add slow5 reading

[Psy-Fer]

Hello,

I added a hacky way to add slow5/blow5 reading to radian.
I didn't want to change the interface or args, as I think that should be left to the authors, but I have shown how easy it is to implement and added some comments for clarity.

I timed myself doing this as well.

~8min for:

• make fork
• load in VS code
• read code and find fast5 reading
• make code changes to add pyslow5
• add some comments

then ~52min for:

• create environment
• install deps
• find errors and switch python
• try again
• try test data
• fix errors
• try slow5 conversion of fast5 test data
• fix errors
• try again
• test slow5 reading
• success!

So about 1h.

Thanks!

P.S:
I've added my notes of this below. I would recommend stating that python3.8 is recommended for installing tensorflow 2.4.4 and to use virtual environments.
Also the tar command puts the output in the wrong folder (I think), I had to move it back to the models folder.

James' Notes:

setting up slow5 hack using fast5dir for *.blow5 file

time taken: 8min

trying to install on laptop

CUDA Version: 11.7 might work? server is 11.1, so will have to see

no python version given. Trying my default of 3.10

no instruction of creating venv, but doing that first

python3 -m venv venv
source venv/bin/activate
export PYSLOW5_ZSTD=1
pip install --upgrade pip
pip install -r requirements.txt

error:

ERROR: Could not find a version that satisfies the requirement tensorflow~=2.4.4 (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.11.0rc0, 2.11.0rc1)
ERROR: No matching distribution found for tensorflow~=2.4.4

looks like the release compatibility on pypi is for python3.8

so using deadsnakes, installing python3.8

sudo apt install python3.8 python3.8-dev python3.8-venv

deactivate
rm -rf ./venv

python3.8 -m venv venv
source venv/bin/activate
export PYSLOW5_ZSTD=1
pip install --upgrade pip
pip install -r requirements.txt

okay that worked! (7min so far)

tar -xvzf radian/models/rnamodel_12mer_pc.tar.gz

testing on test data

cd radian
mkdir out_dir
python3 basecall.py data out_dir

error:

Traceback (most recent call last):
  File "./basecall.py", line 8, in <module>
    import pyslow5
  File "python/pyslow5.pyx", line 1, in init pyslow5
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 80 from PyObject

maybe something to do with numpy

removing and adding again

that doesn't work. Problem is i need to purge the cached pyslow5 build to force it to build again

pip cache purge
pip install pyslow5

now that works

trying again

python3 basecall.py data out_dir

error:
FileNotFoundError: [Errno 2] No such file or directory: 'models/rnamodel_12mer_pc.json'

mv rnamodel_12mer_pc.json radian/models/

can't use my GPU? or something.

(venv) jamfer@garvan-work:~/Dropbox/Bioinformatics/tools/repos/radian/radian$ python3 basecall.py data out_dir
2022-10-27 15:37:50.938231: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-10-27 15:37:50.938255: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-10-27 15:38:07.925487: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2022-10-27 15:38:07.926116: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2022-10-27 15:38:07.935911: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-27 15:38:07.936078: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3050 Ti Laptop GPU computeCapability: 8.6
coreClock: 1.223GHz coreCount: 20 deviceMemorySize: 3.82GiB deviceMemoryBandwidth: 163.94GiB/s
2022-10-27 15:38:07.936166: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936218: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936253: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936287: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936320: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936354: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936385: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936434: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory
2022-10-27 15:38:07.936440: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2022-10-27 15:38:07.936691: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-10-27 15:38:07.937207: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2022-10-27 15:38:07.937223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-10-27 15:38:07.937226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      
2022-10-27 15:38:08.320243: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2022-10-27 15:38:08.320551: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2304000000 Hz
Basecalled read 00256416-5423-47a9-ad91-54a87a6be5e5 in 6.25 sec.
Basecalled read 018097e8-babe-4525-8426-e7cfe568219c in 1.59 sec.
Basecalled read 022542bc-00b3-4d6a-9226-9f5da14af8ea in 4.53 sec.
Basecalled read 04295bc9-d0af-4a85-a59f-fff2ef52ece6 in 5.83 sec.
Basecalled read 049f55ce-f95e-4712-95a7-44a5709134f8 in 3.93 sec.

anyway, works for CPU on a few reads. That's enough to test.

SO now testing slow5....let's convert the fast5 files (we are at 30min now)

cd data
mkdir fast5
cp *.fast5 fast5/
slow5tools f2s -o reads.blow5 fast5/
[list_all_items] Looking for '*.fast5' files in fast5/
[f2s_main] 1 fast5 files found - took 0.000s
[f2s_main] Just before forking, peak RAM = 0.000 GB
[f2s_iop] 1 proceses will be used.
[fast5_group_itr::ERROR] Bad fast5: A primary attribute is missing in the fast5//reads.fast5.
[read_fast5::ERROR] Bad fast5: Could not iterate over the read groups in the fast5 file fast5//reads.fast5.
[f2s_child_worker::ERROR] Could not read contents of the fast5 file 'fast5//reads.fast5'.

lol! okay that's weird...

opens up file in HDFView

oh...there are 2 signal entries. Let's delete the old_signal entry...

try again with slow5tools....and that works now with slow5tools.

Now let's try slow5 as the input.

python3 basecall.py ./data/reads.blow5 out_dir

yep, that works!

(venv) jamfer@garvan-work:~/Dropbox/Bioinformatics/tools/repos/radian/radian$ python3 basecall.py ./data/reads.blow5 out_dir
2022-10-27 17:12:43.659243: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-10-27 17:12:43.659266: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-10-27 17:12:59.056701: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2022-10-27 17:12:59.057282: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2022-10-27 17:12:59.066022: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-10-27 17:12:59.066126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3050 Ti Laptop GPU computeCapability: 8.6
coreClock: 1.223GHz coreCount: 20 deviceMemorySize: 3.82GiB deviceMemoryBandwidth: 163.94GiB/s
2022-10-27 17:12:59.066177: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066204: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066226: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066249: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066271: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066291: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066312: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066333: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory
2022-10-27 17:12:59.066337: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2022-10-27 17:12:59.066507: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-10-27 17:12:59.067950: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2022-10-27 17:12:59.067967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-10-27 17:12:59.067970: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      
2022-10-27 17:12:59.395246: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2022-10-27 17:12:59.395513: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2304000000 Hz
Basecalled read 00256416-5423-47a9-ad91-54a87a6be5e5 in 6.49 sec.
Basecalled read 018097e8-babe-4525-8426-e7cfe568219c in 1.75 sec.
Basecalled read 022542bc-00b3-4d6a-9226-9f5da14af8ea in 4.58 sec.
Basecalled read 04295bc9-d0af-4a85-a59f-fff2ef52ece6 in 6.02 sec.
Basecalled read 049f55ce-f95e-4712-95a7-44a5709134f8 in 4.13 sec.

Winning!

Time: 52min.

Total time to implement and test: ~1h