From 26e1904fa4fa5d7df2a57d3cb9ebc0b64fcdf652 Mon Sep 17 00:00:00 2001 From: Bruno Alvisio Date: Mon, 25 Aug 2025 10:02:08 +0000 Subject: [PATCH] Add LoRA example to Evo2 finetune jupyter notebook Signed-off-by: Bruno Alvisio --- .../examples/fine-tuning-tutorial.ipynb | 180 +++++++++++++++++- 1 file changed, 178 insertions(+), 2 deletions(-) diff --git a/sub-packages/bionemo-evo2/examples/fine-tuning-tutorial.ipynb b/sub-packages/bionemo-evo2/examples/fine-tuning-tutorial.ipynb index ae2089298b..2cd4c6b929 100644 --- a/sub-packages/bionemo-evo2/examples/fine-tuning-tutorial.ipynb +++ b/sub-packages/bionemo-evo2/examples/fine-tuning-tutorial.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "jupyter": { "source_hidden": true @@ -61,12 +61,14 @@ " !rm -rf preprocessed_data\n", " !rm -rf preatraining_demo\n", " !rm -rf pretraining_demo\n", + " !rm -rf lora_finetune_demo\n", " !rm -rf training_data_config.yaml\n", " !rm -rf preprocess_config.yaml\n", " !rm -f chr20.fa.gz\n", " !rm -f chr21.fa.gz\n", " !rm -f chr22.fa.gz\n", - " !rm -f chr20_21_22.fa" + " !rm -f chr20_21_22.fa\n", + " !rm -f brca1_seqs.fasta" ] }, { @@ -661,6 +663,180 @@ "\n", "```" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fine-tuning with LoRA\n", + "\n", + "**LoRA** (Low-Rank Adaptation) is a fine-tuning method that injects small trainable low-rank matrices into specific model layers—while keeping the original pre-trained parameters frozen—to dramatically reduce the number of trainable parameters, accelerating fine-tuning and lowering resource demands. For more detailed information, refer to the original paper: [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685).\n", + "\n", + "The fine-tuning command for using LoRA is almost identical to the one used before except for the additional `--lora-finetune` flag. In this example we are finetuning the model that was downloaded from HF but it is also possible to use the fine-tuned model from the previous step by `--ckpt-dir {final_ckpt_path}` instead. For this example, we use the reduced version of the model with only 4 layers to showcase how the decrease of training loss when using LoRA. " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "lora_result_dir = \"lora_finetune_demo\"\n", + "\n", + "lora_finetune_cmd = f\"\"\"train_evo2 \\\n", + " -d training_data_config.yaml \\\n", + " --dataset-dir ./preprocessed_data \\\n", + " --result-dir {lora_result_dir} \\\n", + " --experiment-name evo2 \\\n", + " --model-size 1b \\\n", + " --devices 1 \\\n", + " --num-nodes 1 \\\n", + " --seq-length 8192 \\\n", + " --micro-batch-size 2 \\\n", + " --lr 1.5 \\\n", + " --min-lr 1.49 \\\n", + " --grad-acc-batches 4 \\\n", + " --max-steps {MAX_STEPS} \\\n", + " --ckpt-dir nemo2_evo2_1b_8k \\\n", + " --clip-grad 250 \\\n", + " --wd 0.001 \\\n", + " --attention-dropout 0.01 \\\n", + " --hidden-dropout 0.01 \\\n", + " --val-check-interval {val_check_interval} \\\n", + " --num-layers 4 \\\n", + " --hybrid-override-pattern SDH* \\\n", + " --activation-checkpoint-recompute-num-layers 2 \\\n", + " --create-tensorboard-logger \\\n", + " --ckpt-async-save \\\n", + " --lora-finetune\"\"\"\n", + "\n", + "print(f\"Running command: {lora_finetune_cmd}\")\n", + "\n", + "result = run_subprocess_safely(lora_finetune_cmd)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "assert result[\"returncode\"] == 0, result" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the TensorBoard event file for the training run\n", + "log_dirs = !find lora_finetune_demo/evo2/dev -name \"events.out.tfevents*\"\n", + "tf_event_file = log_dirs[0]\n", + "\n", + "# Extract data from your event file\n", + "df = tensorboard_to_dataframe(tf_event_file)\n", + "# You can uncomment and modify this to plot multiple metrics once you see what's available\n", + "plot_multiple_training_metrics(df, [\"reduced_train_loss\", \"lr\", \"grad_norm\", \"val_loss\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the model has been finetuned using LoRA, it can be used for prediction as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "# We create a small FASTA file for prediction with BRCA ref. sequences\n", + "fasta_content = \"\"\">>BRCA1_ref_pos_41199726_T_class\n", + "AAGCATGTTAAATACAAGTGGTAGGCAGAATTCTAAAGATGTCCTCCCTTGATTGCTGTCTTCTGGTTATTCATTCCAACAGTAATCTAGGTACTGCTGCGAAGGGACTTTGCAGCTATAATTAAGGCTATGGGCCTTAAGATATGAAGATCACTTTGGATTACCAAGTGGGTGCAATCTAATCACTTGAGCCCTTAGAAACAGAGAAATGTCTCTGGCAGGAGTTAGAAAGATACAGTAGTCAGCAAGATTCAGTGTGAGAAAAACTTGACCACCCATTGCTGGTTTTGAAGATGGAGGCAGACAGGCCAGGAGCCTAGGAATGTGGGGAGCCTCAAGAAGCTGAGAATGATGCCCAGCCAACAGCCAGTGGGAAACAGGGATCTTAGTCCTAGAACTGCAAGGACCCAGAGCCTCTAGAAGGGAACACGCCCTGCGAACACCTTTATTTTGGCCTCATGAAACCTGAAACAAAGAAACAGTTGAGCCACCTCAGACTTCTGACCTTGCAACAATTTGTTTTGGCAGCAACAGGAAATACAAAAGGTATTTAAGCTGCCTCAATAAATCCTAGGAGGTAGATACTATCATTACCCCCATTTTACAGAGTGGGAGACTGAAGCACAGTGAAAAGGCTCTGAGAAAGTCGGCTGGCCTAAGTCTCAAGAACAGTCATTCATGGTGGAAGTGTTTGCTACCAAGTTTATTTGCAGTGTTAACAGCACAACATTTACAAAACGTATTTTGTACAATCAAGTCTTCACTGCCCTTGCACACTGGGGGGGCTAGGGAAGACCTAGTCCTTCCAACAGCTATAAACAGTCCTGGATAATGGGTTTATGAAAAACACTTTTTCTTCCTTCAGCAAGCAAAATTATTTATGAAGCTGTATGGTTTCAGCAACAGGGAGCAAAGGAAAAAAATCACCTCAAAGAAAGCAACAGCTTCCTTCCTGGTGGGATCTGTCATTTTATAGATATGAAATATTCATGCCAGAGGTCTTATATTTTAAGAGGAATGGATTATATACCAGAGCTACAACAATAAACATTTTACTTATTACTAATGAGGAATTAGAAGACTGTCTTTGGAAACCGGTTCTTGAAAATCTTCTGCTGTTTTAGAACACATTCTTTAGAAATCTAGCAAATATATCTCAGACTTTTAGAAATCTCTTCTAGTTTCATTTTCCTTTTTTTTTTTTTTTTTTTGAGCCACAGTCTCACTGTCACCCAGGCTGGAGTGCCGTGGTATGATCTTGGCTCACTGCAACCTCCACCTCCCGGGCTGAAGTGATTCTCCTGCCTTAGCCACCTGAGTAGCTGGGATTACAGGTGTCCACCACCATGACCGGCTAATTTCTGTATTTTTAGTAGAGATGGGGTTTCACCATGTTGGCCAGGCTGGTTTCGAACTCCTGACCTCCAGTGATCTGCCCACCTTGGCCTCCCAAAGTGCTGGGATTACAGGCGTGAGCCACCATGCCCAGGTTTCAAGTTTCCTTTTCATTTCTAATACCTGCCTCAGAATTTCCTCCCCAATGTTCCACTCCAACATTTGAGAACTGCCCAAGGACTATTCTGACTTTAAGTCACATAATCGATCCCAAGCACTCTCCTTCCATTGAAGGGTCTGACTCTCTGCCTTTGTGAACACAGGGTTTTAGAGAAGTAAACTTAGGGAAACCAGCTATTCTCTTGAGGCCAAGCCACTCTGTGCTTCCAGCCCTAAGCCAACAACAGCCTGAATAGAAAGAATAGGGCTGATAAATAATGAATCAGCATCTTGCTCAATTGGTGGCGTTTAAATGGTTTTAAAATCTTCTCAGGTGAAAAATTACCATAATTTTGTGCTCATGGCAGATTTCCAAGGGAGACTTCAAGCAGAAAATCTTTAAGGGACCCTTGCATAGCCAGAAGTCCTTTTCAGGCTGATGTACATAAAATATTTAGTAGCCAGGACAGTAGAAGGACTGAAGAGTGAGAGGAGCTCCCAGGGCCTGGAAAGGCCACTTTGTAAGCTCATTCTTGGGGTCCTGTGGCTCTGTACCTGTGGCTGGCTGCAGTCAGTAGTGGCTGTGGGGGATCTGGGGTATCAGGTAGGTGTCCAGCTCCTGGCACTGGTAGAGTGCTACACTGTCCAACACCCACTCTCGGGTCACCACAGGTGCCTCACACATCTGCCCAATTGCTGGAGACAGAGAACACAAGCAGAGATTAGTGTCAATTCATTCTCCTGGACTAGGCTCTAATCAATCGACTCCAGGGTCCTGGTTGTATGAGTTCTTAGGATTAATGAGGTAGAAGCTAATTTTTTTTTTTTTTTTTTGAGACGGAGTCTTGCTCTGTCGCCGAGGCTAGAGTGTGATGGCGCAATCTCGGCTCATTCAACCTCCGCCTCCTGGGTTCAAGCAATTCTCCTGTCTCTGCCTCCTGAGTAGCTGGAATTACAGGCACATGCCATCACACCCAGCTAATTTTTGTATTTTTAGTAGAGACGGGGGTTTCACAATGTTGGCCAGGCTGCTCTGGAACTCCTGACCTCAGGTGATCCACCCACCTTGGCCTCCCAAAGTGCTGGGATTACAGGCGTGAGCCACTGCACCTGGCCTTTTTTTTTTTTTTTTTTTTTTTTTGAGACGGAGTCTTGCTCTTGTTGCTCAGCCTGGAATGCAATGGCACGATCTCAGCTCACTGCAACCTCCACCTCCCGGGTTCAAGCAATTCTCCTGCCTCAGCCTCCCAAGTAGCAGGGATTACAGGTGCCTGCCACCATGCCAGGCTAATTGTTTTTTCTTTTTTTTCAGATGGAGTCTCACTCTGTCACTCAGGCTGGATTGTGATGGTGTGATCTCAGCTCACTGCAACCTCAACATCCTGGGTTCAAGCGATTCTCCTGCCTCAGTCTCCCAAGTAGCTGGGACTACAAGTGCGTGCCACCATGCCTGGCTAATTTTTTTTAGTATTTTTAGTAGAGATGGGGTTTCGCCATATTGGCCAGGCTGGTCTCAAACTCCTGATGTCAGGTGATCCGCCCTGAGGCTGAGGCAGGAGAATCATTTAAACCCAGGAGGCGGAGGTTGCAGTGAGCCAAGACTGGGCCACTGCACTCCAGCCTGCTAAGTGACAGAGTGAGACTCCACCTCAAAAAAAAAAAAAAAAGGCAATGCTTCAGGACATAAGGCCTTGCTCTGAAGAGGCCCTAGGAGTGACTCCTGGTGACAGTGAAAGCCCACAGCCTCTGGCAACTGTATTAACATGAACTTCAATCTGTTAAAGGAAAGCCACCAGGAAAACAGCACTGTAATTTAACGATGTGGAAAAATGTATGTAATATCTTAAGGAAAAAAGCAAAACAGTGTAATTATGATCACATTTTATAAAATACACGTGTATATATACGCACATATGCCTGGTGGAGTTTTATGGTGATCATCTCCAAGTGGTGGAATTACTGGGATTATTTTATTGTTTTTGTGTAAATTTATACTTTCTTTTTTCTTTTTGAGACACGGTCTCGCTCTGTCGCCCAGGCTGGAGTACAGTGGTGTGATCGTGGCTCACTGAAGCATCAACCTCCTGAGCTCAAGTGATCCTCCCACCTCAGCTTCCCAAGTAGCTGCGACTACAGGCATCTGCCACCACACCCAGCTACTTTTTAAATTTGTTGTACAGATGAAGTCTCCTTATGTTGCCCAGGCTGGTCTCGAACTTCTAGGCTCCCACCTTGACCTCCATCTTGACCTCCCAAAGTGCTGGAATTATAGGCATGAGCCACCATGCCCGGCCTTGATTTATGTTTTTGTGATGAACATTCATATCTTACTCCCACCCCATGGAAACAGTTCATGTATTACTTTTACAATATAAAACAAATAACAATAAAAACATCAAAAAGACATTTTAGCCATTCATTCAACAAATATTTAAAATGTGCCAAGAACTGTGCTACTCAAGCACCAGGTAATGAGTGATAAACCAAACCCATGCAAAAGGACCCCATATAGCACAGGTACATGCAGGCACCTTACCATGGAAGCCATTGTCCTCTGTCCAGGCATCTGGCTGCACAACCACAATTGGGTGGACACCCTGGATCCCCAGGAAGGAAAGAGCATTCAAAGTGTCAAAGTAGGACTACTGGAACTGTCACTTCATCATTTTTTTTGTTTGTTTTTGAGACAGGGTCTTGCTCTGTCACCCAGGCTGGAGTGCAGTGGTGTGATCTCAGCTCACTGCCACCTCTGCCTCCTGGGCTCAAGCAATCCTTCCATCTCAGCCTCCTAAGTAGCTGGAACTACAGACACGTACCACCACCCCTGGCTAATTTTTTTGTATTTTTGGTAGAGACAGGGTTTTGCCATGTTGCCCAGGCTGGTCTCAAACTCCTGGGCTCAACTTCACCCCCGGGATTATAGGCATGAGCCACCGCACCCAGCCTTGGCTAATTTTTAATAATTTTTTTGTAGACATGAGGTCCTACTGTATTGCCCAGGCTGGTCTTCAGCTCCCAGGCTCAAGCGATTCTCCCACCTTGGCCTCCCAGTGTTGTGATTACAGGGGTGGGGCACTGGCCCAGCCCATCATTTCTCTCTCTCTCTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCCGGGCTGGAGTGCAGTGGCGCGATCTTGGCTCACTGCAACCTCCGCCTCCGGGGTTCAAGCGATTCTCCTGCCCCAGCCCCTCAAGTAGCTGGGACTACAGGCGTGCGCCCCTACGCCCAGCTAATTTTTGTATTTTTAGTAGAGACGGGGTTTCGCCATGTTGGTTGGCCAGGATGGTCTCGATCTCTTGACCTCGTGATCTGCCCACCTCAGCCTCCCAAAGTGCTGGGATTACAGGCGTGAGCCACCGCACCTAGCTTTTCTCTCTCTCTCTTTTTTTTTTTTTTTAGACAAAGTCTCACTCTGTCACCCAGTCTGGAGTGCAGTGGTGCAATCTTGGCTCACTGCAACCTCTGCCTCCCACGTTCAAGCGATGCTCACACCTCAACTTCCCAAATAGCTGGCATTACAGGCATGCTCCACCAGGCCTGGCTACTTTTTGTTTTTTTTTTTTTAGTACAGATGGGGTTTCACCATGTTGGCCAGGCTGGTCTCAAACTCCTGACAAGTGATCCACCTGCCTCGGCCTCCCAAAGTGCTGGGATTACAGACATGAGCCACCATGCCCAGCCTCCAGCCCATCATTTCTTGATGATTTGTTGAAACACAGTATGCTGGGGCAGTCACAGAGAGGAGGGGGAGGGACATATGGGAAAAAGAGTTAGAGGGAAAAAGTCTTCCCTCAGTATATTTAATATGTGCAGTTCTCAAATCCTTACCCATCCCTTACAGATGGAGTCTTTTGGCACAGGTATGTGGGCAGAGAAGACTTCTGAGGCTACAGTAGGGGCATCCATAGGGACTGACAGGTGCCAGTCTTGCTCACAGGAGAGAATATTGTGTCCTCCCTCTCTGACAGGGCACCCAATACTTACTGTGCCAAGGGTGAATGATGAAAGCTCCTTCACCACAGAAGCACCACACAGCTGTACCATCCATTCCAGTTGATCTAAAATGGACATTTAGATGTAAAATCACTGCAGTAATCTGCATACTTAACCCAGGCCCTCTACCCTACACTCTCCGGATGAAGGCTTATAGCAAGACCTCTCAATGGGAGAGTCTGTCTCTCTGCTCCAAAGGACAATGGTCTTAAAATAGTAGGGGTATGGATTTTAAGTCAATTTGCCACTGATATGCCATGTACTCTGGTTATCAGTCTCCATAAGGCCACTTGGTATAAGGTTTGATAGTCTCTCAAATAAAATGCTTGAAAGAAAAAAAAATCAAAGATCTAATTTCCATTAATTTGCTAAATTGCTGGCTAAGACACTGTGTGAAAAAACACCCACCTTCCTTCCCTCCCTTCCTCCCTTCATCCTAATTCTGTGTTGGTAACTGATAATCACGGCCACTGAAAATACCATACTTGGTGGTAATTACTGTAAATGTCAAGAGATGGGAAGATAATTCATCCAGTCAAAAAAATACATGTTATCCTGGTTAGAGACTCAGCAGGGAAAGGCTACATGCTGAGCTGGAATCCATATACTCAGGGGAATAAAAATCAGAAGAGACTGTGGAGATGCTGTGTACCTGGAAAATCAGAACTGCCCAATGGGCTCTGTTGGCTGTGTTCTTCAAGACATTCATGTATGTTGCTCTTTCCCATCAGCCCGTTTCTGGGAATTGCTGAGGTGCTCCTGTCTGTCTGACTGAACGAAGGTTGACTAACTCACCCCCAAAATAATACTTTCTGGCAACCCTGGTTTCCACTATACCAAAGTAAAAAAACACTGAGAAATCCTTGAGTGGAACTTGGAAATTTCATAAAAATCCAATTGATAACTAATAAAGAGGATGGAAACAAAATTACATTGCAGCCAGGTTTTCTTGCTGTCACTCATCTGCCTGTGACCAGATGCTAAGGCCTTTCTCTAAGGTGTAAGAGGACCTAAGTCCCTGGGCAGAAGCAGGCACAGGAGGCAAAGGTGGGTAGCTTTTGCTGTGAAAAGAACAAACCAAATTTATACACACACCAAGGCCTTTGTGCTGCGCAACTCCAGAGGTAAGCTTTATAGCAGTCCTAAGCATAACATTGTATTAAGTGTCAAGTTTAATTAGAAAATGTTCATGGAGAGGGCTGGGCACAGTGGCTCATGCCTGTAATCCTAGCACTTTGGGAGGCTGAGGCGGGTGGATCACGAGGTCAGGAGATCGGGACCATATTGGCTAACACGGTGAAACCCCGTCTCTACTAAAAAAAAAATACAAAAAATTAGTCGGGCGTGGTGGCACACGCCTGTAGTCTCAGCTATTTGGGAGGCTGAGGCAGGAGAATCACTTGAACCCGGGAGGTGGAGGTTGCAGTGAGCCGAGATCGCGCCATTGCACTCTGGCCTGGGCAACAGAGACTCGACCTCAAAAAAAAAGAAAAAAAAAAAGAAATGTTCACCGAGAATCTTCCCCTGCTCTGGGCCCGTCCGTGGTGGGCCAGCTGCTGTGCTTTCTTCTATGTAAGTAAATTAAGATGGTTTAGGAAGAGGAGAACTCCTCCTTGATTTTTACCTATCCAAAGATATTTTCTCACTAACATGTTGGCACTAACAGCAGCTCAACGCCATCTGAACACATAACATACTGAATCCTAACTATTAACCACCTTCATGCTCTTGAGAAGGGGGACAAGGTATAGTTTTTTTTTGCCATAGGATAACATTTAGGTGCTGTTTTGTTTGGAGAGTGGTAGAGAAATAGAATAGCCTCTAGAACATTTCAGCAATCTGAGGAACCCCCATCGTGGGATCTTGCTTATAATACTCCACTATGTAAGACAAAGGCTGGTGCTGGAACTCTGGGGTTCTCCCAGGCTCTTACCTGTGGGCATGTTGGTGAAGGGCCCATAGCAACAGATTTCTAGCCCCCTGAAGATCTGGAAGAAGAGAGGAAGAGAGAGGGACAGGGGAATGGAGAGAAGGAAAATCTAGTTATAAAAGAATATTGGCTTTTATTCAAAAAACAGACTTTCAAAAAGGAAGAGCTTTTCTTTTTCTTCTGTTCACCACCTGATGATTTCTGCTGCTACTTCCCAGGGACAAGCAGTCCAATGTCCAGAACACTACTGGATTTCAGAAGATCTTCTTGAAGTGCATATGTAGTTGACCTGCACTCTACAGGCATTCTTTGTCATTCAAGGACTGAGCATCTCACTTTTGTCACCAATCAGGCCAAGGCTCCTCCCTAATGATCTCTGCAGGTGCTTTAACTTGTTAGATGCAAGGGAAAAAAGGTCCTTCTGTATGTTTAATAAGAGGCTTGGATGGCTAGAAACTCAAAGTTATTGGCTGAAGTTTGATGTTTATCCAGACTTGGTACCTCAAGTACTCACTATGACCCCATCAACAGAGGGGTCTATGTTGATTTTAGGTGTACATGCTCCTTGTCTCCTCTGACTTTTTTTTTTTTTTTTGAGACGGAGTCTCTCTCTGTCACCTAGGCTGGAGCGTAGTGGTGCGATCTGGGCTCACTGCAACCTCTGCCTCCAGGGTTCAAGCGATTCTCCTGCCTTAGCCTCCTGAGTAGCTGGGATTACAGGCGCGCACCACCACGCCCGGCTAAT\n", + ">BRCA1_ref_pos_41209074_T_class\n", + "CCACCTCCTGGGTTCAAGTGATTCTCCTGCCTCAGCCTCCCTAGTAGCTGGGATTACAGGTGCACACCACCACACCCGGCTAATTTTTTGTATTTTTAGTAGAAACGGGGTTTCACTATGTTGGCCAGGCTGGTCTTGAACTCCTGACCTCATGATCCACCCGCCTCAGGCTCCCAAAGACAAGTCTGTTTGTTTTAAGGGACAGCATCCATCTGTGCCACTCTCACTACCTGTATGAATAAACCGCACCCCCAACCTGTTGATAGGACCTGCATGCTCATAATGCTAGAAGTTCTCCCCAGGCAGCCAAGTGGAGCCAAATGCTGACATGAAAAGTACAAAAGATTAGGAATGTTTATATCCAGGCTAACACTCAGTGATGAGGATGCCTAGTTATGAGATAAGAAATTTGAAGTTTCGGGCTTGGCGTAGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCTGAGGCAGGCGGATCACGAGGTCAAGAGATGGAGACCATCCCGTCTAACACAGTGAAAACCCGTCTCTACTAAAAATACAAAAAATTAGCTGGGCATGGTGGCGGGCACCTGTAGTCCCAGCTACTTGGGAGGCTGAGGCAGGAGAATGGTGTGAACCCAGGAGGTGGAGTGGAGCTGGTAGTGAGCCGAGACGGCGCCACTGCACTCCAGCCTGGGTGACATAGCGAGACTCTGTCTCAAAAAAAAAAAGAAATTTGAAGTTTCAGGCCGGGCGCGGTGGCCTGTAATCCTAGCACTTTGGGAGGCCGAGGTGGGTGGATCACTTGGGGTCGGGAGTTCAAGACCAGCGTGACCAACATGGAGAAACCCCATCTCTACTAAAAATACAAAATCAGCTGGGCGTGGTGGCACATGCCTGTAATCCTAGCTACTTAGGAGGCTGAGGCAGGAGAATCACTTGAACCCTGGAGGCAGAGATTGCGGTGAGCCAAGATCACGCCATTGTACTTCAGCCTGGGCAAGAAGAGCAAAGCTTCGTCTTAAAAAAAAAAAAAAAGAAATTTGAAGTTTCACCTCTACATATACATTTCTCTTCCAGAGAAAAATACTATGGTGACATTTAGTATTTTCAGAATGTCATTACTTTGACCACATACTTTCCATCATTGCTATTCTGCATGGAGGAAAAAATCCAAAGCACTAGAATTTCTTTTTAAAGAGGGGAAGAATCTGGTGCTAATACTGCCTAGCATACAGTGGAGGAAAAACAGAGGACTGGCTCACATGGCGCTCCCCTGTGGTAAAGGCAGAAGCAGAAATAAGGCCAGCAGCTTTTCTTGGCATCTGGAACAATTACTTGATAGGCTCAGAAGGTAAAAACAGGCTGACCTCAGCAGTTCAAAACTCCAGGCTTCATTTGTGATCATGTCCCAAAGCAGTTTTCCTTAGGAAATTACCTCTACTGGTTCCCTCTTAAGTTTCTATAAAGGGACAGAAAGTGCTCAGTAGAAGGATGTTTTGTTTTGTTTTGAGACAAGGTCTCACAATGTTGGCCAGGCTGGAGTGCAGTAGCACCATCATGGCTCACTGCAGCCTCGACCTCCCAGACTCAAGCATTCTTCTCACCTCAGCCTCCTGAGTAGCTGGGAATACAAGAGTGTGCTACGACACCCAATTAATTTTTAAATTTTTTTGTAGAGACAGGGTCTTACTATGTTGCCCAGGCTAGTCTTGAACTCCTGGGCTCAAGTGATCCTCCTGCCTTGGCCTCCTAAAGTGCTGGGATTACAGAAGTGAGCCGCTGTGCCCAGCTGGGATTTTTTTTTTTTTTTTGAGATGAAGTCTCCCTCTTGTCCCCAGGCTAGAGTGCAGTGGCGCAATCTTGGCTCACTGCAACCTCCGCCTCCTGGGTTCAACCGATTCTCTAGCCTCAGCCTCCCGAGTAATCCTGAGCTAGGATTACAGGTGCCTACCACCACGCCCAGTTAACTTTTGTATTTTTAGTAGAGATGGGGTTTCACCATGTTGGCCAGGCTGGTCTCGAACTCCTGACCTCAGGTGATCCACCCGCCTTGGGCTCCCAAAGTGCTGGGATCACAGGTGTGAGCCACCGCGACCAGCCTGGGATGTTCTGAAATGCCTGGTAGTGCACACTTGAAAAAGGAAGAAAAGCAGACTGGGAACATAGCACTTAGGGAGCTGAGAAAGCAGCCAGCTCAGGCCTGACAGGTAAGTGTGAAATCTTGCAGACTTTCTCCCTGTGAGGAAGTCAGAGTTCTGGCTAAGTCATCTGCTACAATATGATCAGGAGAAAGTCATTGAGTCTTCTTGGGTCTCATCTGCAAAGTTGTTAGAATGCTCCATTTTTTTTCCCTCCGTGAAGCCTTCAACAACTTTTCAAGAAACTATAGTATTGTTGTTAAGAGTTTAGAATTGGAGGCCGGGTGCAGTGGCTCATGCTTGTAATCCCAGCACTTTGGGAAGCCAAGGCGGGTGGATCACTGGAGGCCAGGAGATCAAGGCCAGCCTGGCCAACATGGCAAAACCCCATCTCTAATAAAAATATAAAAATTAAGGCCGGGTGCAGTGGCTCATGCCTGTAATCCCAGCACTTTGGGAGCCTGAGGCGGGCAGATGATCTGAGGTCTGGAATTTGAGACCAGCCTGGCCAACATGTTGAAACCTTGTTTCTATTAAAAATACAAAAAACTATCTCGGTGTGGTGGCACGTGCCTGTAATCCCAGCTATTTGGGAGGCTAAGGCACGAGAATCACTTGAACCTGGGAGCTGGAGACTGCAGTGAGCTGAGATTGCGCCACTGCACTCCAGACTGGCCAACAGAATCAGACAAAAAATTAGCTAGTTGTTGTGGCACATGCCTGTAATCCTAGCCACTTGGGAGGCCAAGGCACAAGAATTGAACAAGGTTTGAACTTGGGAGGTGAAGGTTGCAGTGAGCAGAGATAGTGGCACTGCACTCCAGCCTGGGTGACAGAGTAAGACTCTGGCCCGAAAAATAAATTAACAAAGAATTTAGACTTGGAGCTAGACTTCCTGAGTTCAACTTCTAGTTCCATCACTTATTACCTGTGTGACTTTGAGCAACTTATTCAACCTCTCTGAGCCTCAATTTTCACATCTGTAATATAGGCATAATTACAGTACATAGCTCATAGAGTTGTCATAAAATTGTTAATTAGTTAAAACATGGTTGGGCCTTTAGAGCAGTCCCAGCATAAAGTGATCTTTTTTTTTTTTTTTTCTTTTGAGATCTTCTGTCTGTCGTCTAGGCTGGAGTGCAGTGGCGCAATCTCCGTTCACTGTAGTCCCCTCTTCTTGGGCTCAAGAGATACTCTCAACTCAGCCTTCCGAGTAGCTGGAACTACAAGTACATGCCACCACGCCTGGCTAATTTTTGTATATTTTTTTTGGTAGAGATGGGGTTTTGCCATGTTGCCCAGGCTGGTATCGAACTCCTTGGCTCAAGCGATCTGCCTGCCTCAGCTTCCCAAAGTGCTGGGATAACAGGAACGAGCCGCTGTGCCTGGCCCAGTGAACATTATATAAATTTTAGCTATTATTGGCTGGGCACGGTGGCTCACGCCTATCCCAGCACTTGGGGAGGCCAAGGCGGGAGGATCACAAGGTCAGGGGTTCAAGACCAGCCTGGGCAAGATGAGTTTTCAGTGAGCTGAGATCGCACCACTGCACTCTGGCCTGGGCAATGGAGTGAGACTCCGTCTTGGGAAAAAAAAAAAGAGAGAGAGAGAGCAAGAGAGAGAGAGAGAAAGACACCCCAGTGAAGTGAAAAGAGCAAAGTCTTTAGATTCTCATCTGCTTAAAGTCCCAGCTCTTCCACTTATCAGCTAAGATCTGAACCCGAGACGGGAATCCAAATTACACAGCCTCTCTAAATCTCTTAGTTTTATCATTCATAAAGTAGAGACAATACTTATTTATGTGGTTGGGATGGAAGAGTGAAAAAAGAACCTGTGTGAAAGTATCTAGCACTGTGTATGTATGTAATAAGTCTTACAAAATGAAGCGGCCCATCTCTGCAAAGGGGAGTGGAATACAGAGTGGTGGGGTGAGATTTTTGTCAACTTGAGGGAGGGAGCTTTACCTTTCTGTCCTGGGATTCTCTTGCTCGCTTTGGACCTTGGTGGTTTCTTCCATTGACCACATCTCCTCTGACTTCAAAATCATGCTGAAAGAAACCAAACACAACCCATCAGGATAAGAGAAAGAGAAGCTTCCTTCAATGGAAGTGGAGCAGACACGTCATATTTAAGGCATTCAGGCCAGGCGCAGTGGCTCACCCCTGTAATCCTAGCACTTTGGGAGACTGAGGCAGGCAGATCACTTGAGGTCAGGAGTTCGACACCAGTCTGACCAACATGGTGAAACCTCATCTCTACTAAAAATACAAAAAATTAGTTGGGCGTGGTGGCAGGCACCTGTAATCCCAGCTACTTGGGAGGCTGAGGCAGGAGAATCTTTTGAATGCAGGAGGTGGAGGTTGCAGTGAGCTGAGATGGTGCCACTGCACTCCAGCCTGGGTAACAGAGCGAGACTCCATCTCGCATCTCAAAAAATTAAAAATAAAATTAAAAAAAAAAAAATTAGGCCGGGTGTGGTGGCTCACGCCTGTAATCCCAGGACTTTGGGAGGCCGAGACAGGCGGATCACGAGGTCAGGAGATCGAGACCATCCTGGCTAACACGGTGAAACCCCGTCTCTACTTAAAATACAAAAAAATTAGCCGGGTGTAGTGGCAGGCGCCTGTAGTCCCAGCTACTCGGGAGGCTGAGGCAGGAGAATGGCGTGAACCCGGGAGGCGGAGCTTGCAGTGAGCGGAGATCACGCCACTGCACTCCAGCCTGGGCAACTGAGCGAGACTCCGTCTCAAAACAAACAAACAAACAAACAAAAAAACCAAAAATTAGCTGGGCGTGGTGGCATGCACCTGTAGTCCCAGCTACTCGGGAAGCTGAGGCATGAGAATTGCTGGAATCCAGGAGGCAGAGGTTGCTGTGAGCCACGATCATGCCACTGCACTCCAGCCTGGGTGACAGAGTGAAACTCTGTCTCAAAAAAAAAAAAAAAAAAAAAAAAAATTTAAGGCATTCAGAATTGCCAGGCAGGCAGTAGCTGGGCACCAATGGCTCACACCTATAACCCTAGCACTTTGGCACTTTGGGAGGCTGAGGCAGGAGGACAGCTTGAGACCAGGAGCTGGAGACCCAGCTGGGCAACACAGTGAGACCCCTTCTCTAGAAAAAAGATACATAAAACCTCAGCCAGGCATAGTTGCATGTGCCTGCAGTCCCAATTTCTTGAAAAGCTGAAGCTGGAGGATCACTTGAGCCCAGGAGTTGGAGGCTGCAGTGAACTGTGATCATACCACCACACTCCCGCCTGGGTGACAAAGTGAGACTCTGCTAAAAAAAAACACACACACACACACACATAAAACAAAAAAAAGAAAGAAAGAAAAAGAATTGCTGGGCAGAGATTTGACCAAAAGCAAGTAAAATCATTCTTTTTGGTAGACCAGGTGAAATGACTAGGATCTCAGCTGTTGAAATCAATTCACTGAAGCAAATCTTTTGTGTGCAGAACTTAACAATCGGCTTTTCACCTCACTGGGACTTAATGTATTTTAATTTTCAACATCTATCTCCTCTCAAAACATTCACTTAGCATTTAGATTAGACTGCTAATACTGTGCTGTTATAAAGGTAACTGAAAAATTCCCTATCTCCTAAATCAATTGTATATTATCATCTAAGGAAATAAAGGCTTTTCAGATTTCCACTGAGAGATTTACAGTTAACACAACCAAAATGATAAAATATACATTCAATGCACTAAAGAAAGTTAGAGGTTTTTCATAATTACTATTATGACTGGTCATGGACATGAGAATAAAGAATAATGAGACTGGGGAGAAATTTGAGGAAGAACGCTCAGAAACAATTCTGACTGAAACTCAGATTGTAGGGAAGGCCTTATGTGTTGCCAAGAGTATTGTGAAGAAAACTAAAGGCAAGAGTATAACCACTGGCTATTACTAGGGGAAGAATATCTGACATTTCCTAGTTACTTGAATGAGGAGTCTAGTGATAAATTTGTCAGAAGACTCCCATGTTCATAGTCCTCCTTGTACCCCTGAGCTTCATACAATGTGTCCAACTGCCTACTTAACTTCTCTATCCAGAAGTCTAGTATACATCTCAAAATTCATGCATCTGGCCGGGCACAGTGGCTCACACCTGCAATCCCAGCACTTTGGGAGGCCGAGGTGGGTGGATTACCTGAGGTCAGGAGTTTAAGACCAGCCTGGCCAACATGGTAAAACCCCATCTCTACTAAAAATACAAGTATTAGCCAGGCATTGTGGCAGGTGCCTGTAATCCCAGCTACTCGGGAGGCTGAGGCAGGAAAATCACTTGAACCGGGAGGCGGAGGTTGGAGTGAGCTGAGATCGTGCTACCGCACTCCATGCACTCTAGCCTGGGCAACAGAACGAGATGCTGTCACAACAACAACAACAACAACAACAACAACAACAACAACAACAACAAATTCTCACATCTAAAACAGAGTTCCTGGTTCCATTCCTGCTTCCTGCCTTTCCCACTCCCCCATATTCCCTACCATGCCTTCTTCATCTAATTTAATATTACTAACAAGATCTATTGTTCAAGCCAAAACCCAAGTGTCACTCCTTCAATTTCTCTTTACCTTATCCTCCAAATTTAATCCATTAGCAAGTCCTCTCTTCAAACCCATCCCAAACCAACCTTGTTTTTAACCATCTCCACACCACCAATTACCACAAGGATAAAATCTGAATTCCTTACCACCAAATACTATGTGATCTGGCCCTCATCTATGACCTTCTCCCATTCCTTGTGTAATCTCTGCCTCCACACATAATTTGCAAATTACTCCAGCTACACTGGCCTATTATTATTATTATTATTATTTTTGAGACGGAGTCTTGCTCTTTCGCCCAGCCTGGAGTGCAGTGGCGCAATCTCAGCTCACTGCAATCTCCGCCTCCTGGGTTCAAGCGATTCTCCTGCCCCAGCCTCCCAAGTAGCTGTGATTACAGGCACATGCCACCATTCCCAGCTAATTTTTTTTTGTTTTTGAGATGGAGTTTCACTCTTGTTGCCCAGGCTGGAGTGCAATGGTGCGATCTCAGCTCACCACAACCTCCACCTCCCGGGTTGATGAAGTGATTCTCTTGTCTCAGCCTCCCGTGTAGCTGGGATTAGAGGCACGCGCCACCACGCTGGGCAAATTTTTGTATTTTTAGTAGAGACAGGGTTTCTACCTCAGTGATCTGTCCGCCTTGACCTCCCAAAGTGCTGGGATTACAGGAATGAGCCACCACACCCAGCCGTGCCCAGCTAATTTTTGCATTTTTTAGTAGAGATGGGGTTTTGCCACGTTGGCCAGGCTGGTCTCAAACTCCTGACCTCAGGGGATCTGCCTGCCTCGGCCTCCTAGAGTGCTGGAATTACAGGTGTGAGCCACTGTGCCCGAACCTTTTATCATTATTATTTCTTGAGACAGGAGTCTTGCTCTGTCGTTCAGGCTGGAGTGCAGTGATGCGATCTTGGCTCACTGTAACTCCTACCTTTCGGTTCAAGTGATTCTCCTGCCTCAGCCTCTGGAGTAGCTGGGATTACAGGCACTGGGATTACAGGCACACACCACCACACCATGCTAGTTTTTTGTATTTTTAGTAGAGATGGGGTTTCACCATGTTGGCCAGGCTGGTCTCGAACTCCTGACCTCAAGTGATTTGCCTGCCTTGGCTTCCCAAAGTGCTGGGATTATAGGCACGAGCCACCACACACGACCAACATTGGCCTATCTTTTAAAAAATAAACCAAGCTCTGGCCGGGCACAGTGGCTCACACCTGTGATCCCAGCACTTTGGGAGGTTGAGGTGGTTGGATCACTTGAGTTCAGGAGTTTGAGACCAGCCTGACCAACGTGGTAAAACCCCATCTCTACTAAAAATAAAAACTAGTCGGGTGTGGTAGCACGCGTGCCTGTAATACCAGCTACTCAGGAGGCCAAGGCAGGAGAATTGCTTGAACCCAGGAGACAGAGTTTGCAGTGAGCCAAGATTGTGCCACTGCACTCCAGCCTGGGGGATAGAGGGAGACACCATCTCAAAAAAACCAAAATACAGAAATCAAAA\n", + ">BRCA1_ref_pos_41256913_A_class\n", + "CGGCACACTGCAACCTCCACCTCCCTGGTTCAGTTGATTCTCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGGCGTACACCACCACGCCTAGTTAATTTTTGTATTTTTAGTAGAGACAGGTTTCGCCATGTTGGCCAGGCTGGTCTCACACTCCTGACCTCAGGTGATCTGCCTGCCTCGGCCTCCCAAAGTGCTGGGATTACAGCCATGAGCCACCGTGCCTGGCCAATAACATTTTTAAAAAGGATAAATGATGACAATAAAACCCCAGCACTCCTAAGAACATTTAGTATAGGAACGAGTATTTAAAAGCCCAACATTACTTTTTTTTTCCTTCCTTTTTAGCTCTATATCTTTTGAAAAATGAATGCTGGTACTGTGAGAACTACTTTTATTTTATTTGTATTTTTTTAGACAGAGTCTTGTTCTGTTGCCCAGGCTGGAGCGTGGTGGCGTGATCTCGGTTCACTGCAACCTCTGTCTCCTGGGTTCAAGTGATCCTCCCACCTCAGCCTCCCAAGGAGCTGGGACTACATGCGCACGCCACCATGCCTGGCTAATTTTTGTATTTTTTGTAGCGATGGGGTTTCACCACATTGCCTAGGCTGGTCTTGAACTCTAGGACTCGAGATCCACCCGCCTTGGCCTCCCAAAGTGCTGGGATTACAGGCAAGAGCCACTGCGCCCAGCTAGAACAATTACTTTTTTTTCTTTTTTGAGACAGAGTCTCACTCTACGGCCCAGGCTGGAGTGCAGTGGCGCGATCTTGGCTCGCTGCAACCTCCGCGTCCTAGGTTCAAGCAATTCTTGTGCCTCCGCCTCCCGAGTAGCTGGGATTACAGGTACGTGCCACCACACCCAGCTAATTTTTGTATTTTTAGTAGAGATGGGTTTCACCATGTTGGCCAGGCTGGTCTCAAACTCCTGACCTCAAATTATCTGCCTGCCTCAGCCTCCCGAAGTGCTGGGATTACAGGTGCAAGCTACTACGCCCAGCCCCAGAATGATTACTTTTTAAGATCTCCATCATTCATGCACATATAAGTAACAACAGAGCAAATCACTACATTTTCTTTAACAATTACACCTTTTAAGTTCAAGTAATTTTCCCTTTTTTTAAAAGATAGGGTCTCAGTCACCTGCGCTGGAGTCCAGTGGCACAGTCATAGCTCAACTGCAGCCTTGACTCTCTATGTTAAAGCAATGCTCCAGCCTCAGCCTCCCAAGTAGCTAGGACCACAGGTGCATACCACCACGCCCAGCTAAGTTTTTTTGTTTTTTTTTTGAGACAGAGTCTCACTCTGTCGCCCAGGCTGGATCGCAGTGGCGGGATCTCGGCTCACTGCAAGCTCCACCTCCCGGGTTCATGCTATTCTCCTGCCTCAGCCTCCCGAGTAGCTGGGACTACAGGAGTCCGCCATCACGCTCAGCTAATTTTTCTGTGTTTTTAGTAGAGACGGGCTTTCACCATGTGAGCCAGGATAGTCTTGATCTCCACACCTCGTGATCCACTTGACTCGGCCTCCCAAAGTGCTGGGATTACAGGCGTGAAGCTTTTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCGCCAGGCTGGAGTGCAGTGGCGCAATCTCGGCTCACTGCAACCTCCGACTCTCAGGTTCAAGTGATTCTCCATCCTCAGCCTCTGGAGTAGCTGGGATTACAGGCACGTGCCACCACACCCAGCTAATTTTTGTATTTTTAGTAGAGACCGGGTTTCACCATGTTGGCCAGGGTGGTCTCAATCTCCTGACCTCATGATCCACTGGCCTCAGCCTCCCAAGGTGCTGGGATTGCAGGTGTTAGCCGCCGCACCCAGCCAGTTCTTTTTTTTTTTGCAACTAGATCTTGCTTTGCCGCCCACGCTGAAGTGCAGTGGAGTGATCATAGCTCACTGTAGTCTACATCTCCTGGACTCAAGTGATCCTTCTGTCTCAGCTTCCCAAGTAGCTGGGACTACAGGCACACACCACCATGCCCAGATAATTTAAAAAAACTTTTAATATAGACAAGGACTTGCTATGTTGCCCAAACTGGTCTTGAACTCCTGAGCTCAGGCAATCCTCAAGCCTCCTAGTGCTGGAATTACAGGCGTGAGCCACGGCACCCAGCTACTTTTTTTTTTTTTTAATTGCAGAGACAGTGTCTCACTGTGTTGCCCAAGCTGGGCTCAAAGGACCTCCTACCTCAGCCTCCCAAAGGGTTGGGATTATAGGCATGAGCCATGGCACCCAGCTGAAGTAATTTCCAAATGCTTAAAAATATCAGTGACCTTATGGCTATTTTAGATAAGCATCACAGAGCAGATAAAGCTCCAAAGCAAGGAAGGGCCTATAAGAGCTCTCTTTTTTGGCCGGGCTTGGTGGCTCACGCCTGTAATCCCAGCACTCTGGGAGGCCAAGGAGGGTGGATCACCTGAGGTCAGGAGTTCGAGACCAGCCTGACCAATATGGTGAAACCCCGACTCTACTAAAATTACAAAAATTAGCCAGGTCTGGTGTCGTACCCCTGTAGTCCCAACTACTCAGGAGGCTGAGGCAGGAGAATCACTTGAACCTGGGAGGCGGAGGTTGCAGTGAGCTGACATCGTGTCACTGCGCTCCAGCCTGGGCGACAGAGCGAGACTCTGTCTCAAAAAAAAAAAAAAAAAAGAAATCTGTCTTTTTGGTGTATCTGATGGGGATCTTGCTATGTTGCCTGGGCTGGTCTCAAACCTGGACTCAAGTGATCCTCCCATTTCAGCCTCCTAAGTAGCTGGGATTATAGGTACATTCACCTCACCTGGCTGTAAGGTCTCTCTCTTGATTATATCAGGTACCCTGACCTTCTCTGAACTCTAACTGATTAGAACACTTGTCTAACCACTTAATATACCATCTTTCCTTCTTCACTAACTTTTTATATAAATCTATGAGCATCTAGCACATTCACTAACATATTCACAATGCTCAATAAAGAGATGTTGCCAGAATAAATGAAAATGGTTTTACCGGCCGGGCACAGTGGCTCACACCTGTAATCCCAGCAATTTGGGGAGCCGAGGTGGGTGGATCACCTGAGGTTAGGAGTTCGAGACCAGCCTGGCCAACATGATGAAACCCCGTCTCTACTAAAAATACAAAAATTAGCCTGGCATGGTGGCGCGTGCCTGTAATCCCAGCTACTAAGGGGGCTAAGGCAGGAGGACTGCTTCTAGCCTGGGCCACAGAGCAAGACTCCATCTCAAAAAAAAAAAAGAAAAAAAAAAGAAAAGAAGAAGAAGAAGAAGAAGAAAACAAATGGTTTTACCAAGGAAGGATTTTCGGGTTCACTCTGTAGAAGTCTTTTGGCACGGTTTCTGTAGCCCATACTTTGGATGATAGAAACTTCATCTTTTAGATGTTCAGGAGAGTTATTTTCCTTTTTTGCAAAATTATAGCTGTTTGCATCTGTAAAATACAAGGGAAAACATTATGTTTGCAGTTAGAGAAAAATGTATGAATTATAATCAAAGAAACCAAGAGAAACCCTATGTATGCTCTTTGTTGTGTTAAGACAATGCATTAAGGTTACCTGTGATTTTTTTTAAAAATCAAATTTTAAAAAATCAACTGGAAATTTTACTCCTAGGAAACAAAAACAAATACACAGACCATCAAAGTTATTTAGAGTCCTTGTTTTCTACCTTAACACTAAGAAATTTGCCTATCAATACTGTATTTCACTAAAGCTAAGACACCAACAATGTAAGTTGCACTATTATTTTCTGTATCACTAAGAAAGAAAGCACACAAATTAAACAAATGACACACCCTCAATAGTAAAATGTTCCCAATTTCAGAGATGTTAAGATGTGAAAAATGTGCACCTTACGATTGATAAAATAAGGTGTGAGACCAGTGGGAGTAATTTTTTAAAAATAGATTACAGATACAGAACTAAAATTAACCTAGACTAAAAGGTCTTATCACCACGTCATAGAAAGTAATTGTGCAAACTTCCTGAGTTTTCATGGACAGCACTTGAGTGTCATTCTTGGGATATTCAACACTTACACTCCAAACCTGTGTCAAGCTGAAAAGCACAAATGATTTTCAATAGCTCTTCAACAAGTTGACTAAATCTCGTACTTTCTTGTAGGCTCCTGAAATTAAATTGTTTGAGAAACACACTCAGCAAGTGATTATCAACCTTTTAAGGACACTAAAATAAGAAAAGACATGTAAACAAATAAAAATAAGATGCAGCAACAGTAGAAAACCTCTATAATCAATACATCATTGACATCTGTATAAACCGTGTGATGGCAGTGATTTAGTAACTTTTTGTCATTCATTTAAGCCTACCAAATGCCTAAAATTATCTGATATAATACTGCCCTAAATCCACAGCAGATATAATGCATTGCATAGAAAACTAGAGTACTTTTTTTTTTGTTTAATTTAGAGACAGGGTCTTGCTCTATTGCCCAGGCTAGCATGCAGTGCCACAATCATAGCTCACTGCAGCCTCAGACTTCTGTACTCAAGCTATCCTCCCACTATAGCCTCCCAAGTAGCTAGGACTATGGGCATATGCCACCATGTACATTTATTTATTTATTTTGTTGGTAGTGATGGGGTCTCACTATGTTGCCCAGGCTGATTTCAAACTCCTGGCCTCAAGCAATCATCCCACCTCAGCCTCCTAGAGTGCTAGGGTTATAGGCATGAGCAATCACACCCTGCTGAGCTTACTGTTCTTAACATCTGAACCCTGCCCAATCCCTCACTTAGTTCTGCCTCTCAAGGATTTCTGTCAAATTATGATTATACTTCTTTGAAAACACATAGATAACAATGTCCAAAGGGAGATTTGGGTTTAGTAAAGTGGAATTAAAGCACAATTCATTCATTCCTTTAAGCATCTGAGTGCCTACTATGTACCAAACTTAGGAAAAACAGTGTTGAGACAGACAAAATTACTACTCTCATAGAATTTACATTGTAGCAGAAGAGATACACAATATATAAATACATTGTGTTTGTGTAAATAATTAGATAGAAATAAACACACACCTGTGAACCCCAGCACTTTGGGAGGCCGAGGTGGGCAGATCACCTGAGCTCAGGAGTTACCAGCCTGGGCAACATGGTAAAACCTCATCTCTACAAAAAATACAAAAATTGGCCGGGCATGGTAGCGTGTGCCTGTAGTCCCAGCTATTTGGGAGGCTAAGGCAGGAAGAGGCTGGGAGACAGAGGCTACAGTGAGCCGAGATCACGCCACTGCACTCCAGCCTCAGTGACAGAGCAGGACCCTGTCTTAAAAAAAAAAAAAAAAAGAAAAGGAAACACAATCAAAAATATAAAAACAAGATTAACAGACAATGGGGGCACGGCGATACAGCCCTACTTTACATAAGTCTGCAAGTTTTTACACTAGAAGCATTAGAGAAAGGCAGTAAGTTTCTAATACCTGTATAAGGCAGATGTCCCATAAAACTTTCAGGAAAATAACTTTGGAAATAATTTACTGTGTGCTAAAAACTCTTTTATAAATTTTTCTGATGAATGGTTTTATAGGAACGCTATGTTATTAAATAATTTCTACTTTTTCCTACTGTGGTTGCTTCCAACCTAGCATCATTACCAAATTATATACCTTTTGGTTATATCATTCTTACATAAAGGACACTGTGAAGGCCCTTTCTTCTGGTTGAGAAGTTTCAGCATGCAAAATCTATAAATTATAAAGAAAGAAAGAACAATTTAATTTACTTCCTTTTGTAGAAAGAATACTCAAAAGGCAAATAGCCATGAAAAGATAATCTCACAACTGCCCTTAAGAGCCATTTAAAAAGTAATGGCAGGTGAATTACAAGCAATAGTTTCCTAATTGTTTTTGGATGCTGCATAAGCAAAAACCTAAACTACATAAGCAATGATCTATGAAAACTGAACTTACATGCATATGGCTTACTGTTATGATCACATAAAACTTAATAAAAGTTAATATGGCATATTTAATGATATATTCATAATTATTTCATGCTGCATAATCAAATATAAACGGTAAGTAGTTCATGTAAGATGCTTATAATTAGTTTAAAATCTCAACACCTCACCACAAAGCTATAGTAATCAAGAAAGTGTGGTAATAGCATAAGAATAGACACATACATCAATAAAATGGAATTGACAGTACAGAAATAAACTCATACATACATCTGTGATCAAGCGATTTTCAACAATAATAAATACCAAGACAATTTTTTTTTTTTTTTTTTTTGAGACGGAGTCTCGCTCTGTCTCCCAGGCTGGAGTGCAGTGGCACGATCTCGGCTCACTGCAAGCTCCACCTCCTGGGTTCACGCCATTCTCCTGCCTCAGCCTCCCGAGCAGCTGGGACTACAGGCGCCCAGCACCATGCCCGGCTAATTTTTTTATTTTTAGTAGAGACGGGGTTTCACCATGTTAGCCAGGATGGTCTCCATCTCCTGACCTCGTGATCTGCCCGCCTCTGCCTCCCAAAGTGCTGGGATTACAGGTGTGAGCCACCATGCCTGGCCGACGATTTTTATTTTCTTAATTTTTTTTTTTTTTTTTTGAGACAGACTCACTCTATCACCCAGGCTGGAGGGTAGTGACACAATCTCAGCTCACTACAACCTCCACCTCCTAGGCTCAAGCAATTCTCGTGCCTCAGCCTCCCGAGTAGCTGGGATTACAGACATGCACCACCATACCCAGCTAATTTTTTGTATTTTTAGTTTCACCATGTTGGCTAGTCTTGAACTCCTGGCCTCAAATTGATCCATCTGCCTTGGCCTTCCAGAGTGCTGGGATTACAGGCATGAGCCACCGTGCCCAGGCCAAAGATGATTAAATGGGAAAATAATACTTTTTGTGTTTTTAACAAACAGTGTTGAGACAAAAGTATAACCACATGTAAAAGAATGAAATTATACTCCTATCTCACACCAAATAACAAAAGTTAACTCAAAGTGGATCATAGACCAGAAGTAGAGAGAGCAATGGTGATGATAATTAAAAGCAGCGATTCACTCATACAACCAATATTTACTATGTCTAGTACTATTCTAGATGGAGGGGATTCAATAGAAAAGCAAAATAAGATCCTGCAAAATAAAACATAAAGGACATAATATACTGGTGAAGGGGGTGACTAGGTAGGGAGTAAATAGCAGACACTGACAATGAAGAGACCCAGGAAAGAAAAGATTAAGGGGCTAAAAAAAAAGTCACTAGGGCTGTGCACGGTGGTCCACACCTGTAATCCCAGCACTTGCGGGGTGGGTGAATCACTTGAGGCCAGGAGTTCAAGACCAGCCTGGCCAACATGGTGAAACCCTGTCTCTACTAAATATTCAAAAATTAGCTGGGTGTGGTGGTGGTGCACTGTAGTCCCAGTTACTCGGGAGGCTGAGGCATGAGAATAAGTTGAACCTGTGAGGCGGGGGTTACAGTGAGCCAATATTGCACCATTGCATTCCAGCCTGGGCAACAGAGTGAGATTGTGCCAAAAAAAAAAAAAAGGCATTGGGAATTGTGAGAAAAGACACTTAGAGAATATTTTTTACTAGCCAGGTAATTTAACATTTATTACCCTCTCTTGGCCGGGTGCAGTGGCTCATGTCTGCAATCCCAGGAGGCTGAGGCAGGCAGATCACTTGAGGCCTGGAGTTTGAGACCAGCCTGGCTAACATGGTGAAACCCTGTCTCTACTAAAAATACAACAAAATTAGCCAGGCGTGGTGGGCACACGCCTGAGTCCCAGCTACTTGGGAGGCTGAGGCATGAGAATCACTGGAACCCAGGATGGCAGAGGTTGCAATGAGCTGAGAAGAGTGCCACTGCACTCCACCCTGGGCAACAGAGGAAGACTCTGTCTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGCCAGGAGCGGTGGCTCACGCCTGTAATCCCAACACTTTGGGAGAGGTCAGGAGTTCAAGACCAGCCTGACCAACACGGAGAAACCCTGTCTCTACTAAAAATACAAAAATTAGCCGGGCATGGTGGCATATGCCTGTAATCCTGGCTACTCAGGAGGCTGAGGCAGGAGAATCTCTTGAACCCGGGAGGCGGAGGTTGCAGTAAACTGAGATCGCGCCATTGCACTCCAGCCTGGGCAACAAGAGCGAAACTCCATC\n", + "\"\"\"\n", + "\n", + "with open(\"brca1_seqs.fasta\", \"w\") as f:\n", + " f.write(fasta_content)\n", + "\n", + "\n", + "lora_ckpt_paths = !ls -d lora_finetune_demo/evo2/checkpoints/*-last\n", + "lora_ckpt_path = lora_ckpt_paths[-1]\n", + "predict_output_dir = \"lora_predict\"\n", + "\n", + "predict_cmd = f\"\"\"predict_evo2 \\\n", + " --fasta brca1_seqs.fasta \\\n", + " --ckpt-dir nemo2_evo2_1b_8k \\\n", + " --output-dir {predict_output_dir} \\\n", + " --model-size 1b \\\n", + " --num-layers 4 \\\n", + " --hybrid-override-pattern SDH* \\\n", + " --tensor-parallel-size 1 \\\n", + " --pipeline-model-parallel-size 1 \\\n", + " --context-parallel-size 1 \\\n", + " --output-log-prob-seqs \\\n", + " --lora-checkpoint-path {lora_ckpt_path}\"\"\"\n", + "\n", + "\n", + "print(f\"Running command: {predict_cmd}\")\n", + "\n", + "result = run_subprocess_safely(predict_cmd)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "assert result[\"returncode\"] == 0, result" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'log_probs_seqs': tensor([-4.9490, -4.9865, -5.1852]),\n", + " 'seq_idx': tensor([0, 1, 2])}" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load Predictions\n", + "import glob\n", + "\n", + "import torch\n", + "\n", + "\n", + "torch.load(glob.glob(os.path.join(predict_output_dir, \"predictions__rank_*.pt\"))[0])" + ] } ], "metadata": {