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prosit interactive mode works while server mode fails #8

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tobigithub opened this issue Jun 24, 2019 · 5 comments
Closed

prosit interactive mode works while server mode fails #8

tobigithub opened this issue Jun 24, 2019 · 5 comments

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@tobigithub
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Hello,
the current interactive prosit version works, while the prosit server version creates multiple errors when curling the peptidelist to the server. So I think many of the errors mentioned in the initial #7 #4 are actually flask errors. Or maybe prosit makefile or prosit code issues. Or maybe related to passing flask arguments.

Proof, prosit in interactive mode creates the predictions:

root@8e29fa711bd2:~# ls -l
total 24
drwxr-xr-x 2 root root 4096 May 30 18:27 data.hdf5
-rw-r--r-- 1 root root 1012 Jun 24 06:00 jump.py
drwx------ 2 1000 1000 4096 Jun 24 05:21 model
-rw-r--r-- 1 root root 2027 Jun 24 06:00 msms_prediction.csv
-rw-r--r-- 1 root root  114 Jun 24 06:00 peptidelist.csv
drwxr-xr-x 5 root root 4096 Jun 24 06:00 prosit
root@8e29fa711bd2:~# cat msms_prediction.csv
Intensities     Masses  Matches Modified Sequence       Charge
1.0;0.41259626;0.73062277;0.30424523;0.11640168;0.093716405;0.08098759;0.1923069;0.08597337;0.15234001;0.08258821;0.012585764;0.034173176;0.013465268;0.0030653037;0.0014310256;0.008246724;0.004191969;0.00061403663;0.0013039891;0.0022206986;0.0005943045  175.118952167;322.15434716699997;435.238411167;548.322475167;619.359589167;690.3967031669999;761.4338171669999;263.088246467;360.141010467;431.178124467;502.21523846699995;573.2523524669999;218.122843817;429.74692881699997;132.047761467;180.574143467;216.092700467;251.61125746699997;207.12471403366666;230.80375203366665;254.48279003366665;229.45032313366664  y1;y2;y3;y4;y5;y6;y7;b2;b3;b4;b5;b6;y3(2+);y8(2+);b2(2+);b3(2+);b4(2+);b5(2+);y5(3+);y6(3+);y7(3+);b7(3+)  MMPAAALIM(ox)R  3
0.031838626;0.05340379;0.0008666609;0.16591543;0.6199195;1.0;0.3755796;0.006878452;0.8553704;0.18602312;0.009526462;0.1253908;0.6669085;0.056322724;0.00025479423;0.0041989647     147.112804167;294.148199167;407.232263167;504.285027167;601.3377911670001;698.3905551670001;769.4276691670001;882.5117331670001;245.131825467;316.168939467;413.221703467;301.17253381700004;349.69891581700006;385.21747281700004;158.58810796699998;207.114489967   y1;y2;y3;y4;y5;y6;y7;y8;b2;b3;b4;y5(2+);y6(2+);y7(2+);b3(2+);b4(2+)        MLAPPPIM(ox)K   2
0.6478142;0.7623719;1.0;0.78580517;0.24467614;0.037683975;0.98502636;0.46840557;0.30539873;0.5721372;0.27308717;0.025923852;0.31577614;0.6946651;0.4260823;0.0029871135;0.016204849;0.0243927;0.0019134118;0.027638009;0.008317641;0.0010335244       175.118952167;419.20711116699994;516.259875167;613.312639167;710.3654031670001;132.047761467;288.148872467;359.185986467;472.27005046700003;585.354114467;698.4381784670001;811.5222424670001;258.633575817;307.159957817;355.68633981700003;180.096631467;236.63866346700001;293.180695467;349.722727467;59.044501700333335;237.45998536700003;44.68743813366666        y1;y3;y4;y5;y6;b1;b2;b3;b4;b5;b6;b7;y4(2+);y5(2+);y6(2+);b3(2+);b4(2+);b5(2+);b6(2+);y1(3+);y6(3+);b1(3+)  MRALLLIPPPPM(ox)R       6
root@8e29fa711bd2:~#

code

# Example for manual start of prosit
# runfile for sudo make jump 
# sudo make jump MODEL=/home/xxx/prosit/prosit1

# once inside container
# cp prosit/peptidelist.csv .
# cp prosit/jump.py .
# python jump.py


from prosit import constants
from prosit import maxquant
from prosit import alignment
from prosit import prediction
from prosit import tensorize
import pandas

from prosit import model as model_lib
model_dir = constants.MODEL_DIR
global model
global model_config
model, model_config = model_lib.load(model_dir, trained=True)

# read peptides
df = pandas.read_csv("peptidelist.csv")
tensor = tensorize.peptidelist(df)
result = prediction.predict(tensor, model, model_config)
df_pred = maxquant.convert_prediction(result)

# write files to prediction.csv
# path = "{}prediction.csv".format(model_dir)
path="msms_prediction.csv"
maxquant.write(df_pred, path)

# copy file "msms_prediction.csv" out of container
# find docker ID: sudo docker ps
# sudo docker cp cbfc30fce3f1:/root/msms_prediction.csv .

when running

sudo make server MODEL=/home/xxx/prosit/prosit1

and running in another instance

curl -F "peptides=@examples/peptidelist.csv" http://127.0.0.1:5000/predict/

the following error occurs

* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
[2019-06-24 06:20:20,889] ERROR in app: Exception on /predict/ [POST]
Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 2311, in wsgi_app
    response = self.full_dispatch_request()
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1834, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1737, in handle_user_exception
    reraise(exc_type, exc_value, tb)
  File "/usr/local/lib/python3.5/dist-packages/flask/_compat.py", line 36, in reraise
    raise value
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1832, in full_dispatch_request
    rv = self.dispatch_request()
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1818, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "/root/prosit/server.py", line 28, in predict
    result = prediction.predict(tensor, model, model_config)
  File "/root/prosit/prediction.py", line 14, in predict
    model.compile(optimizer="adam", loss="mse")
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 333, in compile
    sample_weight, mask)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training_utils.py", line 403, in weighted
    score_array = fn(y_true, y_pred)
  File "/usr/local/lib/python3.5/dist-packages/keras/losses.py", line 14, in mean_squared_error
    return K.mean(K.square(y_pred - y_true), axis=-1)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 848, in binary_op_wrapper
    with ops.name_scope(None, op_name, [x, y]) as name:
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 5770, in __enter__
    g = _get_graph_from_inputs(self._values)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 5428, in _get_graph_from_inputs
    _assert_same_graph(original_graph_element, graph_element)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 5364, in _assert_same_graph
    original_item))
ValueError: Tensor("out_target:0", shape=(?, ?), dtype=float32) must be from the same graph as Tensor("out/Reshape:0", shape=(?, ?), dtype=float32).
172.17.0.1 - - [24/Jun/2019 06:20:20] "POST /predict/ HTTP/1.1" 500 -

There is also a keras yaml error and maybe flask can not handle it, or prosit does not contain the appropriate code to ignore this warning?

/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py:349: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
  config = yaml.load(yaml_string)
 * Serving Flask app "server" (lazy loading)
@gessulat
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Try this branch:
https://github.com/kusterlab/prosit/tree/fix/server_issue

I don't quite understand why this happens for some environments and not for others.

This was referenced Jun 24, 2019
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@tobigithub
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@gessulat
Tnx

@tobigithub
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@gessulat
I tried the fixed server branch https://github.com/kusterlab/prosit/tree/fix/server_issue
It runs and gives results, however submitting a second file will break the server.

~/prosit$ curl -F "peptides=@examples/peptidelist.csv" http://127.0.0.1:5000/predict/
Intensities     Masses  Matches Modified Sequence       Charge
1.0;0.41259626;0.73062277;0.30424523;0.11640168;0.093716405;0.08098759;0.1923069;0.08597337;0.15234001;0.08258821;0.012585764;0.034173176;0.013465268;0.0030653037;0.0014310256;0.008246724;0.004191969;0.00061403663;0.0013039891;0.0022206986;0.0005943045  175.118952167;322.15434716699997;435.238411167;548.322475167;619.359589167;690.3967031669999;761.4338171669999;263.088246467;360.141010467;431.178124467;502.21523846699995;573.2523524669999;218.122843817;429.74692881699997;132.047761467;180.574143467;216.092700467;251.61125746699997;207.12471403366666;230.80375203366665;254.48279003366665;229.45032313366664     y1;y2;y3;y4;y5;y6;y7;b2;b3;b4;b5;b6;y3(2+);y8(2+);b2(2+);b3(2+);b4(2+);b5(2+);y5(3+);y6(3+);y7(3+);b7(3+)     MMPAAALIM(ox)R  3
0.031838626;0.05340379;0.0008666609;0.16591543;0.6199195;1.0;0.3755796;0.006878452;0.8553704;0.18602312;0.009526462;0.1253908;0.6669085;0.056322724;0.00025479423;0.0041989647        147.112804167;294.148199167;407.232263167;504.285027167;601.3377911670001;698.3905551670001;769.4276691670001;882.5117331670001;245.131825467;316.168939467;413.221703467;301.17253381700004;349.69891581700006;385.21747281700004;158.58810796699998;207.114489967   y1;y2;y3;y4;y5;y6;y7;y8;b2;b3;b4;y5(2+);y6(2+);y7(2+);b3(2+);b4(2+)   MLAPPPIM(ox)K   2
0.6478142;0.7623719;1.0;0.78580517;0.24467614;0.037683975;0.98502636;0.46840557;0.30539873;0.5721372;0.27308717;0.025923852;0.31577614;0.6946651;0.4260823;0.0029871135;0.016204849;0.0243927;0.0019134118;0.027638009;0.008317641;0.0010335244       175.118952167;419.20711116699994;516.259875167;613.312639167;710.3654031670001;132.047761467;288.148872467;359.185986467;472.27005046700003;585.354114467;698.4381784670001;811.5222424670001;258.633575817;307.159957817;355.68633981700003;180.096631467;236.63866346700001;293.180695467;349.722727467;59.044501700333335;237.45998536700003;44.68743813366666   y1;y3;y4;y5;y6;b1;b2;b3;b4;b5;b6;b7;y4(2+);y5(2+);y6(2+);b3(2+);b4(2+);b5(2+);b6(2+);y1(3+);y6(3+);b1(3+)     MRALLLIPPPPM(ox)R       6

The second time to curl the same CSV file will break the server.

* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
172.17.0.1 - - [24/Jun/2019 22:59:26] "POST /predict/ HTTP/1.1" 200 -
[2019-06-24 23:03:20,541] ERROR in app: Exception on /predict/ [POST]
Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 2311, in wsgi_app
    response = self.full_dispatch_request()
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1834, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1737, in handle_user_exception
    reraise(exc_type, exc_value, tb)
  File "/usr/local/lib/python3.5/dist-packages/flask/_compat.py", line 36, in reraise
    raise value
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1832, in full_dispatch_request
    rv = self.dispatch_request()
  File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1818, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "/root/prosit/server.py", line 29, in predict
    result = prediction.predict(tensor, model, model_config, graph)
  File "/root/prosit/prediction.py", line 17, in predict
    x, verbose=verbose, batch_size=constants.PRED_BATCH_SIZE
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1167, in predict
    steps=steps)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training_arrays.py", line 294, in predict_loop
    batch_outs = f(ins_batch)
  File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 2666, in __call__
    return self._call(inputs)
  File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 2636, in _call
    fetched = self._callable_fn(*array_vals)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1382, in __call__
    run_metadata_ptr)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 519, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value dense_1/kernel
         [[Node: dense_1/kernel/read = Identity[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"](dense_1/kernel)]]
         [[Node: out/Reshape/_23 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_606_out/Reshape", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
172.17.0.1 - - [24/Jun/2019 23:03:20] "POST /predict/ HTTP/1.1" 500 -

basically after one process the GPU memory is not freed. nvidia-smi shows
full memory allocation, even when the GPU is not used anymore.
See also keras-team/keras#12625

|   1  GeForce GTX 108...  Off  | 00000000:03:00.0 Off |                  N/A |
| 30%   49C    P8    17W / 250W |  10787MiB / 11178MiB |      0%      Default |

@gessulat
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gessulat commented Jul 2, 2019

Note that the master branch is updated now. The server should not have the issues as described above anymore.

@tobigithub
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@gessulat thank you,
the new version works great, no hiccups!

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