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(6.1077714, dtype=float32), 'baseline_loss': array(13.289753, dtype=float32), 'entropy_loss': array(-7.2947497, dtype=float32), 'grad_norm_unclipped': array(4.912279, dtype=float32), 'total_loss': array(12.6797, dtype=float32), 'weight_norm': array(15.69555, dtype=float32), 'num_frames': 19200}
I0609 18:44:53.870376<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19160, 'episode_return': -1.0}
I0609 18:44:53.897332<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19160, 'episode_return': 1.0}
I0609 18:44:53.955576<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19160, 'episode_return': -1.0}
I0609 18:44:53.979460<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19160, 'episode_return': -1.0}
I0609 18:44:54.032648<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-2.480914, dtype=float32), 'baseline_loss': array(11.937717, dtype=float32), 'entropy_loss': array(-8.436846, dtype=float32), 'grad_norm_unclipped': array(3.4625907, dtype=float32), 'total_loss': array(3.4035761, dtype=float32), 'weight_norm': array(15.714447, dtype=float32), 'num_frames': 19240}
I0609 18:44:54.054726<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19200, 'episode_return': -1.0}
I0609 18:44:54.082229<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19200, 'episode_return': 1.0}
I0609 18:44:54.141119<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19200, 'episode_return': -1.0}
I0609 18:44:54.164223<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19200, 'episode_return': -1.0}
I0609 18:44:54.212501<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(6.844277, dtype=float32), 'baseline_loss': array(12.771336, dtype=float32), 'entropy_loss': array(-7.566834, dtype=float32), 'grad_norm_unclipped': array(3.177842, dtype=float32), 'total_loss': array(13.154276, dtype=float32), 'weight_norm': array(15.680187, dtype=float32), 'num_frames': 19280}
I0609 18:44:54.245392<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19240, 'episode_return': -1.0}
I0609 18:44:54.264602<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19240, 'episode_return': -1.0}
I0609 18:44:54.331165<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19240, 'episode_return': -1.0}
I0609 18:44:54.349367<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19240, 'episode_return': -1.0}
I0609 18:44:54.405457<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-3.0720258, dtype=float32), 'baseline_loss': array(2.4449716, dtype=float32), 'entropy_loss': array(-8.45744, dtype=float32), 'grad_norm_unclipped': array(6.6179347, dtype=float32), 'total_loss': array(-1.9341145, dtype=float32), 'weight_norm': array(15.682609, dtype=float32), 'num_frames': 19320}
I0609 18:44:54.437191<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19280, 'episode_return': -1.0}
I0609 18:44:54.456379<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19280, 'episode_return': 1.0}
I0609 18:44:54.523197<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19280, 'episode_return': 1.0}
I0609 18:44:54.540910<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19280, 'episode_return': -1.0}
I0609 18:44:54.596846<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(11.91173, dtype=float32), 'baseline_loss': array(24.407772, dtype=float32), 'entropy_loss': array(-8.437069, dtype=float32), 'grad_norm_unclipped': array(12.500327, dtype=float32), 'total_loss': array(24.031244, dtype=float32), 'weight_norm': array(15.644412, dtype=float32), 'num_frames': 19360}
I0609 18:44:54.626531<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19320, 'episode_return': -1.0}
I0609 18:44:54.645592<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19320, 'episode_return': 1.0}
I0609 18:44:54.711541<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19320, 'episode_return': 1.0}
I0609 18:44:54.729357<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19320, 'episode_return': -1.0}
I0609 18:44:54.787699<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(1.1867194, dtype=float32), 'baseline_loss': array(17.611267, dtype=float32), 'entropy_loss': array(-10.509092, dtype=float32), 'grad_norm_unclipped': array(8.335604, dtype=float32), 'total_loss': array(9.887262, dtype=float32), 'weight_norm': array(15.659662, dtype=float32), 'num_frames': 19400}
I0609 18:44:54.814345<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19360, 'episode_return': -1.0}
I0609 18:44:54.834977<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19360, 'episode_return': -1.0}
I0609 18:44:54.901680<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19360, 'episode_return': -1.0}
I0609 18:44:54.919874<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19360, 'episode_return': -1.0}
I0609 18:44:54.974289<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-1.3058836, dtype=float32), 'baseline_loss': array(6.095595, dtype=float32), 'entropy_loss': array(-9.469074, dtype=float32), 'grad_norm_unclipped': array(10.600857, dtype=float32), 'total_loss': array(1.647223, dtype=float32), 'weight_norm': array(15.640868, dtype=float32), 'num_frames': 19440}
I0609 18:44:55.000550<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19400, 'episode_return': 1.0}
I0609 18:44:55.024647<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19400, 'episode_return': -1.0}
I0609 18:44:55.085605<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19400, 'episode_return': -1.0}
I0609 18:44:55.106794<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19400, 'episode_return': -1.0}
I0609 18:44:55.150425<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-0.5962144, dtype=float32), 'baseline_loss': array(11.372883, dtype=float32), 'entropy_loss': array(-10.7711115, dtype=float32), 'grad_norm_unclipped': array(1.0019239, dtype=float32), 'total_loss': array(4.982516, dtype=float32), 'weight_norm': array(15.645232, dtype=float32), 'num_frames': 19480}
I0609 18:44:55.191561<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19440, 'episode_return': -1.0}
I0609 18:44:55.207082<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19440, 'episode_return': -1.0}
I0609 18:44:55.276106<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19440, 'episode_return': -1.0}
I0609 18:44:55.291717<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19440, 'episode_return': -1.0}
I0609 18:44:55.348644<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-5.226397, dtype=float32), 'baseline_loss': array(3.359177, dtype=float32), 'entropy_loss': array(-10.436632, dtype=float32), 'grad_norm_unclipped': array(7.758003, dtype=float32), 'total_loss': array(-3.6511748, dtype=float32), 'weight_norm': array(15.652247, dtype=float32), 'num_frames': 19520}
I0609 18:44:55.374876<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19480, 'episode_return': -1.0}
I0609 18:44:55.395675<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19480, 'episode_return': -1.0}
I0609 18:44:55.457953<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19480, 'episode_return': 1.0}
I0609 18:44:55.479441<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19480, 'episode_return': -1.0}
I0609 18:44:55.535367<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(2.3100102, dtype=float32), 'baseline_loss': array(12.708422, dtype=float32), 'entropy_loss': array(-10.319403, dtype=float32), 'grad_norm_unclipped': array(2.800501, dtype=float32), 'total_loss': array(8.561027, dtype=float32), 'weight_norm': array(15.645624, dtype=float32), 'num_frames': 19560}
I0609 18:44:55.557959<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19520, 'episode_return': -1.0}
I0609 18:44:55.585584<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19520, 'episode_return': -1.0}
I0609 18:44:55.644491<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19520, 'episode_return': -1.0}
I0609 18:44:55.667981<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19520, 'episode_return': 1.0}
I0609 18:44:55.717722<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(3.5716112, dtype=float32), 'baseline_loss': array(12.274258, dtype=float32), 'entropy_loss': array(-10.727091, dtype=float32), 'grad_norm_unclipped': array(2.4339705, dtype=float32), 'total_loss': array(9.601469, dtype=float32), 'weight_norm': array(15.631093, dtype=float32), 'num_frames': 19600}
I0609 18:44:55.751512<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19560, 'episode_return': -1.0}
I0609 18:44:55.764515<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19560, 'episode_return': -1.0}
I0609 18:44:55.836327<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19560, 'episode_return': -1.0}
I0609 18:44:55.851738<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19560, 'episode_return': 1.0}
I0609 18:44:55.906602<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-0.8045523, dtype=float32), 'baseline_loss': array(11.312561, dtype=float32), 'entropy_loss': array(-11.811794, dtype=float32), 'grad_norm_unclipped': array(1.6049904, dtype=float32), 'total_loss': array(4.7336106, dtype=float32), 'weight_norm': array(15.638516, dtype=float32), 'num_frames': 19640}
I0609 18:44:55.943906<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19600, 'episode_return': -1.0}
I0609 18:44:55.954402<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19600, 'episode_return': -1.0}
I0609 18:44:56.029914<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19600, 'episode_return': -1.0}
I0609 18:44:56.040005<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19600, 'episode_return': -1.0}
I0609 18:44:56.094541<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-5.701372, dtype=float32), 'baseline_loss': array(2.9743013, dtype=float32), 'entropy_loss': array(-11.182085, dtype=float32), 'grad_norm_unclipped': array(7.2923098, dtype=float32), 'total_loss': array(-4.326042, dtype=float32), 'weight_norm': array(15.647901, dtype=float32), 'num_frames': 19680}
I0609 18:44:56.131167<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19640, 'episode_return': -1.0}
I0609 18:44:56.143305<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19640, 'episode_return': -1.0}
I0609 18:44:56.217600<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19640, 'episode_return': -1.0}
I0609 18:44:56.227971<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19640, 'episode_return': -1.0}
I0609 18:44:56.281056<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-5.332682, dtype=float32), 'baseline_loss': array(1.6522617, dtype=float32), 'entropy_loss': array(-10.85811, dtype=float32), 'grad_norm_unclipped': array(5.495307, dtype=float32), 'total_loss': array(-4.6151323, dtype=float32), 'weight_norm': array(15.662731, dtype=float32), 'num_frames': 19720}
I0609 18:44:56.327549<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19680, 'episode_return': -1.0}
I0609 18:44:56.328974<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19680, 'episode_return': -1.0}
I0609 18:44:56.414793<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19680, 'episode_return': -1.0}
I0609 18:44:56.416231<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19680, 'episode_return': -1.0}
I0609 18:44:56.472827<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-1.9145008, dtype=float32), 'baseline_loss': array(0.94435024, dtype=float32), 'entropy_loss': array(-10.637964, dtype=float32), 'grad_norm_unclipped': array(4.106118, dtype=float32), 'total_loss': array(-1.5487053, dtype=float32), 'weight_norm':
array(15.661455, dtype=float32), 'num_frames': 19760}
I0609 18:44:56.522465<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19720, 'episode_return': -1.0}
I0609 18:44:56.523910<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19720, 'episode_return': -1.0}
I0609 18:44:56.608373<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19720, 'episode_return': -1.0}
I0609 18:44:56.610147<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19720, 'episode_return': -1.0}
I0609 18:44:56.664880<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-1.3125417, dtype=float32), 'baseline_loss': array(0.499528, dtype=float32), 'entropy_loss': array(-10.818449, dtype=float32), 'grad_norm_unclipped': array(2.9733512, dtype=float32), 'total_loss': array(-1.1709622, dtype=float32), 'weight_norm': array(15.659753, dtype=float32), 'num_frames': 19800}
I0609 18:44:56.712538<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19760, 'episode_return': 1.0}
I0609 18:44:56.716334<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19760, 'episode_return': -1.0}
I0609 18:44:56.796781<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19760, 'episode_return': -1.0}
I0609 18:44:56.801209<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19760, 'episode_return': -1.0}
I0609 18:44:56.857705<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(0.5229235, dtype=float32), 'baseline_loss': array(14.523106, dtype=float32), 'entropy_loss': array(-11.014995, dtype=float32), 'grad_norm_unclipped': array(6.552383, dtype=float32), 'total_loss': array(7.6743264, dtype=float32), 'weight_norm': array(15.672911, dtype=float32), 'num_frames': 19840}
I0609 18:44:56.904172<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19800, 'episode_return': -1.0}
I0609 18:44:56.905970<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19800, 'episode_return': -1.0}
I0609 18:44:56.990608<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19800, 'episode_return': -1.0}
I0609 18:44:56.992043<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19800, 'episode_return': -1.0}
I0609 18:44:57.046815<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(-1.0794313, dtype=float32), 'baseline_loss': array(0.82973915, dtype=float32), 'entropy_loss': array(-9.891457, dtype=float32), 'grad_norm_unclipped': array(3.8528771, dtype=float32), 'total_loss': array(-0.7634763, dtype=float32), 'weight_norm':
array(15.668699, dtype=float32), 'num_frames': 19880}
I0609 18:44:57.095299<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19840, 'episode_return': -1.0}
I0609 18:44:57.099149<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19840, 'episode_return': -1.0}
I0609 18:44:57.179523<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19840, 'episode_return': -1.0}
I0609 18:44:57.183337<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19840, 'episode_return': 1.0}
I0609 18:44:57.241079<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(4.569849, dtype=float32), 'baseline_loss': array(13.9446125, dtype=float32), 'entropy_loss': array(-10.259341, dtype=float32), 'grad_norm_unclipped': array(5.779074, dtype=float32), 'total_loss': array(11.439562, dtype=float32), 'weight_norm': array(15.656496, dtype=float32), 'num_frames': 19920}
I0609 18:44:57.287819<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19880, 'episode_return': 1.0}
I0609 18:44:57.289604<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19880, 'episode_return': -1.0}
I0609 18:44:57.374072<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19880, 'episode_return': -1.0}
I0609 18:44:57.375484<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19880, 'episode_return': -1.0}
I0609 18:44:57.432042<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(3.2217233, dtype=float32), 'baseline_loss': array(12.646129, dtype=float32), 'entropy_loss': array(-10.989743, dtype=float32), 'grad_norm_unclipped': array(4.213028, dtype=float32), 'total_loss': array(9.43489, dtype=float32), 'weight_norm': array(15.649371, dtype=float32), 'num_frames': 19960}
I0609 18:44:57.479253<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19920, 'episode_return': -1.0}
I0609 18:44:57.483644<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19920, 'episode_return': -1.0}
I0609 18:44:57.563747<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19920, 'episode_return': -1.0}
I0609 18:44:57.571015<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19920, 'episode_return': 1.0}
I0609 18:44:57.621879<PHONE_NUMBER>46400 util.py:56] {'PG_loss': array(4.945644, dtype=float32), 'baseline_loss': array(12.138546, dtype=float32), 'entropy_loss': array(-11.671843, dtype=float32), 'grad_norm_unclipped': array(3.3342645, dtype=float32), 'total_loss': array(10.898199, dtype=float32), 'weight_norm': array(15.62801, dtype=float32), 'num_frames': 20000}
I0609 18:44:57.671630<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19960, 'episode_return': -1.0}
I0609 18:44:57.673485<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19960, 'episode_return': -1.0}
I0609 18:44:57.757860<PHONE_NUMBER>37952 util.py:56] {'num_frames': 19960, 'episode_return': 1.0}
I0609 18:44:57.759711<PHONE_NUMBER>37024 util.py:56] {'num_frames': 19960, 'episode_return': -1.0}
I0609 18:44:57.860325<PHONE_NUMBER>37952 util.py:56] {'num_frames': 20000, 'episode_return': -1.0}
I0609 18:44:57.861743<PHONE_NUMBER>37024 util.py:56] {'num_frames': 20000, 'episode_return': -1.0}
I0609 18:44:57.946938<PHONE_NUMBER>37952 util.py:56] {'num_frames': 20000, 'episode_return': 1.0}
I0609 18:44:57.948385<PHONE_NUMBER>37024 util.py:56] {'num_frames': 20000, 'episode_return': -1.0}

 impala_lite.py
I0609 18:32:31.996878<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:32.473560<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:32.678860<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:34.222242<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:34.359391<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:34.459262<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:34.589936<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:34.682597<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:34.823267<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:34.923990<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:35.052270<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:35.146077<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:35.292945<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:35.384402<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:35.515546<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:35.611429<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:35.752809<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:35.844259<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:35.977095<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.067746<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.209460<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.306946<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.459139<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.562179<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.715074<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.821565<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:36.970221<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:37.089010<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:37.243072<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:37.355906<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:37.506855<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:37.617312<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:37.773906<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:37.878585<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:38.023758<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:38.127217<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:38.283544<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:38.398535<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:38.544531<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:38.652665<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:38.813543<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:38.930231<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:39.068565<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:39.177990<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:39.330883<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:39.446516<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:39.589520<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:39.703605<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:39.857005<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:39.963717<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:40.117769<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:40.228975<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:40.392996<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:40.503128<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:40.647203<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:40.762332<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:40.908709<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:41.018952<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:41.162108<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:41.281753<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:41.433916<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:41.543094<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:41.685955<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:41.798732<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:41.952419<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:42.065360<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:42.217259<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:42.326667<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:42.480978<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:42.597905<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:42.727256<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:42.827403<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:42.968495<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:43.064970<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:43.197255<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:43.299557<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:43.436322<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:43.529076<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:43.658361<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:43.751231<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:43.885912<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:43.976411<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:44.107825<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:44.201368<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:44.334867<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:44.428773<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:44.556346<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:44.650984<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:44.785450<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:44.879709<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:45.013044<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:45.105906<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:45.238240<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:45.314389<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:45.440201<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:45.539112<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:45.678764<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:45.774055<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:45.905405<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:45.998960<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:46.137748<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:46.233678<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:46.367779<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:46.461537<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:46.604156<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:46.689795<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:46.825954<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:46.922744<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.063703<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.157088<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.289810<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.387336<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.525219<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.618751<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.751825<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:47.848176<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:47.986406<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:48.081928<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:48.211624<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:48.303036<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:48.441438<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:48.532519<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:48.662615<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:48.758323<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:48.893285<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:48.987548<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:49.117535<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:49.210992<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:49.344335<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:49.437639<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:49.571723<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:49.665100<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:49.818987<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:49.913522<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:50.044390<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:50.137338<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:50.274990<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:50.372178<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:50.503616<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:50.602540<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:50.740304<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:50.832190<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:50.966363<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.057243<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.191009<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.284075<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.408699<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.508146<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.645640<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.742809<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:51.873431<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:51.967190<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.106916<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.205305<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.336991<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.431706<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.571661<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.662561<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.793250<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:52.885597<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.023635<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.116454<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.250097<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.349506<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.484209<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.584233<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:53.720027<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.812085<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:53.952897<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:54.047800<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:54.181136<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:54.276678<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:54.416433<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:54.512052<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:54.648389<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:54.739559<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:54.880494<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:54.973440<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.110407<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.202521<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.344353<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.435967<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.570542<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.658932<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.795304<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:55.886940<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:56.017600<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:56.114738<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:56.250173<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:56.343039<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:56.472380<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:56.570281<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:56.711129<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:56.809088<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:56.940056<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
I0609 18:32:57.034023<PHONE_NUMBER>52704 impala_lite.py:178] Episode ended with reward: 1.0
I0609 18:32:57.171033<PHONE_NUMBER>45408 impala_lite.py:178] Episode ended with reward: -1.0
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I notice that some of the hard-coded hyperparameters seem unusual. For example, you use a learning rate of 1e-1 which is much higher than the hyperparameters used in the paper and the epsilon for RMSProp seems quite high at 0.1.

Thanks for the PR, indeed hparams were chosen pretty arbitrarily when I exported both examples from our internal IMPALA reproduction.
FWIW eps=0.1 as an hparam works quite well for impala on atari, so don't exclude it from consideration!
Section 2 of  https://arxiv.org/pdf/1910.05446.pdf is a good survey of many papers which use very high epsilon values (up to 1!) for RMSProp.

Yes, epsilon can behave in surprising ways. In the IMPALA paper they do search eps=0.1 as a possible hyperparameter, but I could not get catch to learn at that setting.


Please add BC22 to the list of versions

Please add BC22

Document Details
⚠ Do not edit this section. It is required for learn.microsoft.com ➟ GitHub issue linking.

ID: 1bac24fa-c9eb-695a-bdd5-3dbe4109a0dc
Version Independent ID: a09fc47a-123f-a136-d4e5-e986dfc94522
Content: Choosing Runtime Version in AL - Business Central
Content Source: dev-itpro/developer/devenv-choosing-runtime.md
Service: dynamics365-business-central
GitHub Login: @SusanneWindfeldPedersen
Microsoft Alias: solsen

Thanks for catching this. Will be fixed with the next doc refresh.


Xcode versions

Codecov Report

Merging #332 into master will not change coverage.
The diff coverage is n/a.


Only php 7 support?

Is the plan to only support php 7?

No. I had originally planned to use a Websocket Library which required PHP 7, but it turned out to be way too painful. Expect a requirement of PHP >=5.5 since Guzzle 6 requires it.

Great!


chore: rev aks-engine to v0.38.5

See:
https://github.com/Azure/aks-engine/releases/tag/v0.38.5

@mboersma @CecileRobertMichon FYI

/lgtm


Backport no simple copy

What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Updated base. Please help check it.


#1: add support for horses in database initialization

/paulo - can you please review this pull request?

/paulo - can you please review this pull request?

/paulo - can you check now?

/paulo - can you check now?


assets folder Removed from base dir

Created by: esube
For some reason part of the app is expecting some of the UI icons for the network visualization under localhost:8080/assets/... etc directory and when it couldn't find them there, first it throws errors on the browser console. But, then somehow it grabs them elsewhere and you could see the network.
But, moments before they are displayed correctly, you'd see the network with no icons for the layers.
Then, it flashes and replaces them with the actual icons. When you look at the console, you'd see the 404 errors (see the attached image).

Created by: esube
Yeah, it looks like it is related. But, you closed that issue yesterday and I got a clean copy this morning. I will still pull and try to see if that fixes it if you have modified it today.
But, it looks like some where some code is still referencing that assets directory although it didn't break the regular working process.

Created by: brollb
Can you check if your config/configuration.base.js reflects the change in https://github.com/brollb/deepforge/commit/7b5732f964c6657840f611dc5b1071e5f8b31b0b?
It should just be the removal of one line

Created by: brollb
It looks like it is related to https://github.com/brollb/deepforge/issues/185. Have you made sure your project is up to date (git pull)?

Created by: brollb
I just found this problem - although the svgs weren't used, they are still loaded in the newer versions (post 1.4.0) of webgme. This has been fixed in branch 173 and will be merged with the data refactor.
I am bending the rules of the feature branches here (by fixing two issues on one branch) bc these issues both change the seed file and I pretty much can't fix merge conflicts as it is a binary.


attach(field, buffer) should throw an error if filename is missing

.attach(field, buffer) should probably throw an error if the filename argument is not specified, since this will not work.
From what I can work out from RFC2388, a part must have a filename= parameter, or use content-disposition: file in order for it to be identified as a file.
With multiparty at least, such a request results in the file data appearing in the fields map, rather than the files map.
For anyone having issues with trying to attach buffers even when specifying the filename, see #403.
Thanks.

Yes, .attach(field, buffer, {filename: 'need a filename'}) does work, but that is to do with #403 (it shouldn't need an object wrapper).
This issue is about what happens when you don't provide a third argument at all. Superagent should either make this work correctly, or throw a useful error message.

I think this is now fixed in master.


Add runtime error in eval.py if yaml config is improperly formatted with extraneous or missing values

Description
This PR enables us to sanity check our eval yaml configuration files before we run the full eval pipeline. This enables us to catch errors in the YAML config prior to an eval run starting. If a yaml config is improperly formatted with extraneous or missing values, a runtime error will be thrown.
Unit Test:
Added unit tests to make sure we raise omegaconf.errors and/or warn users if the yaml is incorrectly formatted. Warnings are used if the parameter has an optional default value.
Integration Test:
Test 1: Model reaches 0.56 COPA accuracy before and after refactor (1 GPU)
  composer eval/eval.py \
  eval/yamls/hf_eval.yaml \
  icl_tasks=eval/yamls/copa.yaml \
  model_name_or_path=mpt-125m-hf

Test 2: Model gauntlet runs: mcli run -f mcli/mcli-hf-eval.yaml --follow
Main branch: mcli logs -f all-eval-main-nphii5
Ran mosaicml/mpt-7b-instruct eval in: 5852.91842341423 seconds
Printing gauntlet results for all models
| model_name               |   average |   world_knowledge |   commonsense_reasoning |   language_understanding |   symbolic_problem_solving |   reading_comprehension |
|:-------------------------|----------:|------------------:|------------------------:|-------------------------:|---------------------------:|------------------------:|
| mosaicml/mpt-7b-instruct |  0.354255 |          0.398764 |                0.415097 |                 0.371509 |                   0.171216 |                0.414691 |
Printing complete results for all models
| Category                 | Benchmark

chuck/add_yaml_check_eval branch: mcli logs -f all-eval-chuck-branch-UqSCPR
Ran mosaicml/mpt-7b-instruct eval in: 5858.392446279526 seconds
Printing gauntlet results for all models
| model_name               |   average |   world_knowledge |   commonsense_reasoning |   language_understanding |   symbolic_problem_solving |   reading_comprehension |
|:-------------------------|----------:|------------------:|------------------------:|-------------------------:|---------------------------:|------------------------:|
| mosaicml/mpt-7b-instruct |  0.354255 |          0.398764 |                0.415097 |                 0.371509 |                   0.171216 |                0.414691 |
Printing complete results for all models
| Category                 | Benchmark

Issues Addressed
This PR chips away at this tech debt issue.

added model gauntlet eval results that show that this change is non-breaking as results are the same before and after this PR.


Intro to Visualization with Matplotlib

I have written this code of Matplotlib with some docs in order to help the beginners in visualization.

Thanks, Wisaal, keep documenting onwards!

Great Wisal, keep documenting onwards!

@all-contributors please add @Wisal-Mukhtiar for code


Crewai is not working as per the role,goal and backstory

My requirement was use CrewAI to build an agent able to breakdown  Complex input Questions into simple sub questions.I have followed as per the instructions and maintaing agent's structure specified in your documentation page.But failed to perform as per my requirements.failed sample code is as follows:
llm = Ollama(
model = "llama2",
base_url = "http://localhost:11434")
general_agent = Agent(role = "Sub Question Generator",
goal = """Break a complex question in to multiple simple sub-questions""",
backstory = """You are an excellent english professor able to modify complex question into multiple simple questions in a way that everyone can understand.Do not Answer corresponding to any of the questions""",
allow_delegation = False,
verbose = True,
llm = llm)
task = Task(description="""what is Multiple Sclerosis and what are the symptoms in young adults""",
agent = general_agent,
expected_output="String  Answer ")
But the the following (found in your documentation) worked properly to build a calculator like agent
from crewai import Agent, Task, Crew
from langchain.llms import Ollama
import os
os.environ["OPENAI_API_KEY"] = "NA"
llm = Ollama(
model = "llama2",
base_url = "http://localhost:11434")
general_agent = Agent(role = "Math Professor",
goal = """Provide the solution to the students that are asking mathematical questions and give them the answer.""",
backstory = """You are an excellent math professor that likes to solve math questions in a way that everyone can understand your solution""",
allow_delegation = False,
verbose = True,
llm = llm)
task = Task(description="""what is 3 + 5""",
agent = general_agent,
expected_output="A numerical answer.")
crew = Crew(
agents=[general_agent],
tasks=[task],
verbose=2
)
result = crew.kickoff()
print(result)

@HariNuve I believe base_url = "http://localhost:11434/v1"
If this does'nt solve the issue pls drop the error after execution.

@HariNuve what's the output you are getting and what's the format you expect?

@pravincoder Changing the url to  http://localhost:11434/v1 didn't worked,experienced an error as follows :
File "/home/harikrishnan/anaconda3/envs/ml_env/lib/python3.10/site-packages/langchain_core/language_models/llms.py", line 512, in stream
for chunk in self._stream(
File "/home/harikrishnan/anaconda3/envs/ml_env/lib/python3.10/site-packages/langchain_community/llms/ollama.py", line 463, in _stream
for stream_resp in self._create_generate_stream(prompt, stop, **kwargs):
File "/home/harikrishnan/anaconda3/envs/ml_env/lib/python3.10/site-packages/langchain_community/llms/ollama.py", line 176, in _create_generate_stream
yield from self._create_stream(
File "/home/harikrishnan/anaconda3/envs/ml_env/lib/python3.10/site-packages/langchain_community/llms/ollama.py", line 247, in _create_stream
raise OllamaEndpointNotFoundError(
langchain_community.llms.ollama.OllamaEndpointNotFoundError: Ollama call failed with status code 404. Maybe your model is not found and you should pull the model with ollama pull llama2.
At the same time when i used the " http://localhost:11434 " atleast i got the response.

@zinyando Iam expecting breaking down complex questions into simple sub questions.What iam getting is answer to those questions

@zinyando Iam expecting breaking down complex questions into simple sub questions.What iam getting is answer to those questions

@HariNuve I think your issue is related to your prompts not being clear enough for the LLM. I played around with it and got it working. You can definitely improve the output.
from crewai import Agent, Task, Crew
from langchain.llms import Ollama
import os

os.environ["OPENAI_API_KEY"] = "NA"

llm = Ollama(model="llama3.1", base_url="http://localhost:11434")

general_agent = Agent(
    role="Sub Question Generator",
    goal="""Break a complex question in to multiple simple sub-questions""",
    backstory="""You are an excellent english professor able to modify complex question into multiple simple questions in a way that everyone can understand.Do not Answer corresponding to any of the questions""",
    allow_delegation=False,
    verbose=True,
    llm=llm,
)

question = "What is Multiple Sclerosis and what are the symptoms in young adults"

task = Task(
    description=f"Break the following complex question into multiple simple sub-questions: {question}",
    agent=general_agent,
    expected_output="A breakdown of the question without answering it clearly formatted as markdown.",
)

crew = Crew(agents=[general_agent], tasks=[task], verbose=2)

result = crew.kickoff(
    inputs={"question": question},
)

print(result)

Be sure to set the Ollama agent to the one you want to use. An example output I got is below
# Breakdown of Complex Question
## 1. What is Multiple Sclerosis?
### 1.1. Definition
What is the medical definition of Multiple Sclerosis?
### 1.2. Causation
Is multiple sclerosis caused by a viral infection or an autoimmune disorder?
### 1.3. Prevalence
What is the estimated prevalence of multiple sclerosis in young adults?

## 2. What are the Symptoms in Young Adults?
### 2.1. Common Symptoms
What are the most common symptoms experienced by young adults with multiple sclerosis?
### 2.2. Rare Symptoms
Are there any rare or unusual symptoms that can be associated with multiple sclerosis in young adults?
### 2.3. Age-Specific Symptoms
How do the symptoms of multiple sclerosis differ in young adults compared to older adults?

Just so you know, I think these type of questions are better suited to be posted in Discord and not on GitHub issues. You can join it here Join CrewAI Discord


Implementing hash functions

I've used XCTFail instead of XCTEqual(false..., due to this now the the two test classes are not consistent. I'm not sure what's the preferred way of failing the tests, but as soon as I get a comment I can change it to be consistent one way or the other.
Please review it carefully, this is my first swift code.
If the size of the PR is too big I can break it up into several smaller ones

@kjessup is this project still being maintained or is there something wrong with my PR?


Illegal user, project, version names

There is a set of illegal user, project, and version names based on routing that need to be figured out. It's kind of a pain to go through and check all api, frontend routes to see if a project version named settings or something like that will cause issues.

We have a separste issue to sanitize that kind stuff, prolly should be combined in a central place

Oh I guess #220 is similar, but it doesnt talk about conflicting routes, just having illegal characters, this issue is more towards conflicting routes, rather than illegal characters. (although they are similar)

Illegal Usernames

api
authors
linkout
logged-out
new
notifications
staff
admin
organizations
tools

Illegal Version Names

recommended


Configuration 'compile' is obsolete and has been replaced with 'implementation' and 'api'.

android 3.3 have this problem

This is a warning but basically in the qrscanner.gradle file you have to replace compile with implementation since compile was deprecated long time ago

This is a warning but basically in the qrscanner.gradle file you have to replace compile with implementation since compile was deprecated long time ago


Add New City Feature to the plus_circle Icon.

Issue
There should be a feature of adding new cities when we click on the plus_circle button on top right of HomePage.

Feel free to make a Pull request with this feature!


wasmtime.bindgen shouldn't be using __file__

There's actually no guarantee at all that Python source files exist anywhere on the filesystem, and even if they do they might as well be inside a zip archive or even a Rust executable. Because of this using __file__ for anything but debugging or logging, and in particular loading resources, is a fragile practice and packages like wasmtime-py should avoid it.
In practical terms the header of the generated code that looks like this:
from .intrinsics import _decode_utf8, _encode_utf8, _load
import ctypes
import pathlib
import wasmtime

class Root:

    def __init__(self, store: wasmtime.Store) -> None:
        path = pathlib.Path(__file__).parent / ('root.core0.wasm')
        module = wasmtime.Module.from_file(store.engine, path)

should be replaced with:
from .intrinsics import _decode_utf8, _encode_utf8, _load
import ctypes
import pathlib
import wasmtime
try:
    import importlib.resources as importlib_resources
    importlib_resources.files() # check if implicit anchor is supported
except TypeError:
    import importlib_resources

class Root:

    def __init__(self, store: wasmtime.Store) -> None:
        file = importlib_resources.files() / ('root.core0.wasm')
        module = wasmtime.Module.from_file(store.engine, file.read_bytes())

Note that the importlib.resources.files() (the zero argument form) is very new, freshly added in 3.12, and unfortunately consumers of the bindings will have to add the importlib-resources>=5.10 polyfill as a dependency to support all versions of Python.
(Potentially you could have another fallback that uses __file__ if neither the zero argument form is supported nor the polyfill is installed, but I find a three-deep chain of compatibility shims pretty hard to maintain.)

GIven that the component support is relatively "new" in a sense this seems reasonable to me to add! It'd probably be ok to document this dependency for older Python versions to play better with the ecosystem.

It'd probably be ok to document this dependency for older Python versions to play better with the ecosystem.

I think one really easy way to handle this is to add this dependency to wasmtime itself, since by definition if you have wasmtime.bindgen you also have that. This is the approach I took in #224.

Excellent point!


sc-70154-update-qml-readme-to-highlight-image-standards

Ticket

sc-70154-update-qml-readme-to-highlight-image-standards

Changes

Add instructions to README.md about image standards to be followed while writing demos this QML repository.
Requirements sources from this doc
Since QML is public & the notion doc is private (accessible by Xanadu only) added the relevant standards in text form in the README.md

Preview

https://github.com/PennyLaneAI/qml/blob/2e9da1bb2bef3cd8a2eb504459f686ab20b9ec0c/README.md

Thank you ashish!


[BEAM-4775] add GetJobMetrics RPC

Send metrics over the job API

new job API RPC depends on MonitoringInfo protos from the fn api

per discussion with @robertwb, I'm going to move MonitoringInfo into model/pipeline, to avoid introducing a [job API] → [fn API] dep
as such, this PR shouldn't be merged as-is; that proto move should happen first, but I wanted to get this out for Jenkins and first review pass

add Python and Java SDK support for portable metrics over the job API

portable python flink wordcount outputs the integer empty_lines metric!
Flink attempted counter PVR cases are now enabled

R: @robertwb @ajamato
CC: @mxm since I reworked the Flink metrics a bit in particular
Post-Commit Tests Status (on master branch)

Lang
SDK
Apex
Dataflow
Flink
Gearpump
Samza
Spark

Go

---
---
---
---
---
---

Java

Python

---

---
---
---

See .test-infra/jenkins/README for trigger phrase, status and link of all Jenkins jobs.

Run Java Flink PortableValidatesRunner Batch

@Ardagan

Overall, I think this PR is adding way too much code and complexity. I would rather not submit this. I have spent 2 hours reading this so far and have gone through about half. I'll have to look at it some more tomorrow. But I am not yet convinced everything you've added is necessary at the moment. and much the naming and callback passing is quite confusing so its hard to understand what the various methods are doing.

flaky dataflow tests (scan)
> Task :beam-runners-google-cloud-dataflow-java-examples:preCommitLegacyWorker

org.apache.beam.examples.WordCountIT > testE2EWordCount FAILED
    java.lang.RuntimeException at WordCountIT.java:69
        Caused by: java.io.IOException at WordCountIT.java:69

> Task :beam-runners-google-cloud-dataflow-java-examples-streaming:preCommit

org.apache.beam.examples.WordCountIT > testE2EWordCount FAILED
    java.lang.RuntimeException at WordCountIT.java:69
        Caused by: java.io.IOException at WordCountIT.java:69

Run Java_Examples_Dataflow PreCommit

Run Python PreCommit

Some kind of installation error that registered as :beam-sdks-python:lintPy27 failing
py27-lint inst: /home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/target/.tox/dist/apache-beam-2.12.0.dev0.zip
--

ERROR: invocation failed (exit code 1), logfile: /home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/target/.tox/py27-lint/log/py27-lint-2.log
ERROR: actionid: py27-lint
msg: installpkg
cmdargs: ['/home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/target/.tox/py27-lint/bin/python', '/home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/target/.tox/py27-lint/bin/pip', 'install', '--retries', '10', '/home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/target/.tox/dist/apache-beam-2.12.0.dev0.zip[test]']

Flaky cassandra test (BEAM-6722)
> Task :beam-sdks-java-io-cassandra:test

org.apache.beam.sdk.io.cassandra.CassandraIOTest > classMethod FAILED
    java.lang.AssertionError at CassandraIOTest.java:104

Run Java PreCommit

This is a new one, to me: :beam-sdks-python-test-suites-dataflow:preCommitIT
test_streaming_wordcount_it (apache_beam.examples.streaming_wordcount_it_test.StreamingWordCountIT) ... ERROR
--

======================================================================
ERROR: test_wordcount_it (apache_beam.examples.wordcount_it_test.WordCountIT)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/apache_beam/examples/wordcount_it_test.py", line 47, in test_wordcount_it
self._run_wordcount_it(wordcount.run)
File "/home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/apache_beam/examples/wordcount_it_test.py", line 75, in _run_wordcount_it
run_wordcount(test_pipeline.get_full_options_as_args(**extra_opts))
File "/home/jenkins/jenkins-slave/workspace/beam_PreCommit_Python_Commit/src/sdks/python/apache_beam/examples/wordcount.py", line 114, in run
result = p.run()
…
IOError: [Errno 2] No such file or directory: '/tmp/tmpsEVK5k/pickled_main_session'

Run Python PreCommit

hmm

Build timed out (after 120 minutes). Marking the build as aborted.

Some other noise (e.g. :beam-sdks-python:docs FAILED) but I'm guessing the timeout is the real culprit. I guess I will try again…

Run Python PreCommit

Run Portable_Python PreCommit

Seems like the job failed to boot properly:
Using context: Portable_Python ("Run Portable_Python PreCommit")
ERROR: Issue with creating launcher for agent beam9. The agent is being disconnected
[EnvInject] - Loading node environment variables.
ERROR: SEVERE ERROR occurs

Run Java PreCommit

Seems like this is going to be chunked into smaller pieces (starting with #8018 / #8022). Closing.


Eldoc style integration / implementation for Common-Lisp

When hovering over a function there should be function/form signature help (similar to Eldoc in Emacs).

Does this mean the text that appears under the status line or the ability to ask for documentation? I have a feeling the doc feature is already implemented.
If it's text appearing under the status line, that will have to slow down and be considered in the wider context of other languages since UI/UX features like that should be open to all clients.

It would be the signature appearing under the status line, so UI/UX. This may be something outside the scope of Conjure, I wasn't sure and wanted to ask if you felt it fell in the realm of something you wanted to support, or if you feel it should be a separate "extension". There is more context on Discord in the Common-lisp room.


Question: How to significantly expand list of supported currencies?

Do I understand correctly that now XChange support currencies listed here?
Though currently supported by XChange exchanges have much more trading pairs.
I need to get prices for all pairs from supported exchanges for my app.
Does that possible with XChange?
How much efforts could be needed to add such support?

Those currencies are just common ones, but not necessarily all the supported ones. As you know, each exchange supports a different set of currency pairs. Some provide API access to  their list, others don't. We have a mechanism in XChange ties to the meta data (take a look). For most exchanges that have a currency list, XChnage polls them and builds a list. Otherwise, there is a .json file in each module where the currency pairs are defined. If it's out of date, you can supply a path for an override .json file.
I hope that helps. I should add that to the Wiki I suppose.

Thanks. I see there is a way to manage it. Good news. One more off topic question: Could I run XChange as a REST service for this purpose? Also didn't find any info. Regards!

Could I run XChange as a REST or Websocket service for this purpose?

No, but you could build one with XChange being the code that fetches data from multiple exchanges. You'd then wrap that with your REST service however you'd like.

How do you think do this project need REST functionality built in?
I'd like to post bounty on it, but I need rough estimation. Otherwise we will develop this project as a fork that is to be honest not our priority at the moment.

I think a REST interface is beyond the scope of this project for sure. It sounds like the best approach would be a separate project that depends on XChange.

Thanks @timmolter!


[UI][IMPROVEMENT] Display ReUploadedDataSize and NewlyUploadedDataSize of each backup on Volume page

Is your improvement request related to a feature? Please describe (👍 if you like this request)
Describe the bug
To clearly display the backup information to users, we can

Display ReUploadedDataSize and NewlyUploadedDataSize of each backup

Describe the solution you'd like

Describe alternatives you've considered

Additional context

cc @ChanYiLin @innobead

Hello @houhoucoop
Need to complete the Pre Ready-For-Testing Checklist and then move to Ready For Testing. QA will verify the ticket and close it.

It is also advisable to include a short video or at least a screenshot for any UI changes, so QA can quickly understand the behavior of the changes. @houhoucoop @a110605

Verified on master-head (longhorn-ui 5113aa2). However, the ReUploadedDataSize and NewlyUploadedDataSize are displayed on Backup page instead of Volume page as indicated in the issue title. @derekbit Could you please confirm if it's what you intended? Thank you!

I think yes. WDYT? @ChanYiLin

Hi @yangchiu
As discussed with @derekbit
Yes it is correct to display this info in backup page
The title is incorrect.
I will correct the title and we can close the issue


Fails if root gradle project directory is not also root git directory

The plugin fails with this error if the Gradle root directory is a subdirectory of the git repository: "Android App Versioning Gradle Plugin works with git tags but root project 'android' is not a valid git repository"
For instance, in a Ionic app, the Gradle project is within an android directory inside the repository. From the command line, git describe --tags works fine from within any subdirectory of the repository. I don't know if GitClient works the same, but the plugin checks if gitRefsDirectory exists before initializing GitClient.

Thanks for reporting this. I just released 0.8.1 which allows you to specify the git root directory explicitly:
appVersioning {
    gitRootDirectory.set(rootProject.file("../"))
}

Let me know if it works :).

Wow thanks a lot, it works!


⚠ SpeakUpConference.com has degraded performance

In 3afb632, SpeakUpConference.com (https://www.speakupconference.com) experienced degraded performance:

HTTP code: 200
Response time: 15370 ms

Resolved: SpeakUpConference.com performance has improved in a736f30 after 11 minutes.


add pinboard

not sure why it failed - my lib doesn't seem to be mentioned in the build output list of issues

Sorry about that. I took care of the other issues and verified on my machine that this addition to the build plan is fine.


Dramatic Works Template - missing end of book statement

Copied verbatim from: https://forum.librivox.org/viewtopic.php?f=49&t=79828 (forum admin permissions are necessary).
application/views/public/project_launch/dramatic_work.php
Please add the following code to the Dramatic Works Template
[*]
 [/list]
after this code (line 114 on Github)

This is and example of what the code create:
At the end of the book, say (in addition):
"End of The Chimney Corner, by Harriet Beecher Stowe. "

This issue is being addressed in PR #23.

This issue is being addressed in PR #23.

This issue may be closed, as it was in PR 23 merged yesterday.


⚠ SceneTime has degraded performance

In 3e398e0, SceneTime ($ST) experienced degraded performance:

HTTP code: 200
Response time: 1514 ms

Resolved: SceneTime performance has improved in 9a492ef after 8 minutes.


apostrophes are bad for api calls

So far, only seems to affect attachment collection for a small number of agencies, e.g.

skipping Troy FPD
Governor's State University failed <HttpError 400 when requesting https://www.googleapis.com/drive/v3/files?q=name%3D%27Governor%27s+State+University%27&alt=json returned "Invalid Value">
skipping DeWitt County

TODO: fix

TODO: see if you can str.replace(''','') agency name just for Drive API calls in atts.gm.roll_thru()[18:24]

tried to fix with c06602785f9db9092ae6ed2ea86da6871fb87776 but still broken

fixed in f1c5106 and verified


cli/apply: Fix chiselc in Node mode

We currently transform the "bundler entry file" with chiselc, but it's
not the right file to transform. Instead, we need to transform the
actual endpoint file.
Therefore, explicitly transform endpoints before generating the bundler
entry files, and then drop chiselc from the bundler step altogether.
Please note that we first transform all endpoints concurrently before
even starting to run the bundler. This ensures that we always have the
file transformed before bundling. However, we can make this faster by
starting the bundler immediately when chiselc completes. (I did try
this approach, but it turned to be an adventure of its own, so I am
deferring it for later.)
Fixes https://github.com/chiselstrike/chiselstrike/issues/1317

@dekimir For functional testing, we just run our test suite with the optimizer on and check that the tests pass. Of course, they also pass if the optimizer does nothing, which is why I am working on a performance harness. That's how I discovered that the indexing stuff, for example, was not actually getting applied.
We could add a test case checking that chisel apply generates the transformed endpoints in the .gen directory, but I am not sure we want to test that implementation detail because you could imagine chisel apply not generating them and, instead, performing the transformation purely in memory.

@dekimir Looks like I missed this post-merge comment. I am not sure what kind of test case you are thinking of here. The issue was that chisel apply was invoking chiselc on the wrong file (chisel apply internally generates a wrapper endpoint) so chiselc/tests/lit plays no part in testing.
It is also unclear what kind of test case we could add to cli/test/lit because the whole point of running chiselc as part of chisel apply is that it's transparent and the behavior of the endpoints stay the same (except for better performance). I mean, we can, of course, check for the presence of a .gen/<endpoint> file (and even verify contents), but that's an implementation detail that may change in the future.


Sometimes Image is not displayed at all

I have the following code running in the onPostExecute part of an AsyncTask that downloads images.
without photoViewAttacher I have no issues at all but when I put the line
  PhotoViewAttacher attacher = new PhotoViewAttacher(stockImageView);
it sometimes works and I can zoom sometimes it does not even show the image. Any Idea on why this might be happening?
Thank you.
   View footerView =((LayoutInflater) activity.getSystemService(Context.LAYOUT_INFLATER_SERVICE)).inflate(R.layout.footer_stock_details, null, false);

        if(listview.getFooterViewsCount()>0)
        {
            listview.removeFooterView(lastAddedFooter);
        }

        lastAddedFooter=footerView;
        ImageView stockImageView= (ImageView)footerView.findViewById(R.id.yahooImage);
        stockImageView.setImageBitmap(result);
        PhotoViewAttacher attacher = new PhotoViewAttacher(stockImageView);
        listview.addFooterView(footerView);

Here are the gradle dependencies and SDK versions:
dependencies {
    compile fileTree(dir: 'libs', include: ['*.jar'])
    testCompile 'junit:junit:4.12'
    compile files('libs/commons-lang3-3.4.jar')
    compile files('libs/gson-2.3.1.jar')
    compile 'com.android.support:appcompat-v7:23.3.0'
    compile 'com.android.support:design:23.3.0'
    compile 'com.android.support:support-v4:23.3.0'
    compile 'com.commit451:PhotoView:1.2.5'
}

 compileSdkVersion 23
 buildToolsVersion "23.0.3"

I use the PhotoView in the footer of a list and noticed the following:
If the listview does not have too many items on it such that the footer can be directly seen without a need to scroll the listview, The image appears fine with all the functionality.
If there are more items such that the footer is not shown without scrolling  then image does not appear. I suspected  it had something to do with recycling views  but I tried without recycling it and it still not shown.
It displays fine in either case with androids default Imageview.

Honestly, my suggestion would be to drop ListView and place this within a RecyclerView instead. I have had many issues with ListView headers and footers. It is best to move over to using RecyclerView as it has better support and this issues will more than likely go away

@Jawnnypoo   Hello , it has no effective that image is not displayed sometimes when using recyclerView.


feat: Introduced scope.onDestroy() and scope.subscribe(). Added info about API deprecation.

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

4 of 4 (100.0%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 100.0%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
299

Relevant Lines:
299

💛  - Coveralls


Get information whether the server is reachable or not

Imagine a case where an app using the AppCenter Crashes and Analytics API is running on a device with restricted internet access (firewall policies, etc). Is there a way of knowing if the AppCenter's domains are reachable by the API? Is it possible to check if the local log storage is empty in order to be sure that the logs were sent to the server?
I will be deploying a UWP App in several machines where the mentioned above case could be possible, and I would like to know if the data of that user will be available on the AppCenter or not, in order to take different measures in case it will not (report the installation via an automated email or something like that in order to keep track of the deployed versions of the app in the field).
I couldn't find anywhere on the documents the domain name the API reports the logs to (in order to make a quick check if it is accessible with an HTTP/GET or so)

Hey @rubiomiguel06, thanks for reaching out.
Endpoint which is mainly used in the SDK is in.appcenter.ms, and it should be the first place to check if backend is reachable.
As for "how" - there's no public API for that in SDK.


ASan crash in CEventManager::startThread on FreeBSD

Regressed by a6cfe7042897. On DragonFly, FreeBSD, NetBSD, OpenBSD sun_path size is 104.
$ meson setup --buildtype=debug -Db_sanitize=address /tmp/hyprland_build
$ meson compile -C /tmp/hyprland_build -c /dev/null
$ /tmp/hyprland_build/src/Hyprland -c /dev/null
[...]
00:00:00.040 [backend/headless/backend.c:68] Creating headless backend
=================================================================
==85957==ERROR: AddressSanitizer: stack-buffer-overflow on address 0x7fffddfedc2a at pc 0x00000068d48c bp 0x7fffddfedb90 sp 0x7fffddfed348
WRITE of size 107 at 0x7fffddfedc2a thread T17
00:00:00.055 [wayland] unable to lock lockfile /var/run/user/1001/wayland-1.lock, maybe another compositor is running
00:00:00.055 [backend/wayland/backend.c:427] Starting Wayland backend
    #0 0x68d48b in strncpy (/tmp/hyprland_build/src/Hyprland+0x68d48b)
    #1 0x92ebaa in CEventManager::startThread()::$_0::operator()() const EventManager.cpp
    #2 0x92e7b4 in decltype(std::declval<CEventManager::startThread()::$_0>()()) std::__1::__invoke[abi:v160002]<CEventManager::startThread()::$_0>(CEventManager::startThread()::$_0&&) EventManager.cpp
    #3 0x92e78c in void std::__1::__thread_execute[abi:v160002]<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct>>, CEventManager::startThread()::$_0>(std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct>>, CEventManager::startThread()::$_0>&, std::__1::__tuple_indices<>) EventManager.cpp
    #4 0x92e41b in void* std::__1::__thread_proxy[abi:v160002]<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct>>, CEventManager::startThread()::$_0>>(void*) EventManager.cpp
    #5 0x8017beb74 in thread_start /usr/src/lib/libthr/thread/thr_create.c:292:16

Address 0x7fffddfedc2a is located in stack of thread T17 at offset 138 in frame
    #0 0x92e7ef in CEventManager::startThread()::$_0::operator()() const EventManager.cpp

  This frame has 8 object(s):
    [32, 138) 'SERVERADDRESS'
    [176, 200) 'socketPath' <== Memory access at offset 138 partially underflows this variable
    [240, 264) 'ref.tmp' <== Memory access at offset 138 partially underflows this variable
    [304, 320) 'clientAddress'
    [336, 340) 'clientSize'
    [352, 356) 'ACCEPTEDCONNECTION'
    [368, 384) 'ref.tmp28'
    [400, 408) 'ref.tmp29'
HINT: this may be a false positive if your program uses some custom stack unwind mechanism, swapcontext or vfork
      (longjmp and C++ exceptions *are* supported)
Thread T17 created by T0 here:
    #0 0x68859a in pthread_create (/tmp/hyprland_build/src/Hyprland+0x68859a)
    #1 0x93eb78 in std::__1::__libcpp_thread_create[abi:v160002](pthread**, void* (*)(void*), void*) EventManager.cpp
    #2 0x92cf88 in std::__1::thread::thread<CEventManager::startThread()::$_0, void>(CEventManager::startThread()::$_0&&) EventManager.cpp
    #3 0x92cd86 in CEventManager::startThread() (/tmp/hyprland_build/src/Hyprland+0x92cd86)
    #4 0xc523fe in CCompositor::initManagers(eManagersInitStage) (/tmp/hyprland_build/src/Hyprland+0xc523fe)
    #5 0xc51bc4 in CCompositor::initServer() (/tmp/hyprland_build/src/Hyprland+0xc51bc4)
    #6 0xe0c483 in main (/tmp/hyprland_build/src/Hyprland+0xe0c483)
    #7 0x80187d189 in __libc_start1 /usr/src/lib/libc/csu/libc_start1.c:155:7
    #8 0x572541 in _start /usr/src/lib/csu/amd64/crt1_c.c:52:2
    #9 0x801065007  (<unknown module>)

SUMMARY: AddressSanitizer: stack-buffer-overflow (/tmp/hyprland_build/src/Hyprland+0x68d48b) in strncpy
Shadow bytes around the buggy address:
  0x7fffddfed980: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x7fffddfeda00: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x7fffddfeda80: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x7fffddfedb00: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x7fffddfedb80: 00 00 00 00 f1 f1 f1 f1 00 00 00 00 00 00 00 00
=>0x7fffddfedc00: 00 00 00 00 00[02]f2 f2 f2 f2 00 00 00 f2 f2 f2
  0x7fffddfedc80: f2 f2 f8 f8 f8 f2 f2 f2 f2 f2 f8 f8 f2 f2 f8 f2
  0x7fffddfedd00: f8 f2 f8 f8 f2 f2 f8 f3 f3 f3 f3 f3 00 00 00 00
  0x7fffddfedd80: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x7fffddfede00: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x7fffddfede80: 00 00 00 00 00 00 00 00 00 00 00 00 f1 f1 f1 f1
Shadow byte legend (one shadow byte represents 8 application bytes):
  Addressable:           00
  Partially addressable: 01 02 03 04 05 06 07
  Heap left redzone:       fa
  Freed heap region:       fd
  Stack left redzone:      f1
  Stack mid redzone:       f2
  Stack right redzone:     f3
  Stack after return:      f5
  Stack use after scope:   f8
  Global redzone:          f9
  Global init order:       f6
  Poisoned by user:        f7
  Container overflow:      fc
  Array cookie:            ac
  Intra object redzone:    bb
  ASan internal:           fe
  Left alloca redzone:     ca
  Right alloca redzone:    cb
==85957==ABORTING

This also shows up during build but drowns in the noise of other warnings:
hyprctl/main.cpp:83:5: warning: 'strncpy' size argument is too large; destination buffer has size 104, but size argument is 107 [-Wfortify-source]
    strncpy(serverAddress.sun_path, socketPath.c_str(), 107);
    ^
hyprctl/main.cpp:146:5: warning: 'strncpy' size argument is too large; destination buffer has size 104, but size argument is 107 [-Wfortify-source]
    strncpy(serverAddress.sun_path, socketPath.c_str(), 107);
    ^
src/managers/EventManager.cpp:70:9: warning: 'strncpy' size argument is too large; destination buffer has size 104, but size argument is 107 [-Wfortify-source]
        strncpy(SERVERADDRESS.sun_path, socketPath.c_str(), 107);
        ^

Hmm, on Linux sun_path size is 108. I guess, you're leaving one byte for terminating \0 to emulate strlcpy.


Initial implementation of leap exercise

closes #98

@loziniak, can I get a review / merge? I have other exercises I can port if you're available.

Don't worry about it. Thanks for the offer, but already maintain three tracks and contribute heavily to a fourth so I've got my hands full. :)
I don't want to crowd you though. If you want additional exercises ported, I can do that and leave them for your review whenever you're up for it. If you don't, that's fine too.

Ok, understood. What do you think about these changes I suggested? I'll apply them if you won't do it yourself, ok?

Additional exercises would be great of course, I'll happily review anything you contribute :-) Also, if you have any suggestions to new exercise creation process, feel free to express that in form of issues. Your input as maintainer on other tracks is extremely valuable.

I don't see the two comments. Were they part of a review?

Yes, I think so. I hope I didn't mess something up so they are not visible to you:


CayenneLPP owner has changed

The owner of CayenneLPP has changed from sabas1080 to electroniccats. This change broke the ability to compile my WisBlock project. I have manually fixed if by changing the library.json:
{
"owner": "sabas1080",
"name": "CayenneLPP",
"version": ""
}
to
{
"owner": "electroniccats",
"name": "CayenneLPP",
"version": ""
}
Here is the link describing the issue: PlatformIO link

Fixed with V2.0.18