Burrows–Abadi–Needham BAG logic

 

Burrows–Abadi–Needham (BAN) logic

https://en.wikipedia.org/wiki/Burrows%E2%80%93Abadi%E2%80%93Needham_logic#:~:text=Burrows%E2%80%93Abadi%E2%80%93Needham%20logic%20(,secured%20against%20eavesdropping%2C%20or%20both.

PET privacy enhancing techniques

Referecne:  https://www.drivendata.org/competitions/98/nist-federated-learning-1/rules/

privacy-preserving federated learning (PPFL) solutions

 democracy-affirming technologies.

 the global federated model is trained, the parameters related to the local models could be used to learn about the sensitive information contained in the training data of each client. Similarly, the released global model could also be used to infer sensitive information about the training datasets used.

1.4 GOALS AND OBJECTIVES:

• Drive innovation in the technological development and application of novel privacy enhancing technologies;

• Deliver strong privacy guarantees against a set of common threats and privacy attacks; and

• Generate effective models to accomplish a set of predictive or analytical tasks that support the use cases.

Organizers seek to mature federated learning approaches and build trust in adoption by accelerating the development of efficient PPFL solutions that leverage a combination of input and output privacy techniques to:

Phase 1: Concept Paper. Blue Team Participants will produce a technical white paper (“Concept Paper” or “White Paper”) setting out their proposed solution approach. Technical papers will be evaluated by a panel of judges across a set of weighted criteria. Participants will be eligible to win prizes awarded to the top technical papers, ranked by points awarded.

As you propose your technical solutions, be prepared to clearly describe the technical approaches and sketch out proof of or justification for privacy guarantees. Participants should consider a broad range of privacy threats during the model training and model use phases and consider technical and process aspects including but not limited to cryptographic and non-cryptographic methods, and protection needed within the deployment environment.

Successful technical approaches and proofs of privacy guarantees will include the design of any algorithms, protocols, etc. utilized, as well as formal or informal arguments of how the solution will provide privacy guarantees. Participants will clearly list any additional privacy issues specific to the technological approaches used and justify initial enhancements or novelties compared to the current state-of-the-art. Participant submissions must describe how the solution will cater to the types of data provided to participants and how generalizable the solution is to multiple domains. Expected efficiency/scalability of improvements, privacy vs. utility trade off should be articulated, if possible, at this conceptual stage.

Q: what is the definition of privacy guarantee? 

a one-page abstract and a Concept Paper.

Abstract: The one-page abstract must include a title and a brief description of the proposed solution, including the proposed privacy mechanisms and architecture of the federated model. The description should also describe the proposed machine learning model and expected results with regard to accuracy. Successful abstracts will outline how solutions will achieve privacy while minimizing loss to accuracy, a proposed solution, and the anticipated results, as more fully described on the Challenge Website. Abstracts must be submitted by following the instructions on the Challenge Website. Abstracts will be screened by the DrivenData and Organizers’ staff for contest eligibility and used to ensure the composition of the judging panel’s expertise aligns to proposed solutions that will be evaluated throughout the course of the Challenge. Feedback will not be provided.
Concept Paper: The Concept Paper should conceptualize solutions that describe the technical approaches and lay out the proof of privacy guarantees that solve a set of predictive or analytic tasks that support the use cases. Successful Concept Papers will incorporate the originally submitted abstract and be no more than ten pages in length. References will not count towards page length. Participant submissions shall:

• Include a title and abstract for the solution

• Clearly articulate the selected track(s) the solution addresses, understanding of the problem, and opportunities for privacy technology within the current state-of-the-art.

• Clearly describe the technical approaches and proof of privacy guarantees based on their described threat model, including:

• The design of any algorithms, protocols, etc. utilized,

• The formal or informal arguments of how the solution will provide privacy guarantees.

• Clearly list any additional privacy issues specific to the technological approaches used.

• Justify initial enhancement or novelty compared to the state-of-the-art.

• Articulate:

• The expected efficiency and scalability of the privacy solution,

• The expected accuracy and performance of the model,

• The expected tradeoffs between privacy and accuracy/utility,

• How the explainability of model outputs may be impacted by your privacy solution,

• The feasibility of implementing the solution within the competition timeframe.

• Describe how the solution will cater to the types of data provided to participants and articulate what additional work may be needed to generalize the solution to other types of data.

• Articulate the anticipated use and purpose of licensed software.

• Be free from typographical and grammatical errors.

Participants should refer to Section 7 on general submission requirements for additional guidance and style guidelines.
Judges will score the Concept Papers against the weighted criteria outlined in the table below. Solutions will need to carefully consider trade-offs between criteria such as privacy, accuracy, and efficiency, and should take the weightings of the criteria into account when considering these trade-offs. Concept Papers must also demonstrate how acceptable levels of both privacy and accuracy will be achieved – one must not be completely traded off for the other (a fully privacy-preserving but totally inaccurate model is not of use to anyone). Proposals that do not sufficiently demonstrate how both privacy and accuracy will be achieved will not be eligible to score points in the remaining criteria.

federated scope

 Alibaba federated learning

https://github.com/AI-in-Biomedical-Science/FederatedScope#quick-start

Concur travel reimbursement request

 

I submitted CONCUR Philadelphia ICIBM meeting travel reimbursement request. When I uploaded the hotel, there are errors for allowance. I then created Itinerary to add per diem, and the allowance error went away. 

So, next time, I should try create Itinerary first, and then upload hotel. 

To create a Travel Allowance Itinerary for an already created report:

1. Log in to Concur.
2. Click on Expense at the top of the screen.
3. Open your existing expense report.
4. Click Details.
5. Under the Travel Allowance section, click New Itinerary.
6. Populate all required fields (be accurate with dates and times).
7. Click Save.

Active Presenter

 screencast, and video editing

https://atomisystems.com/download/ 

4180 day 3

 zoom, turn live caption on, record

review simple R

learning R with GISAID 

Running in RStudio

Next time, go through JHU data set. 

ODE under uncertainty

 ODE under uncertainty

CyptTen

 

https://ai.facebook.com/blog/crypten-a-new-research-tool-for-secure-machine-learning-with-pytorch/

https://crypten.readthedocs.io/en/latest/

hormophic encrption

 holomorphic encryption: 

Enc(m1) + Enc(m2) = Enc( m1 + m2) 

Enc(m1) x Enc(m2) = Enc( m1 x m2) 

So, an untrusted entity can compute addition or multiplication without decryption. 

https://en.wikipedia.org/wiki/Homomorphic_encryption

Fully homomorphic encryption (FHE)

From Wikipedia:

Fourth-generation FHE[edit]

In 2016, Cheon, Kim, Kim and Song (CKKS)^[35] proposed an approximate homomorphic encryption scheme that supports a special kind of fixed-point arithmetic that is commonly referred to as block floating point arithmetic. The CKKS scheme includes an efficient rescaling operation that scales down an encrypted message after a multiplication. For comparison, such rescaling requires bootstrapping in the BGV and BFV schemes. The rescaling operation makes CKKS scheme the most efficient method for evaluating polynomial approximations, and is the preferred approach for implementing privacy-preserving machine learning applications. The scheme introduces several approximation errors, both nondeterministic and deterministic, that require special handling in practice.^[36]

A 2020 article by Baiyu Li and Daniele Micciancio discusses passive attacks against CKKS, suggesting that the standard IND-CPA definition may not be sufficient in scenarios where decryption results are shared.^[37] The authors apply the attack to four modern homomorphic encryption libraries (HEAAN, SEAL, HElib and PALISADE) and report that it is possible to recover the secret key from decryption results in several parameter configurations. The authors also propose mitigation strategies for these attacks, and include a Responsible Disclosure in the paper suggesting that the homomorphic encryption libraries already implemented mitigations for the attacks before the article became publicly available. Further information on the mitigation strategies implemented in the homomorphic encryption libraries has also been published.^[38][39]

multiple aggregators in federated learning

 Leaf-aggregator, intermediate aggregator, master aggregator, in hierarchical tree based aggregation

https://arxiv.org/pdf/2203.12163.pdf

AdaFed takes associativity one step further. AdaFed mitigates issues with aggregation overlays by avoiding the construction of actual/physical tree topology.

Federated learning (book)

Book series

Federated Learning (FL) requires an aggregator and parties to exchange model updates. (Page 285)

vulnerable to the inference of private data

System entities of the FL system

the attack surface is used to refer to the exposed parameters and data

against data leak

FL-specific attacks often take advantage of the information transmission during FL. 

Differential privacy: differential privacy at the party side or the aggregator side. 

For healthcare data and personal information, there are regulation and compliance requirements [14, 63]

page 285: In FL, training data is not explicitly shared. 

$13.3.1 Secure Aggregation 

 

基于人工智能(AI)的蛋白结构预测工具合集

https://mp.weixin.qq.com/s/7fb4tx0sKXE26IX1nXw4qg

CPSC4180 day 2, Rstudio, Cloud, CoLab

zoom, turn live caption on, record
go over datacamp registration and assignment
R and R studio installation
RStudio Cloud; 
CoLab; Repit
simple R exercis: SimpleR.Rmd download directly form Canvas (GitHub link does not provide direct download as Rmd).   

Running in RStudio (1 hour, how to insert new R chunk, R pub) 
run simpleR in CoLab

cpsc4180, 5180 day 1

 

CPSC4180 day 1, orientation

zoom, turn live caption on, 

introduce myself 

syllabus

Socrative, Room HongQin, anonymous icebreaker, 

datacamp registration

participatory coding and video requirement

video submission with hyper-link. Examples of past student submissions. 

sample student videos, 

past student final report

why R and python

x Email list to calendar invitation

Unum Agile

agile at scale 

Rally BroadCom

Use IBM Z as the mainframe

according to the student intern, AGILE give employee work and life balance. Software engineers do not have to work in the weekends when AGILE replaced waterfall model. 

UNUM plan software engineering work one-year head, in order to request a budget. 

Spring 2023 graduate application

 To apply for Spring 2023 enrollment of PhD program, please apply at https://www.utc.edu/research/graduate-school/prospective-students/graduate-school-admissions

https://www.utc.edu/enrollment-management-and-student-affairs/center-for-global-education/international-student-and-scholar-services/prospective-students

https://www.utc.edu/sites/default/files/2021-03/how-to-apply-f12.pdf

 

The deadline for fall  application is February 15 (for students interested in graduate assistantship)

https://www.utc.edu/engineering-and-computer-science/academic-programs/electrical-engineering/graduate-curriculum/graduate-admission

 

For spring application, the website says Nov 1 as the deadline, but for assistantship, it is advised to complete application as earlier as possible. 

Our graduate school application requires GRE and TOEFL/IELTS scores.  For students with US degrees, please contact Lora Cook to verify whether foreign language exams might be waived. 

 

Thanks again for your interest. Please let me know if I can answer some of your questions,

 

Dear XYZ

 

Currently you have not submitted an application for our university.  Our PhD programs have a firm deadline for our PhD programs as noted below.  Degrees received from with the U.S. in the last two years will be waived from the English requirements.

Thank you for your interest in the University of Tennessee at Chattanooga. My name is Lora, and I will assist you in the application process. 

 

The following requirements must be fulfilled by February 1^st for the fall semestersand by September 1^st for spring semesters. We do not begin new students in the summer semester.

 

 

• Pay a nonrefundable application fee of $40.
• Submit the program-specific graduate admissions form. 
• Submit official transcripts from each college or university you have attended.  
• Report satisfactory English test scores from one of the three test scores (institution code 1831). TOEFL IBT minimum score: 79.  IELTS minimum score: 6. Duolingo minimum score 100.
• Submit a GRE score sheet, our institutional code is 1831.

• Submit a clear copy of your current passport.

 

 

Submit official proof of funding. may be submitted after acceptance by the program.   

 

PhD Program information.

 

Visit www.utc.edu/international for more information regarding housing, orientation, student fees, health insurance, and the admissions process.

 

If you have any questions, please don’t hesitate to ask.

Sincerely,

 

 

gdown on ts, download GoogleDrive file/folder to Linux

use the anyone can view share link on googleDrive 

sh-4.2$ gdown --fuzzy https://drive.google.com/drive/folders/14pXSOuFngosZ5BxVAr_e37Zoeq1pBeDS?usp=sharing

Downloading...

From: https://drive.google.com/drive/folders/14pXSOuFngosZ5BxVAr_e37Zoeq1pBeDS?usp=sharing

To: /gpfs/gsfs1/scr/qinlab/test/14pXSOuFngosZ5BxVAr_e37Zoeq1pBeDS?usp=sharing

895kB [00:00, 43.8MB/s]

-bash-4.2$ gdown --fuzzy https://drive.google.com/file/d/155zRiE0U-VGh40uUdgHSdnIxK0_1GSBa/view?usp=sharing

cecs calendar 2022-2023

 

ML and security

https://www.usenix.org/conference/usenixsecurity22/fall-accepted-papers  

model poisoning

privacy implication of forging? 

data reconstruction attack, threat to distributed machine learning

attacks often solve the gradient matching problem via optimization

A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments

 

A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments

https://github.com/BigDataBiology/SemiBin/

alphafold-2 firefly run, error

[hqin@firefly02 alphafold-2]$ bash alphafold-singularity-run.sh --fasta_paths T1050.fasta 

Mounting /scr -> /mnt/data_dir

Mounting /scr/alphafold-data/uniref90 -> /mnt/uniref90_database_path

Mounting /scr/alphafold-data/mgnify -> /mnt/mgnify_database_path

Mounting /scr/alphafold-data/bfd -> /mnt/bfd_database_path

Mounting /scr/alphafold-data/uniclust30/uniclust30_2018_08 -> /mnt/uniclust30_database_path

Mounting 

-> /mnt/pdb70_database_path

Mounting /scr/alphafold-data/pdb_mmcif -> /mnt/template_mmcif_dir

Mounting /scr/alphafold-data/pdb_mmcif -> /mnt/obsolete_pdbs_path

Mounting /home/hqin/alphafold-2 -> /mnt/fasta_path_0

--data_dir=/mnt/data_dir/alphafold-data --uniref90_database_path=/mnt/uniref90_database_path/uniref90.fasta --mgnify_database_path=/mnt/mgnify_database_path/mgy_clusters_2018_12.fa --bfd_database_path=/mnt/bfd_database_path/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt --uniclust30_database_path=/mnt/uniclust30_database_path/uniclust30_2018_08 --pdb70_database_path=/mnt/pdb70_database_path/pdb70 --template_mmcif_dir=/mnt/template_mmcif_dir/mmcif_files --obsolete_pdbs_path=/mnt/obsolete_pdbs_path/obsolete.dat --fasta_paths=/mnt/fasta_path_0/T1050.fasta --output_dir=/mnt/output --benchmark=0 --logtostderr --max_template_date=2021-12-31

/scr:/mnt/data_dir,/scr/alphafold-data/uniref90:/mnt/uniref90_database_path,/scr/alphafold-data/mgnify:/mnt/mgnify_database_path,/scr/alphafold-data/bfd:/mnt/bfd_database_path,/scr/alphafold-data/uniclust30/uniclust30_2018_08:/mnt/uniclust30_database_path,/scr/alphafold-data/pdb70:/mnt/pdb70_database_path,/scr/alphafold-data/pdb_mmcif:/mnt/template_mmcif_dir,/scr/alphafold-data/pdb_mmcif:/mnt/obsolete_pdbs_path,/home/hqin/alphafold-2:/mnt/fasta_path_0,/home/hqin/alphafold-2:/mnt/output

I0804 20:07:16.598631 140258065744832 templates.py:857] Using precomputed obsolete pdbs /mnt/obsolete_pdbs_path/obsolete.dat.

I0804 20:07:16.809164 140258065744832 xla_bridge.py:244] Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker: 

I0804 20:07:17.299607 140258065744832 xla_bridge.py:244] Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.

I0804 20:07:21.937146 140258065744832 run_alphafold.py:385] Have 5 models: ['model_1', 'model_2', 'model_3', 'model_4', 'model_5']

I0804 20:07:21.937372 140258065744832 run_alphafold.py:397] Using random seed 1266311393757702950 for the data pipeline

I0804 20:07:21.937706 140258065744832 run_alphafold.py:150] Predicting T1050

I0804 20:07:21.938605 140258065744832 jackhmmer.py:130] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmp8lfb38hd/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/T1050.fasta /mnt/uniref90_database_path/uniref90.fasta"

I0804 20:07:21.984835 140258065744832 utils.py:36] Started Jackhmmer (uniref90.fasta) query

I0804 20:13:41.682038 140258065744832 utils.py:40] Finished Jackhmmer (uniref90.fasta) query in 379.697 seconds

I0804 20:13:44.308897 140258065744832 jackhmmer.py:130] Launching subprocess "/usr/bin/jackhmmer -o /dev/null -A /tmp/tmpqgyb_oib/output.sto --noali --F1 0.0005 --F2 5e-05 --F3 5e-07 --incE 0.0001 -E 0.0001 --cpu 8 -N 1 /mnt/fasta_path_0/T1050.fasta /mnt/mgnify_database_path/mgy_clusters_2018_12.fa"

I0804 20:13:44.343975 140258065744832 utils.py:36] Started Jackhmmer (mgy_clusters_2018_12.fa) query

I0804 20:20:40.947294 140258065744832 utils.py:40] Finished Jackhmmer (mgy_clusters_2018_12.fa) query in 416.603 seconds

I0804 20:21:01.298550 140258065744832 hhsearch.py:85] Launching subprocess "/usr/bin/hhsearch -i /tmp/tmpwk1zvn0h/query.a3m -o /tmp/tmpwk1zvn0h/output.hhr -maxseq 1000000 -d /mnt/pdb70_database_path/pdb70"

I0804 20:21:01.353056 140258065744832 utils.py:36] Started HHsearch query

I0804 20:21:01.404658 140258065744832 utils.py:40] Finished HHsearch query in 0.051 seconds

Traceback (most recent call last):

  File "/app/alphafold/run_alphafold.py", line 427, in <module>

    app.run(main)

  File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 312, in run

    _run_main(main, args)

  File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 258, in _run_main

    sys.exit(main(argv))

  File "/app/alphafold/run_alphafold.py", line 412, in main

    is_prokaryote=is_prokaryote)

  File "/app/alphafold/run_alphafold.py", line 164, in predict_structure

    msa_output_dir=msa_output_dir)

  File "/app/alphafold/alphafold/data/pipeline.py", line 179, in process

    pdb_templates_result = self.template_searcher.query(uniref90_msa_as_a3m)

  File "/app/alphafold/alphafold/data/tools/hhsearch.py", line 96, in query

    stdout.decode('utf-8'), stderr[:100_000].decode('utf-8')))

RuntimeError: HHSearch failed:

stdout:

stderr:

- 20:21:01.404 ERROR: In /tmp/hh-suite/src/ffindexdatabase.cpp:11: FFindexDatabase:

- 20:21:01.404 ERROR: could not open file '/mnt/pdb70_database_path/pdb70_cs219.ffdata'

 

MATLAB with X-window forwarding on firefly

 

 

This works on a macbook pro with my acco8nt . Briefly, 

 

Log in with X-window forwarding: 

ssh -X -Y user@firefly.simcenter.utc.edu

 

Then start MATLAB: 

/opt/ohpc/pub/MATLAB/R2020b/bin/glnxa64/MATLAB  

 

A X-window should pop up.

SF 86 form

 

SFS interview reference

QUESTIONNAIRE FOR NATIONAL SECURITY POSITIONS 

https://www.opm.gov/forms/pdf_fill/sf86.pdf

firefly01 alphafold test run

(base) hqin@CS313BQin ~ % d.sh

(base) hqin@CS313BQin ~ % ssh xxx@firefly.simcenter.utc.edu

xxxx@firefly.simcenter.utc.edu's password: 

Activate the web console with: systemctl enable --now cockpit.socket

Last login: Wed Jul 27 10:24:27 2022 from 10.63.72.189

[xxxx@firefly ~]$ ssh firefly01

The authenticity of host 'firefly01 (192.168.223.3)' can't be established.

ECDSA key fingerprint is asdfasfasfasdf

Are you sure you want to continue connecting (yes/no/[fingerprint])? yes

Warning: Permanently added 'firefly01,192.168.223.3' (ECDSA) to the list of known hosts.

xxxx@firefly01's password: 

Activate the web console with: systemctl enable --now cockpit.socket

[xxxx@firefly01 ~]$ ls

 2022-06-16_unmasked.fa                   Downloads          test-scp

 anaconda3                                github             test-ts-hello.tar

'Anaconda3-2021.05-Linux-x86_64 (1).sh'   hellosts.txt       tmp

 bin                                      ibm                tmp.Rmd

 bindings.txt                             lib                tmp.txt

 cdsapp                                   old                tools

 commands.txt                             R                  trash

 csh.backup                               R-4.0.3            unison.log

 -cwd.e8271                               readme.txt        'VirtualBox VMs'

 -cwd.o8271                               scr.hqin           wget-log

 demo                                     simcenter-qinlab

 Desktop                                  T1050

[xxxx@firefly01 ~]$ bash /opt/ohpc/pub/singularity/test-scripts/alphafold-2/alphafold-singularity-run.sh

Mounting /scr -> /mnt/data_dir

Mounting /scr/alphafold-data/uniref90 -> /mnt/uniref90_database_path

Mounting /scr/alphafold-data/mgnify -> /mnt/mgnify_database_path

Mounting /scr/alphafold-data/bfd -> /mnt/bfd_database_path

Mounting /scr/alphafold-data/uniclust30/uniclust30_2018_08 -> /mnt/uniclust30_database_path

Mounting /scr/alphafold-data/pdb70 -> /mnt/pdb70_database_path

Mounting /scr/alphafold-data/pdb_mmcif -> /mnt/template_mmcif_dir

Mounting /scr/alphafold-data/pdb_mmcif -> /mnt/obsolete_pdbs_path

--data_dir=/mnt/data_dir/alphafold-data --uniref90_database_path=/mnt/uniref90_database_path/uniref90.fasta --mgnify_database_path=/mnt/mgnify_database_path/mgy_clusters_2018_12.fa --bfd_database_path=/mnt/bfd_database_path/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt --uniclust30_database_path=/mnt/uniclust30_database_path/uniclust30_2018_08 --pdb70_database_path=/mnt/pdb70_database_path/pdb70 --template_mmcif_dir=/mnt/template_mmcif_dir/mmcif_files --obsolete_pdbs_path=/mnt/obsolete_pdbs_path/obsolete.dat --output_dir=/mnt/output --benchmark=0 --logtostderr --fasta_paths=/opt/ohpc/pub/singularity/test-scripts/alphafold-2/T1050.fasta --max_template_date=2021-12-31

/scr:/mnt/data_dir,/scr/alphafold-data/uniref90:/mnt/uniref90_database_path,/scr/alphafold-data/mgnify:/mnt/mgnify_database_path,/scr/alphafold-data/bfd:/mnt/bfd_database_path,/scr/alphafold-data/uniclust30/uniclust30_2018_08:/mnt/uniclust30_database_path,/scr/alphafold-data/pdb70:/mnt/pdb70_database_path,/scr/alphafold-data/pdb_mmcif:/mnt/template_mmcif_dir,/scr/alphafold-data/pdb_mmcif:/mnt/obsolete_pdbs_path,/home/hqin:/mnt/output

I0801 11:48:55.799806 140491381822400 templates.py:857] Using precomputed obsolete pdbs /mnt/obsolete_pdbs_path/obsolete.dat.

I0801 11:48:56.409429 140491381822400 xla_bridge.py:244] Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker: 

I0801 11:48:57.418743 140491381822400 xla_bridge.py:244] Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.

I0801 11:49:03.148635 140491381822400 run_alphafold.py:385] Have 5 models: ['model_1', 'model_2', 'model_3', 'model_4', 'model_5']

I0801 11:49:03.148812 140491381822400 run_alphafold.py:397] Using random seed 725758993269421837 for the data pipeline

I0801 11:49:03.149077 140491381822400 run_alphafold.py:150] Predicting T1050

Traceback (most recent call last):

  File "/app/alphafold/run_alphafold.py", line 427, in <module>

    app.run(main)

  File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 312, in run

    _run_main(main, args)

  File "/opt/conda/lib/python3.7/site-packages/absl/app.py", line 258, in _run_main

    sys.exit(main(argv))

  File "/app/alphafold/run_alphafold.py", line 412, in main

    is_prokaryote=is_prokaryote)

  File "/app/alphafold/run_alphafold.py", line 164, in predict_structure

    msa_output_dir=msa_output_dir)

  File "/app/alphafold/alphafold/data/pipeline.py", line 148, in process

    with open(input_fasta_path) as f:

FileNotFoundError: [Errno 2] No such file or directory: '/opt/ohpc/pub/singularity/test-scripts/alphafold-2/T1050.fasta'